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Azar Kazemi, Masoumeh Gharib, Nema Mohamadian Roshan, Shirin Taraz Jamshidi, Fabian Stögbauer, Saeid Eslami and Peter J. Schüffler.
Assessment of the Tumor–Stroma Ratio and Tumor-Infiltrating Lymphocytes in Colorectal Cancer: Inter-Observer Agreement Evaluation.
Diagnostics, vol. 13, 14, p. 2339, 2023-07-11 doi: 10.3390/diagnostics13142339
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@article{kazemi_assessment_2023,
    title = {Assessment of the {Tumor}–{Stroma} {Ratio} and {Tumor}-{Infiltrating} {Lymphocytes} in {Colorectal} {Cancer}: {Inter}-{Observer} {Agreement} {Evaluation}},
    volume = {13},
    issn = {2075-4418},
    shorttitle = {Assessment of the {Tumor}–{Stroma} {Ratio} and {Tumor}-{Infiltrating} {Lymphocytes} in {Colorectal} {Cancer}},
    url = {https://www.mdpi.com/2075-4418/13/14/2339},
    doi = {10.3390/diagnostics13142339},
    abstract = {Background: To implement the new marker in clinical practice, reliability assessment, validation, and standardization of utilization must be applied. This study evaluated the reliability of tumor-infiltrating lymphocytes (TILs) and tumor-stroma ratio (TSR) assessment through conventional microscopy by comparing observers’ estimations. Methods: Intratumoral and tumor-front stromal TILs, and TSR, were assessed by three pathologists using 86 CRC HE slides. TSR and TILs were categorized using one and four different proposed cutoff systems, respectively, and agreement was assessed using the intraclass coefficient (ICC) and Cohen’s kappa statistics. Pairwise evaluation of agreement was performed using the Fleiss kappa statistic and the concordance rate and it was visualized by Bland–Altman plots. To investigate the association between biomarkers and patient data, Pearson’s correlation analysis was applied. Results: For the evaluation of intratumoral stromal TILs, ICC of 0.505 (95\% CI: 0.35–0.64) was obtained, kappa values were in the range of 0.21 to 0.38, and concordance rates in the range of 0.61 to 0.72. For the evaluation of tumor-front TILs, ICC was 0.52 (95\% CI: 0.32–0.67), the overall kappa value ranged from 0.24 to 0.30, and the concordance rate ranged from 0.66 to 0.72. For estimating the TSR, the ICC was 0.48 (95\% CI: 0.35–0.60), the kappa value was 0.49 and the concordance rate was 0.76. We observed a significant correlation between tumor grade and the median of TSR (0.29 (95\% CI: 0.032–0.51), p-value = 0.03). Conclusions: The agreement between pathologists in estimating these markers corresponds to poor-to-moderate agreement; implementing immune scores in daily practice requires more concentration in inter-observer agreements.},
    language = {en},
    number = {14},
    urldate = {2023-07-12},
    journal = {Diagnostics},
    author = {Kazemi, Azar and Gharib, Masoumeh and Mohamadian Roshan, Nema and Taraz Jamshidi, Shirin and St\"ogbauer, Fabian and Eslami, Saeid and Sch\"uffler, Peter J.},
    month = jul,
    year = {2023},
    pages = {2339},
}
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TY - JOUR
TI - Assessment of the Tumor–Stroma Ratio and Tumor-Infiltrating Lymphocytes in Colorectal Cancer: Inter-Observer Agreement Evaluation
AU - Kazemi, Azar
AU - Gharib, Masoumeh
AU - Mohamadian Roshan, Nema
AU - Taraz Jamshidi, Shirin
AU - Stögbauer, Fabian
AU - Eslami, Saeid
AU - Schüffler, Peter J.
T2 - Diagnostics
AB - Background: To implement the new marker in clinical practice, reliability assessment, validation, and standardization of utilization must be applied. This study evaluated the reliability of tumor-infiltrating lymphocytes (TILs) and tumor-stroma ratio (TSR) assessment through conventional microscopy by comparing observers’ estimations. Methods: Intratumoral and tumor-front stromal TILs, and TSR, were assessed by three pathologists using 86 CRC HE slides. TSR and TILs were categorized using one and four different proposed cutoff systems, respectively, and agreement was assessed using the intraclass coefficient (ICC) and Cohen’s kappa statistics. Pairwise evaluation of agreement was performed using the Fleiss kappa statistic and the concordance rate and it was visualized by Bland–Altman plots. To investigate the association between biomarkers and patient data, Pearson’s correlation analysis was applied. Results: For the evaluation of intratumoral stromal TILs, ICC of 0.505 (95% CI: 0.35–0.64) was obtained, kappa values were in the range of 0.21 to 0.38, and concordance rates in the range of 0.61 to 0.72. For the evaluation of tumor-front TILs, ICC was 0.52 (95% CI: 0.32–0.67), the overall kappa value ranged from 0.24 to 0.30, and the concordance rate ranged from 0.66 to 0.72. For estimating the TSR, the ICC was 0.48 (95% CI: 0.35–0.60), the kappa value was 0.49 and the concordance rate was 0.76. We observed a significant correlation between tumor grade and the median of TSR (0.29 (95% CI: 0.032–0.51), p-value = 0.03). Conclusions: The agreement between pathologists in estimating these markers corresponds to poor-to-moderate agreement; implementing immune scores in daily practice requires more concentration in inter-observer agreements.
DA - 2023/07/11/
PY - 2023
DO - 10.3390/diagnostics13142339
DP - DOI.org (Crossref)
VL - 13
IS - 14
SP - 2339
J2 - Diagnostics
LA - en
SN - 2075-4418
ST - Assessment of the Tumor–Stroma Ratio and Tumor-Infiltrating Lymphocytes in Colorectal Cancer
UR - https://www.mdpi.com/2075-4418/13/14/2339
Y2 - 2023/07/12/06:44:20
ER -
Background: To implement the new marker in clinical practice, reliability assessment, validation, and standardization of utilization must be applied. This study evaluated the reliability of tumor-infiltrating lymphocytes (TILs) and tumor-stroma ratio (TSR) assessment through conventional microscopy by comparing observers’ estimations. Methods: Intratumoral and tumor-front stromal TILs, and TSR, were assessed by three pathologists using 86 CRC HE slides. TSR and TILs were categorized using one and four different proposed cutoff systems, respectively, and agreement was assessed using the intraclass coefficient (ICC) and Cohen’s kappa statistics. Pairwise evaluation of agreement was performed using the Fleiss kappa statistic and the concordance rate and it was visualized by Bland–Altman plots. To investigate the association between biomarkers and patient data, Pearson’s correlation analysis was applied. Results: For the evaluation of intratumoral stromal TILs, ICC of 0.505 (95% CI: 0.35–0.64) was obtained, kappa values were in the range of 0.21 to 0.38, and concordance rates in the range of 0.61 to 0.72. For the evaluation of tumor-front TILs, ICC was 0.52 (95% CI: 0.32–0.67), the overall kappa value ranged from 0.24 to 0.30, and the concordance rate ranged from 0.66 to 0.72. For estimating the TSR, the ICC was 0.48 (95% CI: 0.35–0.60), the kappa value was 0.49 and the concordance rate was 0.76. We observed a significant correlation between tumor grade and the median of TSR (0.29 (95% CI: 0.032–0.51), p-value = 0.03). Conclusions: The agreement between pathologists in estimating these markers corresponds to poor-to-moderate agreement; implementing immune scores in daily practice requires more concentration in inter-observer agreements.
Peter Schüffler, Katja Steiger and Wilko Weichert.
How to use AI in pathology.
Genes Chromosomes & Cancer, p. gcc.23178, 2023-05-31 doi: 10.1002/gcc.23178
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@article{schuffler_how_2023,
    title = {How to use {\textless}span style="font-variant:small-caps;"{\textgreater}{AI}{\textless}/span{\textgreater} in pathology},
    issn = {1045-2257, 1098-2264},
    shorttitle = {How to use {\textless}span style="font-variant},
    url = {https://onlinelibrary.wiley.com/doi/10.1002/gcc.23178},
    doi = {10.1002/gcc.23178},
    language = {en},
    urldate = {2023-06-01},
    journal = {Genes, Chromosomes and Cancer},
    author = {Sch\"uffler, Peter and Steiger, Katja and Weichert, Wilko},
    month = may,
    year = {2023},
    pages = {gcc.23178},
}
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TY - JOUR
TI - How to use AI in pathology
AU - Schüffler, Peter
AU - Steiger, Katja
AU - Weichert, Wilko
T2 - Genes, Chromosomes and Cancer
DA - 2023/05/31/
PY - 2023
DO - 10.1002/gcc.23178
DP - DOI.org (Crossref)
SP - gcc.23178
J2 - Genes Chromosomes & Cancer
LA - en
SN - 1045-2257, 1098-2264
ST - How to use     month = feb,
    year = {2023},
    pages = {100301},
}
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TY - JOUR
TI - Pan-tumor T-lymphocyte detection using deep neural networks: Recommendations for transfer learning in immunohistochemistry
AU - Wilm, Frauke
AU - Ihling, Christian
AU - Méhes, Gábor
AU - Terracciano, Luigi
AU - Puget, Chloé
AU - Klopfleisch, Robert
AU - Schüffler, Peter
AU - Aubreville, Marc
AU - Maier, Andreas
AU - Mrowiec, Thomas
AU - Breininger, Katharina
T2 - Journal of Pathology Informatics
DA - 2023/02//
PY - 2023
DO - 10.1016/j.jpi.2023.100301
DP - DOI.org (Crossref)
SP - 100301
J2 - Journal of Pathology Informatics
LA - en
SN - 21533539
ST - Pan-tumor T-lymphocyte detection using deep neural networks
UR - https://linkinghub.elsevier.com/retrieve/pii/S2153353923001153
Y2 - 2023/03/09/15:35:15
ER -
Caroline Richter, Eva Mezger, Peter J. Schüffler, Wieland Sommer, Federico Fusco, Katharina Hauner, Sebastian C. Schmid, Jürgen E. Gschwend, Wilko Weichert, Kristina Schwamborn, Dominik Pförringer and Anna Melissa Schlitter.
Pathological Reporting of Radical Prostatectomy Specimens Following ICCR Recommendation: Impact of Electronic Reporting Tool Implementation on Quality and Interdisciplinary Communication in a Large University Hospital.
Current Oncology, vol. 29, 10, p. 7245-7256, 2022/10 doi: 10.3390/curroncol29100571
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@article{richter_pathological_2022,
    title = {Pathological {Reporting} of {Radical} {Prostatectomy} {Specimens} {Following} {ICCR} {Recommendation}: {Impact} of {Electronic} {Reporting} {Tool} {Implementation} on {Quality} and {Interdisciplinary} {Communication} in a {Large} {University} {Hospital}},
    volume = {29},
    copyright = {http://creativecommons.org/licenses/by/3.0/},
    issn = {1718-7729},
    shorttitle = {Pathological {Reporting} of {Radical} {Prostatectomy} {Specimens} {Following} {ICCR} {Recommendation}},
    url = {https://www.mdpi.com/1718-7729/29/10/571},
    doi = {10.3390/curroncol29100571},
    abstract = {Prostate cancer represents one of the most common malignant tumors in male patients in Germany. The pathological reporting of radical prostatectomy specimens following a structured process constitutes an excellent prototype for the introduction of software-based standardized structured reporting in pathology. This can lead to reports of higher quality and could create a fundamental improvement for future AI applications. A software-based reporting template was used to generate standardized structured pathological reports of radical prostatectomy specimens of patients treated at the University Hospital Klinikum rechts der Isar of Technische Universit\"at M\"unchen, Germany. Narrative reports (NR) and standardized structured reports (SSR) were analyzed with regard to completeness, and clinicians’ satisfaction with each report type was evaluated. SSR show considerably higher completeness than NR. A total of 10 categories out of 32 were significantly more complete in SSR than in NR (p {\textless} 0.05). Clinicians awarded overall high scores in NR and SSR reports. One rater acknowledged a significantly higher level of clarity and time saving when comparing SSR to NR. Our findings highlight that the standardized structured reporting of radical prostatectomy specimens, qualifying as level 5 reports, significantly increases objectively measured content quality and the level of completeness. The implementation of nationwide SSR in Germany, particularly in oncologic pathology, can serve pathologists, clinicians, and patients.},
    language = {en},
    number = {10},
    urldate = {2022-09-30},
    journal = {Current Oncology},
    author = {Richter, Caroline and Mezger, Eva and Sch\"uffler, Peter J. and Sommer, Wieland and Fusco, Federico and Hauner, Katharina and Schmid, Sebastian C. and Gschwend, J\"urgen E. and Weichert, Wilko and Schwamborn, Kristina and Pf\"orringer, Dominik and Schlitter, Anna Melissa},
    month = oct,
    year = {2022},
    Publisher: Multidisciplinary Digital Publishing Institute},
    keywords = {pathological reporting, prostate cancer, quality improvement, structured reporting templates},
    pages = {7245--7256},
}
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TY - JOUR
TI - Pathological Reporting of Radical Prostatectomy Specimens Following ICCR Recommendation: Impact of Electronic Reporting Tool Implementation on Quality and Interdisciplinary Communication in a Large University Hospital
AU - Richter, Caroline
AU - Mezger, Eva
AU - Schüffler, Peter J.
AU - Sommer, Wieland
AU - Fusco, Federico
AU - Hauner, Katharina
AU - Schmid, Sebastian C.
AU - Gschwend, Jürgen E.
AU - Weichert, Wilko
AU - Schwamborn, Kristina
AU - Pförringer, Dominik
AU - Schlitter, Anna Melissa
T2 - Current Oncology
AB - Prostate cancer represents one of the most common malignant tumors in male patients in Germany. The pathological reporting of radical prostatectomy specimens following a structured process constitutes an excellent prototype for the introduction of software-based standardized structured reporting in pathology. This can lead to reports of higher quality and could create a fundamental improvement for future AI applications. A software-based reporting template was used to generate standardized structured pathological reports of radical prostatectomy specimens of patients treated at the University Hospital Klinikum rechts der Isar of Technische Universität München, Germany. Narrative reports (NR) and standardized structured reports (SSR) were analyzed with regard to completeness, and clinicians’ satisfaction with each report type was evaluated. SSR show considerably higher completeness than NR. A total of 10 categories out of 32 were significantly more complete in SSR than in NR (p < 0.05). Clinicians awarded overall high scores in NR and SSR reports. One rater acknowledged a significantly higher level of clarity and time saving when comparing SSR to NR. Our findings highlight that the standardized structured reporting of radical prostatectomy specimens, qualifying as level 5 reports, significantly increases objectively measured content quality and the level of completeness. The implementation of nationwide SSR in Germany, particularly in oncologic pathology, can serve pathologists, clinicians, and patients.
DA - 2022/10//
PY - 2022
DO - 10.3390/curroncol29100571
DP - www.mdpi.com
VL - 29
IS - 10
SP - 7245
EP - 7256
LA - en
SN - 1718-7729
ST - Pathological Reporting of Radical Prostatectomy Specimens Following ICCR Recommendation
UR - https://www.mdpi.com/1718-7729/29/10/571
Y2 - 2022/09/30/11:52:43
KW - pathological reporting
KW - prostate cancer
KW - quality improvement
KW - structured reporting templates
ER -
Prostate cancer represents one of the most common malignant tumors in male patients in Germany. The pathological reporting of radical prostatectomy specimens following a structured process constitutes an excellent prototype for the introduction of software-based standardized structured reporting in pathology. This can lead to reports of higher quality and could create a fundamental improvement for future AI applications. A software-based reporting template was used to generate standardized structured pathological reports of radical prostatectomy specimens of patients treated at the University Hospital Klinikum rechts der Isar of Technische Universität München, Germany. Narrative reports (NR) and standardized structured reports (SSR) were analyzed with regard to completeness, and clinicians’ satisfaction with each report type was evaluated. SSR show considerably higher completeness than NR. A total of 10 categories out of 32 were significantly more complete in SSR than in NR (p < 0.05). Clinicians awarded overall high scores in NR and SSR reports. One rater acknowledged a significantly higher level of clarity and time saving when comparing SSR to NR. Our findings highlight that the standardized structured reporting of radical prostatectomy specimens, qualifying as level 5 reports, significantly increases objectively measured content quality and the level of completeness. The implementation of nationwide SSR in Germany, particularly in oncologic pathology, can serve pathologists, clinicians, and patients.
Luca Dan, Janik Israel, Sabrina R. Sarker, J. Rao, Katja Steiger, Alexander Muckenhuber, Peter Schüffler, Wilko Weichert, Dev Kumar Das, T. Thomas and U. Joshi.
Deep learning-assisted differential detection of pancreatic intraepithelial neoplasia (PanIN) and characterization of inflammatory structures in proximity.
105. Jahrestagung der Deutschen Gesellschaft für Pathologie, 2022-06-10 
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@misc{dan_deep_2022,
    address = {M\"unster},
    title = {Deep learning-assisted differential detection of pancreatic intraepithelial neoplasia ({PanIN}) and characterization of inflammatory structures in proximity},
    author = {Dan, Luca and Israel, Janik and Sarker, Sabrina R. and Rao, J. and Steiger, Katja and Muckenhuber, Alexander and Sch\"uffler, Peter and Weichert, Wilko and Das, Dev Kumar and Thomas, T. and Joshi, U.},
    month = jun,
    year = {2022},
}
Download Endnote/RIS citation
TY - SLIDE
TI - Deep learning-assisted differential detection of pancreatic intraepithelial neoplasia (PanIN) and characterization of inflammatory structures in proximity
T2 - 105. Jahrestagung der Deutschen Gesellschaft für Pathologie
A2 - Dan, Luca
A2 - Israel, Janik
A2 - Sarker, Sabrina R.
A2 - Rao, J.
A2 - Steiger, Katja
A2 - Muckenhuber, Alexander
A2 - Schüffler, Peter
A2 - Weichert, Wilko
A2 - Das, Dev Kumar
A2 - Thomas, T.
A2 - Joshi, U.
CY - Münster
DA - 2022/06/10/
PY - 2022
ER -
Ufuk Kurt, Anees Kazi, Nassir Navab and Peter Schüffler.
Federated Learning for Breast Cancer Classification.
105. Jahrestagung der Deutschen Gesellschaft für Pathologie, 2022-06-09 
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@misc{kurt_federated_2022,
    address = {M\"unster},
    type = {Poster},
    title = {Federated {Learning} for {Breast} {Cancer} {Classification}},
    author = {Kurt, Ufuk and Kazi, Anees and Navab, Nassir and Sch\"uffler, Peter},
    month = jun,
    year = {2022},
}
Download Endnote/RIS citation
TY - SLIDE
TI - Federated Learning for Breast Cancer Classification
T2 - 105. Jahrestagung der Deutschen Gesellschaft für Pathologie
A2 - Kurt, Ufuk
A2 - Kazi, Anees
A2 - Navab, Nassir
A2 - Schüffler, Peter
CY - Münster
DA - 2022/06/09/
PY - 2022
M3 - Poster
ER -
Janik Israel, Luca Dan, Sabrina R. Sarker, Fabian Stögbauer, Wilko Weichert, Katja Steiger and Peter Schüffler.
A machine learning approach to classify whole slide images by formalin-fixed, paraffin-embedded or frozen section origin.
105. Jahrestagung der Deutschen Gesellschaft für Pathologie, 2022-06-09 
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@misc{israel_machine_2022,
    address = {M\"unster},
    type = {Poster},
    title = {A machine learning approach to classify whole slide images by formalin-fixed, paraffin-embedded or frozen section origin},
    author = {Israel, Janik and Dan, Luca and Sarker, Sabrina R. and St\"ogbauer, Fabian and Weichert, Wilko and Steiger, Katja and Sch\"uffler, Peter},
    month = jun,
    year = {2022},
}
Download Endnote/RIS citation
TY - SLIDE
TI - A machine learning approach to classify whole slide images by formalin-fixed, paraffin-embedded or frozen section origin
T2 - 105. Jahrestagung der Deutschen Gesellschaft für Pathologie
A2 - Israel, Janik
A2 - Dan, Luca
A2 - Sarker, Sabrina R.
A2 - Stögbauer, Fabian
A2 - Weichert, Wilko
A2 - Steiger, Katja
A2 - Schüffler, Peter
CY - Münster
DA - 2022/06/09/
PY - 2022
M3 - Poster
ER -
Fabian Stögbauer, Manuela Lautizi, Mark Kriegsmann, Hauke Winter, Thomas Muley, Katharina Kriegsmann, Moritz Jesinghaus, Jan Baumbach, Peter Schüffler, Wilko Weichert, Tim Kacprowski and Melanie Boxberg.
Tumour Cell Budding and Spread Through Air Spaces in Squamous Cell Carcinoma of the Lung – Determination and Validation of optimal prognostic cut-offs.
Lung Cancer, 2022-05-02 doi: 10.1016/j.lungcan.2022.04.012
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@article{stogbauer_tumour_2022,
    title = {Tumour {Cell} {Budding} and {Spread} {Through} {Air} {Spaces} in {Squamous} {Cell} {Carcinoma} of the {Lung} – {Determination} and {Validation} of optimal prognostic cut-offs},
    issn = {0169-5002},
    url = {https://www.sciencedirect.com/science/article/pii/S0169500222004251},
    doi = {10.1016/j.lungcan.2022.04.012},
    abstract = {Purpose
    Prognostic stratification of patients with squamous cell carcinomas of the lung (SCC-L) is challenging. Therefore, we investigated several histomorphological parameters (tumour cell budding (TCB), spread through air spaces (STAS), tumour-stroma-ratio, immune cell infiltration) which could potentially serve as prognostic parameters in SCC-L. We aimed to systematically determine optimal cut-off-values and assess the prognostic capability of these patterns. We furthermore assessed interobserver variability (IOV) for prognostically significant patterns TCB and STAS.
    Experimental Design:
    The Cancer Genome Atlas (TCGA) study cohort consisted of 335 patients with SCC-L. Histomorphological parameters analysed comprised TCB, minimal cell nest size (MCNS), STAS, stroma content and immune cell infiltration. The most significant cut-off-values were determined and univariate and multivariate survival outcomes were estimated. The identified cut-off-points were validated in an independent SCC-L cohort (n=346 patients). Two experienced pathologists probed IOV in the validation cohort.
    Results
    In the TCGA study cohort, TCB, STAS and immune cell infiltration were identified as significant prognostic parameters. TCB-high tumours, a high number of STAS foci, extensive STAS for distance of STAS in alveoli and a low immune cell infiltration remained as independent prognostic factors in multivariate Cox proportional hazard analyses for overall survival (OS). The significance of TCB, number of STAS foci and distance of STAS in alveoli for OS could be validated in the validation cohort. IOV reached a Kappa ≥0.89 for prognostic parameters.
    Conclusions
    We determined optimal cut-offs and identified TCB and STAS (number of STAS foci, distance of STAS in alveoli) as independent and uncorrelated prognostic factors for patients with SCC-L. The significance was validated in a large independent cohort. IOV was almost perfect for prognostic parameters. We propose the application of TCB- and STAS-based grading in SCC-L as prognostic morphological classifiers.},
    language = {en},
    urldate = {2022-05-03},
    journal = {Lung Cancer},
    author = {St\"ogbauer, Fabian and Lautizi, Manuela and Kriegsmann, Mark and Winter, Hauke and Muley, Thomas and Kriegsmann, Katharina and Jesinghaus, Moritz and Baumbach, Jan and Sch\"uffler, Peter and Weichert, Wilko and Kacprowski, Tim and Boxberg, Melanie},
    month = may,
    year = {2022},
    keywords = {Budding, Histomorphology, Lung cancer, Prognosis, STAS},
}
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TY - JOUR
TI - Tumour Cell Budding and Spread Through Air Spaces in Squamous Cell Carcinoma of the Lung – Determination and Validation of optimal prognostic cut-offs
AU - Stögbauer, Fabian
AU - Lautizi, Manuela
AU - Kriegsmann, Mark
AU - Winter, Hauke
AU - Muley, Thomas
AU - Kriegsmann, Katharina
AU - Jesinghaus, Moritz
AU - Baumbach, Jan
AU - Schüffler, Peter
AU - Weichert, Wilko
AU - Kacprowski, Tim
AU - Boxberg, Melanie
T2 - Lung Cancer
AB - Purpose
Prognostic stratification of patients with squamous cell carcinomas of the lung (SCC-L) is challenging. Therefore, we investigated several histomorphological parameters (tumour cell budding (TCB), spread through air spaces (STAS), tumour-stroma-ratio, immune cell infiltration) which could potentially serve as prognostic parameters in SCC-L. We aimed to systematically determine optimal cut-off-values and assess the prognostic capability of these patterns. We furthermore assessed interobserver variability (IOV) for prognostically significant patterns TCB and STAS.
Experimental Design:
The Cancer Genome Atlas (TCGA) study cohort consisted of 335 patients with SCC-L. Histomorphological parameters analysed comprised TCB, minimal cell nest size (MCNS), STAS, stroma content and immune cell infiltration. The most significant cut-off-values were determined and univariate and multivariate survival outcomes were estimated. The identified cut-off-points were validated in an independent SCC-L cohort (n=346 patients). Two experienced pathologists probed IOV in the validation cohort.
Results
In the TCGA study cohort, TCB, STAS and immune cell infiltration were identified as significant prognostic parameters. TCB-high tumours, a high number of STAS foci, extensive STAS for distance of STAS in alveoli and a low immune cell infiltration remained as independent prognostic factors in multivariate Cox proportional hazard analyses for overall survival (OS). The significance of TCB, number of STAS foci and distance of STAS in alveoli for OS could be validated in the validation cohort. IOV reached a Kappa ≥0.89 for prognostic parameters.
Conclusions
We determined optimal cut-offs and identified TCB and STAS (number of STAS foci, distance of STAS in alveoli) as independent and uncorrelated prognostic factors for patients with SCC-L. The significance was validated in a large independent cohort. IOV was almost perfect for prognostic parameters. We propose the application of TCB- and STAS-based grading in SCC-L as prognostic morphological classifiers.
DA - 2022/05/02/
PY - 2022
DO - 10.1016/j.lungcan.2022.04.012
DP - ScienceDirect
J2 - Lung Cancer
LA - en
SN - 0169-5002
UR - https://www.sciencedirect.com/science/article/pii/S0169500222004251
Y2 - 2022/05/03/09:33:38
KW - Budding
KW - Histomorphology
KW - Lung cancer
KW - Prognosis
KW - STAS
ER -
Purpose Prognostic stratification of patients with squamous cell carcinomas of the lung (SCC-L) is challenging. Therefore, we investigated several histomorphological parameters (tumour cell budding (TCB), spread through air spaces (STAS), tumour-stroma-ratio, immune cell infiltration) which could potentially serve as prognostic parameters in SCC-L. We aimed to systematically determine optimal cut-off-values and assess the prognostic capability of these patterns. We furthermore assessed interobserver variability (IOV) for prognostically significant patterns TCB and STAS. Experimental Design: The Cancer Genome Atlas (TCGA) study cohort consisted of 335 patients with SCC-L. Histomorphological parameters analysed comprised TCB, minimal cell nest size (MCNS), STAS, stroma content and immune cell infiltration. The most significant cut-off-values were determined and univariate and multivariate survival outcomes were estimated. The identified cut-off-points were validated in an independent SCC-L cohort (n=346 patients). Two experienced pathologists probed IOV in the validation cohort. Results In the TCGA study cohort, TCB, STAS and immune cell infiltration were identified as significant prognostic parameters. TCB-high tumours, a high number of STAS foci, extensive STAS for distance of STAS in alveoli and a low immune cell infiltration remained as independent prognostic factors in multivariate Cox proportional hazard analyses for overall survival (OS). The significance of TCB, number of STAS foci and distance of STAS in alveoli for OS could be validated in the validation cohort. IOV reached a Kappa ≥0.89 for prognostic parameters. Conclusions We determined optimal cut-offs and identified TCB and STAS (number of STAS foci, distance of STAS in alveoli) as independent and uncorrelated prognostic factors for patients with SCC-L. The significance was validated in a large independent cohort. IOV was almost perfect for prognostic parameters. We propose the application of TCB- and STAS-based grading in SCC-L as prognostic morphological classifiers.
Sandra Goetze, Peter Schüffler, Alcibiade Athanasiou, Anika Koetemann, Cedric Poyet, Christian Daniel Fankhauser, Peter J. Wild, Ralph Schiess and Bernd Wollscheid.
Use of MS-GUIDE for identification of protein biomarkers for risk stratification of patients with prostate cancer.
Clin Proteom, vol. 19, 1, p. 9, 04/27/2022 doi: 10.1186/s12014-022-09349-x
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@article{goetze_use_2022,
    title = {Use of {MS}-{GUIDE} for identification of protein biomarkers for risk stratification of patients with prostate cancer},
    volume = {19},
    issn = {1542-6416, 1559-0275},
    url = {https://clinicalproteomicsjournal.biomedcentral.com/articles/10.1186/s12014-022-09349-x},
    doi = {10.1186/s12014-022-09349-x},
    abstract = {Abstract
    
     Background
     Non-invasive liquid biopsies could complement current pathological nomograms for risk stratification of prostate cancer patients. Development and testing of potential liquid biopsy markers is time, resource, and cost-intensive. For most protein targets, no antibodies or ELISAs for efficient clinical cohort pre-evaluation are currently available. We reasoned that mass spectrometry-based prescreening would enable the cost-effective and rational preselection of candidates for subsequent clinical-grade ELISA development.
    
    
     Methods
     Using Mass Spectrometry-GUided Immunoassay DEvelopment (MS-GUIDE), we screened 48 literature-derived biomarker candidates for their potential utility in risk stratification scoring of prostate cancer patients. Parallel reaction monitoring was used to evaluate these 48 potential protein markers in a highly multiplexed fashion in a medium-sized patient cohort of 78 patients with ground-truth prostatectomy and clinical follow-up information. Clinical-grade ELISAs were then developed for two of these candidate proteins and used for significance testing in a larger, independent patient cohort of 263 patients.
    
    
     Results
     Machine learning-based analysis of the parallel reaction monitoring data of the liquid biopsies prequalified fibronectin and vitronectin as candidate biomarkers. We evaluated their predictive value for prostate cancer biochemical recurrence scoring in an independent validation cohort of 263 prostate cancer patients using clinical-grade ELISAs. The results of our prostate cancer risk stratification test were statistically significantly 10\% better than results of the current gold standards PSA alone, PSA plus prostatectomy biopsy Gleason score, or the National Comprehensive Cancer Network score in prediction of recurrence.
    
    
     Conclusion
     Using MS-GUIDE we identified fibronectin and vitronectin as candidate biomarkers for prostate cancer risk stratification.},
    language = {en},
    number = {1},
    urldate = {2022-04-27},
    journal = {Clinical Proteomics},
    author = {Goetze, Sandra and Sch\"uffler, Peter and Athanasiou, Alcibiade and Koetemann, Anika and Poyet, Cedric and Fankhauser, Christian Daniel and Wild, Peter J. and Schiess, Ralph and Wollscheid, Bernd},
    month = apr,
    year = {2022},
    pages = {9},
}
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TY - JOUR
TI - Use of MS-GUIDE for identification of protein biomarkers for risk stratification of patients with prostate cancer
AU - Goetze, Sandra
AU - Schüffler, Peter
AU - Athanasiou, Alcibiade
AU - Koetemann, Anika
AU - Poyet, Cedric
AU - Fankhauser, Christian Daniel
AU - Wild, Peter J.
AU - Schiess, Ralph
AU - Wollscheid, Bernd
T2 - Clinical Proteomics
AB - Abstract

Background
Non-invasive liquid biopsies could complement current pathological nomograms for risk stratification of prostate cancer patients. Development and testing of potential liquid biopsy markers is time, resource, and cost-intensive. For most protein targets, no antibodies or ELISAs for efficient clinical cohort pre-evaluation are currently available. We reasoned that mass spectrometry-based prescreening would enable the cost-effective and rational preselection of candidates for subsequent clinical-grade ELISA development.


Methods
Using Mass Spectrometry-GUided Immunoassay DEvelopment (MS-GUIDE), we screened 48 literature-derived biomarker candidates for their potential utility in risk stratification scoring of prostate cancer patients. Parallel reaction monitoring was used to evaluate these 48 potential protein markers in a highly multiplexed fashion in a medium-sized patient cohort of 78 patients with ground-truth prostatectomy and clinical follow-up information. Clinical-grade ELISAs were then developed for two of these candidate proteins and used for significance testing in a larger, independent patient cohort of 263 patients.


Results
Machine learning-based analysis of the parallel reaction monitoring data of the liquid biopsies prequalified fibronectin and vitronectin as candidate biomarkers. We evaluated their predictive value for prostate cancer biochemical recurrence scoring in an independent validation cohort of 263 prostate cancer patients using clinical-grade ELISAs. The results of our prostate cancer risk stratification test were statistically significantly 10% better than results of the current gold standards PSA alone, PSA plus prostatectomy biopsy Gleason score, or the National Comprehensive Cancer Network score in prediction of recurrence.


Conclusion
Using MS-GUIDE we identified fibronectin and vitronectin as candidate biomarkers for prostate cancer risk stratification.
DA - 2022/04/27/
PY - 2022
DO - 10.1186/s12014-022-09349-x
DP - DOI.org (Crossref)
VL - 19
IS - 1
SP - 9
J2 - Clin Proteom
LA - en
SN - 1542-6416, 1559-0275
UR - https://clinicalproteomicsjournal.biomedcentral.com/articles/10.1186/s12014-022-09349-x
Y2 - 2022/04/27/10:10:35
ER -
Abstract Background Non-invasive liquid biopsies could complement current pathological nomograms for risk stratification of prostate cancer patients. Development and testing of potential liquid biopsy markers is time, resource, and cost-intensive. For most protein targets, no antibodies or ELISAs for efficient clinical cohort pre-evaluation are currently available. We reasoned that mass spectrometry-based prescreening would enable the cost-effective and rational preselection of candidates for subsequent clinical-grade ELISA development. Methods Using Mass Spectrometry-GUided Immunoassay DEvelopment (MS-GUIDE), we screened 48 literature-derived biomarker candidates for their potential utility in risk stratification scoring of prostate cancer patients. Parallel reaction monitoring was used to evaluate these 48 potential protein markers in a highly multiplexed fashion in a medium-sized patient cohort of 78 patients with ground-truth prostatectomy and clinical follow-up information. Clinical-grade ELISAs were then developed for two of these candidate proteins and used for significance testing in a larger, independent patient cohort of 263 patients. Results Machine learning-based analysis of the parallel reaction monitoring data of the liquid biopsies prequalified fibronectin and vitronectin as candidate biomarkers. We evaluated their predictive value for prostate cancer biochemical recurrence scoring in an independent validation cohort of 263 prostate cancer patients using clinical-grade ELISAs. The results of our prostate cancer risk stratification test were statistically significantly 10% better than results of the current gold standards PSA alone, PSA plus prostatectomy biopsy Gleason score, or the National Comprehensive Cancer Network score in prediction of recurrence. Conclusion Using MS-GUIDE we identified fibronectin and vitronectin as candidate biomarkers for prostate cancer risk stratification.
Georg Prokop, Michael Örtl, Marina Fotteler, Peter Schüffler, Johannes Schobel, Walter Swoboda, Jürgen Schlegel and Friederike Liesche-Starnecker.
Quantifying Heterogeneity in Tumors: Proposing a New Method Utilizing Convolutional Neuronal Networks.
Stud Health Technol Inform, vol. 289, p. 397-400, 2022-01-14 doi: 10.3233/shti210942
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@article{prokop_quantifying_2022,
    title = {Quantifying {Heterogeneity} in {Tumors}: {Proposing} a {New} {Method} {Utilizing} {Convolutional} {Neuronal} {Networks}},
    volume = {289},
    issn = {1879-8365},
    shorttitle = {Quantifying {Heterogeneity} in {Tumors}},
    doi = {10.3233/SHTI210942},
    abstract = {Heterogeneity is a hallmark of glioblastoma (GBM), the most common malignant brain tumor, and a key reason for the poor survival rate of patients. However, establishing a clinically applicable, cost-efficient tool to measure and quantify heterogeneity is challenging. We present a novel method in an ongoing study utilizing two convolutional neuronal networks (CNN). After digitizing tumor samples, the first CNN delimitates GBM from normal tissue, the second quantifies heterogeneity within the tumor. Since neuronal networks can detect and interpret underlying and hidden information within images and have the ability to incorporate different information sets (i.e. clinical data and mutational status), this approach might venture towards a next level of integrated diagnosis. It may be applicable to other tumors as well and lead to a more precision-based medicine.},
    language = {eng},
    journal = {Studies in Health Technology and Informatics},
    author = {Prokop, Georg and \"Ortl, Michael and Fotteler, Marina and Sch\"uffler, Peter and Schobel, Johannes and Swoboda, Walter and Schlegel, J\"urgen and Liesche-Starnecker, Friederike},
    month = jan,
    year = {2022},
    pmid = {35062175},
    keywords = {Brain Neoplasms, Convolutional Neuronal Network, Digital Pathology, Glioblastoma, Humans, Neural Networks, Computer, Neuropathology, Precision Medicine, Tumor heterogeneity},
    pages = {397--400},
}
Download Endnote/RIS citation
TY - JOUR
TI - Quantifying Heterogeneity in Tumors: Proposing a New Method Utilizing Convolutional Neuronal Networks
AU - Prokop, Georg
AU - Örtl, Michael
AU - Fotteler, Marina
AU - Schüffler, Peter
AU - Schobel, Johannes
AU - Swoboda, Walter
AU - Schlegel, Jürgen
AU - Liesche-Starnecker, Friederike
T2 - Studies in Health Technology and Informatics
AB - Heterogeneity is a hallmark of glioblastoma (GBM), the most common malignant brain tumor, and a key reason for the poor survival rate of patients. However, establishing a clinically applicable, cost-efficient tool to measure and quantify heterogeneity is challenging. We present a novel method in an ongoing study utilizing two convolutional neuronal networks (CNN). After digitizing tumor samples, the first CNN delimitates GBM from normal tissue, the second quantifies heterogeneity within the tumor. Since neuronal networks can detect and interpret underlying and hidden information within images and have the ability to incorporate different information sets (i.e. clinical data and mutational status), this approach might venture towards a next level of integrated diagnosis. It may be applicable to other tumors as well and lead to a more precision-based medicine.
DA - 2022/01/14/
PY - 2022
DO - 10.3233/SHTI210942
DP - PubMed
VL - 289
SP - 397
EP - 400
J2 - Stud Health Technol Inform
LA - eng
SN - 1879-8365
ST - Quantifying Heterogeneity in Tumors
KW - Brain Neoplasms
KW - Convolutional Neuronal Network
KW - Digital Pathology
KW - Glioblastoma
KW - Humans
KW - Neural Networks, Computer
KW - Neuropathology
KW - Precision Medicine
KW - Tumor heterogeneity
ER -
Heterogeneity is a hallmark of glioblastoma (GBM), the most common malignant brain tumor, and a key reason for the poor survival rate of patients. However, establishing a clinically applicable, cost-efficient tool to measure and quantify heterogeneity is challenging. We present a novel method in an ongoing study utilizing two convolutional neuronal networks (CNN). After digitizing tumor samples, the first CNN delimitates GBM from normal tissue, the second quantifies heterogeneity within the tumor. Since neuronal networks can detect and interpret underlying and hidden information within images and have the ability to incorporate different information sets (i.e. clinical data and mutational status), this approach might venture towards a next level of integrated diagnosis. It may be applicable to other tumors as well and lead to a more precision-based medicine.
Peter J. Schüffler, Evangelos Stamelos, Ishtiaque Ahmed, D. Vijay K. Yarlagadda, Matthew G. Hanna, Victor E. Reuter, David S. Klimstra and Meera Hameed.
Efficient Visualization of Whole Slide Images in Web-based Viewers for Digital Pathology.
Archives of Pathology & Laboratory Medicine, 2022-01-3 doi: 10.5858/arpa.2021-0197-oa
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@article{schuffler_efficient_2022,
    title = {Efficient {Visualization} of {Whole} {Slide} {Images} in {Web}-based {Viewers} for {Digital} {Pathology}},
    issn = {1543-2165, 0003-9985},
    url = {https://meridian.allenpress.com/aplm/article/doi/10.5858/arpa.2021-0197-OA/476350/Efficient-Visualization-of-Whole-Slide-Images-in},
    doi = {10.5858/arpa.2021-0197-OA},
    abstract = {Context.—
     Wide adoption of digital pathology requires efficient visualization and navigation in Web-based digital slide viewers, which is poorly defined.
    
    
     Objective.—
     To define and quantify relevant performance metrics for efficient visualization of cases and slides in digital slide viewers.
    
    
     Design.—
     With a universal slide viewer used in clinical routine diagnostics, we evaluate the impact of slide caching, compression type, tile, and block size of whole slide images generated from Philips, Leica, and 3DHistech scanners on streaming performance on case, slide, and field of view levels.
    
    
     Results.—
     Two hundred thirty-nine pathologists routinely reviewed 60 080 whole slide images over 3 months. The median time to open a case's slides from the laboratory information system was less than 4 seconds, the time to change to a slide within the case was less than 1 second, and the time to render the adjacent field of view when navigating the slide was less than one-quarter of a second. A whole slide image's block size and a viewer tile size of 1024 pixels showed best performance to display a field of view and was preferrable over smaller tiles due to fewer mosaic effects. For Philips, fastest median slide streaming pace was 238 ms per field of view and for 3DHistech, 125 ms. For Leica, the fastest pace of 108 ms per field of view was established with block serving without decompression.
    
    
     Conclusions.—
     This is the first study to systematically assess user-centric slide visualization performance metrics for digital viewers, including time to open a case, time to change a slide, and time to change a field of view. These metrics help to improve the viewer's configuration, leading to an efficient visualization baseline that is widely accepted among pathologists using routine digital pathology.},
    language = {en},
    urldate = {2022-01-05},
    journal = {Archives of Pathology \& Laboratory Medicine},
    author = {Sch\"uffler, Peter J. and Stamelos, Evangelos and Ahmed, Ishtiaque and Yarlagadda, D. Vijay K. and Hanna, Matthew G. and Reuter, Victor E. and Klimstra, David S. and Hameed, Meera},
    month = jan,
    year = {2022},
}
Download Endnote/RIS citation
TY - JOUR
TI - Efficient Visualization of Whole Slide Images in Web-based Viewers for Digital Pathology
AU - Schüffler, Peter J.
AU - Stamelos, Evangelos
AU - Ahmed, Ishtiaque
AU - Yarlagadda, D. Vijay K.
AU - Hanna, Matthew G.
AU - Reuter, Victor E.
AU - Klimstra, David S.
AU - Hameed, Meera
T2 - Archives of Pathology & Laboratory Medicine
AB - Context.—
Wide adoption of digital pathology requires efficient visualization and navigation in Web-based digital slide viewers, which is poorly defined.


Objective.—
To define and quantify relevant performance metrics for efficient visualization of cases and slides in digital slide viewers.


Design.—
With a universal slide viewer used in clinical routine diagnostics, we evaluate the impact of slide caching, compression type, tile, and block size of whole slide images generated from Philips, Leica, and 3DHistech scanners on streaming performance on case, slide, and field of view levels.


Results.—
Two hundred thirty-nine pathologists routinely reviewed 60 080 whole slide images over 3 months. The median time to open a case's slides from the laboratory information system was less than 4 seconds, the time to change to a slide within the case was less than 1 second, and the time to render the adjacent field of view when navigating the slide was less than one-quarter of a second. A whole slide image's block size and a viewer tile size of 1024 pixels showed best performance to display a field of view and was preferrable over smaller tiles due to fewer mosaic effects. For Philips, fastest median slide streaming pace was 238 ms per field of view and for 3DHistech, 125 ms. For Leica, the fastest pace of 108 ms per field of view was established with block serving without decompression.


Conclusions.—
This is the first study to systematically assess user-centric slide visualization performance metrics for digital viewers, including time to open a case, time to change a slide, and time to change a field of view. These metrics help to improve the viewer's configuration, leading to an efficient visualization baseline that is widely accepted among pathologists using routine digital pathology.
DA - 2022/01/03/
PY - 2022
DO - 10.5858/arpa.2021-0197-OA
DP - DOI.org (Crossref)
LA - en
SN - 1543-2165, 0003-9985
UR - https://meridian.allenpress.com/aplm/article/doi/10.5858/arpa.2021-0197-OA/476350/Efficient-Visualization-of-Whole-Slide-Images-in
Y2 - 2022/01/05/09:25:21
ER -
Context.— Wide adoption of digital pathology requires efficient visualization and navigation in Web-based digital slide viewers, which is poorly defined. Objective.— To define and quantify relevant performance metrics for efficient visualization of cases and slides in digital slide viewers. Design.— With a universal slide viewer used in clinical routine diagnostics, we evaluate the impact of slide caching, compression type, tile, and block size of whole slide images generated from Philips, Leica, and 3DHistech scanners on streaming performance on case, slide, and field of view levels. Results.— Two hundred thirty-nine pathologists routinely reviewed 60 080 whole slide images over 3 months. The median time to open a case's slides from the laboratory information system was less than 4 seconds, the time to change to a slide within the case was less than 1 second, and the time to render the adjacent field of view when navigating the slide was less than one-quarter of a second. A whole slide image's block size and a viewer tile size of 1024 pixels showed best performance to display a field of view and was preferrable over smaller tiles due to fewer mosaic effects. For Philips, fastest median slide streaming pace was 238 ms per field of view and for 3DHistech, 125 ms. For Leica, the fastest pace of 108 ms per field of view was established with block serving without decompression. Conclusions.— This is the first study to systematically assess user-centric slide visualization performance metrics for digital viewers, including time to open a case, time to change a slide, and time to change a field of view. These metrics help to improve the viewer's configuration, leading to an efficient visualization baseline that is widely accepted among pathologists using routine digital pathology.
Maximilian Fischer, Peter Neher, Michael Götz, Shuhan Xiao, Silvia Dias Almeida, Peter Schüffler, Alexander Muckenhuber, Rickmer Braren, Jens Kleesiek, Marco Nolden and Klaus Maier-Hein.
Deep Learning on Lossily Compressed Pathology Images: Adverse Effects for ImageNet Pre-trained Models.
In: Yuankai Huo, Bryan A. Millis, Yuyin Zhou, Xiangxue Wang, Adam P. Harrison and Ziyue Xu (eds.) Medical Optical Imaging and Virtual Microscopy Image Analysis, vol. 13578, p. 73-83, Springer Nature Switzerland, ISBN 978-3-031-16960-1 978-3-031-16961-8, 2022 
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@incollection{huo_deep_2022,
    address = {Cham},
    title = {Deep {Learning} on {Lossily} {Compressed} {Pathology} {Images}: {Adverse} {Effects} for {ImageNet} {Pre}-trained {Models}},
    volume = {13578},
    isbn = {978-3-031-16960-1 978-3-031-16961-8},
    shorttitle = {Deep {Learning} on {Lossily} {Compressed} {Pathology} {Images}},
    url = {https://link.springer.com/10.1007/978-3-031-16961-8_8},
    language = {en},
    urldate = {2022-11-14},
    booktitle = {Medical {Optical} {Imaging} and {Virtual} {Microscopy} {Image} {Analysis}},
    publisher = {Springer Nature Switzerland},
    author = {Fischer, Maximilian and Neher, Peter and G\"otz, Michael and Xiao, Shuhan and Almeida, Silvia Dias and Sch\"uffler, Peter and Muckenhuber, Alexander and Braren, Rickmer and Kleesiek, Jens and Nolden, Marco and Maier-Hein, Klaus},
    editor = {Huo, Yuankai and Millis, Bryan A. and Zhou, Yuyin and Wang, Xiangxue and Harrison, Adam P. and Xu, Ziyue},
    year = {2022},
    doi = {10.1007/978-3-031-16961-8_8},
    pages = {73--83},
}
Download Endnote/RIS citation
TY - CHAP
TI - Deep Learning on Lossily Compressed Pathology Images: Adverse Effects for ImageNet Pre-trained Models
AU - Fischer, Maximilian
AU - Neher, Peter
AU - Götz, Michael
AU - Xiao, Shuhan
AU - Almeida, Silvia Dias
AU - Schüffler, Peter
AU - Muckenhuber, Alexander
AU - Braren, Rickmer
AU - Kleesiek, Jens
AU - Nolden, Marco
AU - Maier-Hein, Klaus
T2 - Medical Optical Imaging and Virtual Microscopy Image Analysis
A2 - Huo, Yuankai
A2 - Millis, Bryan A.
A2 - Zhou, Yuyin
A2 - Wang, Xiangxue
A2 - Harrison, Adam P.
A2 - Xu, Ziyue
CY - Cham
DA - 2022///
PY - 2022
DP - DOI.org (Crossref)
VL - 13578
SP - 73
EP - 83
LA - en
PB - Springer Nature Switzerland
SN - 978-3-031-16960-1 978-3-031-16961-8
ST - Deep Learning on Lossily Compressed Pathology Images
UR - https://link.springer.com/10.1007/978-3-031-16961-8_8
Y2 - 2022/11/14/07:47:11
ER -
Peter J Schüffler, Luke Geneslaw, D Vijay K Yarlagadda, Matthew G Hanna, Jennifer Samboy, Evangelos Stamelos, Chad Vanderbilt, John Philip, Marc-Henri Jean, Lorraine Corsale, Allyne Manzo, Neeraj H G Paramasivam, John S Ziegler, Jianjiong Gao, Juan C Perin, Young Suk Kim, Umeshkumar K Bhanot, Michael H A Roehrl, Orly Ardon, Sarah Chiang, Dilip D Giri, Carlie S Sigel, Lee K Tan, Melissa Murray, Christina Virgo, Christine England, Yukako Yagi, S Joseph Sirintrapun, David Klimstra, Meera Hameed, Victor E Reuter and Thomas J Fuchs.
Integrated digital pathology at scale: A solution for clinical diagnostics and cancer research at a large academic medical center.
Journal of the American Medical Informatics Association, vol. 28, 9, p. 1874-1884, July 14, 2021 doi: 10.1093/jamia/ocab085
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@article{schuffler_integrated_2021,
    title = {Integrated digital pathology at scale: {A} solution for clinical diagnostics and cancer research at a large academic medical center},
    volume = {28},
    issn = {1527-974X},
    shorttitle = {Integrated digital pathology at scale},
    url = {https://doi.org/10.1093/jamia/ocab085},
    doi = {10.1093/jamia/ocab085},
    abstract = {Broad adoption of digital pathology (DP) is still lacking, and examples for DP connecting diagnostic, research, and educational use cases are missing. We blueprint a holistic DP solution at a large academic medical center ubiquitously integrated into clinical workflows; researchapplications including molecular, genetic, and tissue databases; and educational processes.We built a vendor-agnostic, integrated viewer for reviewing, annotating, sharing, and quality assurance of digital slides in a clinical or research context. It is the first homegrown viewer cleared by New York State provisional approval in 2020 for primary diagnosis and remote sign-out during the COVID-19 (coronavirus disease 2019) pandemic. We further introduce an interconnected Honest Broker for BioInformatics Technology (HoBBIT) to systematically compile and share large-scale DP research datasets including anonymized images, redacted pathology reports, and clinical data of patients with consent.The solution has been operationally used over 3 years by 926 pathologists and researchers evaluating 288 903 digital slides. A total of 51\% of these were reviewed within 1 month after scanning. Seamless integration of the viewer into 4 hospital systems clearly increases the adoption of DP. HoBBIT directly impacts the translation of knowledge in pathology into effective new health measures, including artificial intelligence–driven detection models for prostate cancer, basal cell carcinoma, and breast cancer metastases, developed and validated on thousands of cases.We highlight major challenges and lessons learned when going digital to provide orientation for other pathologists. Building interconnected solutions will not only increase adoption of DP, but also facilitate next-generation computational pathology at scale for enhanced cancer research.},
    number = {9},
    urldate = {2021-07-14},
    journal = {Journal of the American Medical Informatics Association},
    author = {Sch\"uffler, Peter J and Geneslaw, Luke and Yarlagadda, D Vijay K and Hanna, Matthew G and Samboy, Jennifer and Stamelos, Evangelos and Vanderbilt, Chad and Philip, John and Jean, Marc-Henri and Corsale, Lorraine and Manzo, Allyne and Paramasivam, Neeraj H G and Ziegler, John S and Gao, Jianjiong and Perin, Juan C and Kim, Young Suk and Bhanot, Umeshkumar K and Roehrl, Michael H A and Ardon, Orly and Chiang, Sarah and Giri, Dilip D and Sigel, Carlie S and Tan, Lee K and Murray, Melissa and Virgo, Christina and England, Christine and Yagi, Yukako and Sirintrapun, S Joseph and Klimstra, David and Hameed, Meera and Reuter, Victor E and Fuchs, Thomas J},
    month = jul,
    year = {2021},
    pages = {1874--1884},
}
Download Endnote/RIS citation
TY - JOUR
TI - Integrated digital pathology at scale: A solution for clinical diagnostics and cancer research at a large academic medical center
AU - Schüffler, Peter J
AU - Geneslaw, Luke
AU - Yarlagadda, D Vijay K
AU - Hanna, Matthew G
AU - Samboy, Jennifer
AU - Stamelos, Evangelos
AU - Vanderbilt, Chad
AU - Philip, John
AU - Jean, Marc-Henri
AU - Corsale, Lorraine
AU - Manzo, Allyne
AU - Paramasivam, Neeraj H G
AU - Ziegler, John S
AU - Gao, Jianjiong
AU - Perin, Juan C
AU - Kim, Young Suk
AU - Bhanot, Umeshkumar K
AU - Roehrl, Michael H A
AU - Ardon, Orly
AU - Chiang, Sarah
AU - Giri, Dilip D
AU - Sigel, Carlie S
AU - Tan, Lee K
AU - Murray, Melissa
AU - Virgo, Christina
AU - England, Christine
AU - Yagi, Yukako
AU - Sirintrapun, S Joseph
AU - Klimstra, David
AU - Hameed, Meera
AU - Reuter, Victor E
AU - Fuchs, Thomas J
T2 - Journal of the American Medical Informatics Association
AB - Broad adoption of digital pathology (DP) is still lacking, and examples for DP connecting diagnostic, research, and educational use cases are missing. We blueprint a holistic DP solution at a large academic medical center ubiquitously integrated into clinical workflows; researchapplications including molecular, genetic, and tissue databases; and educational processes.We built a vendor-agnostic, integrated viewer for reviewing, annotating, sharing, and quality assurance of digital slides in a clinical or research context. It is the first homegrown viewer cleared by New York State provisional approval in 2020 for primary diagnosis and remote sign-out during the COVID-19 (coronavirus disease 2019) pandemic. We further introduce an interconnected Honest Broker for BioInformatics Technology (HoBBIT) to systematically compile and share large-scale DP research datasets including anonymized images, redacted pathology reports, and clinical data of patients with consent.The solution has been operationally used over 3 years by 926 pathologists and researchers evaluating 288 903 digital slides. A total of 51% of these were reviewed within 1 month after scanning. Seamless integration of the viewer into 4 hospital systems clearly increases the adoption of DP. HoBBIT directly impacts the translation of knowledge in pathology into effective new health measures, including artificial intelligence–driven detection models for prostate cancer, basal cell carcinoma, and breast cancer metastases, developed and validated on thousands of cases.We highlight major challenges and lessons learned when going digital to provide orientation for other pathologists. Building interconnected solutions will not only increase adoption of DP, but also facilitate next-generation computational pathology at scale for enhanced cancer research.
DA - 2021/07/14/
PY - 2021
DO - 10.1093/jamia/ocab085
DP - Silverchair
VL - 28
IS - 9
SP - 1874
EP - 1884
J2 - Journal of the American Medical Informatics Association
SN - 1527-974X
ST - Integrated digital pathology at scale
UR - https://doi.org/10.1093/jamia/ocab085
Y2 - 2021/07/14/21:13:00
ER -
Broad adoption of digital pathology (DP) is still lacking, and examples for DP connecting diagnostic, research, and educational use cases are missing. We blueprint a holistic DP solution at a large academic medical center ubiquitously integrated into clinical workflows; researchapplications including molecular, genetic, and tissue databases; and educational processes.We built a vendor-agnostic, integrated viewer for reviewing, annotating, sharing, and quality assurance of digital slides in a clinical or research context. It is the first homegrown viewer cleared by New York State provisional approval in 2020 for primary diagnosis and remote sign-out during the COVID-19 (coronavirus disease 2019) pandemic. We further introduce an interconnected Honest Broker for BioInformatics Technology (HoBBIT) to systematically compile and share large-scale DP research datasets including anonymized images, redacted pathology reports, and clinical data of patients with consent.The solution has been operationally used over 3 years by 926 pathologists and researchers evaluating 288 903 digital slides. A total of 51% of these were reviewed within 1 month after scanning. Seamless integration of the viewer into 4 hospital systems clearly increases the adoption of DP. HoBBIT directly impacts the translation of knowledge in pathology into effective new health measures, including artificial intelligence–driven detection models for prostate cancer, basal cell carcinoma, and breast cancer metastases, developed and validated on thousands of cases.We highlight major challenges and lessons learned when going digital to provide orientation for other pathologists. Building interconnected solutions will not only increase adoption of DP, but also facilitate next-generation computational pathology at scale for enhanced cancer research.
Orly Ardon, Victor E. Reuter, Meera Hameed, Lorraine Corsale, Allyne Manzo, Sahussapont J. Sirintrapun, Peter Ntiamoah, Evangelos Stamelos, Peter J. Schueffler, Christine England, David S. Klimstra and Matthew G. Hanna.
Digital Pathology Operations at an NYC Tertiary Cancer Center During the First 4 Months of COVID-19 Pandemic Response.
Academic Pathology, vol. 8, April 28, 2021 doi: 10.1177/23742895211010276
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@article{ardon_digital_2021,
    title = {Digital {Pathology} {Operations} at an {NYC} {Tertiary} {Cancer} {Center} {During} the {First} 4 {Months} of {COVID}-19 {Pandemic} {Response}},
    volume = {8},
    issn = {2374-2895},
    url = {https://doi.org/10.1177/23742895211010276},
    doi = {10.1177/23742895211010276},
    abstract = {Implementation of an infrastructure to support digital pathology began in 2006 at Memorial Sloan Kettering Cancer Center. The public health emergency and COVID-19 pandemic regulations in New York City required a novel workflow to sustain existing operations. While regulatory enforcement discretions offered faculty workspace flexibility, a substantial portion of laboratory and digital pathology workflows require on-site presence of staff. Maintaining social distancing and offering staggered work schedules. Due to a decrease in patients seeking health care at the onset of the pandemic, a temporary decrease in patient specimens was observed. Hospital and travel regulations impacted onsite vendor technical support. Digital glass slide scanning activities onsite proceeded without interruption throughout the pandemic, with challenges including staff who required quarantine due to virus exposure, unrelated illness, family support, or lack of public transportation. During the public health emergency, we validated digital pathology systems for a remote pathology operation. Since March 2020, the departmental digital pathology staff were able to maintain scanning volumes of over 100 000 slides per month. The digital scanning team reprioritized archival slide scanning and participated in a remote sign-out validation and successful submission of New York State approval for a laboratory developed test. Digital pathology offers a health care delivery model where pathologists can perform their sign out duties at remote location and prevent disruptions to critical pathology services for patients seeking care at our institution during emergencies. Development of standard operating procedures to support digital workflows will maintain turnaround times and enable clinical operations during emergency or otherwise unanticipated events.},
    language = {en},
    urldate = {2021-09-01},
    journal = {Academic Pathology},
    author = {Ardon, Orly and Reuter, Victor E. and Hameed, Meera and Corsale, Lorraine and Manzo, Allyne and Sirintrapun, Sahussapont J. and Ntiamoah, Peter and Stamelos, Evangelos and Schueffler, Peter J. and England, Christine and Klimstra, David S. and Hanna, Matthew G.},
    month = apr,
    year = {2021},
    keywords = {COVID-19, clinical, digital pathology, implementation, operations, remote signout, telepathology},
}
Download Endnote/RIS citation
TY - JOUR
TI - Digital Pathology Operations at an NYC Tertiary Cancer Center During the First 4 Months of COVID-19 Pandemic Response
AU - Ardon, Orly
AU - Reuter, Victor E.
AU - Hameed, Meera
AU - Corsale, Lorraine
AU - Manzo, Allyne
AU - Sirintrapun, Sahussapont J.
AU - Ntiamoah, Peter
AU - Stamelos, Evangelos
AU - Schueffler, Peter J.
AU - England, Christine
AU - Klimstra, David S.
AU - Hanna, Matthew G.
T2 - Academic Pathology
AB - Implementation of an infrastructure to support digital pathology began in 2006 at Memorial Sloan Kettering Cancer Center. The public health emergency and COVID-19 pandemic regulations in New York City required a novel workflow to sustain existing operations. While regulatory enforcement discretions offered faculty workspace flexibility, a substantial portion of laboratory and digital pathology workflows require on-site presence of staff. Maintaining social distancing and offering staggered work schedules. Due to a decrease in patients seeking health care at the onset of the pandemic, a temporary decrease in patient specimens was observed. Hospital and travel regulations impacted onsite vendor technical support. Digital glass slide scanning activities onsite proceeded without interruption throughout the pandemic, with challenges including staff who required quarantine due to virus exposure, unrelated illness, family support, or lack of public transportation. During the public health emergency, we validated digital pathology systems for a remote pathology operation. Since March 2020, the departmental digital pathology staff were able to maintain scanning volumes of over 100 000 slides per month. The digital scanning team reprioritized archival slide scanning and participated in a remote sign-out validation and successful submission of New York State approval for a laboratory developed test. Digital pathology offers a health care delivery model where pathologists can perform their sign out duties at remote location and prevent disruptions to critical pathology services for patients seeking care at our institution during emergencies. Development of standard operating procedures to support digital workflows will maintain turnaround times and enable clinical operations during emergency or otherwise unanticipated events.
DA - 2021/04/28/
PY - 2021
DO - 10.1177/23742895211010276
DP - SAGE Journals
VL - 8
J2 - Academic Pathology
LA - en
SN - 2374-2895
UR - https://doi.org/10.1177/23742895211010276
Y2 - 2021/09/01/07:50:28
KW - COVID-19
KW - clinical
KW - digital pathology
KW - implementation
KW - operations
KW - remote signout
KW - telepathology
ER -
Implementation of an infrastructure to support digital pathology began in 2006 at Memorial Sloan Kettering Cancer Center. The public health emergency and COVID-19 pandemic regulations in New York City required a novel workflow to sustain existing operations. While regulatory enforcement discretions offered faculty workspace flexibility, a substantial portion of laboratory and digital pathology workflows require on-site presence of staff. Maintaining social distancing and offering staggered work schedules. Due to a decrease in patients seeking health care at the onset of the pandemic, a temporary decrease in patient specimens was observed. Hospital and travel regulations impacted onsite vendor technical support. Digital glass slide scanning activities onsite proceeded without interruption throughout the pandemic, with challenges including staff who required quarantine due to virus exposure, unrelated illness, family support, or lack of public transportation. During the public health emergency, we validated digital pathology systems for a remote pathology operation. Since March 2020, the departmental digital pathology staff were able to maintain scanning volumes of over 100 000 slides per month. The digital scanning team reprioritized archival slide scanning and participated in a remote sign-out validation and successful submission of New York State approval for a laboratory developed test. Digital pathology offers a health care delivery model where pathologists can perform their sign out duties at remote location and prevent disruptions to critical pathology services for patients seeking care at our institution during emergencies. Development of standard operating procedures to support digital workflows will maintain turnaround times and enable clinical operations during emergency or otherwise unanticipated events.
Peter J. Schüffler, Dig Vijay Kumar Yarlagadda, Chad Vanderbilt and Thomas J. Fuchs.
Overcoming an annotation hurdle: Digitizing pen annotations from whole slide images.
Journal of Pathology Informatics, vol. 12, 1, p. 9, 02/23/2021 doi: 10.4103/jpi.jpi_85_20
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@article{schuffler_overcoming_2021,
    title = {Overcoming an annotation hurdle: {Digitizing} pen annotations from whole slide images},
    volume = {12},
    issn = {2153-3539},
    shorttitle = {Overcoming an annotation hurdle},
    url = {https://www.doi.org/10.4103/jpi.jpi_85_20},
    doi = {10.4103/jpi.jpi_85_20},
    abstract = {Background:The development of artificial intelligence (AI) in pathology frequently relies on digitally annotated whole slide images (WSI). The creation of these annotations – manually drawn by pathologists in digital slide viewers – is time consuming and expensive. At the same time, pathologists routinely annotate glass slides with a pen to outline cancerous regions, for example, for molecular assessment of the tissue. These pen annotations are currently considered artifacts and excluded from computational modeling. Methods:We propose a novel method to segment and fill hand-drawn pen annotations and convert them into a digital format to make them accessible for computational models. Our method is implemented in Python as an open source, publicly available software tool. Results: Our method is able to extract pen annotations from WSI and save them as annotation masks. On a data set of 319 WSI with pen markers, we validate our algorithm segmenting the annotations with an overall Dice metric of 0.942, Precision of 0.955, and Recall of 0.943. Processing all images takes 15 min in contrast to 5 h manual digital annotation time. Further, the approach is robust against text annotations. Conclusions: We envision that our method can take advantage of already pen-annotated slides in scenarios in which the annotations would be helpful for training computational models. We conclude that, considering the large archives of many pathology departments that are currently being digitized, our method will help to collect large numbers of training samples from those data.},
    language = {en},
    number = {1},
    urldate = {2021-02-25},
    journal = {Journal of Pathology Informatics},
    author = {Sch\"uffler, Peter J. and Yarlagadda, Dig Vijay Kumar and Vanderbilt, Chad and Fuchs, Thomas J.},
    month = feb,
    year = {2021},
    Distributor: Medknow Publications and Media Pvt. Ltd.
    Institution: Medknow Publications and Media Pvt. Ltd.
    Label: Medknow Publications and Media Pvt. Ltd.
    Publisher: Medknow Publications},
    pages = {9},
}
Download Endnote/RIS citation
TY - JOUR
TI - Overcoming an annotation hurdle: Digitizing pen annotations from whole slide images
AU - Schüffler, Peter J.
AU - Yarlagadda, Dig Vijay Kumar
AU - Vanderbilt, Chad
AU - Fuchs, Thomas J.
T2 - Journal of Pathology Informatics
AB - Background:The development of artificial intelligence (AI) in pathology frequently relies on digitally annotated whole slide images (WSI). The creation of these annotations – manually drawn by pathologists in digital slide viewers – is time consuming and expensive. At the same time, pathologists routinely annotate glass slides with a pen to outline cancerous regions, for example, for molecular assessment of the tissue. These pen annotations are currently considered artifacts and excluded from computational modeling. Methods:We propose a novel method to segment and fill hand-drawn pen annotations and convert them into a digital format to make them accessible for computational models. Our method is implemented in Python as an open source, publicly available software tool. Results: Our method is able to extract pen annotations from WSI and save them as annotation masks. On a data set of 319 WSI with pen markers, we validate our algorithm segmenting the annotations with an overall Dice metric of 0.942, Precision of 0.955, and Recall of 0.943. Processing all images takes 15 min in contrast to 5 h manual digital annotation time. Further, the approach is robust against text annotations. Conclusions: We envision that our method can take advantage of already pen-annotated slides in scenarios in which the annotations would be helpful for training computational models. We conclude that, considering the large archives of many pathology departments that are currently being digitized, our method will help to collect large numbers of training samples from those data.
DA - 2021/02/23/
PY - 2021
DO - 10.4103/jpi.jpi_85_20
DP - www.jpathinformatics.org
VL - 12
IS - 1
SP - 9
LA - en
SN - 2153-3539
ST - Overcoming an annotation hurdle
UR - https://www.doi.org/10.4103/jpi.jpi_85_20
Y2 - 2021/02/25/18:48:46
ER -
Background:The development of artificial intelligence (AI) in pathology frequently relies on digitally annotated whole slide images (WSI). The creation of these annotations – manually drawn by pathologists in digital slide viewers – is time consuming and expensive. At the same time, pathologists routinely annotate glass slides with a pen to outline cancerous regions, for example, for molecular assessment of the tissue. These pen annotations are currently considered artifacts and excluded from computational modeling. Methods:We propose a novel method to segment and fill hand-drawn pen annotations and convert them into a digital format to make them accessible for computational models. Our method is implemented in Python as an open source, publicly available software tool. Results: Our method is able to extract pen annotations from WSI and save them as annotation masks. On a data set of 319 WSI with pen markers, we validate our algorithm segmenting the annotations with an overall Dice metric of 0.942, Precision of 0.955, and Recall of 0.943. Processing all images takes 15 min in contrast to 5 h manual digital annotation time. Further, the approach is robust against text annotations. Conclusions: We envision that our method can take advantage of already pen-annotated slides in scenarios in which the annotations would be helpful for training computational models. We conclude that, considering the large archives of many pathology departments that are currently being digitized, our method will help to collect large numbers of training samples from those data.
Zhang Li, Jiehua Zhang, Tao Tan, Xichao Teng, Xiaoliang Sun, Hong Zhao, Lihong Liu, Yang Xiao, Byungjae Lee, Yilong Li, Qianni Zhang, Shujiao Sun, Yushan Zheng, Junyu Yan, Ni Li, Yiyu Hong, Junsu Ko, Hyun Jung, Yanling Liu, Yu-cheng Chen, Ching-wei Wang, Vladimir Yurovskiy, Pavel Maevskikh, Vahid Khanagha, Yi Jiang, Li Yu, Zhihong Liu, Daiqiang Li, Peter J. Schuffler, Qifeng Yu, Hui Chen, Yuling Tang and Geert Litjens.
Deep Learning Methods for Lung Cancer Segmentation in Whole-Slide Histopathology Images—The ACDC@LungHP Challenge 2019.
IEEE J. Biomed. Health Inform., vol. 25, 2, p. 429-440, 2/2021 doi: 10.1109/jbhi.2020.3039741
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@article{li_deep_2021,
    title = {Deep {Learning} {Methods} for {Lung} {Cancer} {Segmentation} in {Whole}-{Slide} {Histopathology} {Images}—{The} {ACDC}@{LungHP} {Challenge} 2019},
    volume = {25},
    issn = {2168-2194, 2168-2208},
    url = {https://ieeexplore.ieee.org/document/9265237/},
    doi = {10.1109/JBHI.2020.3039741},
    number = {2},
    urldate = {2022-07-13},
    journal = {IEEE Journal of Biomedical and Health Informatics},
    author = {Li, Zhang and Zhang, Jiehua and Tan, Tao and Teng, Xichao and Sun, Xiaoliang and Zhao, Hong and Liu, Lihong and Xiao, Yang and Lee, Byungjae and Li, Yilong and Zhang, Qianni and Sun, Shujiao and Zheng, Yushan and Yan, Junyu and Li, Ni and Hong, Yiyu and Ko, Junsu and Jung, Hyun and Liu, Yanling and Chen, Yu-cheng and Wang, Ching-wei and Yurovskiy, Vladimir and Maevskikh, Pavel and Khanagha, Vahid and Jiang, Yi and Yu, Li and Liu, Zhihong and Li, Daiqiang and Schuffler, Peter J. and Yu, Qifeng and Chen, Hui and Tang, Yuling and Litjens, Geert},
    month = feb,
    year = {2021},
    pages = {429--440},
}
Download Endnote/RIS citation
TY - JOUR
TI - Deep Learning Methods for Lung Cancer Segmentation in Whole-Slide Histopathology Images—The ACDC@LungHP Challenge 2019
AU - Li, Zhang
AU - Zhang, Jiehua
AU - Tan, Tao
AU - Teng, Xichao
AU - Sun, Xiaoliang
AU - Zhao, Hong
AU - Liu, Lihong
AU - Xiao, Yang
AU - Lee, Byungjae
AU - Li, Yilong
AU - Zhang, Qianni
AU - Sun, Shujiao
AU - Zheng, Yushan
AU - Yan, Junyu
AU - Li, Ni
AU - Hong, Yiyu
AU - Ko, Junsu
AU - Jung, Hyun
AU - Liu, Yanling
AU - Chen, Yu-cheng
AU - Wang, Ching-wei
AU - Yurovskiy, Vladimir
AU - Maevskikh, Pavel
AU - Khanagha, Vahid
AU - Jiang, Yi
AU - Yu, Li
AU - Liu, Zhihong
AU - Li, Daiqiang
AU - Schuffler, Peter J.
AU - Yu, Qifeng
AU - Chen, Hui
AU - Tang, Yuling
AU - Litjens, Geert
T2 - IEEE Journal of Biomedical and Health Informatics
DA - 2021/02//
PY - 2021
DO - 10.1109/JBHI.2020.3039741
DP - DOI.org (Crossref)
VL - 25
IS - 2
SP - 429
EP - 440
J2 - IEEE J. Biomed. Health Inform.
SN - 2168-2194, 2168-2208
UR - https://ieeexplore.ieee.org/document/9265237/
Y2 - 2022/07/13/12:46:14
ER -
Peter J. Schüffler, Gamze Gokturk Ozcan, Hikmat Al-Ahmadie and Thomas J. Fuchs.
Flextilesource: An openseadragon extension for efficient whole-slide image visualization.
J Pathol Inform, vol. 12, 1, p. 31, 2021 doi: 10.4103/jpi.jpi_13_21
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@article{schuffler_flextilesource_2021,
    title = {Flextilesource: {An} openseadragon extension for efficient whole-slide image visualization},
    volume = {12},
    issn = {2153-3539},
    shorttitle = {Flextilesource},
    url = {https://doi.org/10.4103/jpi.jpi_13_21},
    doi = {10.4103/jpi.jpi_13_21},
    language = {en},
    number = {1},
    urldate = {2021-09-14},
    journal = {Journal of Pathology Informatics},
    author = {Sch\"uffler, Peter J. and Ozcan, Gamze Gokturk and Al-Ahmadie, Hikmat and Fuchs, Thomas J.},
    year = {2021},
    pages = {31},
}
Download Endnote/RIS citation
TY - JOUR
TI - Flextilesource: An openseadragon extension for efficient whole-slide image visualization
AU - Schüffler, Peter J.
AU - Ozcan, Gamze Gokturk
AU - Al-Ahmadie, Hikmat
AU - Fuchs, Thomas J.
T2 - Journal of Pathology Informatics
DA - 2021///
PY - 2021
DO - 10.4103/jpi.jpi_13_21
VL - 12
IS - 1
SP - 31
J2 - J Pathol Inform
LA - en
SN - 2153-3539
ST - Flextilesource
UR - https://doi.org/10.4103/jpi.jpi_13_21
Y2 - 2021/09/14/18:42:14
ER -
Matthew G. Hanna, Victor E. Reuter, Orly Ardon, David Kim, Sahussapont Joseph Sirintrapun, Peter J. Schüffler, Klaus J. Busam, Jennifer L. Sauter, Edi Brogi, Lee K. Tan, Bin Xu, Tejus Bale, Narasimhan P. Agaram, Laura H. Tang, Lora H. Ellenson, John Philip, Lorraine Corsale, Evangelos Stamelos, Maria A. Friedlander, Peter Ntiamoah, Marc Labasin, Christine England, David S. Klimstra and Meera Hameed.
Validation of a digital pathology system including remote review during the COVID-19 pandemic.
Modern Pathology, vol. 33, p. 2115–2127, 2020-06-22 doi: 10.1038/s41379-020-0601-5
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@article{hanna_validation_2020,
    title = {Validation of a digital pathology system including remote review during the {COVID}-19 pandemic},
    volume = {33},
    copyright = {2020 The Author(s), under exclusive licence to United States \& Canadian Academy of Pathology},
    issn = {1530-0285},
    url = {https://www.nature.com/articles/s41379-020-0601-5},
    doi = {10.1038/s41379-020-0601-5},
    abstract = {Remote digital pathology allows healthcare systems to maintain pathology operations during public health emergencies. Existing Clinical Laboratory Improvement Amendments regulations require pathologists to electronically verify patient reports from a certified facility. During the 2019 pandemic of COVID-19 disease, caused by the SAR-CoV-2 virus, this requirement potentially exposes pathologists, their colleagues, and household members to the risk of becoming infected. Relaxation of government enforcement of this regulation allows pathologists to review and report pathology specimens from a remote, non-CLIA certified facility. The availability of digital pathology systems can facilitate remote microscopic diagnosis, although formal comprehensive (case-based) validation of remote digital diagnosis has not been reported. All glass slides representing routine clinical signout workload in surgical pathology subspecialties at Memorial Sloan Kettering Cancer Center were scanned on an Aperio GT450 at ×40 equivalent resolution (0.26 µm/pixel). Twelve pathologists from nine surgical pathology subspecialties remotely reviewed and reported complete pathology cases using a digital pathology system from a non-CLIA certified facility through a secure connection. Whole slide images were integrated to and launched within the laboratory information system to a custom vendor-agnostic, whole slide image viewer. Remote signouts utilized consumer-grade computers and monitors (monitor size, 13.3–42 in.; resolution, 1280 × 800–3840 × 2160 pixels) connecting to an institution clinical workstation via secure virtual private network. Pathologists subsequently reviewed all corresponding glass slides using a light microscope within the CLIA-certified department. Intraobserver concordance metrics included reporting elements of top-line diagnosis, margin status, lymphovascular and/or perineural invasion, pathology stage, and ancillary testing. The median whole slide image file size was 1.3 GB; scan time/slide averaged 90 s; and scanned tissue area averaged 612 mm2. Signout sessions included a total of 108 cases, comprised of 254 individual parts and 1196 slides. Major diagnostic equivalency was 100\% between digital and glass slide diagnoses; and overall concordance was 98.8\% (251/254). This study reports validation of primary diagnostic review and reporting of complete pathology cases from a remote site during a public health emergency. Our experience shows high (100\%) intraobserver digital to glass slide major diagnostic concordance when reporting from a remote site. This randomized, prospective study successfully validated remote use of a digital pathology system including operational feasibility supporting remote review and reporting of pathology specimens, and evaluation of remote access performance and usability for remote signout.},
    language = {en},
    urldate = {2020-06-22},
    journal = {Modern Pathology},
    author = {Hanna, Matthew G. and Reuter, Victor E. and Ardon, Orly and Kim, David and Sirintrapun, Sahussapont Joseph and Sch\"uffler, Peter J. and Busam, Klaus J. and Sauter, Jennifer L. and Brogi, Edi and Tan, Lee K. and Xu, Bin and Bale, Tejus and Agaram, Narasimhan P. and Tang, Laura H. and Ellenson, Lora H. and Philip, John and Corsale, Lorraine and Stamelos, Evangelos and Friedlander, Maria A. and Ntiamoah, Peter and Labasin, Marc and England, Christine and Klimstra, David S. and Hameed, Meera},
    month = jun,
    year = {2020},
    pages = {2115--2127},
}
Download Endnote/RIS citation
TY - JOUR
TI - Validation of a digital pathology system including remote review during the COVID-19 pandemic
AU - Hanna, Matthew G.
AU - Reuter, Victor E.
AU - Ardon, Orly
AU - Kim, David
AU - Sirintrapun, Sahussapont Joseph
AU - Schüffler, Peter J.
AU - Busam, Klaus J.
AU - Sauter, Jennifer L.
AU - Brogi, Edi
AU - Tan, Lee K.
AU - Xu, Bin
AU - Bale, Tejus
AU - Agaram, Narasimhan P.
AU - Tang, Laura H.
AU - Ellenson, Lora H.
AU - Philip, John
AU - Corsale, Lorraine
AU - Stamelos, Evangelos
AU - Friedlander, Maria A.
AU - Ntiamoah, Peter
AU - Labasin, Marc
AU - England, Christine
AU - Klimstra, David S.
AU - Hameed, Meera
T2 - Modern Pathology
AB - Remote digital pathology allows healthcare systems to maintain pathology operations during public health emergencies. Existing Clinical Laboratory Improvement Amendments regulations require pathologists to electronically verify patient reports from a certified facility. During the 2019 pandemic of COVID-19 disease, caused by the SAR-CoV-2 virus, this requirement potentially exposes pathologists, their colleagues, and household members to the risk of becoming infected. Relaxation of government enforcement of this regulation allows pathologists to review and report pathology specimens from a remote, non-CLIA certified facility. The availability of digital pathology systems can facilitate remote microscopic diagnosis, although formal comprehensive (case-based) validation of remote digital diagnosis has not been reported. All glass slides representing routine clinical signout workload in surgical pathology subspecialties at Memorial Sloan Kettering Cancer Center were scanned on an Aperio GT450 at ×40 equivalent resolution (0.26 µm/pixel). Twelve pathologists from nine surgical pathology subspecialties remotely reviewed and reported complete pathology cases using a digital pathology system from a non-CLIA certified facility through a secure connection. Whole slide images were integrated to and launched within the laboratory information system to a custom vendor-agnostic, whole slide image viewer. Remote signouts utilized consumer-grade computers and monitors (monitor size, 13.3–42 in.; resolution, 1280 × 800–3840 × 2160 pixels) connecting to an institution clinical workstation via secure virtual private network. Pathologists subsequently reviewed all corresponding glass slides using a light microscope within the CLIA-certified department. Intraobserver concordance metrics included reporting elements of top-line diagnosis, margin status, lymphovascular and/or perineural invasion, pathology stage, and ancillary testing. The median whole slide image file size was 1.3 GB; scan time/slide averaged 90 s; and scanned tissue area averaged 612 mm2. Signout sessions included a total of 108 cases, comprised of 254 individual parts and 1196 slides. Major diagnostic equivalency was 100% between digital and glass slide diagnoses; and overall concordance was 98.8% (251/254). This study reports validation of primary diagnostic review and reporting of complete pathology cases from a remote site during a public health emergency. Our experience shows high (100%) intraobserver digital to glass slide major diagnostic concordance when reporting from a remote site. This randomized, prospective study successfully validated remote use of a digital pathology system including operational feasibility supporting remote review and reporting of pathology specimens, and evaluation of remote access performance and usability for remote signout.
DA - 2020/06/22/
PY - 2020
DO - 10.1038/s41379-020-0601-5
DP - www.nature.com
VL - 33
SP - 2115
EP - 2127
LA - en
SN - 1530-0285
UR - https://www.nature.com/articles/s41379-020-0601-5
Y2 - 2020/06/22/12:46:57
ER -
Remote digital pathology allows healthcare systems to maintain pathology operations during public health emergencies. Existing Clinical Laboratory Improvement Amendments regulations require pathologists to electronically verify patient reports from a certified facility. During the 2019 pandemic of COVID-19 disease, caused by the SAR-CoV-2 virus, this requirement potentially exposes pathologists, their colleagues, and household members to the risk of becoming infected. Relaxation of government enforcement of this regulation allows pathologists to review and report pathology specimens from a remote, non-CLIA certified facility. The availability of digital pathology systems can facilitate remote microscopic diagnosis, although formal comprehensive (case-based) validation of remote digital diagnosis has not been reported. All glass slides representing routine clinical signout workload in surgical pathology subspecialties at Memorial Sloan Kettering Cancer Center were scanned on an Aperio GT450 at ×40 equivalent resolution (0.26 µm/pixel). Twelve pathologists from nine surgical pathology subspecialties remotely reviewed and reported complete pathology cases using a digital pathology system from a non-CLIA certified facility through a secure connection. Whole slide images were integrated to and launched within the laboratory information system to a custom vendor-agnostic, whole slide image viewer. Remote signouts utilized consumer-grade computers and monitors (monitor size, 13.3–42 in.; resolution, 1280 × 800–3840 × 2160 pixels) connecting to an institution clinical workstation via secure virtual private network. Pathologists subsequently reviewed all corresponding glass slides using a light microscope within the CLIA-certified department. Intraobserver concordance metrics included reporting elements of top-line diagnosis, margin status, lymphovascular and/or perineural invasion, pathology stage, and ancillary testing. The median whole slide image file size was 1.3 GB; scan time/slide averaged 90 s; and scanned tissue area averaged 612 mm2. Signout sessions included a total of 108 cases, comprised of 254 individual parts and 1196 slides. Major diagnostic equivalency was 100% between digital and glass slide diagnoses; and overall concordance was 98.8% (251/254). This study reports validation of primary diagnostic review and reporting of complete pathology cases from a remote site during a public health emergency. Our experience shows high (100%) intraobserver digital to glass slide major diagnostic concordance when reporting from a remote site. This randomized, prospective study successfully validated remote use of a digital pathology system including operational feasibility supporting remote review and reporting of pathology specimens, and evaluation of remote access performance and usability for remote signout.
David Kim, Liron Pantanowitz, Peter Schüffler, Dig Vijay Kumar Yarlagadda, Orly Ardon, Victor E. Reuter, Meera Hameed, David S. Klimstra and Matthew G. Hanna.
(Re) Defining the high-power field for digital pathology.
Journal of Pathology Informatics, vol. 11, 1, p. 33, 1/1/2020 doi: 10.4103/jpi.jpi_48_20
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@article{kim_re_2020,
    title = {({Re}) {Defining} the high-power field for digital pathology},
    volume = {11},
    issn = {2153-3539},
    url = {https://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2020;volume=11;issue=1;spage=33;epage=33;aulast=Kim;type=0},
    doi = {10.4103/jpi.jpi_48_20},
    abstract = {{\textless}br{\textgreater}\textbf{Background:} The microscope high-power field (HPF) is the cornerstone for histopathology diagnostic evaluation such as the quantification of mitotic figures, lymphocytes, and tumor grading. With traditional light microscopy, HPFs are typically evaluated by quantifying histologic events in 10 fields of view at × 400 magnification. In the era of digital pathology, new variables are introduced that may affect HPF evaluation. The aim of this study was to determine the parameters that influence HPF in whole slide images (WSIs). \textbf{Materials and Methods:} Glass slides scanned on various devices (Leica's Aperio GT450, AT2, and ScanScope XT; Philips UltraFast Scanner; Hamamatsu's Nanozoomer 2.0HT; and 3DHistech's P1000) were compared to acquired digital slides reviewed on each vendor's respective WSI viewer software (e.g., Aperio ImageScope, ImageScope DX, Philips IMS, 3DHistech CaseViewer, and Hamamatsu NDP.view) and an in-house developed vendor-agnostic viewer. WSIs were reviewed at “×40” equivalent HPF on different sized monitors with varying display resolutions (1900 × 1080–4500 × 3000) and aspect ratios (e.g., Food and Drug Administration [FDA]-cleared 27” Philips PS27QHDCR, FDA-cleared 24” Dell MR2416, 24” Hewlett Packard Z24n G2, and 28” Microsoft Surface Studio). Digital and microscopic HPF areas were calculated and compared. \textbf{Results:} A significant variation of HPF area occurred between differing monitor size and display resolutions with minor differences between WSI viewers. No differences were identified by scanner or WSIs scanned at different resolutions (e.g., 0.5, 0.25, 0.24, and 0.12 μm/pixel). \textbf{Conclusion:} Glass slide HPF at × 400 magnification with conventional light microscopy was not equivalent to “×40” digital HPF areas. Digital HPF quantification may vary due to differences in the tissue area displayed by monitor sizes, display resolutions, and WSI viewers but not by scanner or scanning resolution. These findings will need to be further clinically validated with potentially new digital metrics for evaluation.{\textless}br{\textgreater}},
    language = {en},
    number = {1},
    urldate = {2020-10-28},
    journal = {Journal of Pathology Informatics},
    author = {Kim, David and Pantanowitz, Liron and Sch\"uffler, Peter and Yarlagadda, Dig Vijay Kumar and Ardon, Orly and Reuter, Victor E. and Hameed, Meera and Klimstra, David S. and Hanna, Matthew G.},
    month = jan,
    year = {2020},
    Distributor: Medknow Publications and Media Pvt. Ltd.
    Institution: Medknow Publications and Media Pvt. Ltd.
    Label: Medknow Publications and Media Pvt. Ltd.
    Publisher: Medknow Publications},
    pages = {33},
}
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TY - JOUR
TI - (Re) Defining the high-power field for digital pathology
AU - Kim, David
AU - Pantanowitz, Liron
AU - Schüffler, Peter
AU - Yarlagadda, Dig Vijay Kumar
AU - Ardon, Orly
AU - Reuter, Victor E.
AU - Hameed, Meera
AU - Klimstra, David S.
AU - Hanna, Matthew G.
T2 - Journal of Pathology Informatics
AB -
Background: The microscope high-power field (HPF) is the cornerstone for histopathology diagnostic evaluation such as the quantification of mitotic figures, lymphocytes, and tumor grading. With traditional light microscopy, HPFs are typically evaluated by quantifying histologic events in 10 fields of view at × 400 magnification. In the era of digital pathology, new variables are introduced that may affect HPF evaluation. The aim of this study was to determine the parameters that influence HPF in whole slide images (WSIs). Materials and Methods: Glass slides scanned on various devices (Leica's Aperio GT450, AT2, and ScanScope XT; Philips UltraFast Scanner; Hamamatsu's Nanozoomer 2.0HT; and 3DHistech's P1000) were compared to acquired digital slides reviewed on each vendor's respective WSI viewer software (e.g., Aperio ImageScope, ImageScope DX, Philips IMS, 3DHistech CaseViewer, and Hamamatsu NDP.view) and an in-house developed vendor-agnostic viewer. WSIs were reviewed at “×40” equivalent HPF on different sized monitors with varying display resolutions (1900 × 1080–4500 × 3000) and aspect ratios (e.g., Food and Drug Administration [FDA]-cleared 27” Philips PS27QHDCR, FDA-cleared 24” Dell MR2416, 24” Hewlett Packard Z24n G2, and 28” Microsoft Surface Studio). Digital and microscopic HPF areas were calculated and compared. Results: A significant variation of HPF area occurred between differing monitor size and display resolutions with minor differences between WSI viewers. No differences were identified by scanner or WSIs scanned at different resolutions (e.g., 0.5, 0.25, 0.24, and 0.12 μm/pixel). Conclusion: Glass slide HPF at × 400 magnification with conventional light microscopy was not equivalent to “×40” digital HPF areas. Digital HPF quantification may vary due to differences in the tissue area displayed by monitor sizes, display resolutions, and WSI viewers but not by scanner or scanning resolution. These findings will need to be further clinically validated with potentially new digital metrics for evaluation.

DA - 2020/01/01/
PY - 2020
DO - 10.4103/jpi.jpi_48_20
DP - www.jpathinformatics.org
VL - 11
IS - 1
SP - 33
LA - en
SN - 2153-3539
UR - https://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2020;volume=11;issue=1;spage=33;epage=33;aulast=Kim;type=0
Y2 - 2020/10/28/14:22:22
ER -

Background: The microscope high-power field (HPF) is the cornerstone for histopathology diagnostic evaluation such as the quantification of mitotic figures, lymphocytes, and tumor grading. With traditional light microscopy, HPFs are typically evaluated by quantifying histologic events in 10 fields of view at × 400 magnification. In the era of digital pathology, new variables are introduced that may affect HPF evaluation. The aim of this study was to determine the parameters that influence HPF in whole slide images (WSIs). Materials and Methods: Glass slides scanned on various devices (Leica's Aperio GT450, AT2, and ScanScope XT; Philips UltraFast Scanner; Hamamatsu's Nanozoomer 2.0HT; and 3DHistech's P1000) were compared to acquired digital slides reviewed on each vendor's respective WSI viewer software (e.g., Aperio ImageScope, ImageScope DX, Philips IMS, 3DHistech CaseViewer, and Hamamatsu NDP.view) and an in-house developed vendor-agnostic viewer. WSIs were reviewed at “×40” equivalent HPF on different sized monitors with varying display resolutions (1900 × 1080–4500 × 3000) and aspect ratios (e.g., Food and Drug Administration [FDA]-cleared 27” Philips PS27QHDCR, FDA-cleared 24” Dell MR2416, 24” Hewlett Packard Z24n G2, and 28” Microsoft Surface Studio). Digital and microscopic HPF areas were calculated and compared. Results: A significant variation of HPF area occurred between differing monitor size and display resolutions with minor differences between WSI viewers. No differences were identified by scanner or WSIs scanned at different resolutions (e.g., 0.5, 0.25, 0.24, and 0.12 μm/pixel). Conclusion: Glass slide HPF at × 400 magnification with conventional light microscopy was not equivalent to “×40” digital HPF areas. Digital HPF quantification may vary due to differences in the tissue area displayed by monitor sizes, display resolutions, and WSI viewers but not by scanner or scanning resolution. These findings will need to be further clinically validated with potentially new digital metrics for evaluation.
David Joon Ho, Narasimhan P. Agaram, Peter J. Schüffler, Chad M. Vanderbilt, Marc-Henri Jean, Meera R. Hameed and Thomas J. Fuchs.
Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment.
In: Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu and Leo Joskowicz (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, vol. 12265, p. 540-549, Springer International Publishing, ISBN 978-3-030-59721-4 978-3-030-59722-1, 2020 
PDF    URL   BibTeX   Endnote / RIS   Abstract
Download BibTeX citation
@incollection{martel_deep_2020,
    address = {Cham},
    title = {Deep {Interactive} {Learning}: {An} {Efficient} {Labeling} {Approach} for {Deep} {Learning}-{Based} {Osteosarcoma} {Treatment} {Response} {Assessment}},
    volume = {12265},
    isbn = {978-3-030-59721-4 978-3-030-59722-1},
    shorttitle = {Deep {Interactive} {Learning}},
    url = {http://link.springer.com/10.1007/978-3-030-59722-1_52},
    language = {en},
    urldate = {2020-10-06},
    booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2020},
    publisher = {Springer International Publishing},
    author = {Ho, David Joon and Agaram, Narasimhan P. and Sch\"uffler, Peter J. and Vanderbilt, Chad M. and Jean, Marc-Henri and Hameed, Meera R. and Fuchs, Thomas J.},
    editor = {Martel, Anne L. and Abolmaesumi, Purang and Stoyanov, Danail and Mateus, Diana and Zuluaga, Maria A. and Zhou, S. Kevin and Racoceanu, Daniel and Joskowicz, Leo},
    year = {2020},
    doi = {10.1007/978-3-030-59722-1_52},
    pages = {540--549},
}
Download Endnote/RIS citation
TY - CHAP
TI - Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment
AU - Ho, David Joon
AU - Agaram, Narasimhan P.
AU - Schüffler, Peter J.
AU - Vanderbilt, Chad M.
AU - Jean, Marc-Henri
AU - Hameed, Meera R.
AU - Fuchs, Thomas J.
T2 - Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
A2 - Martel, Anne L.
A2 - Abolmaesumi, Purang
A2 - Stoyanov, Danail
A2 - Mateus, Diana
A2 - Zuluaga, Maria A.
A2 - Zhou, S. Kevin
A2 - Racoceanu, Daniel
A2 - Joskowicz, Leo
CY - Cham
DA - 2020///
PY - 2020
DP - DOI.org (Crossref)
VL - 12265
SP - 540
EP - 549
LA - en
PB - Springer International Publishing
SN - 978-3-030-59721-4 978-3-030-59722-1
ST - Deep Interactive Learning
UR - http://link.springer.com/10.1007/978-3-030-59722-1_52
Y2 - 2020/10/06/08:47:12
ER -
Anne Grabenstetter, Tracy-Ann Moo, Sabina Hajiyeva, Peter J. Schüffler, Pallavi Khattar, Maria A. Friedlander, Maura A. McCormack, Monica Raiss, Emily C. Zabor, Andrea Barrio, Monica Morrow and Marcia Edelweiss.
Accuracy of Intraoperative Frozen Section of Sentinel Lymph Nodes After Neoadjuvant Chemotherapy for Breast Carcinoma.
Am. J. Surg. Pathol., Jun 18, 2019 doi: 10.1097/pas.0000000000001311
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@article{grabenstetter_accuracy_2019,
    title = {Accuracy of {Intraoperative} {Frozen} {Section} of {Sentinel} {Lymph} {Nodes} {After} {Neoadjuvant} {Chemotherapy} for {Breast} {Carcinoma}},
    issn = {1532-0979},
    url = {https://pubmed.ncbi.nlm.nih.gov/31219817/},
    doi = {10.1097/PAS.0000000000001311},
    abstract = {False-negative (FN) intraoperative frozen section (FS) results of sentinel lymph nodes (SLN) have been reported to be more common after neoadjuvant chemotherapy (NAC) in the primary surgical setting. We evaluated SLN FS assessment in breast cancer patients treated with NAC to determine the FN rate and the histomorphologic factors associated with FN results. Patients who had FS SLN assessment following NAC from July 2008 to July 2017 were identified. Of the 711 SLN FS cases, 522 were negative, 181 positive, and 8 deferred. The FN rate was 5.4\% (28/522). There were no false-positive results. Of the 8 deferred cases, 5 were positive on permanent section and 3 were negative. There was a higher frequency of micrometastasis and isolated tumor cells in FN cases (P{\textless}0.001). There was a significant increase in tissue surface area present on permanent section slides compared with FS slides (P{\textless}0.001), highlighting the inherent technical limitations of FS and histologic under-sampling of tissue which leads to most FN results. The majority (25/28, 89\%) of FN cases had metastatic foci identified exclusively on permanent sections and were not due to a true diagnostic interpretation error. FN cases were more frequently estrogen receptor positive (P{\textless}0.001), progesterone receptor positive (P=0.001), human epidermal growth factor receptor-2 negative (P=0.009) and histologic grade 1 (P=0.015), which most likely reflects the lower rates of pathologic complete response in these tumors. Despite its limitations, FS is a reliable modality to assess the presence of SLN metastases in NAC treated patients.},
    language = {eng},
    journal = {The American Journal of Surgical Pathology},
    author = {Grabenstetter, Anne and Moo, Tracy-Ann and Hajiyeva, Sabina and Sch\"uffler, Peter J. and Khattar, Pallavi and Friedlander, Maria A. and McCormack, Maura A. and Raiss, Monica and Zabor, Emily C. and Barrio, Andrea and Morrow, Monica and Edelweiss, Marcia},
    month = jun,
    year = {2019},
    pmid = {31219817},
}
Download Endnote/RIS citation
TY - JOUR
TI - Accuracy of Intraoperative Frozen Section of Sentinel Lymph Nodes After Neoadjuvant Chemotherapy for Breast Carcinoma
AU - Grabenstetter, Anne
AU - Moo, Tracy-Ann
AU - Hajiyeva, Sabina
AU - Schüffler, Peter J.
AU - Khattar, Pallavi
AU - Friedlander, Maria A.
AU - McCormack, Maura A.
AU - Raiss, Monica
AU - Zabor, Emily C.
AU - Barrio, Andrea
AU - Morrow, Monica
AU - Edelweiss, Marcia
T2 - The American Journal of Surgical Pathology
AB - False-negative (FN) intraoperative frozen section (FS) results of sentinel lymph nodes (SLN) have been reported to be more common after neoadjuvant chemotherapy (NAC) in the primary surgical setting. We evaluated SLN FS assessment in breast cancer patients treated with NAC to determine the FN rate and the histomorphologic factors associated with FN results. Patients who had FS SLN assessment following NAC from July 2008 to July 2017 were identified. Of the 711 SLN FS cases, 522 were negative, 181 positive, and 8 deferred. The FN rate was 5.4% (28/522). There were no false-positive results. Of the 8 deferred cases, 5 were positive on permanent section and 3 were negative. There was a higher frequency of micrometastasis and isolated tumor cells in FN cases (P<0.001). There was a significant increase in tissue surface area present on permanent section slides compared with FS slides (P<0.001), highlighting the inherent technical limitations of FS and histologic under-sampling of tissue which leads to most FN results. The majority (25/28, 89%) of FN cases had metastatic foci identified exclusively on permanent sections and were not due to a true diagnostic interpretation error. FN cases were more frequently estrogen receptor positive (P<0.001), progesterone receptor positive (P=0.001), human epidermal growth factor receptor-2 negative (P=0.009) and histologic grade 1 (P=0.015), which most likely reflects the lower rates of pathologic complete response in these tumors. Despite its limitations, FS is a reliable modality to assess the presence of SLN metastases in NAC treated patients.
DA - 2019/06/18/
PY - 2019
DO - 10.1097/PAS.0000000000001311
DP - PubMed
J2 - Am. J. Surg. Pathol.
LA - eng
SN - 1532-0979
UR - https://pubmed.ncbi.nlm.nih.gov/31219817/
ER -
False-negative (FN) intraoperative frozen section (FS) results of sentinel lymph nodes (SLN) have been reported to be more common after neoadjuvant chemotherapy (NAC) in the primary surgical setting. We evaluated SLN FS assessment in breast cancer patients treated with NAC to determine the FN rate and the histomorphologic factors associated with FN results. Patients who had FS SLN assessment following NAC from July 2008 to July 2017 were identified. Of the 711 SLN FS cases, 522 were negative, 181 positive, and 8 deferred. The FN rate was 5.4% (28/522). There were no false-positive results. Of the 8 deferred cases, 5 were positive on permanent section and 3 were negative. There was a higher frequency of micrometastasis and isolated tumor cells in FN cases (P<0.001). There was a significant increase in tissue surface area present on permanent section slides compared with FS slides (P<0.001), highlighting the inherent technical limitations of FS and histologic under-sampling of tissue which leads to most FN results. The majority (25/28, 89%) of FN cases had metastatic foci identified exclusively on permanent sections and were not due to a true diagnostic interpretation error. FN cases were more frequently estrogen receptor positive (P<0.001), progesterone receptor positive (P=0.001), human epidermal growth factor receptor-2 negative (P=0.009) and histologic grade 1 (P=0.015), which most likely reflects the lower rates of pathologic complete response in these tumors. Despite its limitations, FS is a reliable modality to assess the presence of SLN metastases in NAC treated patients.
Matthew G. Hanna, Victor E. Reuter, Meera R. Hameed, Lee K. Tan, Sarah Chiang, Carlie Sigel, Travis Hollmann, Dilip Giri, Jennifer Samboy, Carlos Moradel, Andrea Rosado, John R. Otilano, Christine England, Lorraine Corsale, Evangelos Stamelos, Yukako Yagi, Peter J. Schüffler, Thomas Fuchs, David S. Klimstra and S. Joseph Sirintrapun.
Whole slide imaging equivalency and efficiency study: experience at a large academic center.
Modern Pathology, vol. 32, p. 916–928, 2019-02-18 doi: 10.1038/s41379-019-0205-0
PDF    URL   BibTeX   Endnote / RIS   Abstract
Download BibTeX citation
@article{hanna_whole_2019,
    title = {Whole slide imaging equivalency and efficiency study: experience at a large academic center},
    volume = {32},
    copyright = {2019 United States \& Canadian Academy of Pathology},
    issn = {1530-0285},
    shorttitle = {Whole slide imaging equivalency and efficiency study},
    url = {https://www.nature.com/articles/s41379-019-0205-0},
    doi = {10.1038/s41379-019-0205-0},
    abstract = {Whole slide imaging is Food and Drug Administration-approved for primary diagnosis in the United States of America; however, relatively few pathology departments in the country have fully implemented an enterprise wide digital pathology system enabled for primary diagnosis. Digital pathology has significant potential to transform pathology practice with several published studies documenting some level of diagnostic equivalence between digital and conventional systems. However, whole slide imaging also has significant potential to disrupt pathology practice, due to the differences in efficiency of manipulating digital images vis-\`a-vis glass slides, and studies on the efficiency of actual digital pathology workload are lacking. Our randomized, equivalency and efficiency study aimed to replicate clinical workflow, comparing conventional microscopy to a complete digital pathology signout using whole slide images, evaluating the equivalency and efficiency of glass slide to whole slide image reporting, reflective of true pathology practice workloads in the clinical setting. All glass slides representing an entire day’s routine clinical signout workload for six different anatomic pathology subspecialties at Memorial Sloan Kettering Cancer Center were scanned on Leica Aperio AT2 at ×40 (0.25 µm/pixel). Integration of whole slide images for each accessioned case is through an interface between the Leica eSlide manager database and the laboratory information system, Cerner CoPathPlus. Pathologists utilized a standard institution computer workstation and viewed whole slide images through an internally developed, vendor agnostic whole slide image viewer, named the “MSK Slide Viewer”. Subspecialized pathologists first reported on glass slides from surgical pathology cases using routine clinical workflow. Glass slides were de-identified, scanned, and re-accessioned in the laboratory information system test environment. After a washout period of 13 weeks, pathologists reported the same clinical workload using whole slide image integrated within the laboratory information system. Intraobserver equivalency metrics included top-line diagnosis, margin status, lymphovascular and/or perineural invasion, pathology stage, and the need to order ancillary testing (i.e., recuts, immunohistochemistry). Turnaround time (efficiency) evaluation was defined by the start of each case when opened in the laboratory information system and when the case was completed for that day (i.e., case sent to signout queue or pending ancillary studies). Eight pathologists participated from the following subspecialties: bone and soft tissue, genitourinary, gastrointestinal, breast, gynecologic, and dermatopathology. Glass slides signouts comprised of 204 cases, encompassing 2091 glass slides; and digital signouts comprised of 199 cases, encompassing 2073 whole slide images. The median whole slide image file size was 1.54 GB; scan time/slide, 6 min 24 s; and scan area 32.1 × 18.52 mm. Overall diagnostic equivalency (e.g., top-line diagnosis) was 99.3\% between digital and glass slide signout; however, signout using whole slide images showed a median overall 19\% decrease in efficiency per case. No significant difference by reader, subspecialty, or specimen type was identified. Our experience is the most comprehensive study to date and shows high intraobserver whole slide image to glass slide equivalence in reporting of true clinical workflows and workloads. Efficiency needs to improve for digital pathology to gain more traction among pathologists.},
    language = {En},
    urldate = {2019-02-21},
    journal = {Modern Pathology},
    author = {Hanna, Matthew G. and Reuter, Victor E. and Hameed, Meera R. and Tan, Lee K. and Chiang, Sarah and Sigel, Carlie and Hollmann, Travis and Giri, Dilip and Samboy, Jennifer and Moradel, Carlos and Rosado, Andrea and Otilano, John R. and England, Christine and Corsale, Lorraine and Stamelos, Evangelos and Yagi, Yukako and Sch\"uffler, Peter J. and Fuchs, Thomas and Klimstra, David S. and Sirintrapun, S. Joseph},
    month = feb,
    year = {2019},
    pages = {916--928},
}
Download Endnote/RIS citation
TY - JOUR
TI - Whole slide imaging equivalency and efficiency study: experience at a large academic center
AU - Hanna, Matthew G.
AU - Reuter, Victor E.
AU - Hameed, Meera R.
AU - Tan, Lee K.
AU - Chiang, Sarah
AU - Sigel, Carlie
AU - Hollmann, Travis
AU - Giri, Dilip
AU - Samboy, Jennifer
AU - Moradel, Carlos
AU - Rosado, Andrea
AU - Otilano, John R.
AU - England, Christine
AU - Corsale, Lorraine
AU - Stamelos, Evangelos
AU - Yagi, Yukako
AU - Schüffler, Peter J.
AU - Fuchs, Thomas
AU - Klimstra, David S.
AU - Sirintrapun, S. Joseph
T2 - Modern Pathology
AB - Whole slide imaging is Food and Drug Administration-approved for primary diagnosis in the United States of America; however, relatively few pathology departments in the country have fully implemented an enterprise wide digital pathology system enabled for primary diagnosis. Digital pathology has significant potential to transform pathology practice with several published studies documenting some level of diagnostic equivalence between digital and conventional systems. However, whole slide imaging also has significant potential to disrupt pathology practice, due to the differences in efficiency of manipulating digital images vis-à-vis glass slides, and studies on the efficiency of actual digital pathology workload are lacking. Our randomized, equivalency and efficiency study aimed to replicate clinical workflow, comparing conventional microscopy to a complete digital pathology signout using whole slide images, evaluating the equivalency and efficiency of glass slide to whole slide image reporting, reflective of true pathology practice workloads in the clinical setting. All glass slides representing an entire day’s routine clinical signout workload for six different anatomic pathology subspecialties at Memorial Sloan Kettering Cancer Center were scanned on Leica Aperio AT2 at ×40 (0.25 µm/pixel). Integration of whole slide images for each accessioned case is through an interface between the Leica eSlide manager database and the laboratory information system, Cerner CoPathPlus. Pathologists utilized a standard institution computer workstation and viewed whole slide images through an internally developed, vendor agnostic whole slide image viewer, named the “MSK Slide Viewer”. Subspecialized pathologists first reported on glass slides from surgical pathology cases using routine clinical workflow. Glass slides were de-identified, scanned, and re-accessioned in the laboratory information system test environment. After a washout period of 13 weeks, pathologists reported the same clinical workload using whole slide image integrated within the laboratory information system. Intraobserver equivalency metrics included top-line diagnosis, margin status, lymphovascular and/or perineural invasion, pathology stage, and the need to order ancillary testing (i.e., recuts, immunohistochemistry). Turnaround time (efficiency) evaluation was defined by the start of each case when opened in the laboratory information system and when the case was completed for that day (i.e., case sent to signout queue or pending ancillary studies). Eight pathologists participated from the following subspecialties: bone and soft tissue, genitourinary, gastrointestinal, breast, gynecologic, and dermatopathology. Glass slides signouts comprised of 204 cases, encompassing 2091 glass slides; and digital signouts comprised of 199 cases, encompassing 2073 whole slide images. The median whole slide image file size was 1.54 GB; scan time/slide, 6 min 24 s; and scan area 32.1 × 18.52 mm. Overall diagnostic equivalency (e.g., top-line diagnosis) was 99.3% between digital and glass slide signout; however, signout using whole slide images showed a median overall 19% decrease in efficiency per case. No significant difference by reader, subspecialty, or specimen type was identified. Our experience is the most comprehensive study to date and shows high intraobserver whole slide image to glass slide equivalence in reporting of true clinical workflows and workloads. Efficiency needs to improve for digital pathology to gain more traction among pathologists.
DA - 2019/02/18/
PY - 2019
DO - 10.1038/s41379-019-0205-0
DP - www.nature.com
VL - 32
SP - 916
EP - 928
LA - En
SN - 1530-0285
ST - Whole slide imaging equivalency and efficiency study
UR - https://www.nature.com/articles/s41379-019-0205-0
Y2 - 2019/02/21/21:15:25
ER -
Whole slide imaging is Food and Drug Administration-approved for primary diagnosis in the United States of America; however, relatively few pathology departments in the country have fully implemented an enterprise wide digital pathology system enabled for primary diagnosis. Digital pathology has significant potential to transform pathology practice with several published studies documenting some level of diagnostic equivalence between digital and conventional systems. However, whole slide imaging also has significant potential to disrupt pathology practice, due to the differences in efficiency of manipulating digital images vis-à-vis glass slides, and studies on the efficiency of actual digital pathology workload are lacking. Our randomized, equivalency and efficiency study aimed to replicate clinical workflow, comparing conventional microscopy to a complete digital pathology signout using whole slide images, evaluating the equivalency and efficiency of glass slide to whole slide image reporting, reflective of true pathology practice workloads in the clinical setting. All glass slides representing an entire day’s routine clinical signout workload for six different anatomic pathology subspecialties at Memorial Sloan Kettering Cancer Center were scanned on Leica Aperio AT2 at ×40 (0.25 µm/pixel). Integration of whole slide images for each accessioned case is through an interface between the Leica eSlide manager database and the laboratory information system, Cerner CoPathPlus. Pathologists utilized a standard institution computer workstation and viewed whole slide images through an internally developed, vendor agnostic whole slide image viewer, named the “MSK Slide Viewer”. Subspecialized pathologists first reported on glass slides from surgical pathology cases using routine clinical workflow. Glass slides were de-identified, scanned, and re-accessioned in the laboratory information system test environment. After a washout period of 13 weeks, pathologists reported the same clinical workload using whole slide image integrated within the laboratory information system. Intraobserver equivalency metrics included top-line diagnosis, margin status, lymphovascular and/or perineural invasion, pathology stage, and the need to order ancillary testing (i.e., recuts, immunohistochemistry). Turnaround time (efficiency) evaluation was defined by the start of each case when opened in the laboratory information system and when the case was completed for that day (i.e., case sent to signout queue or pending ancillary studies). Eight pathologists participated from the following subspecialties: bone and soft tissue, genitourinary, gastrointestinal, breast, gynecologic, and dermatopathology. Glass slides signouts comprised of 204 cases, encompassing 2091 glass slides; and digital signouts comprised of 199 cases, encompassing 2073 whole slide images. The median whole slide image file size was 1.54 GB; scan time/slide, 6 min 24 s; and scan area 32.1 × 18.52 mm. Overall diagnostic equivalency (e.g., top-line diagnosis) was 99.3% between digital and glass slide signout; however, signout using whole slide images showed a median overall 19% decrease in efficiency per case. No significant difference by reader, subspecialty, or specimen type was identified. Our experience is the most comprehensive study to date and shows high intraobserver whole slide image to glass slide equivalence in reporting of true clinical workflows and workloads. Efficiency needs to improve for digital pathology to gain more traction among pathologists.
Carl A. J. Puylaert, Jeroen A. W. Tielbeek, Peter J. Schüffler, C. Yung Nio, Karin Horsthuis, Banafsche Mearadji, Cyriel Y. Ponsioen, Frans M. Vos and Jaap Stoker.
Comparison of contrast-enhanced and diffusion-weighted MRI in assessment of the terminal ileum in Crohn’s disease patients.
Abdominal Radiology, vol. 44, p. 398–405, 2018-8-14 doi: 10.1007/s00261-018-1734-6
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@article{puylaert_comparison_2018,
    title = {Comparison of contrast-enhanced and diffusion-weighted {MRI} in assessment of the terminal ileum in {Crohn}’s disease patients},
    volume = {44},
    issn = {2366-004X, 2366-0058},
    url = {http://link.springer.com/10.1007/s00261-018-1734-6},
    doi = {10.1007/s00261-018-1734-6},
    language = {en},
    urldate = {2018-09-04},
    journal = {Abdominal Radiology},
    author = {Puylaert, Carl A. J. and Tielbeek, Jeroen A. W. and Sch\"uffler, Peter J. and Nio, C. Yung and Horsthuis, Karin and Mearadji, Banafsche and Ponsioen, Cyriel Y. and Vos, Frans M. and Stoker, Jaap},
    month = aug,
    year = {2018},
    pages = {398--405},
}
Download Endnote/RIS citation
TY - JOUR
TI - Comparison of contrast-enhanced and diffusion-weighted MRI in assessment of the terminal ileum in Crohn’s disease patients
AU - Puylaert, Carl A. J.
AU - Tielbeek, Jeroen A. W.
AU - Schüffler, Peter J.
AU - Nio, C. Yung
AU - Horsthuis, Karin
AU - Mearadji, Banafsche
AU - Ponsioen, Cyriel Y.
AU - Vos, Frans M.
AU - Stoker, Jaap
T2 - Abdominal Radiology
DA - 2018/08/14/
PY - 2018
DO - 10.1007/s00261-018-1734-6
DP - Crossref
VL - 44
SP - 398
EP - 405
LA - en
SN - 2366-004X, 2366-0058
UR - http://link.springer.com/10.1007/s00261-018-1734-6
Y2 - 2018/09/04/23:18:09
ER -
Christian D. Fankhauser, Peter J. Schüffler, Silke Gillessen, Aurelius Omlin, Niels J. Rupp, Jan H. Rueschoff, Thomas Hermanns, Cedric Poyet, Tullio Sulser, Holger Moch and Peter J. Wild.
Comprehensive immunohistochemical analysis of PD-L1 shows scarce expression in castration-resistant prostate cancer.
Oncotarget, vol. 9, 12, 2018-02-13 doi: 10.18632/oncotarget.22888
PDF   URL   BibTeX   Endnote / RIS   Abstract
Download BibTeX citation
@article{fankhauser_comprehensive_2018,
    title = {Comprehensive immunohistochemical analysis of {PD}-{L1} shows scarce expression in castration-resistant prostate cancer},
    volume = {9},
    issn = {1949-2553},
    url = {http://www.oncotarget.com/fulltext/22888},
    doi = {10.18632/oncotarget.22888},
    language = {en},
    number = {12},
    urldate = {2018-05-31},
    journal = {Oncotarget},
    author = {Fankhauser, Christian D. and Sch\"uffler, Peter J. and Gillessen, Silke and Omlin, Aurelius and Rupp, Niels J. and Rueschoff, Jan H. and Hermanns, Thomas and Poyet, Cedric and Sulser, Tullio and Moch, Holger and Wild, Peter J.},
    month = feb,
    year = {2018},
}
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TY - JOUR
TI - Comprehensive immunohistochemical analysis of PD-L1 shows scarce expression in castration-resistant prostate cancer
AU - Fankhauser, Christian D.
AU - Schüffler, Peter J.
AU - Gillessen, Silke
AU - Omlin, Aurelius
AU - Rupp, Niels J.
AU - Rueschoff, Jan H.
AU - Hermanns, Thomas
AU - Poyet, Cedric
AU - Sulser, Tullio
AU - Moch, Holger
AU - Wild, Peter J.
T2 - Oncotarget
DA - 2018/02/13/
PY - 2018
DO - 10.18632/oncotarget.22888
DP - Crossref
VL - 9
IS - 12
LA - en
SN - 1949-2553
UR - http://www.oncotarget.com/fulltext/22888
Y2 - 2018/05/31/17:41:01
ER -
Gabriele Campanella, Arjun R. Rajanna, Lorraine Corsale, Peter J. Schüffler, Yukako Yagi and Thomas J. Fuchs.
Towards machine learned quality control: A benchmark for sharpness quantification in digital pathology.
Computerized Medical Imaging and Graphics, vol. 65, p. 142-151, 04/2018 doi: 10.1016/j.compmedimag.2017.09.001
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@article{campanella_towards_2018,
    title = {Towards machine learned quality control: {A} benchmark for sharpness quantification in digital pathology},
    volume = {65},
    issn = {08956111},
    shorttitle = {Towards machine learned quality control},
    url = {https://linkinghub.elsevier.com/retrieve/pii/S0895611117300800},
    doi = {10.1016/j.compmedimag.2017.09.001},
    language = {en},
    urldate = {2019-11-26},
    journal = {Computerized Medical Imaging and Graphics},
    author = {Campanella, Gabriele and Rajanna, Arjun R. and Corsale, Lorraine and Sch\"uffler, Peter J. and Yagi, Yukako and Fuchs, Thomas J.},
    month = apr,
    year = {2018},
    pages = {142--151},
}
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TY - JOUR
TI - Towards machine learned quality control: A benchmark for sharpness quantification in digital pathology
AU - Campanella, Gabriele
AU - Rajanna, Arjun R.
AU - Corsale, Lorraine
AU - Schüffler, Peter J.
AU - Yagi, Yukako
AU - Fuchs, Thomas J.
T2 - Computerized Medical Imaging and Graphics
DA - 2018/04//
PY - 2018
DO - 10.1016/j.compmedimag.2017.09.001
DP - DOI.org (Crossref)
VL - 65
SP - 142
EP - 151
J2 - Computerized Medical Imaging and Graphics
LA - en
SN - 08956111
ST - Towards machine learned quality control
UR - https://linkinghub.elsevier.com/retrieve/pii/S0895611117300800
Y2 - 2019/11/26/18:28:10
ER -
Carl A.J. Puylaert, Peter J. Schüffler, Robiel E. Naziroglu, Jeroen A.W. Tielbeek, Zhang Li, Jesica C. Makanyanga, Charlotte J. Tutein Nolthenius, C. Yung Nio, Douglas A. Pendsé, Alex Menys, Cyriel Y. Ponsioen, David Atkinson, Alastair Forbes, Joachim M. Buhmann, Thomas J. Fuchs, Haralambos Hatzakis, Lucas J. van Vliet, Jaap Stoker, Stuart A. Taylor and Frans M. Vos.
Semiautomatic Assessment of the Terminal Ileum and Colon in Patients with Crohn Disease Using MRI (the VIGOR++ Project).
Academic Radiology, vol. 25, 8, p. 1038-1045, 2/2018 doi: 10.1016/j.acra.2017.12.024
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@article{puylaert_semiautomatic_2018,
    title = {Semiautomatic {Assessment} of the {Terminal} {Ileum} and {Colon} in {Patients} with {Crohn} {Disease} {Using} {MRI} (the {VIGOR}++ {Project})},
    volume = {25},
    issn = {10766332},
    url = {http://linkinghub.elsevier.com/retrieve/pii/S1076633218300060},
    doi = {10.1016/j.acra.2017.12.024},
    language = {en},
    number = {8},
    urldate = {2018-05-21},
    journal = {Academic Radiology},
    author = {Puylaert, Carl A.J. and Sch\"uffler, Peter J. and Naziroglu, Robiel E. and Tielbeek, Jeroen A.W. and Li, Zhang and Makanyanga, Jesica C. and Tutein Nolthenius, Charlotte J. and Nio, C. Yung and Pends\'e, Douglas A. and Menys, Alex and Ponsioen, Cyriel Y. and Atkinson, David and Forbes, Alastair and Buhmann, Joachim M. and Fuchs, Thomas J. and Hatzakis, Haralambos and van Vliet, Lucas J. and Stoker, Jaap and Taylor, Stuart A. and Vos, Frans M.},
    month = feb,
    year = {2018},
    pages = {1038--1045},
}
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TY - JOUR
TI - Semiautomatic Assessment of the Terminal Ileum and Colon in Patients with Crohn Disease Using MRI (the VIGOR++ Project)
AU - Puylaert, Carl A.J.
AU - Schüffler, Peter J.
AU - Naziroglu, Robiel E.
AU - Tielbeek, Jeroen A.W.
AU - Li, Zhang
AU - Makanyanga, Jesica C.
AU - Tutein Nolthenius, Charlotte J.
AU - Nio, C. Yung
AU - Pendsé, Douglas A.
AU - Menys, Alex
AU - Ponsioen, Cyriel Y.
AU - Atkinson, David
AU - Forbes, Alastair
AU - Buhmann, Joachim M.
AU - Fuchs, Thomas J.
AU - Hatzakis, Haralambos
AU - van Vliet, Lucas J.
AU - Stoker, Jaap
AU - Taylor, Stuart A.
AU - Vos, Frans M.
T2 - Academic Radiology
DA - 2018/02//
PY - 2018
DO - 10.1016/j.acra.2017.12.024
DP - Crossref
VL - 25
IS - 8
SP - 1038
EP - 1045
LA - en
SN - 10766332
UR - http://linkinghub.elsevier.com/retrieve/pii/S1076633218300060
Y2 - 2018/05/21/12:24:37
ER -
Peter J. Schüffler, Qing Zhong, Peter J. Wild and Thomas J. Fuchs.
Computational Pathology.
In: Johannes Haybäck (ed.) Mechanisms of Molecular Carcinogenesis - Volume 2, 1st ed. 2017 edition, Springer, ISBN 3-319-53660-5, 21. Jun. 2017 
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@incollection{schuffler_computational_2017,
    edition = {1st ed. 2017 edition},
    title = {Computational {Pathology}},
    isbn = {3-319-53660-5},
    url = {http://www.springer.com/de/book/9783319536606},
    booktitle = {Mechanisms of {Molecular} {Carcinogenesis} - {Volume} 2},
    publisher = {Springer},
    author = {Sch\"uffler, Peter J. and Zhong, Qing and Wild, Peter J. and Fuchs, Thomas J.},
    editor = {Hayb\"ack, Johannes},
    month = jun,
    year = {2017},
}
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TY - CHAP
TI - Computational Pathology
AU - Schüffler, Peter J.
AU - Zhong, Qing
AU - Wild, Peter J.
AU - Fuchs, Thomas J.
T2 - Mechanisms of Molecular Carcinogenesis - Volume 2
A2 - Haybäck, Johannes
DA - 2017/06/21/
PY - 2017
ET - 1st ed. 2017 edition
PB - Springer
SN - 3-319-53660-5
UR - http://www.springer.com/de/book/9783319536606
ER -
Gabriele Abbati, Stefan Bauer, Sebastian Winklhofer, Peter J. Schüffler, Ulrike Held, Jakob M. Burgstaller, Johann Steurer and Joachim M. Buhmann.
MRI-Based Surgical Planning for Lumbar Spinal Stenosis.
In: Maxime Descoteaux, Lena Maier-Hein, Alfred Franz, Pierre Jannin, D. Louis Collins and Simon Duchesne (eds.) Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017, vol. 10435, p. 116-124, Lecture Notes in Computer Science, Springer, ISBN 978-3-319-66179-7, 2017 
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@incollection{descoteaux_mri-based_2017,
    address = {Cham},
    series = {Lecture {Notes} in {Computer} {Science}},
    title = {{MRI}-{Based} {Surgical} {Planning} for {Lumbar} {Spinal} {Stenosis}},
    volume = {10435},
    isbn = {978-3-319-66179-7},
    url = {http://link.springer.com/10.1007/978-3-319-66179-7_14},
    urldate = {2017-09-18},
    booktitle = {Medical {Image} {Computing} and {Computer}-{Assisted} {Intervention} − {MICCAI} 2017},
    publisher = {Springer},
    author = {Abbati, Gabriele and Bauer, Stefan and Winklhofer, Sebastian and Sch\"uffler, Peter J. and Held, Ulrike and Burgstaller, Jakob M. and Steurer, Johann and Buhmann, Joachim M.},
    editor = {Descoteaux, Maxime and Maier-Hein, Lena and Franz, Alfred and Jannin, Pierre and Collins, D. Louis and Duchesne, Simon},
    year = {2017},
    doi = {10.1007/978-3-319-66179-7_14},
    pages = {116--124},
}
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TY - CHAP
TI - MRI-Based Surgical Planning for Lumbar Spinal Stenosis
AU - Abbati, Gabriele
AU - Bauer, Stefan
AU - Winklhofer, Sebastian
AU - Schüffler, Peter J.
AU - Held, Ulrike
AU - Burgstaller, Jakob M.
AU - Steurer, Johann
AU - Buhmann, Joachim M.
T2 - Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017
A2 - Descoteaux, Maxime
A2 - Maier-Hein, Lena
A2 - Franz, Alfred
A2 - Jannin, Pierre
A2 - Collins, D. Louis
A2 - Duchesne, Simon
T3 - Lecture Notes in Computer Science
CY - Cham
DA - 2017///
PY - 2017
DP - CrossRef
VL - 10435
SP - 116
EP - 124
PB - Springer
SN - 978-3-319-66179-7
UR - http://link.springer.com/10.1007/978-3-319-66179-7_14
Y2 - 2017/09/18/12:44:49
ER -
Peter J. Schüffler, Judy Sarungbam, Hassan Muhammad, Ed Reznik, Satish K. Tickoo and Thomas J. Fuchs.
Mitochondria-based Renal Cell Carcinoma Subtyping: Learning from Deep vs. Flat Feature Representations.
Proceedings of the 1st Machine Learning for Healthcare Conference, Machine Learning for Healthcare, vol. 56, p. 191-208, Proceedings of Machine Learning Research, PMLR, 18 Aug 2016 
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@inproceedings{schuffler_mitochondria-based_2016,
    address = {Los Angeles},
    series = {Proceedings of {Machine} {Learning} {Research}},
    title = {Mitochondria-based {Renal} {Cell} {Carcinoma} {Subtyping}: {Learning} from {Deep} vs. {Flat} {Feature} {Representations}},
    volume = {56},
    url = {http://proceedings.mlr.press/v56/Schuffler16.html},
    abstract = {Accurate subtyping of renal cell carcinoma (RCC) is of crucial importance for understanding disease progression and for making informed treatment decisions. New discoveries of significant mitochondria alterations between subtypes makes immunohistochemical (IHC) staining based image classification an imperative.
    Until now, accurate quantification and subtyping was made impossible by huge IHC variations, the absence of cell membrane staining for cytoplasm segmentation as well as the complete lack of systems for robust and reproducible image based classification.
    In this paper we present a comprehensive classification framework to overcome these challenges for tissue microarrays (TMA) of RCCs. We compare and evaluate models based on domain specific hand-crafted "flat"-features versus "deep" feature representations from various layers of a pre-trained convolutional neural network (CNN).
    The best model reaches a cross-validation accuracy of 89\%, which demonstrates for the first time, that robust mitochondria-based subtyping of renal cancer is feasible.},
    language = {English},
    booktitle = {Proceedings of the 1st {Machine} {Learning} for {Healthcare} {Conference}},
    publisher = {PMLR},
    author = {Sch\"uffler, Peter J. and Sarungbam, Judy and Muhammad, Hassan and Reznik, Ed and Tickoo, Satish K. and Fuchs, Thomas J.},
    editor = {Finale, Doshi-Valez and Fackler, Jim and Kale, David and Wallace, Byron and Weins, Jenna},
    month = aug,
    year = {2016},
    pages = {191--208},
}
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TY - CONF
TI - Mitochondria-based Renal Cell Carcinoma Subtyping: Learning from Deep vs. Flat Feature Representations
AU - Schüffler, Peter J.
AU - Sarungbam, Judy
AU - Muhammad, Hassan
AU - Reznik, Ed
AU - Tickoo, Satish K.
AU - Fuchs, Thomas J.
T2 - Machine Learning for Healthcare
A2 - Finale, Doshi-Valez
A2 - Fackler, Jim
A2 - Kale, David
A2 - Wallace, Byron
A2 - Weins, Jenna
T3 - Proceedings of Machine Learning Research
AB - Accurate subtyping of renal cell carcinoma (RCC) is of crucial importance for understanding disease progression and for making informed treatment decisions. New discoveries of significant mitochondria alterations between subtypes makes immunohistochemical (IHC) staining based image classification an imperative.
Until now, accurate quantification and subtyping was made impossible by huge IHC variations, the absence of cell membrane staining for cytoplasm segmentation as well as the complete lack of systems for robust and reproducible image based classification.
In this paper we present a comprehensive classification framework to overcome these challenges for tissue microarrays (TMA) of RCCs. We compare and evaluate models based on domain specific hand-crafted "flat"-features versus "deep" feature representations from various layers of a pre-trained convolutional neural network (CNN).
The best model reaches a cross-validation accuracy of 89%, which demonstrates for the first time, that robust mitochondria-based subtyping of renal cancer is feasible.
C1 - Los Angeles
C3 - Proceedings of the 1st Machine Learning for Healthcare Conference
DA - 2016/08/18/
PY - 2016
VL - 56
SP - 191
EP - 208
LA - English
PB - PMLR
UR - http://proceedings.mlr.press/v56/Schuffler16.html
ER -
Accurate subtyping of renal cell carcinoma (RCC) is of crucial importance for understanding disease progression and for making informed treatment decisions. New discoveries of significant mitochondria alterations between subtypes makes immunohistochemical (IHC) staining based image classification an imperative. Until now, accurate quantification and subtyping was made impossible by huge IHC variations, the absence of cell membrane staining for cytoplasm segmentation as well as the complete lack of systems for robust and reproducible image based classification. In this paper we present a comprehensive classification framework to overcome these challenges for tissue microarrays (TMA) of RCCs. We compare and evaluate models based on domain specific hand-crafted "flat"-features versus "deep" feature representations from various layers of a pre-trained convolutional neural network (CNN). The best model reaches a cross-validation accuracy of 89%, which demonstrates for the first time, that robust mitochondria-based subtyping of renal cancer is feasible.
Qing Zhong, Jan H. Rüschoff, Tiannan Guo, Maria Gabrani, Peter J. Schüffler, Markus Rechsteiner, Yansheng Liu, Thomas J. Fuchs, Niels J. Rupp, Christian Fankhauser, Joachim M. Buhmann, Sven Perner, Cédric Poyet, Miriam Blattner, Davide Soldini, Holger Moch, Mark A. Rubin, Aurelia Noske, Josef Rüschoff, Michael C. Haffner, Wolfram Jochum and Peter J. Wild.
Image-Based Computational Quantification and Visualization of Genetic Alterations and Tumour Heterogeneity.
Scientific Reports, vol. 6, p. 24146, 2016 doi: 10.1038/srep24146
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@article{zhong_image-based_2016,
    title = {Image-{Based} {Computational} {Quantification} and {Visualization} of {Genetic} {Alterations} and {Tumour} {Heterogeneity}},
    volume = {6},
    issn = {2045-2322},
    url = {http://www.nature.com/articles/srep24146},
    doi = {10.1038/srep24146},
    urldate = {2016-04-12},
    journal = {Scientific Reports},
    author = {Zhong, Qing and R\"uschoff, Jan H. and Guo, Tiannan and Gabrani, Maria and Sch\"uffler, Peter J. and Rechsteiner, Markus and Liu, Yansheng and Fuchs, Thomas J. and Rupp, Niels J. and Fankhauser, Christian and Buhmann, Joachim M. and Perner, Sven and Poyet, C\'edric and Blattner, Miriam and Soldini, Davide and Moch, Holger and Rubin, Mark A. and Noske, Aurelia and R\"uschoff, Josef and Haffner, Michael C. and Jochum, Wolfram and Wild, Peter J.},
    year = {2016},
    pages = {24146},
}
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TY - JOUR
TI - Image-Based Computational Quantification and Visualization of Genetic Alterations and Tumour Heterogeneity
AU - Zhong, Qing
AU - Rüschoff, Jan H.
AU - Guo, Tiannan
AU - Gabrani, Maria
AU - Schüffler, Peter J.
AU - Rechsteiner, Markus
AU - Liu, Yansheng
AU - Fuchs, Thomas J.
AU - Rupp, Niels J.
AU - Fankhauser, Christian
AU - Buhmann, Joachim M.
AU - Perner, Sven
AU - Poyet, Cédric
AU - Blattner, Miriam
AU - Soldini, Davide
AU - Moch, Holger
AU - Rubin, Mark A.
AU - Noske, Aurelia
AU - Rüschoff, Josef
AU - Haffner, Michael C.
AU - Jochum, Wolfram
AU - Wild, Peter J.
T2 - Scientific Reports
DA - 2016///
PY - 2016
DO - 10.1038/srep24146
DP - CrossRef
VL - 6
SP - 24146
SN - 2045-2322
UR - http://www.nature.com/articles/srep24146
Y2 - 2016/04/12/01:49:30
ER -
Andrew J. Schaumberg, S. Joseph Sirintrapun, Hikmat A. Al-Ahmadie, Peter J. Schüffler and Thomas J. Fuchs.
DeepScope: Nonintrusive Whole Slide Saliency Annotation and Prediction from Pathologists at the Microscope.
13th International Conference on Computational Intelligence methods for Bioinformatics and Biostatistics, CIBB, 2016 
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@inproceedings{schaumberg_deepscope_2016,
    address = {Stirling, United Kingdom},
    title = {{DeepScope}: {Nonintrusive} {Whole} {Slide} {Saliency} {Annotation} and {Prediction} from {Pathologists} at the {Microscope}},
    shorttitle = {{DeepScope}},
    url = {http://www.cs.stir.ac.uk/events/cibb2016/},
    abstract = {Modern digital pathology departments have grown to produce whole-slide image data at petabyte scale, an unprecedented treasure chest for medical machine learning tasks.
    Unfortunately, most digital slides are not annotated at the image level, hindering large-scale application of supervised learning. Manual labeling is prohibitive, requiring pathologists with decades of training and outstanding clinical service responsibilities. This problem is further aggravated by the United States Food and Drug Administration's ruling that primary diagnosis must come from a glass slide rather than a digital image.
    We present the first end-to-end framework to overcome this problem, gathering annotations in a nonintrusive manner during a pathologist's routine clinical work: (i) microscope-specific 3D-printed commodity camera mounts are used to video record the glass-slide-based clinical diagnosis process; (ii) after routine scanning of the whole slide, the video frames are registered to the digital slide; (iii) motion and observing time are estimated to generate a spatial and temporal saliency map of the whole slide.
    Demonstrating the utility of these annotations, we train a convolutional neural network that detects diagnosis-relevant salient regions, then report accuracy of 85.15\% in bladder and 91.50\% in prostate, with 75.00\% accuracy when training on prostate but predicting in bladder, despite different pathologists examining the different tissues.},
    language = {English},
    booktitle = {13th {International} {Conference} on {Computational} {Intelligence} methods for {Bioinformatics} and {Biostatistics}},
    author = {Schaumberg, Andrew J. and Sirintrapun, S. Joseph and Al-Ahmadie, Hikmat A. and Sch\"uffler, Peter J. and Fuchs, Thomas J.},
    year = {2016},
}
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TY - CONF
TI - DeepScope: Nonintrusive Whole Slide Saliency Annotation and Prediction from Pathologists at the Microscope
AU - Schaumberg, Andrew J.
AU - Sirintrapun, S. Joseph
AU - Al-Ahmadie, Hikmat A.
AU - Schüffler, Peter J.
AU - Fuchs, Thomas J.
T2 - CIBB
AB - Modern digital pathology departments have grown to produce whole-slide image data at petabyte scale, an unprecedented treasure chest for medical machine learning tasks.
Unfortunately, most digital slides are not annotated at the image level, hindering large-scale application of supervised learning. Manual labeling is prohibitive, requiring pathologists with decades of training and outstanding clinical service responsibilities. This problem is further aggravated by the United States Food and Drug Administration's ruling that primary diagnosis must come from a glass slide rather than a digital image.
We present the first end-to-end framework to overcome this problem, gathering annotations in a nonintrusive manner during a pathologist's routine clinical work: (i) microscope-specific 3D-printed commodity camera mounts are used to video record the glass-slide-based clinical diagnosis process; (ii) after routine scanning of the whole slide, the video frames are registered to the digital slide; (iii) motion and observing time are estimated to generate a spatial and temporal saliency map of the whole slide.
Demonstrating the utility of these annotations, we train a convolutional neural network that detects diagnosis-relevant salient regions, then report accuracy of 85.15% in bladder and 91.50% in prostate, with 75.00% accuracy when training on prostate but predicting in bladder, despite different pathologists examining the different tissues.
C1 - Stirling, United Kingdom
C3 - 13th International Conference on Computational Intelligence methods for Bioinformatics and Biostatistics
DA - 2016///
PY - 2016
LA - English
ST - DeepScope
UR - http://www.cs.stir.ac.uk/events/cibb2016/
ER -
Modern digital pathology departments have grown to produce whole-slide image data at petabyte scale, an unprecedented treasure chest for medical machine learning tasks. Unfortunately, most digital slides are not annotated at the image level, hindering large-scale application of supervised learning. Manual labeling is prohibitive, requiring pathologists with decades of training and outstanding clinical service responsibilities. This problem is further aggravated by the United States Food and Drug Administration's ruling that primary diagnosis must come from a glass slide rather than a digital image. We present the first end-to-end framework to overcome this problem, gathering annotations in a nonintrusive manner during a pathologist's routine clinical work: (i) microscope-specific 3D-printed commodity camera mounts are used to video record the glass-slide-based clinical diagnosis process; (ii) after routine scanning of the whole slide, the video frames are registered to the digital slide; (iii) motion and observing time are estimated to generate a spatial and temporal saliency map of the whole slide. Demonstrating the utility of these annotations, we train a convolutional neural network that detects diagnosis-relevant salient regions, then report accuracy of 85.15% in bladder and 91.50% in prostate, with 75.00% accuracy when training on prostate but predicting in bladder, despite different pathologists examining the different tissues.
Hassan Muhammad, Peter J. Schüffler, Judy Sarungbam, Satish K. Tickoo and Thomas Fuchs.
Classifying Renal Cell Carcinoma by Using Convolutional Neural Networks to Deconstruct Pathological Images.
Vincent du Vigneaud Memorial Research Symposium, 2016 
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@misc{muhammad_classifying_2016,
    address = {Weill Cornell Medicine},
    type = {Poster},
    title = {Classifying {Renal} {Cell} {Carcinoma} by {Using} {Convolutional} {Neural} {Networks} to {Deconstruct} {Pathological} {Images}.},
    author = {Muhammad, Hassan},
    collaborator = {Sch\"uffler, Peter J. and Sarungbam, Judy and Tickoo, Satish K. and Fuchs, Thomas},
    year = {2016},
}
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TY - SLIDE
TI - Classifying Renal Cell Carcinoma by Using Convolutional Neural Networks to Deconstruct Pathological Images.
T2 - Vincent du Vigneaud Memorial Research Symposium
A2 - Muhammad, Hassan
CY - Weill Cornell Medicine
DA - 2016///
PY - 2016
M3 - Poster
ER -
Jakob M. Burgstaller, Peter J. Schüffler, Joachim M. Buhmann, Gustav Andreisek, Sebastian Winklhofer, Filippo Del Grande, Michèle Mattle, Florian Brunner, Georgios Karakoumis, Johann Steurer and Ulrike Held.
Is There an Association Between Pain and Magnetic Resonance Imaging Parameters in Patients With Lumbar Spinal Stenosis?
Spine, vol. 41, 17, p. 1053-1062, 2016 doi: 10.1097/brs.0000000000001544
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@article{burgstaller_is_2016,
    title = {Is {There} an {Association} {Between} {Pain} and {Magnetic} {Resonance} {Imaging} {Parameters} in {Patients} {With} {Lumbar} {Spinal} {Stenosis}?},
    volume = {41},
    issn = {0362-2436},
    shorttitle = {Is {There} an {Association} {Between} {Pain} and {Magnetic} {Resonance} {Imaging} {Parameters} in {Patients} {With} {Lumbar} {Spinal} {Stenosis}?},
    url = {http://Insights.ovid.com/crossref?an=00007632-201609010-00015},
    doi = {10.1097/BRS.0000000000001544},
    abstract = {STUDY DESIGN:
    A prospective multicenter cohort study.
    OBJECTIVE:
    The aim of this study was to identify an association between pain and magnetic resonance imaging (MRI) parameters in patients with lumbar spinal stenosis (LSS).
    SUMMARY OF BACKGROUND DATA:
    At present, the relationship between abnormal MRI findings and pain in patients with LSS is still unclear.
    METHODS:
    First, we conducted a systematic literature search. We identified relationships of relevant MRI parameters and pain in patients with LSS. Second, we addressed the study question with a thorough descriptive and graphical analysis to establish a relationship between MRI parameters and pain using data of the LSS outcome study (LSOS).
    RESULTS:
    In the systematic review including four papers about the associations between radiological findings in the MRI and pain, the authors of two articles reported no association and two of them did. Of the latters, only one study found a moderate correlation between leg pain measured by Visual Analog Scale (VAS) and the degree of stenosis assessed by spine surgeons. In the data of the LSOS study, we could not identify a relevant association between any of the MRI parameters and buttock, leg, and back pain, quantified by the Spinal Stenosis Measure (SSM) and the Numeric Rating Scale (NRS). Even by restricting the analysis to the level of the lumbar spine with the most prominent radiological "stenosis," no relevant association could be shown.
    CONCLUSION:
    Despite a thorough analysis of the data, we were not able to prove any correlation between radiological findings (MRI) and the severity of pain. There is a need for innovative "methods/techniques" to learn more about the causal relationship between radiological findings and the patients' pain-related complaints.
    LEVEL OF EVIDENCE:
    2.},
    language = {en},
    number = {17},
    urldate = {2017-02-11},
    journal = {Spine},
    author = {Burgstaller, Jakob M. and Sch\"uffler, Peter J. and Buhmann, Joachim M. and Andreisek, Gustav and Winklhofer, Sebastian and Del Grande, Filippo and Mattle, Mich\`ele and Brunner, Florian and Karakoumis, Georgios and Steurer, Johann and Held, Ulrike},
    year = {2016},
    pages = {1053--1062},
}
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TY - JOUR
TI - Is There an Association Between Pain and Magnetic Resonance Imaging Parameters in Patients With Lumbar Spinal Stenosis?
AU - Burgstaller, Jakob M.
AU - Schüffler, Peter J.
AU - Buhmann, Joachim M.
AU - Andreisek, Gustav
AU - Winklhofer, Sebastian
AU - Del Grande, Filippo
AU - Mattle, Michèle
AU - Brunner, Florian
AU - Karakoumis, Georgios
AU - Steurer, Johann
AU - Held, Ulrike
T2 - Spine
AB - STUDY DESIGN:
A prospective multicenter cohort study.
OBJECTIVE:
The aim of this study was to identify an association between pain and magnetic resonance imaging (MRI) parameters in patients with lumbar spinal stenosis (LSS).
SUMMARY OF BACKGROUND DATA:
At present, the relationship between abnormal MRI findings and pain in patients with LSS is still unclear.
METHODS:
First, we conducted a systematic literature search. We identified relationships of relevant MRI parameters and pain in patients with LSS. Second, we addressed the study question with a thorough descriptive and graphical analysis to establish a relationship between MRI parameters and pain using data of the LSS outcome study (LSOS).
RESULTS:
In the systematic review including four papers about the associations between radiological findings in the MRI and pain, the authors of two articles reported no association and two of them did. Of the latters, only one study found a moderate correlation between leg pain measured by Visual Analog Scale (VAS) and the degree of stenosis assessed by spine surgeons. In the data of the LSOS study, we could not identify a relevant association between any of the MRI parameters and buttock, leg, and back pain, quantified by the Spinal Stenosis Measure (SSM) and the Numeric Rating Scale (NRS). Even by restricting the analysis to the level of the lumbar spine with the most prominent radiological "stenosis," no relevant association could be shown.
CONCLUSION:
Despite a thorough analysis of the data, we were not able to prove any correlation between radiological findings (MRI) and the severity of pain. There is a need for innovative "methods/techniques" to learn more about the causal relationship between radiological findings and the patients' pain-related complaints.
LEVEL OF EVIDENCE:
2.
DA - 2016///
PY - 2016
DO - 10.1097/BRS.0000000000001544
DP - CrossRef
VL - 41
IS - 17
SP - 1053
EP - 1062
LA - en
SN - 0362-2436
ST - Is There an Association Between Pain and Magnetic Resonance Imaging Parameters in Patients With Lumbar Spinal Stenosis?
UR - http://Insights.ovid.com/crossref?an=00007632-201609010-00015
Y2 - 2017/02/11/00:39:09
ER -
STUDY DESIGN: A prospective multicenter cohort study. OBJECTIVE: The aim of this study was to identify an association between pain and magnetic resonance imaging (MRI) parameters in patients with lumbar spinal stenosis (LSS). SUMMARY OF BACKGROUND DATA: At present, the relationship between abnormal MRI findings and pain in patients with LSS is still unclear. METHODS: First, we conducted a systematic literature search. We identified relationships of relevant MRI parameters and pain in patients with LSS. Second, we addressed the study question with a thorough descriptive and graphical analysis to establish a relationship between MRI parameters and pain using data of the LSS outcome study (LSOS). RESULTS: In the systematic review including four papers about the associations between radiological findings in the MRI and pain, the authors of two articles reported no association and two of them did. Of the latters, only one study found a moderate correlation between leg pain measured by Visual Analog Scale (VAS) and the degree of stenosis assessed by spine surgeons. In the data of the LSOS study, we could not identify a relevant association between any of the MRI parameters and buttock, leg, and back pain, quantified by the Spinal Stenosis Measure (SSM) and the Numeric Rating Scale (NRS). Even by restricting the analysis to the level of the lumbar spine with the most prominent radiological "stenosis," no relevant association could be shown. CONCLUSION: Despite a thorough analysis of the data, we were not able to prove any correlation between radiological findings (MRI) and the severity of pain. There is a need for innovative "methods/techniques" to learn more about the causal relationship between radiological findings and the patients' pain-related complaints. LEVEL OF EVIDENCE: 2.
Stefan Bauer, Nicolas Carion, Peter Schüffler, Thomas Fuchs, Peter Wild and Joachim M. Buhmann.
Multi-Organ Cancer Classification and Survival Analysis.
arXiv:1606.00897 [cs, q-bio, stat], 2016 
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@article{bauer_multi-organ_2016,
    title = {Multi-{Organ} {Cancer} {Classification} and {Survival} {Analysis}},
    url = {http://arxiv.org/abs/1606.00897},
    abstract = {Accurate and robust cell nuclei classification is the cornerstone for a wider range of tasks in digital and Computational Pathology. However, most machine learning systems require extensive labeling from expert pathologists for each individual problem at hand, with no or limited abilities for knowledge transfer between datasets and organ sites. In this paper we implement and evaluate a variety of deep neural network models and model ensembles for nuclei classification in renal cell cancer (RCC) and prostate cancer (PCa). We propose a convolutional neural network system based on residual learning which significantly improves over the state-of-the-art in cell nuclei classification. However, the main thrust of our work is to demonstrate for the first time the models ability to transfer the learned concepts from one organ type to another, with robust performance. Finally, we show that the combination of tissue types during training increases not only classification accuracy but also overall survival analysis. The best model, trained on combined data of RCC and PCa, exhibits optimal performance on PCa classification and better survival group stratification than an expert pathologist (\$p=0.006\$). All code, image data and expert labels are made publicly available to serve as benchmark for the community for future research into computational pathology.},
    urldate = {2016-06-16},
    journal = {arXiv:1606.00897 [cs, q-bio, stat]},
    author = {Bauer, Stefan and Carion, Nicolas and Sch\"uffler, Peter and Fuchs, Thomas and Wild, Peter and Buhmann, Joachim M.},
    year = {2016},
    keywords = {Computer Science - Learning, Quantitative Biology - Quantitative Methods, Quantitative Biology - Tissues and Organs, Statistics - Machine Learning},
}
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TY - JOUR
TI - Multi-Organ Cancer Classification and Survival Analysis
AU - Bauer, Stefan
AU - Carion, Nicolas
AU - Schüffler, Peter
AU - Fuchs, Thomas
AU - Wild, Peter
AU - Buhmann, Joachim M.
T2 - arXiv:1606.00897 [cs, q-bio, stat]
AB - Accurate and robust cell nuclei classification is the cornerstone for a wider range of tasks in digital and Computational Pathology. However, most machine learning systems require extensive labeling from expert pathologists for each individual problem at hand, with no or limited abilities for knowledge transfer between datasets and organ sites. In this paper we implement and evaluate a variety of deep neural network models and model ensembles for nuclei classification in renal cell cancer (RCC) and prostate cancer (PCa). We propose a convolutional neural network system based on residual learning which significantly improves over the state-of-the-art in cell nuclei classification. However, the main thrust of our work is to demonstrate for the first time the models ability to transfer the learned concepts from one organ type to another, with robust performance. Finally, we show that the combination of tissue types during training increases not only classification accuracy but also overall survival analysis. The best model, trained on combined data of RCC and PCa, exhibits optimal performance on PCa classification and better survival group stratification than an expert pathologist ($p=0.006$). All code, image data and expert labels are made publicly available to serve as benchmark for the community for future research into computational pathology.
DA - 2016///
PY - 2016
DP - arXiv.org
UR - http://arxiv.org/abs/1606.00897
Y2 - 2016/06/16/01:52:59
KW - Computer Science - Learning
KW - Quantitative Biology - Quantitative Methods
KW - Quantitative Biology - Tissues and Organs
KW - Statistics - Machine Learning
ER -
Accurate and robust cell nuclei classification is the cornerstone for a wider range of tasks in digital and Computational Pathology. However, most machine learning systems require extensive labeling from expert pathologists for each individual problem at hand, with no or limited abilities for knowledge transfer between datasets and organ sites. In this paper we implement and evaluate a variety of deep neural network models and model ensembles for nuclei classification in renal cell cancer (RCC) and prostate cancer (PCa). We propose a convolutional neural network system based on residual learning which significantly improves over the state-of-the-art in cell nuclei classification. However, the main thrust of our work is to demonstrate for the first time the models ability to transfer the learned concepts from one organ type to another, with robust performance. Finally, we show that the combination of tissue types during training increases not only classification accuracy but also overall survival analysis. The best model, trained on combined data of RCC and PCa, exhibits optimal performance on PCa classification and better survival group stratification than an expert pathologist ($p=0.006$). All code, image data and expert labels are made publicly available to serve as benchmark for the community for future research into computational pathology.
Peter J. Schüffler, Denis Schapiro, Charlotte Giesen, Hao A. O. Wang, Bernd Bodenmiller and Joachim M. Buhmann.
Automatic single cell segmentation on highly multiplexed tissue images.
Cytometry Part A, vol. 87, 10, p. 936-942, 10/2015 doi: 10.1002/cyto.a.22702
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@article{schuffler_automatic_2015,
    title = {Automatic single cell segmentation on highly multiplexed tissue images},
    volume = {87},
    issn = {15524922},
    shorttitle = {Automatic single cell segmentation on highly multiplexed tissue images},
    url = {http://doi.wiley.com/10.1002/cyto.a.22702},
    doi = {10.1002/cyto.a.22702},
    abstract = {The combination of mass cytometry and immunohistochemistry (IHC) enables new histopathological imaging methods in which dozens of proteins and protein modifications can be visualized simultaneously in a single tissue section. The power of multiplexing combined with spatial information and quantification was recently illustrated on breast cancer tissue and was described as next-generation IHC. Robust, accurate, and high-throughput cell segmentation is crucial for the analysis of this new generation of IHC data. To this end, we propose a watershed-based cell segmentation, which uses a nuclear marker and multiple membrane markers, the latter automatically selected based on their correlation. In comparison with the state-of-the-art segmentation pipelines, which are only using a single marker for object detection, we could show that the use of multiple markers can significantly increase the segmentation power, and thus, multiplexed information should be used and not ignored during the segmentation. Furthermore, we provide a novel, user-friendly open-source toolbox for the automatic segmentation of multiplexed histopathological images. (c) 2015 International Society for Advancement of Cytometry.},
    language = {en},
    number = {10},
    urldate = {2017-02-14},
    journal = {Cytometry Part A},
    author = {Sch\"uffler, Peter J. and Schapiro, Denis and Giesen, Charlotte and Wang, Hao A. O. and Bodenmiller, Bernd and Buhmann, Joachim M.},
    month = oct,
    year = {2015},
    pages = {936--942},
}
Download Endnote/RIS citation
TY - JOUR
TI - Automatic single cell segmentation on highly multiplexed tissue images
AU - Schüffler, Peter J.
AU - Schapiro, Denis
AU - Giesen, Charlotte
AU - Wang, Hao A. O.
AU - Bodenmiller, Bernd
AU - Buhmann, Joachim M.
T2 - Cytometry Part A
AB - The combination of mass cytometry and immunohistochemistry (IHC) enables new histopathological imaging methods in which dozens of proteins and protein modifications can be visualized simultaneously in a single tissue section. The power of multiplexing combined with spatial information and quantification was recently illustrated on breast cancer tissue and was described as next-generation IHC. Robust, accurate, and high-throughput cell segmentation is crucial for the analysis of this new generation of IHC data. To this end, we propose a watershed-based cell segmentation, which uses a nuclear marker and multiple membrane markers, the latter automatically selected based on their correlation. In comparison with the state-of-the-art segmentation pipelines, which are only using a single marker for object detection, we could show that the use of multiple markers can significantly increase the segmentation power, and thus, multiplexed information should be used and not ignored during the segmentation. Furthermore, we provide a novel, user-friendly open-source toolbox for the automatic segmentation of multiplexed histopathological images. (c) 2015 International Society for Advancement of Cytometry.
DA - 2015/10//
PY - 2015
DO - 10.1002/cyto.a.22702
DP - CrossRef
VL - 87
IS - 10
SP - 936
EP - 942
LA - en
SN - 15524922
ST - Automatic single cell segmentation on highly multiplexed tissue images
UR - http://doi.wiley.com/10.1002/cyto.a.22702
Y2 - 2017/02/14/19:42:24
ER -
The combination of mass cytometry and immunohistochemistry (IHC) enables new histopathological imaging methods in which dozens of proteins and protein modifications can be visualized simultaneously in a single tissue section. The power of multiplexing combined with spatial information and quantification was recently illustrated on breast cancer tissue and was described as next-generation IHC. Robust, accurate, and high-throughput cell segmentation is crucial for the analysis of this new generation of IHC data. To this end, we propose a watershed-based cell segmentation, which uses a nuclear marker and multiple membrane markers, the latter automatically selected based on their correlation. In comparison with the state-of-the-art segmentation pipelines, which are only using a single marker for object detection, we could show that the use of multiple markers can significantly increase the segmentation power, and thus, multiplexed information should be used and not ignored during the segmentation. Furthermore, we provide a novel, user-friendly open-source toolbox for the automatic segmentation of multiplexed histopathological images. (c) 2015 International Society for Advancement of Cytometry.
D. Mahapatra, P. J. Schüffler, F. M. Vos and J. M. Buhmann.
Crohn's Disease Segmentation from MRI Using Learned Image Priors.
Proceedings IEEE ISBI 2015, p. 625-628, 2015 
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@article{mahapatra_crohns_2015,
    title = {Crohn's {Disease} {Segmentation} from {MRI} {Using} {Learned} {Image} {Priors}},
    url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7163951},
    abstract = {We use a Field of Experts (FoE) model to segment abdominal regions from MRI affected with Crohns Disease (CD). FoE learns a prior model of diseased and normal bowel, and background non-bowel tissues from manually annotated training images. Unlike current approaches, FoE does not rely on hand designed features but learns the most discriminative features (in the form of filters) for different classes. FoE filter responses are integrated into a Random forest (RF) model that outputs probability maps for the test image and finally segments the diseased region. Experimental results show our method achieves significantly better performance than existing methods.},
    journal = {Proceedings IEEE ISBI 2015},
    author = {Mahapatra, D. and Sch\"uffler, P. J. and Vos, F. M. and Buhmann, J. M.},
    year = {2015},
    pages = {625--628},
}
Download Endnote/RIS citation
TY - JOUR
TI - Crohn's Disease Segmentation from MRI Using Learned Image Priors
AU - Mahapatra, D.
AU - Schüffler, P. J.
AU - Vos, F. M.
AU - Buhmann, J. M.
T2 - Proceedings IEEE ISBI 2015
AB - We use a Field of Experts (FoE) model to segment abdominal regions from MRI affected with Crohns Disease (CD). FoE learns a prior model of diseased and normal bowel, and background non-bowel tissues from manually annotated training images. Unlike current approaches, FoE does not rely on hand designed features but learns the most discriminative features (in the form of filters) for different classes. FoE filter responses are integrated into a Random forest (RF) model that outputs probability maps for the test image and finally segments the diseased region. Experimental results show our method achieves significantly better performance than existing methods.
DA - 2015///
PY - 2015
SP - 625
EP - 628
UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7163951
ER -
We use a Field of Experts (FoE) model to segment abdominal regions from MRI affected with Crohns Disease (CD). FoE learns a prior model of diseased and normal bowel, and background non-bowel tissues from manually annotated training images. Unlike current approaches, FoE does not rely on hand designed features but learns the most discriminative features (in the form of filters) for different classes. FoE filter responses are integrated into a Random forest (RF) model that outputs probability maps for the test image and finally segments the diseased region. Experimental results show our method achieves significantly better performance than existing methods.
Peter J. Schüffler, Dwarikanath Mahapatra, Franciscus M. Vos and Joachim M. Buhmann.
Computer Aided Crohn's Disease Severity Assessment in MRI.
VIGOR++ Workshop 2014 - Showcase of Research Outcomes and Future Outlook, 2014 
Best Poster Award
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@misc{schuffler_computer_2014,
    address = {London},
    type = {Poster},
    title = {Computer {Aided} {Crohn}'s {Disease} {Severity} {Assessment} in {MRI}},
    url = {https://www.researchgate.net/publication/262198303_Computer_Aided_Crohns_Disease_Severity_Assessment_in_MRI},
    author = {Sch\"uffler, Peter J. and Mahapatra, Dwarikanath and Vos, Franciscus M. and Buhmann, Joachim M.},
    year = {2014},
}
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TY - SLIDE
TI - Computer Aided Crohn's Disease Severity Assessment in MRI
T2 - VIGOR++ Workshop 2014 - Showcase of Research Outcomes and Future Outlook
A2 - Schüffler, Peter J.
A2 - Mahapatra, Dwarikanath
A2 - Vos, Franciscus M.
A2 - Buhmann, Joachim M.
CY - London
DA - 2014///
PY - 2014
M3 - Poster
UR - https://www.researchgate.net/publication/262198303_Computer_Aided_Crohns_Disease_Severity_Assessment_in_MRI
ER -
C. Giesen, H. A. Wang, D. Schapiro, N. Zivanovic, A. Jacobs, B. Hattendorf, P. J. Schüffler, D. Grolimund, J. M. Buhmann, S. Brandt, Z. Varga, P. J. Wild, D. Gunther and B. Bodenmiller.
Highly Multiplexed Imaging of Tumor Tissues with Subcellular Resolution by Mass Cytometry.
Nature methods, vol. 11, p. 417-22, 2014 doi: 10.1038/nmeth.2869
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@article{giesen_highly_2014,
    title = {Highly {Multiplexed} {Imaging} of {Tumor} {Tissues} with {Subcellular} {Resolution} by {Mass} {Cytometry}},
    volume = {11},
    issn = {1548-7105 (Electronic) 1548-7091 (Linking)},
    url = {http://www.nature.com/nmeth/journal/v11/n4/full/nmeth.2869.html},
    doi = {10.1038/nmeth.2869},
    abstract = {Mass cytometry enables high-dimensional, single-cell analysis of cell type and state. In mass cytometry, rare earth metals are used as reporters on antibodies. Analysis of metal abundances using the mass cytometer allows determination of marker expression in individual cells. Mass cytometry has previously been applied only to cell suspensions. To gain spatial information, we have coupled immunohistochemical and immunocytochemical methods with high-resolution laser ablation to CyTOF mass cytometry. This approach enables the simultaneous imaging of 32 proteins and protein modifications at subcellular resolution; with the availability of additional isotopes, measurement of over 100 markers will be possible. We applied imaging mass cytometry to human breast cancer samples, allowing delineation of cell subpopulations and cell-cell interactions and highlighting tumor heterogeneity. Imaging mass cytometry complements existing imaging approaches. It will enable basic studies of tissue heterogeneity and function and support the transition of medicine toward individualized molecularly targeted diagnosis and therapies.},
    journal = {Nat Methods},
    author = {Giesen, C. and Wang, H. A. and Schapiro, D. and Zivanovic, N. and Jacobs, A. and Hattendorf, B. and Sch\"uffler, P. J. and Grolimund, D. and Buhmann, J. M. and Brandt, S. and Varga, Z. and Wild, P. J. and Gunther, D. and Bodenmiller, B.},
    year = {2014},
    pages = {417--22},
}
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TY - JOUR
TI - Highly Multiplexed Imaging of Tumor Tissues with Subcellular Resolution by Mass Cytometry
AU - Giesen, C.
AU - Wang, H. A.
AU - Schapiro, D.
AU - Zivanovic, N.
AU - Jacobs, A.
AU - Hattendorf, B.
AU - Schüffler, P. J.
AU - Grolimund, D.
AU - Buhmann, J. M.
AU - Brandt, S.
AU - Varga, Z.
AU - Wild, P. J.
AU - Gunther, D.
AU - Bodenmiller, B.
T2 - Nat Methods
AB - Mass cytometry enables high-dimensional, single-cell analysis of cell type and state. In mass cytometry, rare earth metals are used as reporters on antibodies. Analysis of metal abundances using the mass cytometer allows determination of marker expression in individual cells. Mass cytometry has previously been applied only to cell suspensions. To gain spatial information, we have coupled immunohistochemical and immunocytochemical methods with high-resolution laser ablation to CyTOF mass cytometry. This approach enables the simultaneous imaging of 32 proteins and protein modifications at subcellular resolution; with the availability of additional isotopes, measurement of over 100 markers will be possible. We applied imaging mass cytometry to human breast cancer samples, allowing delineation of cell subpopulations and cell-cell interactions and highlighting tumor heterogeneity. Imaging mass cytometry complements existing imaging approaches. It will enable basic studies of tissue heterogeneity and function and support the transition of medicine toward individualized molecularly targeted diagnosis and therapies.
DA - 2014///
PY - 2014
DO - 10.1038/nmeth.2869
VL - 11
SP - 417
EP - 22
J2 - Nature methods
SN - 1548-7105 (Electronic) 1548-7091 (Linking)
UR - http://www.nature.com/nmeth/journal/v11/n4/full/nmeth.2869.html
ER -
Mass cytometry enables high-dimensional, single-cell analysis of cell type and state. In mass cytometry, rare earth metals are used as reporters on antibodies. Analysis of metal abundances using the mass cytometer allows determination of marker expression in individual cells. Mass cytometry has previously been applied only to cell suspensions. To gain spatial information, we have coupled immunohistochemical and immunocytochemical methods with high-resolution laser ablation to CyTOF mass cytometry. This approach enables the simultaneous imaging of 32 proteins and protein modifications at subcellular resolution; with the availability of additional isotopes, measurement of over 100 markers will be possible. We applied imaging mass cytometry to human breast cancer samples, allowing delineation of cell subpopulations and cell-cell interactions and highlighting tumor heterogeneity. Imaging mass cytometry complements existing imaging approaches. It will enable basic studies of tissue heterogeneity and function and support the transition of medicine toward individualized molecularly targeted diagnosis and therapies.
P. J. Schüffler, D. Mahapatra, R. E. Naziroglu, Z. Li, C. A. J. Puylaert, R. Andriantsimiavona, F. M. Vos, D. A. Pendsé, C. Yung Nio, J. Stoker, S. A. Taylor and J. M. Buhmann.
Semi-Automatic Crohn's Disease Severity Estimation on MR Imaging.
6th MICCAI Workshop on Abdominal Imaging - Computational and Clinical Applications, 2014 
PDF   URL   BibTeX   Endnote / RIS   Abstract
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@inproceedings{schuffler_semi-automatic_2014,
    title = {Semi-{Automatic} {Crohn}'s {Disease} {Severity} {Estimation} on {MR} {Imaging}},
    url = {http://link.springer.com/chapter/10.1007%2F978-3-319-13692-9_12},
    abstract = {Crohn’s disease (CD) is a chronic inflammatory bowel disease which can be visualized by magnetic resonance imaging (MRI). For CD grading, several non-invasive MRI based severity scores are known, most prominent the MaRIA and AIS. As these scores rely on manual MRI readings for individual bowel segments by trained radiologists, automated MRI assessment has been more and more focused in recent research. We show on a dataset of 27 CD patients that semi-automatically measured bowel wall thickness (ABWT) and dynamic contrast enhancement (DCE) completely outperform manual scorings: the segmental correlation to the Crohn’s Disease Endoscopic Index of Severity (CDEIS) of ABWT and DCE is significantly higher (r = .78) than that of MaRIA (r = .45) or AIS (r = .51). Also on a per-patient basis, the models with ABWT and DCE show significantly higher correlation (r = .69) to global CDEIS than MaRIA (r = .46).},
    author = {Sch\"uffler, P. J. and Mahapatra, D. and Naziroglu, R. E. and Li, Z. and Puylaert, C. A. J. and Andriantsimiavona, R. and Vos, F. M. and Pends\'e, D. A. and Nio, C. Yung and Stoker, J. and Taylor, S. A. and Buhmann, J. M.},
    year = {2014},
}
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TY - CONF
TI - Semi-Automatic Crohn's Disease Severity Estimation on MR Imaging
AU - Schüffler, P. J.
AU - Mahapatra, D.
AU - Naziroglu, R. E.
AU - Li, Z.
AU - Puylaert, C. A. J.
AU - Andriantsimiavona, R.
AU - Vos, F. M.
AU - Pendsé, D. A.
AU - Nio, C. Yung
AU - Stoker, J.
AU - Taylor, S. A.
AU - Buhmann, J. M.
T2 - 6th MICCAI Workshop on Abdominal Imaging - Computational and Clinical Applications
AB - Crohn’s disease (CD) is a chronic inflammatory bowel disease which can be visualized by magnetic resonance imaging (MRI). For CD grading, several non-invasive MRI based severity scores are known, most prominent the MaRIA and AIS. As these scores rely on manual MRI readings for individual bowel segments by trained radiologists, automated MRI assessment has been more and more focused in recent research. We show on a dataset of 27 CD patients that semi-automatically measured bowel wall thickness (ABWT) and dynamic contrast enhancement (DCE) completely outperform manual scorings: the segmental correlation to the Crohn’s Disease Endoscopic Index of Severity (CDEIS) of ABWT and DCE is significantly higher (r = .78) than that of MaRIA (r = .45) or AIS (r = .51). Also on a per-patient basis, the models with ABWT and DCE show significantly higher correlation (r = .69) to global CDEIS than MaRIA (r = .46).
DA - 2014///
PY - 2014
UR - http://link.springer.com/chapter/10.1007%2F978-3-319-13692-9_12
ER -
Crohn’s disease (CD) is a chronic inflammatory bowel disease which can be visualized by magnetic resonance imaging (MRI). For CD grading, several non-invasive MRI based severity scores are known, most prominent the MaRIA and AIS. As these scores rely on manual MRI readings for individual bowel segments by trained radiologists, automated MRI assessment has been more and more focused in recent research. We show on a dataset of 27 CD patients that semi-automatically measured bowel wall thickness (ABWT) and dynamic contrast enhancement (DCE) completely outperform manual scorings: the segmental correlation to the Crohn’s Disease Endoscopic Index of Severity (CDEIS) of ABWT and DCE is significantly higher (r = .78) than that of MaRIA (r = .45) or AIS (r = .51). Also on a per-patient basis, the models with ABWT and DCE show significantly higher correlation (r = .69) to global CDEIS than MaRIA (r = .46).
Peter J. Schüffler, Thomas J. Fuchs, Cheng S. Ong, Peter Wild and Joachim M. Buhmann.
TMARKER: A Free Software Toolkit for Histopathological Cell Counting and Staining Estimation.
Journal of Pathology Informatics, vol. 4, 2, p. 2, 2013 doi: 10.4103/2153-3539.109804
PDF   URL   BibTeX   Endnote / RIS   Abstract
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@article{schuffler_tmarker_2013,
    title = {{TMARKER}: {A} {Free} {Software} {Toolkit} for {Histopathological} {Cell} {Counting} and {Staining} {Estimation}},
    volume = {4},
    url = {https://www.sciencedirect.com/science/article/pii/S2153353922006629?via%3Dihub},
    doi = {10.4103/2153-3539.109804},
    abstract = {Background: Histological tissue analysis often involves manual cell counting and staining estimation of cancerous cells. These assessments are extremely time consuming, highly subjective and prone to error, since immunohistochemically stained cancer tissues usually show high variability in cell sizes, morphological structures and staining quality. To facilitate reproducible analysis in clinical practice as well as for cancer research, objective computer assisted staining estimation is highly desirable. Methods: We employ machine learning algorithms as randomized decision trees and support vector machines for nucleus detection and classification. Superpixels as segmentation over the tissue image are classified into foreground and background and thereafter into malignant and benign, learning from the user's feedback. As a fast alternative without nucleus classification, the existing color deconvolution method is incorporated. Results: Our program TMARKER connects already available workflows for computational pathology and immunohistochemical tissue rating with modern active learning algorithms from machine learning and computer vision. On a test dataset of human renal clear cell carcinoma and prostate carcinoma, the performance of the used algorithms is equivalent to two independent pathologists for nucleus detection and classification. Conclusion: We present a novel, free and operating system independent software package for computational cell counting and staining estimation, supporting IHC stained tissue analysis in clinic and for research. Proprietary toolboxes for similar tasks are expensive, bound to specific commercial hardware (e.g. a microscope) and mostly not quantitatively validated in terms of performance and reproducibility. We are confident that the presented software package will proof valuable for the scientific community and we anticipate a broader application domain due to the possibility to interactively learn models for new image types.},
    number = {2},
    journal = {Journal of Pathology Informatics},
    author = {Sch\"uffler, Peter J. and Fuchs, Thomas J. and Ong, Cheng S. and Wild, Peter and Buhmann, Joachim M.},
    year = {2013},
    pages = {2},
}
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TY - JOUR
TI - TMARKER: A Free Software Toolkit for Histopathological Cell Counting and Staining Estimation
AU - Schüffler, Peter J.
AU - Fuchs, Thomas J.
AU - Ong, Cheng S.
AU - Wild, Peter
AU - Buhmann, Joachim M.
T2 - Journal of Pathology Informatics
AB - Background: Histological tissue analysis often involves manual cell counting and staining estimation of cancerous cells. These assessments are extremely time consuming, highly subjective and prone to error, since immunohistochemically stained cancer tissues usually show high variability in cell sizes, morphological structures and staining quality. To facilitate reproducible analysis in clinical practice as well as for cancer research, objective computer assisted staining estimation is highly desirable. Methods: We employ machine learning algorithms as randomized decision trees and support vector machines for nucleus detection and classification. Superpixels as segmentation over the tissue image are classified into foreground and background and thereafter into malignant and benign, learning from the user's feedback. As a fast alternative without nucleus classification, the existing color deconvolution method is incorporated. Results: Our program TMARKER connects already available workflows for computational pathology and immunohistochemical tissue rating with modern active learning algorithms from machine learning and computer vision. On a test dataset of human renal clear cell carcinoma and prostate carcinoma, the performance of the used algorithms is equivalent to two independent pathologists for nucleus detection and classification. Conclusion: We present a novel, free and operating system independent software package for computational cell counting and staining estimation, supporting IHC stained tissue analysis in clinic and for research. Proprietary toolboxes for similar tasks are expensive, bound to specific commercial hardware (e.g. a microscope) and mostly not quantitatively validated in terms of performance and reproducibility. We are confident that the presented software package will proof valuable for the scientific community and we anticipate a broader application domain due to the possibility to interactively learn models for new image types.
DA - 2013///
PY - 2013
DO - 10.4103/2153-3539.109804
VL - 4
IS - 2
SP - 2
UR - https://www.sciencedirect.com/science/article/pii/S2153353922006629?via%3Dihub
ER -
Background: Histological tissue analysis often involves manual cell counting and staining estimation of cancerous cells. These assessments are extremely time consuming, highly subjective and prone to error, since immunohistochemically stained cancer tissues usually show high variability in cell sizes, morphological structures and staining quality. To facilitate reproducible analysis in clinical practice as well as for cancer research, objective computer assisted staining estimation is highly desirable. Methods: We employ machine learning algorithms as randomized decision trees and support vector machines for nucleus detection and classification. Superpixels as segmentation over the tissue image are classified into foreground and background and thereafter into malignant and benign, learning from the user's feedback. As a fast alternative without nucleus classification, the existing color deconvolution method is incorporated. Results: Our program TMARKER connects already available workflows for computational pathology and immunohistochemical tissue rating with modern active learning algorithms from machine learning and computer vision. On a test dataset of human renal clear cell carcinoma and prostate carcinoma, the performance of the used algorithms is equivalent to two independent pathologists for nucleus detection and classification. Conclusion: We present a novel, free and operating system independent software package for computational cell counting and staining estimation, supporting IHC stained tissue analysis in clinic and for research. Proprietary toolboxes for similar tasks are expensive, bound to specific commercial hardware (e.g. a microscope) and mostly not quantitatively validated in terms of performance and reproducibility. We are confident that the presented software package will proof valuable for the scientific community and we anticipate a broader application domain due to the possibility to interactively learn models for new image types.
Peter J. Schüffler, Dwarikanath Mahapatra, Jeroen A. W. Tielbeek, Franciscus M. Vos, Jesica Makanyanga, Doug A. Pendsé, C. Yung Nio, Jaap Stoker, Stuart A. Taylor and Joachim M. Buhmann.
A Model Development Pipeline for Crohn's Disease Severity Assessment from Magnetic Resonance Images.
In: Hiroyuki Yoshida, Simon Warfield and Michael Vannier (eds.) Abdominal Imaging. Computation and Clinical Applications, vol. 8198, p. 1-10, Lecture Notes in Computer Science, Springer Berlin Heidelberg, ISBN 978-3-642-41082-6, 2013 
Outstanding Paper Award
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@incollection{schuffler_model_2013,
    series = {Lecture {Notes} in {Computer} {Science}},
    title = {A {Model} {Development} {Pipeline} for {Crohn}'s {Disease} {Severity} {Assessment} from {Magnetic} {Resonance} {Images}},
    volume = {8198},
    isbn = {978-3-642-41082-6},
    url = {http://link.springer.com/chapter/10.1007%2F978-3-642-41083-3_1},
    abstract = {Crohn's Disease affects the intestinal tract of a patient and can have varying severity which influences treatment strategy. The clinical severity score CDEIS (Crohn’s Disease Endoscopic Index of severity) ranges from 0 to 44 and is measured by endoscopy. In this paper we investigate the potential of non-invasive magnetic resonance imaging to assess this severity, together with the underlying question which features are most relevant for this estimation task. We propose a new general and modular pipeline that uses machine learning techniques to quantify disease severity from MR images and show its value on Crohn’s Disease severity assessment on 30 patients scored by 4 medical experts. With the pipeline, we can obtain a magnetic resonance imaging score which outperforms two existing reference scores MaRIA and AIS.},
    booktitle = {Abdominal {Imaging}. {Computation} and {Clinical} {Applications}},
    publisher = {Springer Berlin Heidelberg},
    author = {Sch\"uffler, Peter J. and Mahapatra, Dwarikanath and Tielbeek, Jeroen A. W. and Vos, Franciscus M. and Makanyanga, Jesica and Pends\'e, Doug A. and Nio, C. Yung and Stoker, Jaap and Taylor, Stuart A. and Buhmann, Joachim M.},
    editor = {Yoshida, Hiroyuki and Warfield, Simon and Vannier, Michael},
    year = {2013},
    keywords = {AIS, CDEIS, Crohn’s Disease, MaRIA, abdominal MRI},
    pages = {1--10},
}
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TY - CHAP
TI - A Model Development Pipeline for Crohn's Disease Severity Assessment from Magnetic Resonance Images
AU - Schüffler, Peter J.
AU - Mahapatra, Dwarikanath
AU - Tielbeek, Jeroen A. W.
AU - Vos, Franciscus M.
AU - Makanyanga, Jesica
AU - Pendsé, Doug A.
AU - Nio, C. Yung
AU - Stoker, Jaap
AU - Taylor, Stuart A.
AU - Buhmann, Joachim M.
T2 - Abdominal Imaging. Computation and Clinical Applications
A2 - Yoshida, Hiroyuki
A2 - Warfield, Simon
A2 - Vannier, Michael
T3 - Lecture Notes in Computer Science
AB - Crohn's Disease affects the intestinal tract of a patient and can have varying severity which influences treatment strategy. The clinical severity score CDEIS (Crohn’s Disease Endoscopic Index of severity) ranges from 0 to 44 and is measured by endoscopy. In this paper we investigate the potential of non-invasive magnetic resonance imaging to assess this severity, together with the underlying question which features are most relevant for this estimation task. We propose a new general and modular pipeline that uses machine learning techniques to quantify disease severity from MR images and show its value on Crohn’s Disease severity assessment on 30 patients scored by 4 medical experts. With the pipeline, we can obtain a magnetic resonance imaging score which outperforms two existing reference scores MaRIA and AIS.
DA - 2013///
PY - 2013
VL - 8198
SP - 1
EP - 10
PB - Springer Berlin Heidelberg
SN - 978-3-642-41082-6
UR - http://link.springer.com/chapter/10.1007%2F978-3-642-41083-3_1
KW - AIS
KW - CDEIS
KW - Crohn’s Disease
KW - MaRIA
KW - abdominal MRI
ER -
Crohn's Disease affects the intestinal tract of a patient and can have varying severity which influences treatment strategy. The clinical severity score CDEIS (Crohn’s Disease Endoscopic Index of severity) ranges from 0 to 44 and is measured by endoscopy. In this paper we investigate the potential of non-invasive magnetic resonance imaging to assess this severity, together with the underlying question which features are most relevant for this estimation task. We propose a new general and modular pipeline that uses machine learning techniques to quantify disease severity from MR images and show its value on Crohn’s Disease severity assessment on 30 patients scored by 4 medical experts. With the pipeline, we can obtain a magnetic resonance imaging score which outperforms two existing reference scores MaRIA and AIS.
D. Mahapatra, P. J. Schüffler, J. A. W. Tielbeek, J. C. Makanyanga, J. Stoker, S. A. Taylor, F. M. Vos and J. M. Buhmann.
Automatic Detection and Segmentation of Crohn's Disease Tissues from Abdominal MRI.
IEEE Transactions on Medical Imaging, vol. 32, p. 2332-2347, 2013 doi: 10.1109/tmi.2013.2282124
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@article{mahapatra_automatic_2013,
    title = {Automatic {Detection} and {Segmentation} of {Crohn}'s {Disease} {Tissues} from {Abdominal} {MRI}},
    volume = {32},
    issn = {0278-0062},
    url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6600949},
    doi = {10.1109/TMI.2013.2282124},
    abstract = {We propose an information processing pipeline for segmenting parts of the bowel in abdominal magnetic resonance images that are affected with Crohn's disease. Given a magnetic resonance imaging test volume, it is first oversegmented into supervoxels and each supervoxel is analyzed to detect presence of Crohn's disease using random forest (RF) classifiers. The supervoxels identified as containing diseased tissues define the volume of interest (VOI). All voxels within the VOI are further investigated to segment the diseased region. Probability maps are generated for each voxel using a second set of RF classifiers which give the probabilities of each voxel being diseased, normal or background. The negative log-likelihood of these maps are used as penalty costs in a graph cut segmentation framework. Low level features like intensity statistics, texture anisotropy and curvature asymmetry, and high level context features are used at different stages. Smoothness constraints are imposed based on semantic information (importance of each feature to the classification task) derived from the second set of learned RF classifiers. Experimental results show that our method achieves high segmentation accuracy with Dice metric values of 0.90 ± 0.04 and Hausdorff distance of 7.3 ± 0.8 mm. Semantic information and context features are an integral part of our method and are robust to different levels of added noise.},
    journal = {IEEE Transactions on Medical Imaging},
    author = {Mahapatra, D. and Sch\"uffler, P. J. and Tielbeek, J. A. W. and Makanyanga, J. C. and Stoker, J. and Taylor, S. A. and Vos, F. M. and Buhmann, J. M.},
    year = {2013},
    keywords = {Anisotropic magnetoresistance, Context, Crohn\&\#x2019, Diseases, Entropy, Image segmentation, Radio frequency, content, graph cut, image features, probability maps, random forests, s disease, segmentation, semantic information, shape, supervoxels},
    pages = {2332--2347},
}
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TY - JOUR
TI - Automatic Detection and Segmentation of Crohn's Disease Tissues from Abdominal MRI
AU - Mahapatra, D.
AU - Schüffler, P. J.
AU - Tielbeek, J. A. W.
AU - Makanyanga, J. C.
AU - Stoker, J.
AU - Taylor, S. A.
AU - Vos, F. M.
AU - Buhmann, J. M.
T2 - IEEE Transactions on Medical Imaging
AB - We propose an information processing pipeline for segmenting parts of the bowel in abdominal magnetic resonance images that are affected with Crohn's disease. Given a magnetic resonance imaging test volume, it is first oversegmented into supervoxels and each supervoxel is analyzed to detect presence of Crohn's disease using random forest (RF) classifiers. The supervoxels identified as containing diseased tissues define the volume of interest (VOI). All voxels within the VOI are further investigated to segment the diseased region. Probability maps are generated for each voxel using a second set of RF classifiers which give the probabilities of each voxel being diseased, normal or background. The negative log-likelihood of these maps are used as penalty costs in a graph cut segmentation framework. Low level features like intensity statistics, texture anisotropy and curvature asymmetry, and high level context features are used at different stages. Smoothness constraints are imposed based on semantic information (importance of each feature to the classification task) derived from the second set of learned RF classifiers. Experimental results show that our method achieves high segmentation accuracy with Dice metric values of 0.90 ± 0.04 and Hausdorff distance of 7.3 ± 0.8 mm. Semantic information and context features are an integral part of our method and are robust to different levels of added noise.
DA - 2013///
PY - 2013
DO - 10.1109/TMI.2013.2282124
VL - 32
SP - 2332
EP - 2347
SN - 0278-0062
UR - http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6600949
KW - Anisotropic magnetoresistance
KW - Context
KW - Crohn’
KW - Diseases
KW - Entropy
KW - Image segmentation
KW - Radio frequency
KW - content
KW - graph cut
KW - image features
KW - probability maps
KW - random forests
KW - s disease
KW - segmentation
KW - semantic information
KW - shape
KW - supervoxels
ER -
We propose an information processing pipeline for segmenting parts of the bowel in abdominal magnetic resonance images that are affected with Crohn's disease. Given a magnetic resonance imaging test volume, it is first oversegmented into supervoxels and each supervoxel is analyzed to detect presence of Crohn's disease using random forest (RF) classifiers. The supervoxels identified as containing diseased tissues define the volume of interest (VOI). All voxels within the VOI are further investigated to segment the diseased region. Probability maps are generated for each voxel using a second set of RF classifiers which give the probabilities of each voxel being diseased, normal or background. The negative log-likelihood of these maps are used as penalty costs in a graph cut segmentation framework. Low level features like intensity statistics, texture anisotropy and curvature asymmetry, and high level context features are used at different stages. Smoothness constraints are imposed based on semantic information (importance of each feature to the classification task) derived from the second set of learned RF classifiers. Experimental results show that our method achieves high segmentation accuracy with Dice metric values of 0.90 ± 0.04 and Hausdorff distance of 7.3 ± 0.8 mm. Semantic information and context features are an integral part of our method and are robust to different levels of added noise.
Peter J. Schueffler, Niels Rupp, Cheng S. Ong, Joachim M. Buhmann, Thomas J. Fuchs and Peter J. Wild.
TMARKER: A Robust and Free Software Toolkit for Histopathological Cell Counting and Immunohistochemical Staining Estimations.
German Society of Pathology 97th Annual Meeting, 2013 
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@inproceedings{schueffler_tmarker_2013,
    title = {{TMARKER}: {A} {Robust} and {Free} {Software} {Toolkit} for {Histopathological} {Cell} {Counting} and {Immunohistochemical} {Staining} {Estimations}},
    url = {http://link.springer.com/article/10.1007%2Fs00292-013-1765-2},
    abstract = {Aims. Assessment of immunhistochemical staining intensity and percentage of positive cells generally suffers from high inter- and intra-observer variability. Besides, estimation of the intensity and percentage of immunostained cells often involves manual cell counting and is therefore time consuming.
    Methods. A novel, free, and open-source software toolkit was developed, connecting already available work flows for computational pathology and immunohistochemical tissue assessment with modern active learning algorithms from machine learning and computer vision.
    Results. A new and platform independent software program, called TMARKER, was developed and introduced. The validity and robustness of the used algorithms has been shown on a test dataset of human renal clear cell carcinomas and prostate carcinomas.
    Conclusions. This new software program together with a user-friendly Java-based graphical user interface enabled comprehensive computational assistance in pathological tissue rating. Routine use of this software toolkit may provide more reliable and robust biomarkers for treatment decision.},
    booktitle = {German {Society} of {Pathology} 97th {Annual} {Meeting}},
    author = {Schueffler, Peter J. and Rupp, Niels and Ong, Cheng S. and Buhmann, Joachim M. and Fuchs, Thomas J. and Wild, Peter J.},
    year = {2013},
}
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TY - CONF
TI - TMARKER: A Robust and Free Software Toolkit for Histopathological Cell Counting and Immunohistochemical Staining Estimations
AU - Schueffler, Peter J.
AU - Rupp, Niels
AU - Ong, Cheng S.
AU - Buhmann, Joachim M.
AU - Fuchs, Thomas J.
AU - Wild, Peter J.
AB - Aims. Assessment of immunhistochemical staining intensity and percentage of positive cells generally suffers from high inter- and intra-observer variability. Besides, estimation of the intensity and percentage of immunostained cells often involves manual cell counting and is therefore time consuming.
Methods. A novel, free, and open-source software toolkit was developed, connecting already available work flows for computational pathology and immunohistochemical tissue assessment with modern active learning algorithms from machine learning and computer vision.
Results. A new and platform independent software program, called TMARKER, was developed and introduced. The validity and robustness of the used algorithms has been shown on a test dataset of human renal clear cell carcinomas and prostate carcinomas.
Conclusions. This new software program together with a user-friendly Java-based graphical user interface enabled comprehensive computational assistance in pathological tissue rating. Routine use of this software toolkit may provide more reliable and robust biomarkers for treatment decision.
C3 - German Society of Pathology 97th Annual Meeting
DA - 2013///
PY - 2013
UR - http://link.springer.com/article/10.1007%2Fs00292-013-1765-2
ER -
Aims. Assessment of immunhistochemical staining intensity and percentage of positive cells generally suffers from high inter- and intra-observer variability. Besides, estimation of the intensity and percentage of immunostained cells often involves manual cell counting and is therefore time consuming. Methods. A novel, free, and open-source software toolkit was developed, connecting already available work flows for computational pathology and immunohistochemical tissue assessment with modern active learning algorithms from machine learning and computer vision. Results. A new and platform independent software program, called TMARKER, was developed and introduced. The validity and robustness of the used algorithms has been shown on a test dataset of human renal clear cell carcinomas and prostate carcinomas. Conclusions. This new software program together with a user-friendly Java-based graphical user interface enabled comprehensive computational assistance in pathological tissue rating. Routine use of this software toolkit may provide more reliable and robust biomarkers for treatment decision.
Peter J. Schueffler, Thomas J. Fuchs, Cheng Soon Ong, Volker Roth and Joachim M. Buhmann.
Automated Analysis of Tissue Micro-Array Images on the Example of Renal Cell Carcinoma.
In: Similarity-Based Pattern Analysis and Recognition, p. 219–246, Advances in Computer Vision and Pattern Recognition, Springer, ISBN 978-1-4471-5627-7, 2013 
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@incollection{schueffler_automated_2013,
    address = {London},
    series = {Advances in {Computer} {Vision} and {Pattern} {Recognition}},
    title = {Automated {Analysis} of {Tissue} {Micro}-{Array} {Images} on the {Example} of {Renal} {Cell} {Carcinoma}},
    isbn = {978-1-4471-5627-7},
    url = {https://www.springer.com/computer/image+processing/book/978-1-4471-5627-7},
    abstract = {Automated tissue micro-array analysis forms a challenging problem in computational pathology. The detection of cell nuclei, the classification into malignant and benign as well as the evaluation of their protein expression pattern by immunohistochemical staining are crucial routine steps for human cancer research and oncology. Computational assistance in this field can extremely accelerate the high throughput of the upcoming patient data as well as facilitate the reproducibility and objectivity of qualitative and quantitative measures. In this chapter, we describe an automated pipeline for staining estimation of tissue micro-array images, which comprises nucleus detection, nucleus segmentation, nucleus classification and staining estimation among cancerous nuclei. This pipeline is a practical example for the importance of non-metric effects in this kind of image analysis, e.g., the use of shape information and non-Euclidean kernels improve the nucleus classification performance significantly. The pipeline is explained and validated on a renal clear cell carcinoma dataset with MIB-1 stained tissue micro-array images and survival data of 133 patients. Further, the pipeline is implemented for medical use and research purpose in the free program TMARKER.},
    booktitle = {Similarity-{Based} {Pattern} {Analysis} and {Recognition}},
    publisher = {Springer},
    author = {Schueffler, Peter J. and Fuchs, Thomas J. and Ong, Cheng Soon and Roth, Volker and Buhmann, Joachim M.},
    year = {2013},
    pages = {219--246},
}
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TY - CHAP
TI - Automated Analysis of Tissue Micro-Array Images on the Example of Renal Cell Carcinoma
AU - Schueffler, Peter J.
AU - Fuchs, Thomas J.
AU - Ong, Cheng Soon
AU - Roth, Volker
AU - Buhmann, Joachim M.
T2 - Similarity-Based Pattern Analysis and Recognition
T3 - Advances in Computer Vision and Pattern Recognition
AB - Automated tissue micro-array analysis forms a challenging problem in computational pathology. The detection of cell nuclei, the classification into malignant and benign as well as the evaluation of their protein expression pattern by immunohistochemical staining are crucial routine steps for human cancer research and oncology. Computational assistance in this field can extremely accelerate the high throughput of the upcoming patient data as well as facilitate the reproducibility and objectivity of qualitative and quantitative measures. In this chapter, we describe an automated pipeline for staining estimation of tissue micro-array images, which comprises nucleus detection, nucleus segmentation, nucleus classification and staining estimation among cancerous nuclei. This pipeline is a practical example for the importance of non-metric effects in this kind of image analysis, e.g., the use of shape information and non-Euclidean kernels improve the nucleus classification performance significantly. The pipeline is explained and validated on a renal clear cell carcinoma dataset with MIB-1 stained tissue micro-array images and survival data of 133 patients. Further, the pipeline is implemented for medical use and research purpose in the free program TMARKER.
CY - London
DA - 2013///
PY - 2013
SP - 219
EP - 246
PB - Springer
SN - 978-1-4471-5627-7
UR - https://www.springer.com/computer/image+processing/book/978-1-4471-5627-7
ER -
Automated tissue micro-array analysis forms a challenging problem in computational pathology. The detection of cell nuclei, the classification into malignant and benign as well as the evaluation of their protein expression pattern by immunohistochemical staining are crucial routine steps for human cancer research and oncology. Computational assistance in this field can extremely accelerate the high throughput of the upcoming patient data as well as facilitate the reproducibility and objectivity of qualitative and quantitative measures. In this chapter, we describe an automated pipeline for staining estimation of tissue micro-array images, which comprises nucleus detection, nucleus segmentation, nucleus classification and staining estimation among cancerous nuclei. This pipeline is a practical example for the importance of non-metric effects in this kind of image analysis, e.g., the use of shape information and non-Euclidean kernels improve the nucleus classification performance significantly. The pipeline is explained and validated on a renal clear cell carcinoma dataset with MIB-1 stained tissue micro-array images and survival data of 133 patients. Further, the pipeline is implemented for medical use and research purpose in the free program TMARKER.
Niels J. Rupp, Igor Cima, Ralph Schiess, Peter J. Schüffler, Thomas J. Fuchs, Niklaus Frankhauser, Martin Kälin, Silke Gillessen, Ruedi Aebersold, Wilhelm Krek, Mark A. Rubin, Holger Moch and Peter J. Wild.
Serum and Prostate Cancer Tissue Signatures of ERG Rearrangement Derived from Quantitative Analysis of the PTEN Conditional Knockout Mouse Proteome.
Symposium of the German Society for Pathology, 2012 
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@inproceedings{rupp_serum_2012,
    title = {Serum and {Prostate} {Cancer} {Tissue} {Signatures} of {ERG} {Rearrangement} {Derived} from {Quantitative} {Analysis} of the {PTEN} {Conditional} {Knockout} {Mouse} {Proteome}},
    abstract = {Aims: Applying a systems biology approach to assess serum and tissue signatures of ERG rearrangement in patients with prostate cancer with the goal of determining downstream molecular targets for gene fusion cancers.
    Methods: Prostate tissue from a conditional PTEN knockout mouse model of prostate cancer was investigated, using selective enrichment of N-glycopeptides and mass spectrometry-based label-free quantification. Mouse tissue signatures were validated in sera and tissue of mice (n=12) and humans (n=105) by selected reaction monitoring (SRM), ELISA, and immunohistochemistry. ERG rearrangement status was assessed using fluorescence in situ hybridization (FISH) on two independent tissue microarray based prostatectomy cohorts (n=41, n=348). A Random forest model was trained and validated to identify serum and tissue signatures of ERG rearrangement.
    Results: A comprehensive PTEN dependent protein catalogue representing over 700 glycoproteins was established. TMPRSS2-ERG gene fusions occured in 41\% (17/41) of analyzable prostate cancers of the training cohort. ERG dependent serum signatures could be found. Serum signatures were tested on prostate cancer tissue microarrays using immunohistochemistry (test cohort).
    Conclusions: This is the first study to demonstrate a proteomic signature of ERG rearrangement prostate cancer. Given the challenges of directly targeting ETS transcription factors, this study has potential clinical implications providing important insights into future targetable downstream pathways.},
    booktitle = {Symposium of the {German} {Society} for {Pathology}},
    author = {Rupp, Niels J. and Cima, Igor and Schiess, Ralph and Sch\"uffler, Peter J. and Fuchs, Thomas J. and Frankhauser, Niklaus and K\"alin, Martin and Gillessen, Silke and Aebersold, Ruedi and Krek, Wilhelm and Rubin, Mark A. and Moch, Holger and Wild, Peter J.},
    year = {2012},
}
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TY - CONF
TI - Serum and Prostate Cancer Tissue Signatures of ERG Rearrangement Derived from Quantitative Analysis of the PTEN Conditional Knockout Mouse Proteome
AU - Rupp, Niels J.
AU - Cima, Igor
AU - Schiess, Ralph
AU - Schüffler, Peter J.
AU - Fuchs, Thomas J.
AU - Frankhauser, Niklaus
AU - Kälin, Martin
AU - Gillessen, Silke
AU - Aebersold, Ruedi
AU - Krek, Wilhelm
AU - Rubin, Mark A.
AU - Moch, Holger
AU - Wild, Peter J.
AB - Aims: Applying a systems biology approach to assess serum and tissue signatures of ERG rearrangement in patients with prostate cancer with the goal of determining downstream molecular targets for gene fusion cancers.
Methods: Prostate tissue from a conditional PTEN knockout mouse model of prostate cancer was investigated, using selective enrichment of N-glycopeptides and mass spectrometry-based label-free quantification. Mouse tissue signatures were validated in sera and tissue of mice (n=12) and humans (n=105) by selected reaction monitoring (SRM), ELISA, and immunohistochemistry. ERG rearrangement status was assessed using fluorescence in situ hybridization (FISH) on two independent tissue microarray based prostatectomy cohorts (n=41, n=348). A Random forest model was trained and validated to identify serum and tissue signatures of ERG rearrangement.
Results: A comprehensive PTEN dependent protein catalogue representing over 700 glycoproteins was established. TMPRSS2-ERG gene fusions occured in 41% (17/41) of analyzable prostate cancers of the training cohort. ERG dependent serum signatures could be found. Serum signatures were tested on prostate cancer tissue microarrays using immunohistochemistry (test cohort).
Conclusions: This is the first study to demonstrate a proteomic signature of ERG rearrangement prostate cancer. Given the challenges of directly targeting ETS transcription factors, this study has potential clinical implications providing important insights into future targetable downstream pathways.
C3 - Symposium of the German Society for Pathology
DA - 2012///
PY - 2012
ER -
Aims: Applying a systems biology approach to assess serum and tissue signatures of ERG rearrangement in patients with prostate cancer with the goal of determining downstream molecular targets for gene fusion cancers. Methods: Prostate tissue from a conditional PTEN knockout mouse model of prostate cancer was investigated, using selective enrichment of N-glycopeptides and mass spectrometry-based label-free quantification. Mouse tissue signatures were validated in sera and tissue of mice (n=12) and humans (n=105) by selected reaction monitoring (SRM), ELISA, and immunohistochemistry. ERG rearrangement status was assessed using fluorescence in situ hybridization (FISH) on two independent tissue microarray based prostatectomy cohorts (n=41, n=348). A Random forest model was trained and validated to identify serum and tissue signatures of ERG rearrangement. Results: A comprehensive PTEN dependent protein catalogue representing over 700 glycoproteins was established. TMPRSS2-ERG gene fusions occured in 41% (17/41) of analyzable prostate cancers of the training cohort. ERG dependent serum signatures could be found. Serum signatures were tested on prostate cancer tissue microarrays using immunohistochemistry (test cohort). Conclusions: This is the first study to demonstrate a proteomic signature of ERG rearrangement prostate cancer. Given the challenges of directly targeting ETS transcription factors, this study has potential clinical implications providing important insights into future targetable downstream pathways.
Peter J. Schueffler, Thomas J. Fuchs, Cheng S. Ong, Peter Wild and Joachim M. Buhmann.
TMARKER: A User-Friendly Open-Source Assistance for Tma Grading and Cell Counting.
Histopathology Image Analysis (HIMA) Workshop at the 15th International Conference on Medical Image Computing and Computer Assisted Intervention MICCAI, 2012 
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@inproceedings{schueffler_tmarker_2012,
    title = {{TMARKER}: {A} {User}-{Friendly} {Open}-{Source} {Assistance} for {Tma} {Grading} and {Cell} {Counting}},
    booktitle = {Histopathology {Image} {Analysis} ({HIMA}) {Workshop} at the 15th {International} {Conference} on {Medical} {Image} {Computing} and {Computer} {Assisted} {Intervention} {MICCAI}},
    author = {Schueffler, Peter J. and Fuchs, Thomas J. and Ong, Cheng S. and Wild, Peter and Buhmann, Joachim M.},
    year = {2012},
}
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TY - CONF
TI - TMARKER: A User-Friendly Open-Source Assistance for Tma Grading and Cell Counting
AU - Schueffler, Peter J.
AU - Fuchs, Thomas J.
AU - Ong, Cheng S.
AU - Wild, Peter
AU - Buhmann, Joachim M.
C3 - Histopathology Image Analysis (HIMA) Workshop at the 15th International Conference on Medical Image Computing and Computer Assisted Intervention MICCAI
DA - 2012///
PY - 2012
ER -
Igor Cima, Ralph Schiess, Peter Wild, Martin Kaelin, Peter Schueffler, Vinzenz Lange, Paola Picotti, Reto Ossola, Arnoud Templeton, Olga Schubert, Thomas J. Fuchs, Thomas Leippold, Stephen Wyler, Jens Zehetner, Wolfram Jochum, Joachim Buhmann, Thomas Cerny, Holger Moch, Silke Gillessen, Ruedi Aebersold and Wilhelm Krek.
Cancer Genetics-Guided Discovery of Serum Biomarker Signatures for Diagnosis and Prognosis of Prostate Cancer.
PNAS: Proceedings of the National Academy of Sciences, vol. 108, 8, p. 3342-3347, 2011 doi: 10.1073/pnas.1013699108
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@article{cima_cancer_2011,
    title = {Cancer {Genetics}-{Guided} {Discovery} of {Serum} {Biomarker} {Signatures} for {Diagnosis} and {Prognosis} of {Prostate} {Cancer}},
    volume = {108},
    url = {http://www.pnas.org/content/108/8/3342.abstract},
    doi = {10.1073/pnas.1013699108},
    abstract = {A key barrier to the realization of personalized medicine for cancer is the identification of biomarkers. Here we describe a two-stage strategy for the discovery of serum biomarker signatures corresponding to specific cancer-causing mutations and its application to prostate cancer (PCa) in the context of the commonly occurring phosphatase and tensin homolog (PTEN) tumor-suppressor gene inactivation. In the first stage of our approach, we identified 775 N-linked glycoproteins from sera and prostate tissue of wild-type and Pten-null mice. Using label-free quantitative proteomics, we showed that Pten inactivation leads to measurable perturbations in the murine prostate and serum glycoproteome. Following bioinformatic prioritization, in a second stage we applied targeted proteomics to detect and quantify 39 human ortholog candidate biomarkers in the sera of PCa patients and control individuals. The resulting proteomic profiles were analyzed by machine learning to build predictive regression models for tissue PTEN status and diagnosis and grading of PCa. Our approach suggests a general path to rational cancer biomarker discovery and initial validation guided by cancer genetics and based on the integration of experimental mouse models, proteomics-based technologies, and computational modeling.},
    number = {8},
    journal = {PNAS: Proceedings of the National Academy of Sciences},
    author = {Cima, Igor and Schiess, Ralph and Wild, Peter and Kaelin, Martin and Schueffler, Peter and Lange, Vinzenz and Picotti, Paola and Ossola, Reto and Templeton, Arnoud and Schubert, Olga and Fuchs, Thomas J. and Leippold, Thomas and Wyler, Stephen and Zehetner, Jens and Jochum, Wolfram and Buhmann, Joachim and Cerny, Thomas and Moch, Holger and Gillessen, Silke and Aebersold, Ruedi and Krek, Wilhelm},
    year = {2011},
    pages = {3342--3347},
}
Download Endnote/RIS citation
TY - JOUR
TI - Cancer Genetics-Guided Discovery of Serum Biomarker Signatures for Diagnosis and Prognosis of Prostate Cancer
AU - Cima, Igor
AU - Schiess, Ralph
AU - Wild, Peter
AU - Kaelin, Martin
AU - Schueffler, Peter
AU - Lange, Vinzenz
AU - Picotti, Paola
AU - Ossola, Reto
AU - Templeton, Arnoud
AU - Schubert, Olga
AU - Fuchs, Thomas J.
AU - Leippold, Thomas
AU - Wyler, Stephen
AU - Zehetner, Jens
AU - Jochum, Wolfram
AU - Buhmann, Joachim
AU - Cerny, Thomas
AU - Moch, Holger
AU - Gillessen, Silke
AU - Aebersold, Ruedi
AU - Krek, Wilhelm
T2 - PNAS: Proceedings of the National Academy of Sciences
AB - A key barrier to the realization of personalized medicine for cancer is the identification of biomarkers. Here we describe a two-stage strategy for the discovery of serum biomarker signatures corresponding to specific cancer-causing mutations and its application to prostate cancer (PCa) in the context of the commonly occurring phosphatase and tensin homolog (PTEN) tumor-suppressor gene inactivation. In the first stage of our approach, we identified 775 N-linked glycoproteins from sera and prostate tissue of wild-type and Pten-null mice. Using label-free quantitative proteomics, we showed that Pten inactivation leads to measurable perturbations in the murine prostate and serum glycoproteome. Following bioinformatic prioritization, in a second stage we applied targeted proteomics to detect and quantify 39 human ortholog candidate biomarkers in the sera of PCa patients and control individuals. The resulting proteomic profiles were analyzed by machine learning to build predictive regression models for tissue PTEN status and diagnosis and grading of PCa. Our approach suggests a general path to rational cancer biomarker discovery and initial validation guided by cancer genetics and based on the integration of experimental mouse models, proteomics-based technologies, and computational modeling.
DA - 2011///
PY - 2011
DO - 10.1073/pnas.1013699108
VL - 108
IS - 8
SP - 3342
EP - 3347
UR - http://www.pnas.org/content/108/8/3342.abstract
ER -
A key barrier to the realization of personalized medicine for cancer is the identification of biomarkers. Here we describe a two-stage strategy for the discovery of serum biomarker signatures corresponding to specific cancer-causing mutations and its application to prostate cancer (PCa) in the context of the commonly occurring phosphatase and tensin homolog (PTEN) tumor-suppressor gene inactivation. In the first stage of our approach, we identified 775 N-linked glycoproteins from sera and prostate tissue of wild-type and Pten-null mice. Using label-free quantitative proteomics, we showed that Pten inactivation leads to measurable perturbations in the murine prostate and serum glycoproteome. Following bioinformatic prioritization, in a second stage we applied targeted proteomics to detect and quantify 39 human ortholog candidate biomarkers in the sera of PCa patients and control individuals. The resulting proteomic profiles were analyzed by machine learning to build predictive regression models for tissue PTEN status and diagnosis and grading of PCa. Our approach suggests a general path to rational cancer biomarker discovery and initial validation guided by cancer genetics and based on the integration of experimental mouse models, proteomics-based technologies, and computational modeling.
Peter J. Schueffler, Thomas J. Fuchs, Cheng Soon Ong, Volker Roth and Joachim M. Buhmann.
Computational TMA Analysis and Cell Nucleus Classification of Renal Cell Carcinoma.
Proceedings of the 32nd DAGM conference on Pattern recognition, p. 202–211, Springer-Verlag, Berlin, Heidelberg, ISBN 3-642-15985-0 978-3-642-15985-5, 2010 doi: 10.1007/978-3-642-15986-2_21
PDF   URL   BibTeX   Endnote / RIS   Abstract
Download BibTeX citation
@inproceedings{schueffler_computational_2010,
    address = {Darmstadt, Germany},
    title = {Computational {TMA} {Analysis} and {Cell} {Nucleus} {Classification} of {Renal} {Cell} {Carcinoma}},
    isbn = {3-642-15985-0 978-3-642-15985-5},
    url = {http://portal.acm.org/citation.cfm?id=1926258.1926281},
    doi = {10.1007/978-3-642-15986-2_21},
    abstract = {We consider an automated processing pipeline for tissue micro array analysis (TMA) of renal cell carcinoma. It consists of several consecutive tasks, which can be mapped to machine learning challenges. We investigate three of these tasks, namely nuclei segmentation, nuclei classification and staining estimation. We argue for a holistic view of the processing pipeline, as it is not obvious whether performance improvements at individual steps improve overall accuracy. The experimental results show that classification accuracy, which is comparable to trained human experts, can be achieved by using support vector machines (SVM) with appropriate kernels. Furthermore, we provide evidence that the shape of cell nuclei increases the classification performance. Most importantly, these improvements in classification accuracy result in corresponding improvements for the medically relevant estimation of immunohistochemical staining.},
    booktitle = {Proceedings of the 32nd {DAGM} conference on {Pattern} recognition},
    publisher = {Springer-Verlag, Berlin, Heidelberg},
    author = {Schueffler, Peter J. and Fuchs, Thomas J. and Ong, Cheng Soon and Roth, Volker and Buhmann, Joachim M.},
    year = {2010},
    pages = {202--211},
}
Download Endnote/RIS citation
TY - CONF
TI - Computational TMA Analysis and Cell Nucleus Classification of Renal Cell Carcinoma
AU - Schueffler, Peter J.
AU - Fuchs, Thomas J.
AU - Ong, Cheng Soon
AU - Roth, Volker
AU - Buhmann, Joachim M.
AB - We consider an automated processing pipeline for tissue micro array analysis (TMA) of renal cell carcinoma. It consists of several consecutive tasks, which can be mapped to machine learning challenges. We investigate three of these tasks, namely nuclei segmentation, nuclei classification and staining estimation. We argue for a holistic view of the processing pipeline, as it is not obvious whether performance improvements at individual steps improve overall accuracy. The experimental results show that classification accuracy, which is comparable to trained human experts, can be achieved by using support vector machines (SVM) with appropriate kernels. Furthermore, we provide evidence that the shape of cell nuclei increases the classification performance. Most importantly, these improvements in classification accuracy result in corresponding improvements for the medically relevant estimation of immunohistochemical staining.
C1 - Darmstadt, Germany
C3 - Proceedings of the 32nd DAGM conference on Pattern recognition
DA - 2010///
PY - 2010
DO - 10.1007/978-3-642-15986-2_21
SP - 202
EP - 211
PB - Springer-Verlag, Berlin, Heidelberg
SN - 3-642-15985-0 978-3-642-15985-5
UR - http://portal.acm.org/citation.cfm?id=1926258.1926281
ER -
We consider an automated processing pipeline for tissue micro array analysis (TMA) of renal cell carcinoma. It consists of several consecutive tasks, which can be mapped to machine learning challenges. We investigate three of these tasks, namely nuclei segmentation, nuclei classification and staining estimation. We argue for a holistic view of the processing pipeline, as it is not obvious whether performance improvements at individual steps improve overall accuracy. The experimental results show that classification accuracy, which is comparable to trained human experts, can be achieved by using support vector machines (SVM) with appropriate kernels. Furthermore, we provide evidence that the shape of cell nuclei increases the classification performance. Most importantly, these improvements in classification accuracy result in corresponding improvements for the medically relevant estimation of immunohistochemical staining.
Jurgen Veeck, Peter Wild, Thomas J. Fuchs, Peter Schueffler, Arndt Hartmann, Ruth Knuchel and Edgar Dahl.
Prognostic Relevance of Wnt-Inhibitory Factor-1 (WIF1) and Dickkopf-3 (DKK3) Promoter Methylation in Human Breast Cancer.
BMC Cancer, vol. 9, 1, p. 217, 2009 doi: 10.1186/1471-2407-9-217
PDF    URL   BibTeX   Endnote / RIS   Abstract
Download BibTeX citation
@article{veeck_prognostic_2009,
    title = {Prognostic {Relevance} of {Wnt}-{Inhibitory} {Factor}-1 ({WIF1}) and {Dickkopf}-3 ({DKK3}) {Promoter} {Methylation} in {Human} {Breast} {Cancer}},
    volume = {9},
    issn = {1471-2407},
    url = {http://www.biomedcentral.com/1471-2407/9/217},
    doi = {10.1186/1471-2407-9-217},
    abstract = {Background
    Secreted Wnt signaling antagonists have recently been described as frequent targets of epigenetic inactivation in human tumor entities. Since gene silencing of certain Wnt antagonists was found to be correlated with adverse patient survival in cancer, we aimed at investigating a potential prognostic impact of the two Wnt antagonizing molecules WIF1 and DKK3 in breast cancer, which are frequently silenced by promoter methylation in this disease.
    Methods
    WIF1 and DKK3 promoter methylation were assessed by methylation-specific PCR with bisulfite-converted DNA from 19 normal breast tissues and 150 primary breast carcinomas. Promoter methylation was interpreted in a qualitative, binary fashion. Statistical evaluations included two-sided Fisher's exact tests, univariate log-rank tests of Kaplan-Meier curves as well as multivariate Cox regression analyses.
    Results
    WIF1 and DKK3 promoter methylation were detected in 63.3\% (95/150) and 61.3\% (92/150) of breast carcinoma samples, respectively. In normal breast tissues, WIF1 methylation was present in 0\% (0/19) and DKK3 methylation in 5.3\% (1/19) of samples. In breast carcinomas, WIF1 methylation was significantly associated with methylation of DKK3 (p = 0.009). Methylation of either gene was not associated with clinicopathological parameters, except for DKK3 methylation being associated with patient age (p = 0.007). In univariate analysis, WIF1 methylation was not associated with clinical patient outcome. In contrast, DKK3 methylation was a prognostic factor in patient overall survival (OS) and disease-free survival (DFS). Estimated OS rates after 10 years were 54\% for patients with DKK3 -methylated tumors, in contrast to patients without DKK3 methylation in the tumor, who had a favorable 97\% OS after 10 years (p {\textless} 0.001). Likewise, DFS at 10 years for patients harboring DKK3 methylation in the tumor was 58\%, compared with 78\% for patients with unmethylated DKK3 (p = 0.037). Multivariate analyses revealed that DKK3 methylation was an independent prognostic factor predicting poor OS (hazard ratio (HR): 14.4; 95\% confidence interval (CI): 1.9–111.6; p = 0.011), and short DFS (HR: 2.5; 95\% CI: 1.0–6.0; p = 0.047) in breast cancer.
    Conclusion
    Although the Wnt antagonist genes WIF1 and DKK3 show a very similar frequency of promoter methylation in human breast cancer, only DKK3 methylation proves as a novel prognostic marker potentially useful in the clinical management of this disease.},
    number = {1},
    journal = {BMC Cancer},
    author = {Veeck, Jurgen and Wild, Peter and Fuchs, Thomas J. and Schueffler, Peter and Hartmann, Arndt and Knuchel, Ruth and Dahl, Edgar},
    year = {2009},
    pages = {217},
}
Download Endnote/RIS citation
TY - JOUR
TI - Prognostic Relevance of Wnt-Inhibitory Factor-1 (WIF1) and Dickkopf-3 (DKK3) Promoter Methylation in Human Breast Cancer
AU - Veeck, Jurgen
AU - Wild, Peter
AU - Fuchs, Thomas J.
AU - Schueffler, Peter
AU - Hartmann, Arndt
AU - Knuchel, Ruth
AU - Dahl, Edgar
T2 - BMC Cancer
AB - Background
Secreted Wnt signaling antagonists have recently been described as frequent targets of epigenetic inactivation in human tumor entities. Since gene silencing of certain Wnt antagonists was found to be correlated with adverse patient survival in cancer, we aimed at investigating a potential prognostic impact of the two Wnt antagonizing molecules WIF1 and DKK3 in breast cancer, which are frequently silenced by promoter methylation in this disease.
Methods
WIF1 and DKK3 promoter methylation were assessed by methylation-specific PCR with bisulfite-converted DNA from 19 normal breast tissues and 150 primary breast carcinomas. Promoter methylation was interpreted in a qualitative, binary fashion. Statistical evaluations included two-sided Fisher's exact tests, univariate log-rank tests of Kaplan-Meier curves as well as multivariate Cox regression analyses.
Results
WIF1 and DKK3 promoter methylation were detected in 63.3% (95/150) and 61.3% (92/150) of breast carcinoma samples, respectively. In normal breast tissues, WIF1 methylation was present in 0% (0/19) and DKK3 methylation in 5.3% (1/19) of samples. In breast carcinomas, WIF1 methylation was significantly associated with methylation of DKK3 (p = 0.009). Methylation of either gene was not associated with clinicopathological parameters, except for DKK3 methylation being associated with patient age (p = 0.007). In univariate analysis, WIF1 methylation was not associated with clinical patient outcome. In contrast, DKK3 methylation was a prognostic factor in patient overall survival (OS) and disease-free survival (DFS). Estimated OS rates after 10 years were 54% for patients with DKK3 -methylated tumors, in contrast to patients without DKK3 methylation in the tumor, who had a favorable 97% OS after 10 years (p < 0.001). Likewise, DFS at 10 years for patients harboring DKK3 methylation in the tumor was 58%, compared with 78% for patients with unmethylated DKK3 (p = 0.037). Multivariate analyses revealed that DKK3 methylation was an independent prognostic factor predicting poor OS (hazard ratio (HR): 14.4; 95% confidence interval (CI): 1.9–111.6; p = 0.011), and short DFS (HR: 2.5; 95% CI: 1.0–6.0; p = 0.047) in breast cancer.
Conclusion
Although the Wnt antagonist genes WIF1 and DKK3 show a very similar frequency of promoter methylation in human breast cancer, only DKK3 methylation proves as a novel prognostic marker potentially useful in the clinical management of this disease.
DA - 2009///
PY - 2009
DO - 10.1186/1471-2407-9-217
VL - 9
IS - 1
SP - 217
SN - 1471-2407
UR - http://www.biomedcentral.com/1471-2407/9/217
ER -
Background Secreted Wnt signaling antagonists have recently been described as frequent targets of epigenetic inactivation in human tumor entities. Since gene silencing of certain Wnt antagonists was found to be correlated with adverse patient survival in cancer, we aimed at investigating a potential prognostic impact of the two Wnt antagonizing molecules WIF1 and DKK3 in breast cancer, which are frequently silenced by promoter methylation in this disease. Methods WIF1 and DKK3 promoter methylation were assessed by methylation-specific PCR with bisulfite-converted DNA from 19 normal breast tissues and 150 primary breast carcinomas. Promoter methylation was interpreted in a qualitative, binary fashion. Statistical evaluations included two-sided Fisher's exact tests, univariate log-rank tests of Kaplan-Meier curves as well as multivariate Cox regression analyses. Results WIF1 and DKK3 promoter methylation were detected in 63.3% (95/150) and 61.3% (92/150) of breast carcinoma samples, respectively. In normal breast tissues, WIF1 methylation was present in 0% (0/19) and DKK3 methylation in 5.3% (1/19) of samples. In breast carcinomas, WIF1 methylation was significantly associated with methylation of DKK3 (p = 0.009). Methylation of either gene was not associated with clinicopathological parameters, except for DKK3 methylation being associated with patient age (p = 0.007). In univariate analysis, WIF1 methylation was not associated with clinical patient outcome. In contrast, DKK3 methylation was a prognostic factor in patient overall survival (OS) and disease-free survival (DFS). Estimated OS rates after 10 years were 54% for patients with DKK3 -methylated tumors, in contrast to patients without DKK3 methylation in the tumor, who had a favorable 97% OS after 10 years (p < 0.001). Likewise, DFS at 10 years for patients harboring DKK3 methylation in the tumor was 58%, compared with 78% for patients with unmethylated DKK3 (p = 0.037). Multivariate analyses revealed that DKK3 methylation was an independent prognostic factor predicting poor OS (hazard ratio (HR): 14.4; 95% confidence interval (CI): 1.9–111.6; p = 0.011), and short DFS (HR: 2.5; 95% CI: 1.0–6.0; p = 0.047) in breast cancer. Conclusion Although the Wnt antagonist genes WIF1 and DKK3 show a very similar frequency of promoter methylation in human breast cancer, only DKK3 methylation proves as a novel prognostic marker potentially useful in the clinical management of this disease.





_autopubs.cshtml:

Azar Kazemi, Masoumeh Gharib, Nema Mohamadian Roshan, Shirin Taraz Jamshidi, Fabian Stögbauer, Saeid Eslami and Peter J. Schüffler.
Assessment of the Tumor–Stroma Ratio and Tumor-Infiltrating Lymphocytes in Colorectal Cancer: Inter-Observer Agreement Evaluation.
Diagnostics, vol. 13, 14, p. 2339, 2023-07-11 doi: 10.3390/diagnostics13142339
PDF    URL   BibTeX   Endnote / RIS   Abstract
Download BibTeX citation
@article{kazemi_assessment_2023,
    title = {Assessment of the {Tumor}–{Stroma} {Ratio} and {Tumor}-{Infiltrating} {Lymphocytes} in {Colorectal} {Cancer}: {Inter}-{Observer} {Agreement} {Evaluation}},
    volume = {13},
    issn = {2075-4418},
    shorttitle = {Assessment of the {Tumor}–{Stroma} {Ratio} and {Tumor}-{Infiltrating} {Lymphocytes} in {Colorectal} {Cancer}},
    url = {https://www.mdpi.com/2075-4418/13/14/2339},
    doi = {10.3390/diagnostics13142339},
    abstract = {Background: To implement the new marker in clinical practice, reliability assessment, validation, and standardization of utilization must be applied. This study evaluated the reliability of tumor-infiltrating lymphocytes (TILs) and tumor-stroma ratio (TSR) assessment through conventional microscopy by comparing observers’ estimations. Methods: Intratumoral and tumor-front stromal TILs, and TSR, were assessed by three pathologists using 86 CRC HE slides. TSR and TILs were categorized using one and four different proposed cutoff systems, respectively, and agreement was assessed using the intraclass coefficient (ICC) and Cohen’s kappa statistics. Pairwise evaluation of agreement was performed using the Fleiss kappa statistic and the concordance rate and it was visualized by Bland–Altman plots. To investigate the association between biomarkers and patient data, Pearson’s correlation analysis was applied. Results: For the evaluation of intratumoral stromal TILs, ICC of 0.505 (95\% CI: 0.35–0.64) was obtained, kappa values were in the range of 0.21 to 0.38, and concordance rates in the range of 0.61 to 0.72. For the evaluation of tumor-front TILs, ICC was 0.52 (95\% CI: 0.32–0.67), the overall kappa value ranged from 0.24 to 0.30, and the concordance rate ranged from 0.66 to 0.72. For estimating the TSR, the ICC was 0.48 (95\% CI: 0.35–0.60), the kappa value was 0.49 and the concordance rate was 0.76. We observed a significant correlation between tumor grade and the median of TSR (0.29 (95\% CI: 0.032–0.51), p-value = 0.03). Conclusions: The agreement between pathologists in estimating these markers corresponds to poor-to-moderate agreement; implementing immune scores in daily practice requires more concentration in inter-observer agreements.},
    language = {en},
    number = {14},
    urldate = {2023-07-12},
    journal = {Diagnostics},
    author = {Kazemi, Azar and Gharib, Masoumeh and Mohamadian Roshan, Nema and Taraz Jamshidi, Shirin and St\"ogbauer, Fabian and Eslami, Saeid and Sch\"uffler, Peter J.},
    month = jul,
    year = {2023},
    pages = {2339},
}
Download Endnote/RIS citation
TY - JOUR
TI - Assessment of the Tumor–Stroma Ratio and Tumor-Infiltrating Lymphocytes in Colorectal Cancer: Inter-Observer Agreement Evaluation
AU - Kazemi, Azar
AU - Gharib, Masoumeh
AU - Mohamadian Roshan, Nema
AU - Taraz Jamshidi, Shirin
AU - Stögbauer, Fabian
AU - Eslami, Saeid
AU - Schüffler, Peter J.
T2 - Diagnostics
AB - Background: To implement the new marker in clinical practice, reliability assessment, validation, and standardization of utilization must be applied. This study evaluated the reliability of tumor-infiltrating lymphocytes (TILs) and tumor-stroma ratio (TSR) assessment through conventional microscopy by comparing observers’ estimations. Methods: Intratumoral and tumor-front stromal TILs, and TSR, were assessed by three pathologists using 86 CRC HE slides. TSR and TILs were categorized using one and four different proposed cutoff systems, respectively, and agreement was assessed using the intraclass coefficient (ICC) and Cohen’s kappa statistics. Pairwise evaluation of agreement was performed using the Fleiss kappa statistic and the concordance rate and it was visualized by Bland–Altman plots. To investigate the association between biomarkers and patient data, Pearson’s correlation analysis was applied. Results: For the evaluation of intratumoral stromal TILs, ICC of 0.505 (95% CI: 0.35–0.64) was obtained, kappa values were in the range of 0.21 to 0.38, and concordance rates in the range of 0.61 to 0.72. For the evaluation of tumor-front TILs, ICC was 0.52 (95% CI: 0.32–0.67), the overall kappa value ranged from 0.24 to 0.30, and the concordance rate ranged from 0.66 to 0.72. For estimating the TSR, the ICC was 0.48 (95% CI: 0.35–0.60), the kappa value was 0.49 and the concordance rate was 0.76. We observed a significant correlation between tumor grade and the median of TSR (0.29 (95% CI: 0.032–0.51), p-value = 0.03). Conclusions: The agreement between pathologists in estimating these markers corresponds to poor-to-moderate agreement; implementing immune scores in daily practice requires more concentration in inter-observer agreements.
DA - 2023/07/11/
PY - 2023
DO - 10.3390/diagnostics13142339
DP - DOI.org (Crossref)
VL - 13
IS - 14
SP - 2339
J2 - Diagnostics
LA - en
SN - 2075-4418
ST - Assessment of the Tumor–Stroma Ratio and Tumor-Infiltrating Lymphocytes in Colorectal Cancer
UR - https://www.mdpi.com/2075-4418/13/14/2339
Y2 - 2023/07/12/06:44:20
ER -
Background: To implement the new marker in clinical practice, reliability assessment, validation, and standardization of utilization must be applied. This study evaluated the reliability of tumor-infiltrating lymphocytes (TILs) and tumor-stroma ratio (TSR) assessment through conventional microscopy by comparing observers’ estimations. Methods: Intratumoral and tumor-front stromal TILs, and TSR, were assessed by three pathologists using 86 CRC HE slides. TSR and TILs were categorized using one and four different proposed cutoff systems, respectively, and agreement was assessed using the intraclass coefficient (ICC) and Cohen’s kappa statistics. Pairwise evaluation of agreement was performed using the Fleiss kappa statistic and the concordance rate and it was visualized by Bland–Altman plots. To investigate the association between biomarkers and patient data, Pearson’s correlation analysis was applied. Results: For the evaluation of intratumoral stromal TILs, ICC of 0.505 (95% CI: 0.35–0.64) was obtained, kappa values were in the range of 0.21 to 0.38, and concordance rates in the range of 0.61 to 0.72. For the evaluation of tumor-front TILs, ICC was 0.52 (95% CI: 0.32–0.67), the overall kappa value ranged from 0.24 to 0.30, and the concordance rate ranged from 0.66 to 0.72. For estimating the TSR, the ICC was 0.48 (95% CI: 0.35–0.60), the kappa value was 0.49 and the concordance rate was 0.76. We observed a significant correlation between tumor grade and the median of TSR (0.29 (95% CI: 0.032–0.51), p-value = 0.03). Conclusions: The agreement between pathologists in estimating these markers corresponds to poor-to-moderate agreement; implementing immune scores in daily practice requires more concentration in inter-observer agreements.
Peter Schüffler, Katja Steiger and Wilko Weichert.
How to use AI in pathology.
Genes Chromosomes & Cancer, p. gcc.23178, 2023-05-31 doi: 10.1002/gcc.23178
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@article{schuffler_how_2023,
    title = {How to use {\textless}span style="font-variant:small-caps;"{\textgreater}{AI}{\textless}/span{\textgreater} in pathology},
    issn = {1045-2257, 1098-2264},
    shorttitle = {How to use {\textless}span style="font-variant},
    url = {https://onlinelibrary.wiley.com/doi/10.1002/gcc.23178},
    doi = {10.1002/gcc.23178},
    language = {en},
    urldate = {2023-06-01},
    journal = {Genes, Chromosomes and Cancer},
    author = {Sch\"uffler, Peter and Steiger, Katja and Weichert, Wilko},
    month = may,
    year = {2023},
    pages = {gcc.23178},
}
Download Endnote/RIS citation
TY - JOUR
TI - How to use AI in pathology
AU - Schüffler, Peter
AU - Steiger, Katja
AU - Weichert, Wilko
T2 - Genes, Chromosomes and Cancer
DA - 2023/05/31/
PY - 2023
DO - 10.1002/gcc.23178
DP - DOI.org (Crossref)
SP - gcc.23178
J2 - Genes Chromosomes & Cancer
LA - en
SN - 1045-2257, 1098-2264
ST - How to use     month = feb,
    year = {2023},
    pages = {100301},
}
Download Endnote/RIS citation
TY - JOUR
TI - Pan-tumor T-lymphocyte detection using deep neural networks: Recommendations for transfer learning in immunohistochemistry
AU - Wilm, Frauke
AU - Ihling, Christian
AU - Méhes, Gábor
AU - Terracciano, Luigi
AU - Puget, Chloé
AU - Klopfleisch, Robert
AU - Schüffler, Peter
AU - Aubreville, Marc
AU - Maier, Andreas
AU - Mrowiec, Thomas
AU - Breininger, Katharina
T2 - Journal of Pathology Informatics
DA - 2023/02//
PY - 2023
DO - 10.1016/j.jpi.2023.100301
DP - DOI.org (Crossref)
SP - 100301
J2 - Journal of Pathology Informatics
LA - en
SN - 21533539
ST - Pan-tumor T-lymphocyte detection using deep neural networks
UR - https://linkinghub.elsevier.com/retrieve/pii/S2153353923001153
Y2 - 2023/03/09/15:35:15
ER -
Caroline Richter, Eva Mezger, Peter J. Schüffler, Wieland Sommer, Federico Fusco, Katharina Hauner, Sebastian C. Schmid, Jürgen E. Gschwend, Wilko Weichert, Kristina Schwamborn, Dominik Pförringer and Anna Melissa Schlitter.
Pathological Reporting of Radical Prostatectomy Specimens Following ICCR Recommendation: Impact of Electronic Reporting Tool Implementation on Quality and Interdisciplinary Communication in a Large University Hospital.
Current Oncology, vol. 29, 10, p. 7245-7256, 2022/10 doi: 10.3390/curroncol29100571
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@article{richter_pathological_2022,
    title = {Pathological {Reporting} of {Radical} {Prostatectomy} {Specimens} {Following} {ICCR} {Recommendation}: {Impact} of {Electronic} {Reporting} {Tool} {Implementation} on {Quality} and {Interdisciplinary} {Communication} in a {Large} {University} {Hospital}},
    volume = {29},
    copyright = {http://creativecommons.org/licenses/by/3.0/},
    issn = {1718-7729},
    shorttitle = {Pathological {Reporting} of {Radical} {Prostatectomy} {Specimens} {Following} {ICCR} {Recommendation}},
    url = {https://www.mdpi.com/1718-7729/29/10/571},
    doi = {10.3390/curroncol29100571},
    abstract = {Prostate cancer represents one of the most common malignant tumors in male patients in Germany. The pathological reporting of radical prostatectomy specimens following a structured process constitutes an excellent prototype for the introduction of software-based standardized structured reporting in pathology. This can lead to reports of higher quality and could create a fundamental improvement for future AI applications. A software-based reporting template was used to generate standardized structured pathological reports of radical prostatectomy specimens of patients treated at the University Hospital Klinikum rechts der Isar of Technische Universit\"at M\"unchen, Germany. Narrative reports (NR) and standardized structured reports (SSR) were analyzed with regard to completeness, and clinicians’ satisfaction with each report type was evaluated. SSR show considerably higher completeness than NR. A total of 10 categories out of 32 were significantly more complete in SSR than in NR (p {\textless} 0.05). Clinicians awarded overall high scores in NR and SSR reports. One rater acknowledged a significantly higher level of clarity and time saving when comparing SSR to NR. Our findings highlight that the standardized structured reporting of radical prostatectomy specimens, qualifying as level 5 reports, significantly increases objectively measured content quality and the level of completeness. The implementation of nationwide SSR in Germany, particularly in oncologic pathology, can serve pathologists, clinicians, and patients.},
    language = {en},
    number = {10},
    urldate = {2022-09-30},
    journal = {Current Oncology},
    author = {Richter, Caroline and Mezger, Eva and Sch\"uffler, Peter J. and Sommer, Wieland and Fusco, Federico and Hauner, Katharina and Schmid, Sebastian C. and Gschwend, J\"urgen E. and Weichert, Wilko and Schwamborn, Kristina and Pf\"orringer, Dominik and Schlitter, Anna Melissa},
    month = oct,
    year = {2022},
    Publisher: Multidisciplinary Digital Publishing Institute},
    keywords = {pathological reporting, prostate cancer, quality improvement, structured reporting templates},
    pages = {7245--7256},
}
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TY - JOUR
TI - Pathological Reporting of Radical Prostatectomy Specimens Following ICCR Recommendation: Impact of Electronic Reporting Tool Implementation on Quality and Interdisciplinary Communication in a Large University Hospital
AU - Richter, Caroline
AU - Mezger, Eva
AU - Schüffler, Peter J.
AU - Sommer, Wieland
AU - Fusco, Federico
AU - Hauner, Katharina
AU - Schmid, Sebastian C.
AU - Gschwend, Jürgen E.
AU - Weichert, Wilko
AU - Schwamborn, Kristina
AU - Pförringer, Dominik
AU - Schlitter, Anna Melissa
T2 - Current Oncology
AB - Prostate cancer represents one of the most common malignant tumors in male patients in Germany. The pathological reporting of radical prostatectomy specimens following a structured process constitutes an excellent prototype for the introduction of software-based standardized structured reporting in pathology. This can lead to reports of higher quality and could create a fundamental improvement for future AI applications. A software-based reporting template was used to generate standardized structured pathological reports of radical prostatectomy specimens of patients treated at the University Hospital Klinikum rechts der Isar of Technische Universität München, Germany. Narrative reports (NR) and standardized structured reports (SSR) were analyzed with regard to completeness, and clinicians’ satisfaction with each report type was evaluated. SSR show considerably higher completeness than NR. A total of 10 categories out of 32 were significantly more complete in SSR than in NR (p < 0.05). Clinicians awarded overall high scores in NR and SSR reports. One rater acknowledged a significantly higher level of clarity and time saving when comparing SSR to NR. Our findings highlight that the standardized structured reporting of radical prostatectomy specimens, qualifying as level 5 reports, significantly increases objectively measured content quality and the level of completeness. The implementation of nationwide SSR in Germany, particularly in oncologic pathology, can serve pathologists, clinicians, and patients.
DA - 2022/10//
PY - 2022
DO - 10.3390/curroncol29100571
DP - www.mdpi.com
VL - 29
IS - 10
SP - 7245
EP - 7256
LA - en
SN - 1718-7729
ST - Pathological Reporting of Radical Prostatectomy Specimens Following ICCR Recommendation
UR - https://www.mdpi.com/1718-7729/29/10/571
Y2 - 2022/09/30/11:52:43
KW - pathological reporting
KW - prostate cancer
KW - quality improvement
KW - structured reporting templates
ER -
Prostate cancer represents one of the most common malignant tumors in male patients in Germany. The pathological reporting of radical prostatectomy specimens following a structured process constitutes an excellent prototype for the introduction of software-based standardized structured reporting in pathology. This can lead to reports of higher quality and could create a fundamental improvement for future AI applications. A software-based reporting template was used to generate standardized structured pathological reports of radical prostatectomy specimens of patients treated at the University Hospital Klinikum rechts der Isar of Technische Universität München, Germany. Narrative reports (NR) and standardized structured reports (SSR) were analyzed with regard to completeness, and clinicians’ satisfaction with each report type was evaluated. SSR show considerably higher completeness than NR. A total of 10 categories out of 32 were significantly more complete in SSR than in NR (p < 0.05). Clinicians awarded overall high scores in NR and SSR reports. One rater acknowledged a significantly higher level of clarity and time saving when comparing SSR to NR. Our findings highlight that the standardized structured reporting of radical prostatectomy specimens, qualifying as level 5 reports, significantly increases objectively measured content quality and the level of completeness. The implementation of nationwide SSR in Germany, particularly in oncologic pathology, can serve pathologists, clinicians, and patients.
Luca Dan, Janik Israel, Sabrina R. Sarker, J. Rao, Katja Steiger, Alexander Muckenhuber, Peter Schüffler, Wilko Weichert, Dev Kumar Das, T. Thomas and U. Joshi.
Deep learning-assisted differential detection of pancreatic intraepithelial neoplasia (PanIN) and characterization of inflammatory structures in proximity.
105. Jahrestagung der Deutschen Gesellschaft für Pathologie, 2022-06-10 
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@misc{dan_deep_2022,
    address = {M\"unster},
    title = {Deep learning-assisted differential detection of pancreatic intraepithelial neoplasia ({PanIN}) and characterization of inflammatory structures in proximity},
    author = {Dan, Luca and Israel, Janik and Sarker, Sabrina R. and Rao, J. and Steiger, Katja and Muckenhuber, Alexander and Sch\"uffler, Peter and Weichert, Wilko and Das, Dev Kumar and Thomas, T. and Joshi, U.},
    month = jun,
    year = {2022},
}
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TY - SLIDE
TI - Deep learning-assisted differential detection of pancreatic intraepithelial neoplasia (PanIN) and characterization of inflammatory structures in proximity
T2 - 105. Jahrestagung der Deutschen Gesellschaft für Pathologie
A2 - Dan, Luca
A2 - Israel, Janik
A2 - Sarker, Sabrina R.
A2 - Rao, J.
A2 - Steiger, Katja
A2 - Muckenhuber, Alexander
A2 - Schüffler, Peter
A2 - Weichert, Wilko
A2 - Das, Dev Kumar
A2 - Thomas, T.
A2 - Joshi, U.
CY - Münster
DA - 2022/06/10/
PY - 2022
ER -
Ufuk Kurt, Anees Kazi, Nassir Navab and Peter Schüffler.
Federated Learning for Breast Cancer Classification.
105. Jahrestagung der Deutschen Gesellschaft für Pathologie, 2022-06-09 
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@misc{kurt_federated_2022,
    address = {M\"unster},
    type = {Poster},
    title = {Federated {Learning} for {Breast} {Cancer} {Classification}},
    author = {Kurt, Ufuk and Kazi, Anees and Navab, Nassir and Sch\"uffler, Peter},
    month = jun,
    year = {2022},
}
Download Endnote/RIS citation
TY - SLIDE
TI - Federated Learning for Breast Cancer Classification
T2 - 105. Jahrestagung der Deutschen Gesellschaft für Pathologie
A2 - Kurt, Ufuk
A2 - Kazi, Anees
A2 - Navab, Nassir
A2 - Schüffler, Peter
CY - Münster
DA - 2022/06/09/
PY - 2022
M3 - Poster
ER -
Janik Israel, Luca Dan, Sabrina R. Sarker, Fabian Stögbauer, Wilko Weichert, Katja Steiger and Peter Schüffler.
A machine learning approach to classify whole slide images by formalin-fixed, paraffin-embedded or frozen section origin.
105. Jahrestagung der Deutschen Gesellschaft für Pathologie, 2022-06-09 
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@misc{israel_machine_2022,
    address = {M\"unster},
    type = {Poster},
    title = {A machine learning approach to classify whole slide images by formalin-fixed, paraffin-embedded or frozen section origin},
    author = {Israel, Janik and Dan, Luca and Sarker, Sabrina R. and St\"ogbauer, Fabian and Weichert, Wilko and Steiger, Katja and Sch\"uffler, Peter},
    month = jun,
    year = {2022},
}
Download Endnote/RIS citation
TY - SLIDE
TI - A machine learning approach to classify whole slide images by formalin-fixed, paraffin-embedded or frozen section origin
T2 - 105. Jahrestagung der Deutschen Gesellschaft für Pathologie
A2 - Israel, Janik
A2 - Dan, Luca
A2 - Sarker, Sabrina R.
A2 - Stögbauer, Fabian
A2 - Weichert, Wilko
A2 - Steiger, Katja
A2 - Schüffler, Peter
CY - Münster
DA - 2022/06/09/
PY - 2022
M3 - Poster
ER -
Fabian Stögbauer, Manuela Lautizi, Mark Kriegsmann, Hauke Winter, Thomas Muley, Katharina Kriegsmann, Moritz Jesinghaus, Jan Baumbach, Peter Schüffler, Wilko Weichert, Tim Kacprowski and Melanie Boxberg.
Tumour Cell Budding and Spread Through Air Spaces in Squamous Cell Carcinoma of the Lung – Determination and Validation of optimal prognostic cut-offs.
Lung Cancer, 2022-05-02 doi: 10.1016/j.lungcan.2022.04.012
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@article{stogbauer_tumour_2022,
    title = {Tumour {Cell} {Budding} and {Spread} {Through} {Air} {Spaces} in {Squamous} {Cell} {Carcinoma} of the {Lung} – {Determination} and {Validation} of optimal prognostic cut-offs},
    issn = {0169-5002},
    url = {https://www.sciencedirect.com/science/article/pii/S0169500222004251},
    doi = {10.1016/j.lungcan.2022.04.012},
    abstract = {Purpose
    Prognostic stratification of patients with squamous cell carcinomas of the lung (SCC-L) is challenging. Therefore, we investigated several histomorphological parameters (tumour cell budding (TCB), spread through air spaces (STAS), tumour-stroma-ratio, immune cell infiltration) which could potentially serve as prognostic parameters in SCC-L. We aimed to systematically determine optimal cut-off-values and assess the prognostic capability of these patterns. We furthermore assessed interobserver variability (IOV) for prognostically significant patterns TCB and STAS.
    Experimental Design:
    The Cancer Genome Atlas (TCGA) study cohort consisted of 335 patients with SCC-L. Histomorphological parameters analysed comprised TCB, minimal cell nest size (MCNS), STAS, stroma content and immune cell infiltration. The most significant cut-off-values were determined and univariate and multivariate survival outcomes were estimated. The identified cut-off-points were validated in an independent SCC-L cohort (n=346 patients). Two experienced pathologists probed IOV in the validation cohort.
    Results
    In the TCGA study cohort, TCB, STAS and immune cell infiltration were identified as significant prognostic parameters. TCB-high tumours, a high number of STAS foci, extensive STAS for distance of STAS in alveoli and a low immune cell infiltration remained as independent prognostic factors in multivariate Cox proportional hazard analyses for overall survival (OS). The significance of TCB, number of STAS foci and distance of STAS in alveoli for OS could be validated in the validation cohort. IOV reached a Kappa ≥0.89 for prognostic parameters.
    Conclusions
    We determined optimal cut-offs and identified TCB and STAS (number of STAS foci, distance of STAS in alveoli) as independent and uncorrelated prognostic factors for patients with SCC-L. The significance was validated in a large independent cohort. IOV was almost perfect for prognostic parameters. We propose the application of TCB- and STAS-based grading in SCC-L as prognostic morphological classifiers.},
    language = {en},
    urldate = {2022-05-03},
    journal = {Lung Cancer},
    author = {St\"ogbauer, Fabian and Lautizi, Manuela and Kriegsmann, Mark and Winter, Hauke and Muley, Thomas and Kriegsmann, Katharina and Jesinghaus, Moritz and Baumbach, Jan and Sch\"uffler, Peter and Weichert, Wilko and Kacprowski, Tim and Boxberg, Melanie},
    month = may,
    year = {2022},
    keywords = {Budding, Histomorphology, Lung cancer, Prognosis, STAS},
}
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TY - JOUR
TI - Tumour Cell Budding and Spread Through Air Spaces in Squamous Cell Carcinoma of the Lung – Determination and Validation of optimal prognostic cut-offs
AU - Stögbauer, Fabian
AU - Lautizi, Manuela
AU - Kriegsmann, Mark
AU - Winter, Hauke
AU - Muley, Thomas
AU - Kriegsmann, Katharina
AU - Jesinghaus, Moritz
AU - Baumbach, Jan
AU - Schüffler, Peter
AU - Weichert, Wilko
AU - Kacprowski, Tim
AU - Boxberg, Melanie
T2 - Lung Cancer
AB - Purpose
Prognostic stratification of patients with squamous cell carcinomas of the lung (SCC-L) is challenging. Therefore, we investigated several histomorphological parameters (tumour cell budding (TCB), spread through air spaces (STAS), tumour-stroma-ratio, immune cell infiltration) which could potentially serve as prognostic parameters in SCC-L. We aimed to systematically determine optimal cut-off-values and assess the prognostic capability of these patterns. We furthermore assessed interobserver variability (IOV) for prognostically significant patterns TCB and STAS.
Experimental Design:
The Cancer Genome Atlas (TCGA) study cohort consisted of 335 patients with SCC-L. Histomorphological parameters analysed comprised TCB, minimal cell nest size (MCNS), STAS, stroma content and immune cell infiltration. The most significant cut-off-values were determined and univariate and multivariate survival outcomes were estimated. The identified cut-off-points were validated in an independent SCC-L cohort (n=346 patients). Two experienced pathologists probed IOV in the validation cohort.
Results
In the TCGA study cohort, TCB, STAS and immune cell infiltration were identified as significant prognostic parameters. TCB-high tumours, a high number of STAS foci, extensive STAS for distance of STAS in alveoli and a low immune cell infiltration remained as independent prognostic factors in multivariate Cox proportional hazard analyses for overall survival (OS). The significance of TCB, number of STAS foci and distance of STAS in alveoli for OS could be validated in the validation cohort. IOV reached a Kappa ≥0.89 for prognostic parameters.
Conclusions
We determined optimal cut-offs and identified TCB and STAS (number of STAS foci, distance of STAS in alveoli) as independent and uncorrelated prognostic factors for patients with SCC-L. The significance was validated in a large independent cohort. IOV was almost perfect for prognostic parameters. We propose the application of TCB- and STAS-based grading in SCC-L as prognostic morphological classifiers.
DA - 2022/05/02/
PY - 2022
DO - 10.1016/j.lungcan.2022.04.012
DP - ScienceDirect
J2 - Lung Cancer
LA - en
SN - 0169-5002
UR - https://www.sciencedirect.com/science/article/pii/S0169500222004251
Y2 - 2022/05/03/09:33:38
KW - Budding
KW - Histomorphology
KW - Lung cancer
KW - Prognosis
KW - STAS
ER -
Purpose Prognostic stratification of patients with squamous cell carcinomas of the lung (SCC-L) is challenging. Therefore, we investigated several histomorphological parameters (tumour cell budding (TCB), spread through air spaces (STAS), tumour-stroma-ratio, immune cell infiltration) which could potentially serve as prognostic parameters in SCC-L. We aimed to systematically determine optimal cut-off-values and assess the prognostic capability of these patterns. We furthermore assessed interobserver variability (IOV) for prognostically significant patterns TCB and STAS. Experimental Design: The Cancer Genome Atlas (TCGA) study cohort consisted of 335 patients with SCC-L. Histomorphological parameters analysed comprised TCB, minimal cell nest size (MCNS), STAS, stroma content and immune cell infiltration. The most significant cut-off-values were determined and univariate and multivariate survival outcomes were estimated. The identified cut-off-points were validated in an independent SCC-L cohort (n=346 patients). Two experienced pathologists probed IOV in the validation cohort. Results In the TCGA study cohort, TCB, STAS and immune cell infiltration were identified as significant prognostic parameters. TCB-high tumours, a high number of STAS foci, extensive STAS for distance of STAS in alveoli and a low immune cell infiltration remained as independent prognostic factors in multivariate Cox proportional hazard analyses for overall survival (OS). The significance of TCB, number of STAS foci and distance of STAS in alveoli for OS could be validated in the validation cohort. IOV reached a Kappa ≥0.89 for prognostic parameters. Conclusions We determined optimal cut-offs and identified TCB and STAS (number of STAS foci, distance of STAS in alveoli) as independent and uncorrelated prognostic factors for patients with SCC-L. The significance was validated in a large independent cohort. IOV was almost perfect for prognostic parameters. We propose the application of TCB- and STAS-based grading in SCC-L as prognostic morphological classifiers.
Sandra Goetze, Peter Schüffler, Alcibiade Athanasiou, Anika Koetemann, Cedric Poyet, Christian Daniel Fankhauser, Peter J. Wild, Ralph Schiess and Bernd Wollscheid.
Use of MS-GUIDE for identification of protein biomarkers for risk stratification of patients with prostate cancer.
Clin Proteom, vol. 19, 1, p. 9, 04/27/2022 doi: 10.1186/s12014-022-09349-x
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@article{goetze_use_2022,
    title = {Use of {MS}-{GUIDE} for identification of protein biomarkers for risk stratification of patients with prostate cancer},
    volume = {19},
    issn = {1542-6416, 1559-0275},
    url = {https://clinicalproteomicsjournal.biomedcentral.com/articles/10.1186/s12014-022-09349-x},
    doi = {10.1186/s12014-022-09349-x},
    abstract = {Abstract
    
     Background
     Non-invasive liquid biopsies could complement current pathological nomograms for risk stratification of prostate cancer patients. Development and testing of potential liquid biopsy markers is time, resource, and cost-intensive. For most protein targets, no antibodies or ELISAs for efficient clinical cohort pre-evaluation are currently available. We reasoned that mass spectrometry-based prescreening would enable the cost-effective and rational preselection of candidates for subsequent clinical-grade ELISA development.
    
    
     Methods
     Using Mass Spectrometry-GUided Immunoassay DEvelopment (MS-GUIDE), we screened 48 literature-derived biomarker candidates for their potential utility in risk stratification scoring of prostate cancer patients. Parallel reaction monitoring was used to evaluate these 48 potential protein markers in a highly multiplexed fashion in a medium-sized patient cohort of 78 patients with ground-truth prostatectomy and clinical follow-up information. Clinical-grade ELISAs were then developed for two of these candidate proteins and used for significance testing in a larger, independent patient cohort of 263 patients.
    
    
     Results
     Machine learning-based analysis of the parallel reaction monitoring data of the liquid biopsies prequalified fibronectin and vitronectin as candidate biomarkers. We evaluated their predictive value for prostate cancer biochemical recurrence scoring in an independent validation cohort of 263 prostate cancer patients using clinical-grade ELISAs. The results of our prostate cancer risk stratification test were statistically significantly 10\% better than results of the current gold standards PSA alone, PSA plus prostatectomy biopsy Gleason score, or the National Comprehensive Cancer Network score in prediction of recurrence.
    
    
     Conclusion
     Using MS-GUIDE we identified fibronectin and vitronectin as candidate biomarkers for prostate cancer risk stratification.},
    language = {en},
    number = {1},
    urldate = {2022-04-27},
    journal = {Clinical Proteomics},
    author = {Goetze, Sandra and Sch\"uffler, Peter and Athanasiou, Alcibiade and Koetemann, Anika and Poyet, Cedric and Fankhauser, Christian Daniel and Wild, Peter J. and Schiess, Ralph and Wollscheid, Bernd},
    month = apr,
    year = {2022},
    pages = {9},
}
Download Endnote/RIS citation
TY - JOUR
TI - Use of MS-GUIDE for identification of protein biomarkers for risk stratification of patients with prostate cancer
AU - Goetze, Sandra
AU - Schüffler, Peter
AU - Athanasiou, Alcibiade
AU - Koetemann, Anika
AU - Poyet, Cedric
AU - Fankhauser, Christian Daniel
AU - Wild, Peter J.
AU - Schiess, Ralph
AU - Wollscheid, Bernd
T2 - Clinical Proteomics
AB - Abstract

Background
Non-invasive liquid biopsies could complement current pathological nomograms for risk stratification of prostate cancer patients. Development and testing of potential liquid biopsy markers is time, resource, and cost-intensive. For most protein targets, no antibodies or ELISAs for efficient clinical cohort pre-evaluation are currently available. We reasoned that mass spectrometry-based prescreening would enable the cost-effective and rational preselection of candidates for subsequent clinical-grade ELISA development.


Methods
Using Mass Spectrometry-GUided Immunoassay DEvelopment (MS-GUIDE), we screened 48 literature-derived biomarker candidates for their potential utility in risk stratification scoring of prostate cancer patients. Parallel reaction monitoring was used to evaluate these 48 potential protein markers in a highly multiplexed fashion in a medium-sized patient cohort of 78 patients with ground-truth prostatectomy and clinical follow-up information. Clinical-grade ELISAs were then developed for two of these candidate proteins and used for significance testing in a larger, independent patient cohort of 263 patients.


Results
Machine learning-based analysis of the parallel reaction monitoring data of the liquid biopsies prequalified fibronectin and vitronectin as candidate biomarkers. We evaluated their predictive value for prostate cancer biochemical recurrence scoring in an independent validation cohort of 263 prostate cancer patients using clinical-grade ELISAs. The results of our prostate cancer risk stratification test were statistically significantly 10% better than results of the current gold standards PSA alone, PSA plus prostatectomy biopsy Gleason score, or the National Comprehensive Cancer Network score in prediction of recurrence.


Conclusion
Using MS-GUIDE we identified fibronectin and vitronectin as candidate biomarkers for prostate cancer risk stratification.
DA - 2022/04/27/
PY - 2022
DO - 10.1186/s12014-022-09349-x
DP - DOI.org (Crossref)
VL - 19
IS - 1
SP - 9
J2 - Clin Proteom
LA - en
SN - 1542-6416, 1559-0275
UR - https://clinicalproteomicsjournal.biomedcentral.com/articles/10.1186/s12014-022-09349-x
Y2 - 2022/04/27/10:10:35
ER -
Abstract Background Non-invasive liquid biopsies could complement current pathological nomograms for risk stratification of prostate cancer patients. Development and testing of potential liquid biopsy markers is time, resource, and cost-intensive. For most protein targets, no antibodies or ELISAs for efficient clinical cohort pre-evaluation are currently available. We reasoned that mass spectrometry-based prescreening would enable the cost-effective and rational preselection of candidates for subsequent clinical-grade ELISA development. Methods Using Mass Spectrometry-GUided Immunoassay DEvelopment (MS-GUIDE), we screened 48 literature-derived biomarker candidates for their potential utility in risk stratification scoring of prostate cancer patients. Parallel reaction monitoring was used to evaluate these 48 potential protein markers in a highly multiplexed fashion in a medium-sized patient cohort of 78 patients with ground-truth prostatectomy and clinical follow-up information. Clinical-grade ELISAs were then developed for two of these candidate proteins and used for significance testing in a larger, independent patient cohort of 263 patients. Results Machine learning-based analysis of the parallel reaction monitoring data of the liquid biopsies prequalified fibronectin and vitronectin as candidate biomarkers. We evaluated their predictive value for prostate cancer biochemical recurrence scoring in an independent validation cohort of 263 prostate cancer patients using clinical-grade ELISAs. The results of our prostate cancer risk stratification test were statistically significantly 10% better than results of the current gold standards PSA alone, PSA plus prostatectomy biopsy Gleason score, or the National Comprehensive Cancer Network score in prediction of recurrence. Conclusion Using MS-GUIDE we identified fibronectin and vitronectin as candidate biomarkers for prostate cancer risk stratification.
Georg Prokop, Michael Örtl, Marina Fotteler, Peter Schüffler, Johannes Schobel, Walter Swoboda, Jürgen Schlegel and Friederike Liesche-Starnecker.
Quantifying Heterogeneity in Tumors: Proposing a New Method Utilizing Convolutional Neuronal Networks.
Stud Health Technol Inform, vol. 289, p. 397-400, 2022-01-14 doi: 10.3233/shti210942
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@article{prokop_quantifying_2022,
    title = {Quantifying {Heterogeneity} in {Tumors}: {Proposing} a {New} {Method} {Utilizing} {Convolutional} {Neuronal} {Networks}},
    volume = {289},
    issn = {1879-8365},
    shorttitle = {Quantifying {Heterogeneity} in {Tumors}},
    doi = {10.3233/SHTI210942},
    abstract = {Heterogeneity is a hallmark of glioblastoma (GBM), the most common malignant brain tumor, and a key reason for the poor survival rate of patients. However, establishing a clinically applicable, cost-efficient tool to measure and quantify heterogeneity is challenging. We present a novel method in an ongoing study utilizing two convolutional neuronal networks (CNN). After digitizing tumor samples, the first CNN delimitates GBM from normal tissue, the second quantifies heterogeneity within the tumor. Since neuronal networks can detect and interpret underlying and hidden information within images and have the ability to incorporate different information sets (i.e. clinical data and mutational status), this approach might venture towards a next level of integrated diagnosis. It may be applicable to other tumors as well and lead to a more precision-based medicine.},
    language = {eng},
    journal = {Studies in Health Technology and Informatics},
    author = {Prokop, Georg and \"Ortl, Michael and Fotteler, Marina and Sch\"uffler, Peter and Schobel, Johannes and Swoboda, Walter and Schlegel, J\"urgen and Liesche-Starnecker, Friederike},
    month = jan,
    year = {2022},
    pmid = {35062175},
    keywords = {Brain Neoplasms, Convolutional Neuronal Network, Digital Pathology, Glioblastoma, Humans, Neural Networks, Computer, Neuropathology, Precision Medicine, Tumor heterogeneity},
    pages = {397--400},
}
Download Endnote/RIS citation
TY - JOUR
TI - Quantifying Heterogeneity in Tumors: Proposing a New Method Utilizing Convolutional Neuronal Networks
AU - Prokop, Georg
AU - Örtl, Michael
AU - Fotteler, Marina
AU - Schüffler, Peter
AU - Schobel, Johannes
AU - Swoboda, Walter
AU - Schlegel, Jürgen
AU - Liesche-Starnecker, Friederike
T2 - Studies in Health Technology and Informatics
AB - Heterogeneity is a hallmark of glioblastoma (GBM), the most common malignant brain tumor, and a key reason for the poor survival rate of patients. However, establishing a clinically applicable, cost-efficient tool to measure and quantify heterogeneity is challenging. We present a novel method in an ongoing study utilizing two convolutional neuronal networks (CNN). After digitizing tumor samples, the first CNN delimitates GBM from normal tissue, the second quantifies heterogeneity within the tumor. Since neuronal networks can detect and interpret underlying and hidden information within images and have the ability to incorporate different information sets (i.e. clinical data and mutational status), this approach might venture towards a next level of integrated diagnosis. It may be applicable to other tumors as well and lead to a more precision-based medicine.
DA - 2022/01/14/
PY - 2022
DO - 10.3233/SHTI210942
DP - PubMed
VL - 289
SP - 397
EP - 400
J2 - Stud Health Technol Inform
LA - eng
SN - 1879-8365
ST - Quantifying Heterogeneity in Tumors
KW - Brain Neoplasms
KW - Convolutional Neuronal Network
KW - Digital Pathology
KW - Glioblastoma
KW - Humans
KW - Neural Networks, Computer
KW - Neuropathology
KW - Precision Medicine
KW - Tumor heterogeneity
ER -
Heterogeneity is a hallmark of glioblastoma (GBM), the most common malignant brain tumor, and a key reason for the poor survival rate of patients. However, establishing a clinically applicable, cost-efficient tool to measure and quantify heterogeneity is challenging. We present a novel method in an ongoing study utilizing two convolutional neuronal networks (CNN). After digitizing tumor samples, the first CNN delimitates GBM from normal tissue, the second quantifies heterogeneity within the tumor. Since neuronal networks can detect and interpret underlying and hidden information within images and have the ability to incorporate different information sets (i.e. clinical data and mutational status), this approach might venture towards a next level of integrated diagnosis. It may be applicable to other tumors as well and lead to a more precision-based medicine.
Peter J. Schüffler, Evangelos Stamelos, Ishtiaque Ahmed, D. Vijay K. Yarlagadda, Matthew G. Hanna, Victor E. Reuter, David S. Klimstra and Meera Hameed.
Efficient Visualization of Whole Slide Images in Web-based Viewers for Digital Pathology.
Archives of Pathology & Laboratory Medicine, 2022-01-3 doi: 10.5858/arpa.2021-0197-oa
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@article{schuffler_efficient_2022,
    title = {Efficient {Visualization} of {Whole} {Slide} {Images} in {Web}-based {Viewers} for {Digital} {Pathology}},
    issn = {1543-2165, 0003-9985},
    url = {https://meridian.allenpress.com/aplm/article/doi/10.5858/arpa.2021-0197-OA/476350/Efficient-Visualization-of-Whole-Slide-Images-in},
    doi = {10.5858/arpa.2021-0197-OA},
    abstract = {Context.—
     Wide adoption of digital pathology requires efficient visualization and navigation in Web-based digital slide viewers, which is poorly defined.
    
    
     Objective.—
     To define and quantify relevant performance metrics for efficient visualization of cases and slides in digital slide viewers.
    
    
     Design.—
     With a universal slide viewer used in clinical routine diagnostics, we evaluate the impact of slide caching, compression type, tile, and block size of whole slide images generated from Philips, Leica, and 3DHistech scanners on streaming performance on case, slide, and field of view levels.
    
    
     Results.—
     Two hundred thirty-nine pathologists routinely reviewed 60 080 whole slide images over 3 months. The median time to open a case's slides from the laboratory information system was less than 4 seconds, the time to change to a slide within the case was less than 1 second, and the time to render the adjacent field of view when navigating the slide was less than one-quarter of a second. A whole slide image's block size and a viewer tile size of 1024 pixels showed best performance to display a field of view and was preferrable over smaller tiles due to fewer mosaic effects. For Philips, fastest median slide streaming pace was 238 ms per field of view and for 3DHistech, 125 ms. For Leica, the fastest pace of 108 ms per field of view was established with block serving without decompression.
    
    
     Conclusions.—
     This is the first study to systematically assess user-centric slide visualization performance metrics for digital viewers, including time to open a case, time to change a slide, and time to change a field of view. These metrics help to improve the viewer's configuration, leading to an efficient visualization baseline that is widely accepted among pathologists using routine digital pathology.},
    language = {en},
    urldate = {2022-01-05},
    journal = {Archives of Pathology \& Laboratory Medicine},
    author = {Sch\"uffler, Peter J. and Stamelos, Evangelos and Ahmed, Ishtiaque and Yarlagadda, D. Vijay K. and Hanna, Matthew G. and Reuter, Victor E. and Klimstra, David S. and Hameed, Meera},
    month = jan,
    year = {2022},
}
Download Endnote/RIS citation
TY - JOUR
TI - Efficient Visualization of Whole Slide Images in Web-based Viewers for Digital Pathology
AU - Schüffler, Peter J.
AU - Stamelos, Evangelos
AU - Ahmed, Ishtiaque
AU - Yarlagadda, D. Vijay K.
AU - Hanna, Matthew G.
AU - Reuter, Victor E.
AU - Klimstra, David S.
AU - Hameed, Meera
T2 - Archives of Pathology & Laboratory Medicine
AB - Context.—
Wide adoption of digital pathology requires efficient visualization and navigation in Web-based digital slide viewers, which is poorly defined.


Objective.—
To define and quantify relevant performance metrics for efficient visualization of cases and slides in digital slide viewers.


Design.—
With a universal slide viewer used in clinical routine diagnostics, we evaluate the impact of slide caching, compression type, tile, and block size of whole slide images generated from Philips, Leica, and 3DHistech scanners on streaming performance on case, slide, and field of view levels.


Results.—
Two hundred thirty-nine pathologists routinely reviewed 60 080 whole slide images over 3 months. The median time to open a case's slides from the laboratory information system was less than 4 seconds, the time to change to a slide within the case was less than 1 second, and the time to render the adjacent field of view when navigating the slide was less than one-quarter of a second. A whole slide image's block size and a viewer tile size of 1024 pixels showed best performance to display a field of view and was preferrable over smaller tiles due to fewer mosaic effects. For Philips, fastest median slide streaming pace was 238 ms per field of view and for 3DHistech, 125 ms. For Leica, the fastest pace of 108 ms per field of view was established with block serving without decompression.


Conclusions.—
This is the first study to systematically assess user-centric slide visualization performance metrics for digital viewers, including time to open a case, time to change a slide, and time to change a field of view. These metrics help to improve the viewer's configuration, leading to an efficient visualization baseline that is widely accepted among pathologists using routine digital pathology.
DA - 2022/01/03/
PY - 2022
DO - 10.5858/arpa.2021-0197-OA
DP - DOI.org (Crossref)
LA - en
SN - 1543-2165, 0003-9985
UR - https://meridian.allenpress.com/aplm/article/doi/10.5858/arpa.2021-0197-OA/476350/Efficient-Visualization-of-Whole-Slide-Images-in
Y2 - 2022/01/05/09:25:21
ER -
Context.— Wide adoption of digital pathology requires efficient visualization and navigation in Web-based digital slide viewers, which is poorly defined. Objective.— To define and quantify relevant performance metrics for efficient visualization of cases and slides in digital slide viewers. Design.— With a universal slide viewer used in clinical routine diagnostics, we evaluate the impact of slide caching, compression type, tile, and block size of whole slide images generated from Philips, Leica, and 3DHistech scanners on streaming performance on case, slide, and field of view levels. Results.— Two hundred thirty-nine pathologists routinely reviewed 60 080 whole slide images over 3 months. The median time to open a case's slides from the laboratory information system was less than 4 seconds, the time to change to a slide within the case was less than 1 second, and the time to render the adjacent field of view when navigating the slide was less than one-quarter of a second. A whole slide image's block size and a viewer tile size of 1024 pixels showed best performance to display a field of view and was preferrable over smaller tiles due to fewer mosaic effects. For Philips, fastest median slide streaming pace was 238 ms per field of view and for 3DHistech, 125 ms. For Leica, the fastest pace of 108 ms per field of view was established with block serving without decompression. Conclusions.— This is the first study to systematically assess user-centric slide visualization performance metrics for digital viewers, including time to open a case, time to change a slide, and time to change a field of view. These metrics help to improve the viewer's configuration, leading to an efficient visualization baseline that is widely accepted among pathologists using routine digital pathology.
Maximilian Fischer, Peter Neher, Michael Götz, Shuhan Xiao, Silvia Dias Almeida, Peter Schüffler, Alexander Muckenhuber, Rickmer Braren, Jens Kleesiek, Marco Nolden and Klaus Maier-Hein.
Deep Learning on Lossily Compressed Pathology Images: Adverse Effects for ImageNet Pre-trained Models.
In: Yuankai Huo, Bryan A. Millis, Yuyin Zhou, Xiangxue Wang, Adam P. Harrison and Ziyue Xu (eds.) Medical Optical Imaging and Virtual Microscopy Image Analysis, vol. 13578, p. 73-83, Springer Nature Switzerland, ISBN 978-3-031-16960-1 978-3-031-16961-8, 2022 
PDF    URL   BibTeX   Endnote / RIS   Abstract
Download BibTeX citation
@incollection{huo_deep_2022,
    address = {Cham},
    title = {Deep {Learning} on {Lossily} {Compressed} {Pathology} {Images}: {Adverse} {Effects} for {ImageNet} {Pre}-trained {Models}},
    volume = {13578},
    isbn = {978-3-031-16960-1 978-3-031-16961-8},
    shorttitle = {Deep {Learning} on {Lossily} {Compressed} {Pathology} {Images}},
    url = {https://link.springer.com/10.1007/978-3-031-16961-8_8},
    language = {en},
    urldate = {2022-11-14},
    booktitle = {Medical {Optical} {Imaging} and {Virtual} {Microscopy} {Image} {Analysis}},
    publisher = {Springer Nature Switzerland},
    author = {Fischer, Maximilian and Neher, Peter and G\"otz, Michael and Xiao, Shuhan and Almeida, Silvia Dias and Sch\"uffler, Peter and Muckenhuber, Alexander and Braren, Rickmer and Kleesiek, Jens and Nolden, Marco and Maier-Hein, Klaus},
    editor = {Huo, Yuankai and Millis, Bryan A. and Zhou, Yuyin and Wang, Xiangxue and Harrison, Adam P. and Xu, Ziyue},
    year = {2022},
    doi = {10.1007/978-3-031-16961-8_8},
    pages = {73--83},
}
Download Endnote/RIS citation
TY - CHAP
TI - Deep Learning on Lossily Compressed Pathology Images: Adverse Effects for ImageNet Pre-trained Models
AU - Fischer, Maximilian
AU - Neher, Peter
AU - Götz, Michael
AU - Xiao, Shuhan
AU - Almeida, Silvia Dias
AU - Schüffler, Peter
AU - Muckenhuber, Alexander
AU - Braren, Rickmer
AU - Kleesiek, Jens
AU - Nolden, Marco
AU - Maier-Hein, Klaus
T2 - Medical Optical Imaging and Virtual Microscopy Image Analysis
A2 - Huo, Yuankai
A2 - Millis, Bryan A.
A2 - Zhou, Yuyin
A2 - Wang, Xiangxue
A2 - Harrison, Adam P.
A2 - Xu, Ziyue
CY - Cham
DA - 2022///
PY - 2022
DP - DOI.org (Crossref)
VL - 13578
SP - 73
EP - 83
LA - en
PB - Springer Nature Switzerland
SN - 978-3-031-16960-1 978-3-031-16961-8
ST - Deep Learning on Lossily Compressed Pathology Images
UR - https://link.springer.com/10.1007/978-3-031-16961-8_8
Y2 - 2022/11/14/07:47:11
ER -
Peter J Schüffler, Luke Geneslaw, D Vijay K Yarlagadda, Matthew G Hanna, Jennifer Samboy, Evangelos Stamelos, Chad Vanderbilt, John Philip, Marc-Henri Jean, Lorraine Corsale, Allyne Manzo, Neeraj H G Paramasivam, John S Ziegler, Jianjiong Gao, Juan C Perin, Young Suk Kim, Umeshkumar K Bhanot, Michael H A Roehrl, Orly Ardon, Sarah Chiang, Dilip D Giri, Carlie S Sigel, Lee K Tan, Melissa Murray, Christina Virgo, Christine England, Yukako Yagi, S Joseph Sirintrapun, David Klimstra, Meera Hameed, Victor E Reuter and Thomas J Fuchs.
Integrated digital pathology at scale: A solution for clinical diagnostics and cancer research at a large academic medical center.
Journal of the American Medical Informatics Association, vol. 28, 9, p. 1874-1884, July 14, 2021 doi: 10.1093/jamia/ocab085
PDF    URL   BibTeX   Endnote / RIS   Abstract
Download BibTeX citation
@article{schuffler_integrated_2021,
    title = {Integrated digital pathology at scale: {A} solution for clinical diagnostics and cancer research at a large academic medical center},
    volume = {28},
    issn = {1527-974X},
    shorttitle = {Integrated digital pathology at scale},
    url = {https://doi.org/10.1093/jamia/ocab085},
    doi = {10.1093/jamia/ocab085},
    abstract = {Broad adoption of digital pathology (DP) is still lacking, and examples for DP connecting diagnostic, research, and educational use cases are missing. We blueprint a holistic DP solution at a large academic medical center ubiquitously integrated into clinical workflows; researchapplications including molecular, genetic, and tissue databases; and educational processes.We built a vendor-agnostic, integrated viewer for reviewing, annotating, sharing, and quality assurance of digital slides in a clinical or research context. It is the first homegrown viewer cleared by New York State provisional approval in 2020 for primary diagnosis and remote sign-out during the COVID-19 (coronavirus disease 2019) pandemic. We further introduce an interconnected Honest Broker for BioInformatics Technology (HoBBIT) to systematically compile and share large-scale DP research datasets including anonymized images, redacted pathology reports, and clinical data of patients with consent.The solution has been operationally used over 3 years by 926 pathologists and researchers evaluating 288 903 digital slides. A total of 51\% of these were reviewed within 1 month after scanning. Seamless integration of the viewer into 4 hospital systems clearly increases the adoption of DP. HoBBIT directly impacts the translation of knowledge in pathology into effective new health measures, including artificial intelligence–driven detection models for prostate cancer, basal cell carcinoma, and breast cancer metastases, developed and validated on thousands of cases.We highlight major challenges and lessons learned when going digital to provide orientation for other pathologists. Building interconnected solutions will not only increase adoption of DP, but also facilitate next-generation computational pathology at scale for enhanced cancer research.},
    number = {9},
    urldate = {2021-07-14},
    journal = {Journal of the American Medical Informatics Association},
    author = {Sch\"uffler, Peter J and Geneslaw, Luke and Yarlagadda, D Vijay K and Hanna, Matthew G and Samboy, Jennifer and Stamelos, Evangelos and Vanderbilt, Chad and Philip, John and Jean, Marc-Henri and Corsale, Lorraine and Manzo, Allyne and Paramasivam, Neeraj H G and Ziegler, John S and Gao, Jianjiong and Perin, Juan C and Kim, Young Suk and Bhanot, Umeshkumar K and Roehrl, Michael H A and Ardon, Orly and Chiang, Sarah and Giri, Dilip D and Sigel, Carlie S and Tan, Lee K and Murray, Melissa and Virgo, Christina and England, Christine and Yagi, Yukako and Sirintrapun, S Joseph and Klimstra, David and Hameed, Meera and Reuter, Victor E and Fuchs, Thomas J},
    month = jul,
    year = {2021},
    pages = {1874--1884},
}
Download Endnote/RIS citation
TY - JOUR
TI - Integrated digital pathology at scale: A solution for clinical diagnostics and cancer research at a large academic medical center
AU - Schüffler, Peter J
AU - Geneslaw, Luke
AU - Yarlagadda, D Vijay K
AU - Hanna, Matthew G
AU - Samboy, Jennifer
AU - Stamelos, Evangelos
AU - Vanderbilt, Chad
AU - Philip, John
AU - Jean, Marc-Henri
AU - Corsale, Lorraine
AU - Manzo, Allyne
AU - Paramasivam, Neeraj H G
AU - Ziegler, John S
AU - Gao, Jianjiong
AU - Perin, Juan C
AU - Kim, Young Suk
AU - Bhanot, Umeshkumar K
AU - Roehrl, Michael H A
AU - Ardon, Orly
AU - Chiang, Sarah
AU - Giri, Dilip D
AU - Sigel, Carlie S
AU - Tan, Lee K
AU - Murray, Melissa
AU - Virgo, Christina
AU - England, Christine
AU - Yagi, Yukako
AU - Sirintrapun, S Joseph
AU - Klimstra, David
AU - Hameed, Meera
AU - Reuter, Victor E
AU - Fuchs, Thomas J
T2 - Journal of the American Medical Informatics Association
AB - Broad adoption of digital pathology (DP) is still lacking, and examples for DP connecting diagnostic, research, and educational use cases are missing. We blueprint a holistic DP solution at a large academic medical center ubiquitously integrated into clinical workflows; researchapplications including molecular, genetic, and tissue databases; and educational processes.We built a vendor-agnostic, integrated viewer for reviewing, annotating, sharing, and quality assurance of digital slides in a clinical or research context. It is the first homegrown viewer cleared by New York State provisional approval in 2020 for primary diagnosis and remote sign-out during the COVID-19 (coronavirus disease 2019) pandemic. We further introduce an interconnected Honest Broker for BioInformatics Technology (HoBBIT) to systematically compile and share large-scale DP research datasets including anonymized images, redacted pathology reports, and clinical data of patients with consent.The solution has been operationally used over 3 years by 926 pathologists and researchers evaluating 288 903 digital slides. A total of 51% of these were reviewed within 1 month after scanning. Seamless integration of the viewer into 4 hospital systems clearly increases the adoption of DP. HoBBIT directly impacts the translation of knowledge in pathology into effective new health measures, including artificial intelligence–driven detection models for prostate cancer, basal cell carcinoma, and breast cancer metastases, developed and validated on thousands of cases.We highlight major challenges and lessons learned when going digital to provide orientation for other pathologists. Building interconnected solutions will not only increase adoption of DP, but also facilitate next-generation computational pathology at scale for enhanced cancer research.
DA - 2021/07/14/
PY - 2021
DO - 10.1093/jamia/ocab085
DP - Silverchair
VL - 28
IS - 9
SP - 1874
EP - 1884
J2 - Journal of the American Medical Informatics Association
SN - 1527-974X
ST - Integrated digital pathology at scale
UR - https://doi.org/10.1093/jamia/ocab085
Y2 - 2021/07/14/21:13:00
ER -
Broad adoption of digital pathology (DP) is still lacking, and examples for DP connecting diagnostic, research, and educational use cases are missing. We blueprint a holistic DP solution at a large academic medical center ubiquitously integrated into clinical workflows; researchapplications including molecular, genetic, and tissue databases; and educational processes.We built a vendor-agnostic, integrated viewer for reviewing, annotating, sharing, and quality assurance of digital slides in a clinical or research context. It is the first homegrown viewer cleared by New York State provisional approval in 2020 for primary diagnosis and remote sign-out during the COVID-19 (coronavirus disease 2019) pandemic. We further introduce an interconnected Honest Broker for BioInformatics Technology (HoBBIT) to systematically compile and share large-scale DP research datasets including anonymized images, redacted pathology reports, and clinical data of patients with consent.The solution has been operationally used over 3 years by 926 pathologists and researchers evaluating 288 903 digital slides. A total of 51% of these were reviewed within 1 month after scanning. Seamless integration of the viewer into 4 hospital systems clearly increases the adoption of DP. HoBBIT directly impacts the translation of knowledge in pathology into effective new health measures, including artificial intelligence–driven detection models for prostate cancer, basal cell carcinoma, and breast cancer metastases, developed and validated on thousands of cases.We highlight major challenges and lessons learned when going digital to provide orientation for other pathologists. Building interconnected solutions will not only increase adoption of DP, but also facilitate next-generation computational pathology at scale for enhanced cancer research.
Orly Ardon, Victor E. Reuter, Meera Hameed, Lorraine Corsale, Allyne Manzo, Sahussapont J. Sirintrapun, Peter Ntiamoah, Evangelos Stamelos, Peter J. Schueffler, Christine England, David S. Klimstra and Matthew G. Hanna.
Digital Pathology Operations at an NYC Tertiary Cancer Center During the First 4 Months of COVID-19 Pandemic Response.
Academic Pathology, vol. 8, April 28, 2021 doi: 10.1177/23742895211010276
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@article{ardon_digital_2021,
    title = {Digital {Pathology} {Operations} at an {NYC} {Tertiary} {Cancer} {Center} {During} the {First} 4 {Months} of {COVID}-19 {Pandemic} {Response}},
    volume = {8},
    issn = {2374-2895},
    url = {https://doi.org/10.1177/23742895211010276},
    doi = {10.1177/23742895211010276},
    abstract = {Implementation of an infrastructure to support digital pathology began in 2006 at Memorial Sloan Kettering Cancer Center. The public health emergency and COVID-19 pandemic regulations in New York City required a novel workflow to sustain existing operations. While regulatory enforcement discretions offered faculty workspace flexibility, a substantial portion of laboratory and digital pathology workflows require on-site presence of staff. Maintaining social distancing and offering staggered work schedules. Due to a decrease in patients seeking health care at the onset of the pandemic, a temporary decrease in patient specimens was observed. Hospital and travel regulations impacted onsite vendor technical support. Digital glass slide scanning activities onsite proceeded without interruption throughout the pandemic, with challenges including staff who required quarantine due to virus exposure, unrelated illness, family support, or lack of public transportation. During the public health emergency, we validated digital pathology systems for a remote pathology operation. Since March 2020, the departmental digital pathology staff were able to maintain scanning volumes of over 100 000 slides per month. The digital scanning team reprioritized archival slide scanning and participated in a remote sign-out validation and successful submission of New York State approval for a laboratory developed test. Digital pathology offers a health care delivery model where pathologists can perform their sign out duties at remote location and prevent disruptions to critical pathology services for patients seeking care at our institution during emergencies. Development of standard operating procedures to support digital workflows will maintain turnaround times and enable clinical operations during emergency or otherwise unanticipated events.},
    language = {en},
    urldate = {2021-09-01},
    journal = {Academic Pathology},
    author = {Ardon, Orly and Reuter, Victor E. and Hameed, Meera and Corsale, Lorraine and Manzo, Allyne and Sirintrapun, Sahussapont J. and Ntiamoah, Peter and Stamelos, Evangelos and Schueffler, Peter J. and England, Christine and Klimstra, David S. and Hanna, Matthew G.},
    month = apr,
    year = {2021},
    keywords = {COVID-19, clinical, digital pathology, implementation, operations, remote signout, telepathology},
}
Download Endnote/RIS citation
TY - JOUR
TI - Digital Pathology Operations at an NYC Tertiary Cancer Center During the First 4 Months of COVID-19 Pandemic Response
AU - Ardon, Orly
AU - Reuter, Victor E.
AU - Hameed, Meera
AU - Corsale, Lorraine
AU - Manzo, Allyne
AU - Sirintrapun, Sahussapont J.
AU - Ntiamoah, Peter
AU - Stamelos, Evangelos
AU - Schueffler, Peter J.
AU - England, Christine
AU - Klimstra, David S.
AU - Hanna, Matthew G.
T2 - Academic Pathology
AB - Implementation of an infrastructure to support digital pathology began in 2006 at Memorial Sloan Kettering Cancer Center. The public health emergency and COVID-19 pandemic regulations in New York City required a novel workflow to sustain existing operations. While regulatory enforcement discretions offered faculty workspace flexibility, a substantial portion of laboratory and digital pathology workflows require on-site presence of staff. Maintaining social distancing and offering staggered work schedules. Due to a decrease in patients seeking health care at the onset of the pandemic, a temporary decrease in patient specimens was observed. Hospital and travel regulations impacted onsite vendor technical support. Digital glass slide scanning activities onsite proceeded without interruption throughout the pandemic, with challenges including staff who required quarantine due to virus exposure, unrelated illness, family support, or lack of public transportation. During the public health emergency, we validated digital pathology systems for a remote pathology operation. Since March 2020, the departmental digital pathology staff were able to maintain scanning volumes of over 100 000 slides per month. The digital scanning team reprioritized archival slide scanning and participated in a remote sign-out validation and successful submission of New York State approval for a laboratory developed test. Digital pathology offers a health care delivery model where pathologists can perform their sign out duties at remote location and prevent disruptions to critical pathology services for patients seeking care at our institution during emergencies. Development of standard operating procedures to support digital workflows will maintain turnaround times and enable clinical operations during emergency or otherwise unanticipated events.
DA - 2021/04/28/
PY - 2021
DO - 10.1177/23742895211010276
DP - SAGE Journals
VL - 8
J2 - Academic Pathology
LA - en
SN - 2374-2895
UR - https://doi.org/10.1177/23742895211010276
Y2 - 2021/09/01/07:50:28
KW - COVID-19
KW - clinical
KW - digital pathology
KW - implementation
KW - operations
KW - remote signout
KW - telepathology
ER -
Implementation of an infrastructure to support digital pathology began in 2006 at Memorial Sloan Kettering Cancer Center. The public health emergency and COVID-19 pandemic regulations in New York City required a novel workflow to sustain existing operations. While regulatory enforcement discretions offered faculty workspace flexibility, a substantial portion of laboratory and digital pathology workflows require on-site presence of staff. Maintaining social distancing and offering staggered work schedules. Due to a decrease in patients seeking health care at the onset of the pandemic, a temporary decrease in patient specimens was observed. Hospital and travel regulations impacted onsite vendor technical support. Digital glass slide scanning activities onsite proceeded without interruption throughout the pandemic, with challenges including staff who required quarantine due to virus exposure, unrelated illness, family support, or lack of public transportation. During the public health emergency, we validated digital pathology systems for a remote pathology operation. Since March 2020, the departmental digital pathology staff were able to maintain scanning volumes of over 100 000 slides per month. The digital scanning team reprioritized archival slide scanning and participated in a remote sign-out validation and successful submission of New York State approval for a laboratory developed test. Digital pathology offers a health care delivery model where pathologists can perform their sign out duties at remote location and prevent disruptions to critical pathology services for patients seeking care at our institution during emergencies. Development of standard operating procedures to support digital workflows will maintain turnaround times and enable clinical operations during emergency or otherwise unanticipated events.
Peter J. Schüffler, Dig Vijay Kumar Yarlagadda, Chad Vanderbilt and Thomas J. Fuchs.
Overcoming an annotation hurdle: Digitizing pen annotations from whole slide images.
Journal of Pathology Informatics, vol. 12, 1, p. 9, 02/23/2021 doi: 10.4103/jpi.jpi_85_20
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@article{schuffler_overcoming_2021,
    title = {Overcoming an annotation hurdle: {Digitizing} pen annotations from whole slide images},
    volume = {12},
    issn = {2153-3539},
    shorttitle = {Overcoming an annotation hurdle},
    url = {https://www.doi.org/10.4103/jpi.jpi_85_20},
    doi = {10.4103/jpi.jpi_85_20},
    abstract = {Background:The development of artificial intelligence (AI) in pathology frequently relies on digitally annotated whole slide images (WSI). The creation of these annotations – manually drawn by pathologists in digital slide viewers – is time consuming and expensive. At the same time, pathologists routinely annotate glass slides with a pen to outline cancerous regions, for example, for molecular assessment of the tissue. These pen annotations are currently considered artifacts and excluded from computational modeling. Methods:We propose a novel method to segment and fill hand-drawn pen annotations and convert them into a digital format to make them accessible for computational models. Our method is implemented in Python as an open source, publicly available software tool. Results: Our method is able to extract pen annotations from WSI and save them as annotation masks. On a data set of 319 WSI with pen markers, we validate our algorithm segmenting the annotations with an overall Dice metric of 0.942, Precision of 0.955, and Recall of 0.943. Processing all images takes 15 min in contrast to 5 h manual digital annotation time. Further, the approach is robust against text annotations. Conclusions: We envision that our method can take advantage of already pen-annotated slides in scenarios in which the annotations would be helpful for training computational models. We conclude that, considering the large archives of many pathology departments that are currently being digitized, our method will help to collect large numbers of training samples from those data.},
    language = {en},
    number = {1},
    urldate = {2021-02-25},
    journal = {Journal of Pathology Informatics},
    author = {Sch\"uffler, Peter J. and Yarlagadda, Dig Vijay Kumar and Vanderbilt, Chad and Fuchs, Thomas J.},
    month = feb,
    year = {2021},
    Distributor: Medknow Publications and Media Pvt. Ltd.
    Institution: Medknow Publications and Media Pvt. Ltd.
    Label: Medknow Publications and Media Pvt. Ltd.
    Publisher: Medknow Publications},
    pages = {9},
}
Download Endnote/RIS citation
TY - JOUR
TI - Overcoming an annotation hurdle: Digitizing pen annotations from whole slide images
AU - Schüffler, Peter J.
AU - Yarlagadda, Dig Vijay Kumar
AU - Vanderbilt, Chad
AU - Fuchs, Thomas J.
T2 - Journal of Pathology Informatics
AB - Background:The development of artificial intelligence (AI) in pathology frequently relies on digitally annotated whole slide images (WSI). The creation of these annotations – manually drawn by pathologists in digital slide viewers – is time consuming and expensive. At the same time, pathologists routinely annotate glass slides with a pen to outline cancerous regions, for example, for molecular assessment of the tissue. These pen annotations are currently considered artifacts and excluded from computational modeling. Methods:We propose a novel method to segment and fill hand-drawn pen annotations and convert them into a digital format to make them accessible for computational models. Our method is implemented in Python as an open source, publicly available software tool. Results: Our method is able to extract pen annotations from WSI and save them as annotation masks. On a data set of 319 WSI with pen markers, we validate our algorithm segmenting the annotations with an overall Dice metric of 0.942, Precision of 0.955, and Recall of 0.943. Processing all images takes 15 min in contrast to 5 h manual digital annotation time. Further, the approach is robust against text annotations. Conclusions: We envision that our method can take advantage of already pen-annotated slides in scenarios in which the annotations would be helpful for training computational models. We conclude that, considering the large archives of many pathology departments that are currently being digitized, our method will help to collect large numbers of training samples from those data.
DA - 2021/02/23/
PY - 2021
DO - 10.4103/jpi.jpi_85_20
DP - www.jpathinformatics.org
VL - 12
IS - 1
SP - 9
LA - en
SN - 2153-3539
ST - Overcoming an annotation hurdle
UR - https://www.doi.org/10.4103/jpi.jpi_85_20
Y2 - 2021/02/25/18:48:46
ER -
Background:The development of artificial intelligence (AI) in pathology frequently relies on digitally annotated whole slide images (WSI). The creation of these annotations – manually drawn by pathologists in digital slide viewers – is time consuming and expensive. At the same time, pathologists routinely annotate glass slides with a pen to outline cancerous regions, for example, for molecular assessment of the tissue. These pen annotations are currently considered artifacts and excluded from computational modeling. Methods:We propose a novel method to segment and fill hand-drawn pen annotations and convert them into a digital format to make them accessible for computational models. Our method is implemented in Python as an open source, publicly available software tool. Results: Our method is able to extract pen annotations from WSI and save them as annotation masks. On a data set of 319 WSI with pen markers, we validate our algorithm segmenting the annotations with an overall Dice metric of 0.942, Precision of 0.955, and Recall of 0.943. Processing all images takes 15 min in contrast to 5 h manual digital annotation time. Further, the approach is robust against text annotations. Conclusions: We envision that our method can take advantage of already pen-annotated slides in scenarios in which the annotations would be helpful for training computational models. We conclude that, considering the large archives of many pathology departments that are currently being digitized, our method will help to collect large numbers of training samples from those data.
Zhang Li, Jiehua Zhang, Tao Tan, Xichao Teng, Xiaoliang Sun, Hong Zhao, Lihong Liu, Yang Xiao, Byungjae Lee, Yilong Li, Qianni Zhang, Shujiao Sun, Yushan Zheng, Junyu Yan, Ni Li, Yiyu Hong, Junsu Ko, Hyun Jung, Yanling Liu, Yu-cheng Chen, Ching-wei Wang, Vladimir Yurovskiy, Pavel Maevskikh, Vahid Khanagha, Yi Jiang, Li Yu, Zhihong Liu, Daiqiang Li, Peter J. Schuffler, Qifeng Yu, Hui Chen, Yuling Tang and Geert Litjens.
Deep Learning Methods for Lung Cancer Segmentation in Whole-Slide Histopathology Images—The ACDC@LungHP Challenge 2019.
IEEE J. Biomed. Health Inform., vol. 25, 2, p. 429-440, 2/2021 doi: 10.1109/jbhi.2020.3039741
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@article{li_deep_2021,
    title = {Deep {Learning} {Methods} for {Lung} {Cancer} {Segmentation} in {Whole}-{Slide} {Histopathology} {Images}—{The} {ACDC}@{LungHP} {Challenge} 2019},
    volume = {25},
    issn = {2168-2194, 2168-2208},
    url = {https://ieeexplore.ieee.org/document/9265237/},
    doi = {10.1109/JBHI.2020.3039741},
    number = {2},
    urldate = {2022-07-13},
    journal = {IEEE Journal of Biomedical and Health Informatics},
    author = {Li, Zhang and Zhang, Jiehua and Tan, Tao and Teng, Xichao and Sun, Xiaoliang and Zhao, Hong and Liu, Lihong and Xiao, Yang and Lee, Byungjae and Li, Yilong and Zhang, Qianni and Sun, Shujiao and Zheng, Yushan and Yan, Junyu and Li, Ni and Hong, Yiyu and Ko, Junsu and Jung, Hyun and Liu, Yanling and Chen, Yu-cheng and Wang, Ching-wei and Yurovskiy, Vladimir and Maevskikh, Pavel and Khanagha, Vahid and Jiang, Yi and Yu, Li and Liu, Zhihong and Li, Daiqiang and Schuffler, Peter J. and Yu, Qifeng and Chen, Hui and Tang, Yuling and Litjens, Geert},
    month = feb,
    year = {2021},
    pages = {429--440},
}
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TY - JOUR
TI - Deep Learning Methods for Lung Cancer Segmentation in Whole-Slide Histopathology Images—The ACDC@LungHP Challenge 2019
AU - Li, Zhang
AU - Zhang, Jiehua
AU - Tan, Tao
AU - Teng, Xichao
AU - Sun, Xiaoliang
AU - Zhao, Hong
AU - Liu, Lihong
AU - Xiao, Yang
AU - Lee, Byungjae
AU - Li, Yilong
AU - Zhang, Qianni
AU - Sun, Shujiao
AU - Zheng, Yushan
AU - Yan, Junyu
AU - Li, Ni
AU - Hong, Yiyu
AU - Ko, Junsu
AU - Jung, Hyun
AU - Liu, Yanling
AU - Chen, Yu-cheng
AU - Wang, Ching-wei
AU - Yurovskiy, Vladimir
AU - Maevskikh, Pavel
AU - Khanagha, Vahid
AU - Jiang, Yi
AU - Yu, Li
AU - Liu, Zhihong
AU - Li, Daiqiang
AU - Schuffler, Peter J.
AU - Yu, Qifeng
AU - Chen, Hui
AU - Tang, Yuling
AU - Litjens, Geert
T2 - IEEE Journal of Biomedical and Health Informatics
DA - 2021/02//
PY - 2021
DO - 10.1109/JBHI.2020.3039741
DP - DOI.org (Crossref)
VL - 25
IS - 2
SP - 429
EP - 440
J2 - IEEE J. Biomed. Health Inform.
SN - 2168-2194, 2168-2208
UR - https://ieeexplore.ieee.org/document/9265237/
Y2 - 2022/07/13/12:46:14
ER -
Peter J. Schüffler, Gamze Gokturk Ozcan, Hikmat Al-Ahmadie and Thomas J. Fuchs.
Flextilesource: An openseadragon extension for efficient whole-slide image visualization.
J Pathol Inform, vol. 12, 1, p. 31, 2021 doi: 10.4103/jpi.jpi_13_21
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@article{schuffler_flextilesource_2021,
    title = {Flextilesource: {An} openseadragon extension for efficient whole-slide image visualization},
    volume = {12},
    issn = {2153-3539},
    shorttitle = {Flextilesource},
    url = {https://doi.org/10.4103/jpi.jpi_13_21},
    doi = {10.4103/jpi.jpi_13_21},
    language = {en},
    number = {1},
    urldate = {2021-09-14},
    journal = {Journal of Pathology Informatics},
    author = {Sch\"uffler, Peter J. and Ozcan, Gamze Gokturk and Al-Ahmadie, Hikmat and Fuchs, Thomas J.},
    year = {2021},
    pages = {31},
}
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TY - JOUR
TI - Flextilesource: An openseadragon extension for efficient whole-slide image visualization
AU - Schüffler, Peter J.
AU - Ozcan, Gamze Gokturk
AU - Al-Ahmadie, Hikmat
AU - Fuchs, Thomas J.
T2 - Journal of Pathology Informatics
DA - 2021///
PY - 2021
DO - 10.4103/jpi.jpi_13_21
VL - 12
IS - 1
SP - 31
J2 - J Pathol Inform
LA - en
SN - 2153-3539
ST - Flextilesource
UR - https://doi.org/10.4103/jpi.jpi_13_21
Y2 - 2021/09/14/18:42:14
ER -
Matthew G. Hanna, Victor E. Reuter, Orly Ardon, David Kim, Sahussapont Joseph Sirintrapun, Peter J. Schüffler, Klaus J. Busam, Jennifer L. Sauter, Edi Brogi, Lee K. Tan, Bin Xu, Tejus Bale, Narasimhan P. Agaram, Laura H. Tang, Lora H. Ellenson, John Philip, Lorraine Corsale, Evangelos Stamelos, Maria A. Friedlander, Peter Ntiamoah, Marc Labasin, Christine England, David S. Klimstra and Meera Hameed.
Validation of a digital pathology system including remote review during the COVID-19 pandemic.
Modern Pathology, vol. 33, p. 2115–2127, 2020-06-22 doi: 10.1038/s41379-020-0601-5
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@article{hanna_validation_2020,
    title = {Validation of a digital pathology system including remote review during the {COVID}-19 pandemic},
    volume = {33},
    copyright = {2020 The Author(s), under exclusive licence to United States \& Canadian Academy of Pathology},
    issn = {1530-0285},
    url = {https://www.nature.com/articles/s41379-020-0601-5},
    doi = {10.1038/s41379-020-0601-5},
    abstract = {Remote digital pathology allows healthcare systems to maintain pathology operations during public health emergencies. Existing Clinical Laboratory Improvement Amendments regulations require pathologists to electronically verify patient reports from a certified facility. During the 2019 pandemic of COVID-19 disease, caused by the SAR-CoV-2 virus, this requirement potentially exposes pathologists, their colleagues, and household members to the risk of becoming infected. Relaxation of government enforcement of this regulation allows pathologists to review and report pathology specimens from a remote, non-CLIA certified facility. The availability of digital pathology systems can facilitate remote microscopic diagnosis, although formal comprehensive (case-based) validation of remote digital diagnosis has not been reported. All glass slides representing routine clinical signout workload in surgical pathology subspecialties at Memorial Sloan Kettering Cancer Center were scanned on an Aperio GT450 at ×40 equivalent resolution (0.26 µm/pixel). Twelve pathologists from nine surgical pathology subspecialties remotely reviewed and reported complete pathology cases using a digital pathology system from a non-CLIA certified facility through a secure connection. Whole slide images were integrated to and launched within the laboratory information system to a custom vendor-agnostic, whole slide image viewer. Remote signouts utilized consumer-grade computers and monitors (monitor size, 13.3–42 in.; resolution, 1280 × 800–3840 × 2160 pixels) connecting to an institution clinical workstation via secure virtual private network. Pathologists subsequently reviewed all corresponding glass slides using a light microscope within the CLIA-certified department. Intraobserver concordance metrics included reporting elements of top-line diagnosis, margin status, lymphovascular and/or perineural invasion, pathology stage, and ancillary testing. The median whole slide image file size was 1.3 GB; scan time/slide averaged 90 s; and scanned tissue area averaged 612 mm2. Signout sessions included a total of 108 cases, comprised of 254 individual parts and 1196 slides. Major diagnostic equivalency was 100\% between digital and glass slide diagnoses; and overall concordance was 98.8\% (251/254). This study reports validation of primary diagnostic review and reporting of complete pathology cases from a remote site during a public health emergency. Our experience shows high (100\%) intraobserver digital to glass slide major diagnostic concordance when reporting from a remote site. This randomized, prospective study successfully validated remote use of a digital pathology system including operational feasibility supporting remote review and reporting of pathology specimens, and evaluation of remote access performance and usability for remote signout.},
    language = {en},
    urldate = {2020-06-22},
    journal = {Modern Pathology},
    author = {Hanna, Matthew G. and Reuter, Victor E. and Ardon, Orly and Kim, David and Sirintrapun, Sahussapont Joseph and Sch\"uffler, Peter J. and Busam, Klaus J. and Sauter, Jennifer L. and Brogi, Edi and Tan, Lee K. and Xu, Bin and Bale, Tejus and Agaram, Narasimhan P. and Tang, Laura H. and Ellenson, Lora H. and Philip, John and Corsale, Lorraine and Stamelos, Evangelos and Friedlander, Maria A. and Ntiamoah, Peter and Labasin, Marc and England, Christine and Klimstra, David S. and Hameed, Meera},
    month = jun,
    year = {2020},
    pages = {2115--2127},
}
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TY - JOUR
TI - Validation of a digital pathology system including remote review during the COVID-19 pandemic
AU - Hanna, Matthew G.
AU - Reuter, Victor E.
AU - Ardon, Orly
AU - Kim, David
AU - Sirintrapun, Sahussapont Joseph
AU - Schüffler, Peter J.
AU - Busam, Klaus J.
AU - Sauter, Jennifer L.
AU - Brogi, Edi
AU - Tan, Lee K.
AU - Xu, Bin
AU - Bale, Tejus
AU - Agaram, Narasimhan P.
AU - Tang, Laura H.
AU - Ellenson, Lora H.
AU - Philip, John
AU - Corsale, Lorraine
AU - Stamelos, Evangelos
AU - Friedlander, Maria A.
AU - Ntiamoah, Peter
AU - Labasin, Marc
AU - England, Christine
AU - Klimstra, David S.
AU - Hameed, Meera
T2 - Modern Pathology
AB - Remote digital pathology allows healthcare systems to maintain pathology operations during public health emergencies. Existing Clinical Laboratory Improvement Amendments regulations require pathologists to electronically verify patient reports from a certified facility. During the 2019 pandemic of COVID-19 disease, caused by the SAR-CoV-2 virus, this requirement potentially exposes pathologists, their colleagues, and household members to the risk of becoming infected. Relaxation of government enforcement of this regulation allows pathologists to review and report pathology specimens from a remote, non-CLIA certified facility. The availability of digital pathology systems can facilitate remote microscopic diagnosis, although formal comprehensive (case-based) validation of remote digital diagnosis has not been reported. All glass slides representing routine clinical signout workload in surgical pathology subspecialties at Memorial Sloan Kettering Cancer Center were scanned on an Aperio GT450 at ×40 equivalent resolution (0.26 µm/pixel). Twelve pathologists from nine surgical pathology subspecialties remotely reviewed and reported complete pathology cases using a digital pathology system from a non-CLIA certified facility through a secure connection. Whole slide images were integrated to and launched within the laboratory information system to a custom vendor-agnostic, whole slide image viewer. Remote signouts utilized consumer-grade computers and monitors (monitor size, 13.3–42 in.; resolution, 1280 × 800–3840 × 2160 pixels) connecting to an institution clinical workstation via secure virtual private network. Pathologists subsequently reviewed all corresponding glass slides using a light microscope within the CLIA-certified department. Intraobserver concordance metrics included reporting elements of top-line diagnosis, margin status, lymphovascular and/or perineural invasion, pathology stage, and ancillary testing. The median whole slide image file size was 1.3 GB; scan time/slide averaged 90 s; and scanned tissue area averaged 612 mm2. Signout sessions included a total of 108 cases, comprised of 254 individual parts and 1196 slides. Major diagnostic equivalency was 100% between digital and glass slide diagnoses; and overall concordance was 98.8% (251/254). This study reports validation of primary diagnostic review and reporting of complete pathology cases from a remote site during a public health emergency. Our experience shows high (100%) intraobserver digital to glass slide major diagnostic concordance when reporting from a remote site. This randomized, prospective study successfully validated remote use of a digital pathology system including operational feasibility supporting remote review and reporting of pathology specimens, and evaluation of remote access performance and usability for remote signout.
DA - 2020/06/22/
PY - 2020
DO - 10.1038/s41379-020-0601-5
DP - www.nature.com
VL - 33
SP - 2115
EP - 2127
LA - en
SN - 1530-0285
UR - https://www.nature.com/articles/s41379-020-0601-5
Y2 - 2020/06/22/12:46:57
ER -
Remote digital pathology allows healthcare systems to maintain pathology operations during public health emergencies. Existing Clinical Laboratory Improvement Amendments regulations require pathologists to electronically verify patient reports from a certified facility. During the 2019 pandemic of COVID-19 disease, caused by the SAR-CoV-2 virus, this requirement potentially exposes pathologists, their colleagues, and household members to the risk of becoming infected. Relaxation of government enforcement of this regulation allows pathologists to review and report pathology specimens from a remote, non-CLIA certified facility. The availability of digital pathology systems can facilitate remote microscopic diagnosis, although formal comprehensive (case-based) validation of remote digital diagnosis has not been reported. All glass slides representing routine clinical signout workload in surgical pathology subspecialties at Memorial Sloan Kettering Cancer Center were scanned on an Aperio GT450 at ×40 equivalent resolution (0.26 µm/pixel). Twelve pathologists from nine surgical pathology subspecialties remotely reviewed and reported complete pathology cases using a digital pathology system from a non-CLIA certified facility through a secure connection. Whole slide images were integrated to and launched within the laboratory information system to a custom vendor-agnostic, whole slide image viewer. Remote signouts utilized consumer-grade computers and monitors (monitor size, 13.3–42 in.; resolution, 1280 × 800–3840 × 2160 pixels) connecting to an institution clinical workstation via secure virtual private network. Pathologists subsequently reviewed all corresponding glass slides using a light microscope within the CLIA-certified department. Intraobserver concordance metrics included reporting elements of top-line diagnosis, margin status, lymphovascular and/or perineural invasion, pathology stage, and ancillary testing. The median whole slide image file size was 1.3 GB; scan time/slide averaged 90 s; and scanned tissue area averaged 612 mm2. Signout sessions included a total of 108 cases, comprised of 254 individual parts and 1196 slides. Major diagnostic equivalency was 100% between digital and glass slide diagnoses; and overall concordance was 98.8% (251/254). This study reports validation of primary diagnostic review and reporting of complete pathology cases from a remote site during a public health emergency. Our experience shows high (100%) intraobserver digital to glass slide major diagnostic concordance when reporting from a remote site. This randomized, prospective study successfully validated remote use of a digital pathology system including operational feasibility supporting remote review and reporting of pathology specimens, and evaluation of remote access performance and usability for remote signout.
David Kim, Liron Pantanowitz, Peter Schüffler, Dig Vijay Kumar Yarlagadda, Orly Ardon, Victor E. Reuter, Meera Hameed, David S. Klimstra and Matthew G. Hanna.
(Re) Defining the high-power field for digital pathology.
Journal of Pathology Informatics, vol. 11, 1, p. 33, 1/1/2020 doi: 10.4103/jpi.jpi_48_20
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@article{kim_re_2020,
    title = {({Re}) {Defining} the high-power field for digital pathology},
    volume = {11},
    issn = {2153-3539},
    url = {https://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2020;volume=11;issue=1;spage=33;epage=33;aulast=Kim;type=0},
    doi = {10.4103/jpi.jpi_48_20},
    abstract = {{\textless}br{\textgreater}\textbf{Background:} The microscope high-power field (HPF) is the cornerstone for histopathology diagnostic evaluation such as the quantification of mitotic figures, lymphocytes, and tumor grading. With traditional light microscopy, HPFs are typically evaluated by quantifying histologic events in 10 fields of view at × 400 magnification. In the era of digital pathology, new variables are introduced that may affect HPF evaluation. The aim of this study was to determine the parameters that influence HPF in whole slide images (WSIs). \textbf{Materials and Methods:} Glass slides scanned on various devices (Leica's Aperio GT450, AT2, and ScanScope XT; Philips UltraFast Scanner; Hamamatsu's Nanozoomer 2.0HT; and 3DHistech's P1000) were compared to acquired digital slides reviewed on each vendor's respective WSI viewer software (e.g., Aperio ImageScope, ImageScope DX, Philips IMS, 3DHistech CaseViewer, and Hamamatsu NDP.view) and an in-house developed vendor-agnostic viewer. WSIs were reviewed at “×40” equivalent HPF on different sized monitors with varying display resolutions (1900 × 1080–4500 × 3000) and aspect ratios (e.g., Food and Drug Administration [FDA]-cleared 27” Philips PS27QHDCR, FDA-cleared 24” Dell MR2416, 24” Hewlett Packard Z24n G2, and 28” Microsoft Surface Studio). Digital and microscopic HPF areas were calculated and compared. \textbf{Results:} A significant variation of HPF area occurred between differing monitor size and display resolutions with minor differences between WSI viewers. No differences were identified by scanner or WSIs scanned at different resolutions (e.g., 0.5, 0.25, 0.24, and 0.12 μm/pixel). \textbf{Conclusion:} Glass slide HPF at × 400 magnification with conventional light microscopy was not equivalent to “×40” digital HPF areas. Digital HPF quantification may vary due to differences in the tissue area displayed by monitor sizes, display resolutions, and WSI viewers but not by scanner or scanning resolution. These findings will need to be further clinically validated with potentially new digital metrics for evaluation.{\textless}br{\textgreater}},
    language = {en},
    number = {1},
    urldate = {2020-10-28},
    journal = {Journal of Pathology Informatics},
    author = {Kim, David and Pantanowitz, Liron and Sch\"uffler, Peter and Yarlagadda, Dig Vijay Kumar and Ardon, Orly and Reuter, Victor E. and Hameed, Meera and Klimstra, David S. and Hanna, Matthew G.},
    month = jan,
    year = {2020},
    Distributor: Medknow Publications and Media Pvt. Ltd.
    Institution: Medknow Publications and Media Pvt. Ltd.
    Label: Medknow Publications and Media Pvt. Ltd.
    Publisher: Medknow Publications},
    pages = {33},
}
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TY - JOUR
TI - (Re) Defining the high-power field for digital pathology
AU - Kim, David
AU - Pantanowitz, Liron
AU - Schüffler, Peter
AU - Yarlagadda, Dig Vijay Kumar
AU - Ardon, Orly
AU - Reuter, Victor E.
AU - Hameed, Meera
AU - Klimstra, David S.
AU - Hanna, Matthew G.
T2 - Journal of Pathology Informatics
AB -
Background: The microscope high-power field (HPF) is the cornerstone for histopathology diagnostic evaluation such as the quantification of mitotic figures, lymphocytes, and tumor grading. With traditional light microscopy, HPFs are typically evaluated by quantifying histologic events in 10 fields of view at × 400 magnification. In the era of digital pathology, new variables are introduced that may affect HPF evaluation. The aim of this study was to determine the parameters that influence HPF in whole slide images (WSIs). Materials and Methods: Glass slides scanned on various devices (Leica's Aperio GT450, AT2, and ScanScope XT; Philips UltraFast Scanner; Hamamatsu's Nanozoomer 2.0HT; and 3DHistech's P1000) were compared to acquired digital slides reviewed on each vendor's respective WSI viewer software (e.g., Aperio ImageScope, ImageScope DX, Philips IMS, 3DHistech CaseViewer, and Hamamatsu NDP.view) and an in-house developed vendor-agnostic viewer. WSIs were reviewed at “×40” equivalent HPF on different sized monitors with varying display resolutions (1900 × 1080–4500 × 3000) and aspect ratios (e.g., Food and Drug Administration [FDA]-cleared 27” Philips PS27QHDCR, FDA-cleared 24” Dell MR2416, 24” Hewlett Packard Z24n G2, and 28” Microsoft Surface Studio). Digital and microscopic HPF areas were calculated and compared. Results: A significant variation of HPF area occurred between differing monitor size and display resolutions with minor differences between WSI viewers. No differences were identified by scanner or WSIs scanned at different resolutions (e.g., 0.5, 0.25, 0.24, and 0.12 μm/pixel). Conclusion: Glass slide HPF at × 400 magnification with conventional light microscopy was not equivalent to “×40” digital HPF areas. Digital HPF quantification may vary due to differences in the tissue area displayed by monitor sizes, display resolutions, and WSI viewers but not by scanner or scanning resolution. These findings will need to be further clinically validated with potentially new digital metrics for evaluation.

DA - 2020/01/01/
PY - 2020
DO - 10.4103/jpi.jpi_48_20
DP - www.jpathinformatics.org
VL - 11
IS - 1
SP - 33
LA - en
SN - 2153-3539
UR - https://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2020;volume=11;issue=1;spage=33;epage=33;aulast=Kim;type=0
Y2 - 2020/10/28/14:22:22
ER -

Background: The microscope high-power field (HPF) is the cornerstone for histopathology diagnostic evaluation such as the quantification of mitotic figures, lymphocytes, and tumor grading. With traditional light microscopy, HPFs are typically evaluated by quantifying histologic events in 10 fields of view at × 400 magnification. In the era of digital pathology, new variables are introduced that may affect HPF evaluation. The aim of this study was to determine the parameters that influence HPF in whole slide images (WSIs). Materials and Methods: Glass slides scanned on various devices (Leica's Aperio GT450, AT2, and ScanScope XT; Philips UltraFast Scanner; Hamamatsu's Nanozoomer 2.0HT; and 3DHistech's P1000) were compared to acquired digital slides reviewed on each vendor's respective WSI viewer software (e.g., Aperio ImageScope, ImageScope DX, Philips IMS, 3DHistech CaseViewer, and Hamamatsu NDP.view) and an in-house developed vendor-agnostic viewer. WSIs were reviewed at “×40” equivalent HPF on different sized monitors with varying display resolutions (1900 × 1080–4500 × 3000) and aspect ratios (e.g., Food and Drug Administration [FDA]-cleared 27” Philips PS27QHDCR, FDA-cleared 24” Dell MR2416, 24” Hewlett Packard Z24n G2, and 28” Microsoft Surface Studio). Digital and microscopic HPF areas were calculated and compared. Results: A significant variation of HPF area occurred between differing monitor size and display resolutions with minor differences between WSI viewers. No differences were identified by scanner or WSIs scanned at different resolutions (e.g., 0.5, 0.25, 0.24, and 0.12 μm/pixel). Conclusion: Glass slide HPF at × 400 magnification with conventional light microscopy was not equivalent to “×40” digital HPF areas. Digital HPF quantification may vary due to differences in the tissue area displayed by monitor sizes, display resolutions, and WSI viewers but not by scanner or scanning resolution. These findings will need to be further clinically validated with potentially new digital metrics for evaluation.
David Joon Ho, Narasimhan P. Agaram, Peter J. Schüffler, Chad M. Vanderbilt, Marc-Henri Jean, Meera R. Hameed and Thomas J. Fuchs.
Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment.
In: Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu and Leo Joskowicz (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, vol. 12265, p. 540-549, Springer International Publishing, ISBN 978-3-030-59721-4 978-3-030-59722-1, 2020 
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@incollection{martel_deep_2020,
    address = {Cham},
    title = {Deep {Interactive} {Learning}: {An} {Efficient} {Labeling} {Approach} for {Deep} {Learning}-{Based} {Osteosarcoma} {Treatment} {Response} {Assessment}},
    volume = {12265},
    isbn = {978-3-030-59721-4 978-3-030-59722-1},
    shorttitle = {Deep {Interactive} {Learning}},
    url = {http://link.springer.com/10.1007/978-3-030-59722-1_52},
    language = {en},
    urldate = {2020-10-06},
    booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2020},
    publisher = {Springer International Publishing},
    author = {Ho, David Joon and Agaram, Narasimhan P. and Sch\"uffler, Peter J. and Vanderbilt, Chad M. and Jean, Marc-Henri and Hameed, Meera R. and Fuchs, Thomas J.},
    editor = {Martel, Anne L. and Abolmaesumi, Purang and Stoyanov, Danail and Mateus, Diana and Zuluaga, Maria A. and Zhou, S. Kevin and Racoceanu, Daniel and Joskowicz, Leo},
    year = {2020},
    doi = {10.1007/978-3-030-59722-1_52},
    pages = {540--549},
}
Download Endnote/RIS citation
TY - CHAP
TI - Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment
AU - Ho, David Joon
AU - Agaram, Narasimhan P.
AU - Schüffler, Peter J.
AU - Vanderbilt, Chad M.
AU - Jean, Marc-Henri
AU - Hameed, Meera R.
AU - Fuchs, Thomas J.
T2 - Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
A2 - Martel, Anne L.
A2 - Abolmaesumi, Purang
A2 - Stoyanov, Danail
A2 - Mateus, Diana
A2 - Zuluaga, Maria A.
A2 - Zhou, S. Kevin
A2 - Racoceanu, Daniel
A2 - Joskowicz, Leo
CY - Cham
DA - 2020///
PY - 2020
DP - DOI.org (Crossref)
VL - 12265
SP - 540
EP - 549
LA - en
PB - Springer International Publishing
SN - 978-3-030-59721-4 978-3-030-59722-1
ST - Deep Interactive Learning
UR - http://link.springer.com/10.1007/978-3-030-59722-1_52
Y2 - 2020/10/06/08:47:12
ER -
Anne Grabenstetter, Tracy-Ann Moo, Sabina Hajiyeva, Peter J. Schüffler, Pallavi Khattar, Maria A. Friedlander, Maura A. McCormack, Monica Raiss, Emily C. Zabor, Andrea Barrio, Monica Morrow and Marcia Edelweiss.
Accuracy of Intraoperative Frozen Section of Sentinel Lymph Nodes After Neoadjuvant Chemotherapy for Breast Carcinoma.
Am. J. Surg. Pathol., Jun 18, 2019 doi: 10.1097/pas.0000000000001311
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@article{grabenstetter_accuracy_2019,
    title = {Accuracy of {Intraoperative} {Frozen} {Section} of {Sentinel} {Lymph} {Nodes} {After} {Neoadjuvant} {Chemotherapy} for {Breast} {Carcinoma}},
    issn = {1532-0979},
    url = {https://pubmed.ncbi.nlm.nih.gov/31219817/},
    doi = {10.1097/PAS.0000000000001311},
    abstract = {False-negative (FN) intraoperative frozen section (FS) results of sentinel lymph nodes (SLN) have been reported to be more common after neoadjuvant chemotherapy (NAC) in the primary surgical setting. We evaluated SLN FS assessment in breast cancer patients treated with NAC to determine the FN rate and the histomorphologic factors associated with FN results. Patients who had FS SLN assessment following NAC from July 2008 to July 2017 were identified. Of the 711 SLN FS cases, 522 were negative, 181 positive, and 8 deferred. The FN rate was 5.4\% (28/522). There were no false-positive results. Of the 8 deferred cases, 5 were positive on permanent section and 3 were negative. There was a higher frequency of micrometastasis and isolated tumor cells in FN cases (P{\textless}0.001). There was a significant increase in tissue surface area present on permanent section slides compared with FS slides (P{\textless}0.001), highlighting the inherent technical limitations of FS and histologic under-sampling of tissue which leads to most FN results. The majority (25/28, 89\%) of FN cases had metastatic foci identified exclusively on permanent sections and were not due to a true diagnostic interpretation error. FN cases were more frequently estrogen receptor positive (P{\textless}0.001), progesterone receptor positive (P=0.001), human epidermal growth factor receptor-2 negative (P=0.009) and histologic grade 1 (P=0.015), which most likely reflects the lower rates of pathologic complete response in these tumors. Despite its limitations, FS is a reliable modality to assess the presence of SLN metastases in NAC treated patients.},
    language = {eng},
    journal = {The American Journal of Surgical Pathology},
    author = {Grabenstetter, Anne and Moo, Tracy-Ann and Hajiyeva, Sabina and Sch\"uffler, Peter J. and Khattar, Pallavi and Friedlander, Maria A. and McCormack, Maura A. and Raiss, Monica and Zabor, Emily C. and Barrio, Andrea and Morrow, Monica and Edelweiss, Marcia},
    month = jun,
    year = {2019},
    pmid = {31219817},
}
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TY - JOUR
TI - Accuracy of Intraoperative Frozen Section of Sentinel Lymph Nodes After Neoadjuvant Chemotherapy for Breast Carcinoma
AU - Grabenstetter, Anne
AU - Moo, Tracy-Ann
AU - Hajiyeva, Sabina
AU - Schüffler, Peter J.
AU - Khattar, Pallavi
AU - Friedlander, Maria A.
AU - McCormack, Maura A.
AU - Raiss, Monica
AU - Zabor, Emily C.
AU - Barrio, Andrea
AU - Morrow, Monica
AU - Edelweiss, Marcia
T2 - The American Journal of Surgical Pathology
AB - False-negative (FN) intraoperative frozen section (FS) results of sentinel lymph nodes (SLN) have been reported to be more common after neoadjuvant chemotherapy (NAC) in the primary surgical setting. We evaluated SLN FS assessment in breast cancer patients treated with NAC to determine the FN rate and the histomorphologic factors associated with FN results. Patients who had FS SLN assessment following NAC from July 2008 to July 2017 were identified. Of the 711 SLN FS cases, 522 were negative, 181 positive, and 8 deferred. The FN rate was 5.4% (28/522). There were no false-positive results. Of the 8 deferred cases, 5 were positive on permanent section and 3 were negative. There was a higher frequency of micrometastasis and isolated tumor cells in FN cases (P<0.001). There was a significant increase in tissue surface area present on permanent section slides compared with FS slides (P<0.001), highlighting the inherent technical limitations of FS and histologic under-sampling of tissue which leads to most FN results. The majority (25/28, 89%) of FN cases had metastatic foci identified exclusively on permanent sections and were not due to a true diagnostic interpretation error. FN cases were more frequently estrogen receptor positive (P<0.001), progesterone receptor positive (P=0.001), human epidermal growth factor receptor-2 negative (P=0.009) and histologic grade 1 (P=0.015), which most likely reflects the lower rates of pathologic complete response in these tumors. Despite its limitations, FS is a reliable modality to assess the presence of SLN metastases in NAC treated patients.
DA - 2019/06/18/
PY - 2019
DO - 10.1097/PAS.0000000000001311
DP - PubMed
J2 - Am. J. Surg. Pathol.
LA - eng
SN - 1532-0979
UR - https://pubmed.ncbi.nlm.nih.gov/31219817/
ER -
False-negative (FN) intraoperative frozen section (FS) results of sentinel lymph nodes (SLN) have been reported to be more common after neoadjuvant chemotherapy (NAC) in the primary surgical setting. We evaluated SLN FS assessment in breast cancer patients treated with NAC to determine the FN rate and the histomorphologic factors associated with FN results. Patients who had FS SLN assessment following NAC from July 2008 to July 2017 were identified. Of the 711 SLN FS cases, 522 were negative, 181 positive, and 8 deferred. The FN rate was 5.4% (28/522). There were no false-positive results. Of the 8 deferred cases, 5 were positive on permanent section and 3 were negative. There was a higher frequency of micrometastasis and isolated tumor cells in FN cases (P<0.001). There was a significant increase in tissue surface area present on permanent section slides compared with FS slides (P<0.001), highlighting the inherent technical limitations of FS and histologic under-sampling of tissue which leads to most FN results. The majority (25/28, 89%) of FN cases had metastatic foci identified exclusively on permanent sections and were not due to a true diagnostic interpretation error. FN cases were more frequently estrogen receptor positive (P<0.001), progesterone receptor positive (P=0.001), human epidermal growth factor receptor-2 negative (P=0.009) and histologic grade 1 (P=0.015), which most likely reflects the lower rates of pathologic complete response in these tumors. Despite its limitations, FS is a reliable modality to assess the presence of SLN metastases in NAC treated patients.
Matthew G. Hanna, Victor E. Reuter, Meera R. Hameed, Lee K. Tan, Sarah Chiang, Carlie Sigel, Travis Hollmann, Dilip Giri, Jennifer Samboy, Carlos Moradel, Andrea Rosado, John R. Otilano, Christine England, Lorraine Corsale, Evangelos Stamelos, Yukako Yagi, Peter J. Schüffler, Thomas Fuchs, David S. Klimstra and S. Joseph Sirintrapun.
Whole slide imaging equivalency and efficiency study: experience at a large academic center.
Modern Pathology, vol. 32, p. 916–928, 2019-02-18 doi: 10.1038/s41379-019-0205-0
PDF    URL   BibTeX   Endnote / RIS   Abstract
Download BibTeX citation
@article{hanna_whole_2019,
    title = {Whole slide imaging equivalency and efficiency study: experience at a large academic center},
    volume = {32},
    copyright = {2019 United States \& Canadian Academy of Pathology},
    issn = {1530-0285},
    shorttitle = {Whole slide imaging equivalency and efficiency study},
    url = {https://www.nature.com/articles/s41379-019-0205-0},
    doi = {10.1038/s41379-019-0205-0},
    abstract = {Whole slide imaging is Food and Drug Administration-approved for primary diagnosis in the United States of America; however, relatively few pathology departments in the country have fully implemented an enterprise wide digital pathology system enabled for primary diagnosis. Digital pathology has significant potential to transform pathology practice with several published studies documenting some level of diagnostic equivalence between digital and conventional systems. However, whole slide imaging also has significant potential to disrupt pathology practice, due to the differences in efficiency of manipulating digital images vis-\`a-vis glass slides, and studies on the efficiency of actual digital pathology workload are lacking. Our randomized, equivalency and efficiency study aimed to replicate clinical workflow, comparing conventional microscopy to a complete digital pathology signout using whole slide images, evaluating the equivalency and efficiency of glass slide to whole slide image reporting, reflective of true pathology practice workloads in the clinical setting. All glass slides representing an entire day’s routine clinical signout workload for six different anatomic pathology subspecialties at Memorial Sloan Kettering Cancer Center were scanned on Leica Aperio AT2 at ×40 (0.25 µm/pixel). Integration of whole slide images for each accessioned case is through an interface between the Leica eSlide manager database and the laboratory information system, Cerner CoPathPlus. Pathologists utilized a standard institution computer workstation and viewed whole slide images through an internally developed, vendor agnostic whole slide image viewer, named the “MSK Slide Viewer”. Subspecialized pathologists first reported on glass slides from surgical pathology cases using routine clinical workflow. Glass slides were de-identified, scanned, and re-accessioned in the laboratory information system test environment. After a washout period of 13 weeks, pathologists reported the same clinical workload using whole slide image integrated within the laboratory information system. Intraobserver equivalency metrics included top-line diagnosis, margin status, lymphovascular and/or perineural invasion, pathology stage, and the need to order ancillary testing (i.e., recuts, immunohistochemistry). Turnaround time (efficiency) evaluation was defined by the start of each case when opened in the laboratory information system and when the case was completed for that day (i.e., case sent to signout queue or pending ancillary studies). Eight pathologists participated from the following subspecialties: bone and soft tissue, genitourinary, gastrointestinal, breast, gynecologic, and dermatopathology. Glass slides signouts comprised of 204 cases, encompassing 2091 glass slides; and digital signouts comprised of 199 cases, encompassing 2073 whole slide images. The median whole slide image file size was 1.54 GB; scan time/slide, 6 min 24 s; and scan area 32.1 × 18.52 mm. Overall diagnostic equivalency (e.g., top-line diagnosis) was 99.3\% between digital and glass slide signout; however, signout using whole slide images showed a median overall 19\% decrease in efficiency per case. No significant difference by reader, subspecialty, or specimen type was identified. Our experience is the most comprehensive study to date and shows high intraobserver whole slide image to glass slide equivalence in reporting of true clinical workflows and workloads. Efficiency needs to improve for digital pathology to gain more traction among pathologists.},
    language = {En},
    urldate = {2019-02-21},
    journal = {Modern Pathology},
    author = {Hanna, Matthew G. and Reuter, Victor E. and Hameed, Meera R. and Tan, Lee K. and Chiang, Sarah and Sigel, Carlie and Hollmann, Travis and Giri, Dilip and Samboy, Jennifer and Moradel, Carlos and Rosado, Andrea and Otilano, John R. and England, Christine and Corsale, Lorraine and Stamelos, Evangelos and Yagi, Yukako and Sch\"uffler, Peter J. and Fuchs, Thomas and Klimstra, David S. and Sirintrapun, S. Joseph},
    month = feb,
    year = {2019},
    pages = {916--928},
}
Download Endnote/RIS citation
TY - JOUR
TI - Whole slide imaging equivalency and efficiency study: experience at a large academic center
AU - Hanna, Matthew G.
AU - Reuter, Victor E.
AU - Hameed, Meera R.
AU - Tan, Lee K.
AU - Chiang, Sarah
AU - Sigel, Carlie
AU - Hollmann, Travis
AU - Giri, Dilip
AU - Samboy, Jennifer
AU - Moradel, Carlos
AU - Rosado, Andrea
AU - Otilano, John R.
AU - England, Christine
AU - Corsale, Lorraine
AU - Stamelos, Evangelos
AU - Yagi, Yukako
AU - Schüffler, Peter J.
AU - Fuchs, Thomas
AU - Klimstra, David S.
AU - Sirintrapun, S. Joseph
T2 - Modern Pathology
AB - Whole slide imaging is Food and Drug Administration-approved for primary diagnosis in the United States of America; however, relatively few pathology departments in the country have fully implemented an enterprise wide digital pathology system enabled for primary diagnosis. Digital pathology has significant potential to transform pathology practice with several published studies documenting some level of diagnostic equivalence between digital and conventional systems. However, whole slide imaging also has significant potential to disrupt pathology practice, due to the differences in efficiency of manipulating digital images vis-à-vis glass slides, and studies on the efficiency of actual digital pathology workload are lacking. Our randomized, equivalency and efficiency study aimed to replicate clinical workflow, comparing conventional microscopy to a complete digital pathology signout using whole slide images, evaluating the equivalency and efficiency of glass slide to whole slide image reporting, reflective of true pathology practice workloads in the clinical setting. All glass slides representing an entire day’s routine clinical signout workload for six different anatomic pathology subspecialties at Memorial Sloan Kettering Cancer Center were scanned on Leica Aperio AT2 at ×40 (0.25 µm/pixel). Integration of whole slide images for each accessioned case is through an interface between the Leica eSlide manager database and the laboratory information system, Cerner CoPathPlus. Pathologists utilized a standard institution computer workstation and viewed whole slide images through an internally developed, vendor agnostic whole slide image viewer, named the “MSK Slide Viewer”. Subspecialized pathologists first reported on glass slides from surgical pathology cases using routine clinical workflow. Glass slides were de-identified, scanned, and re-accessioned in the laboratory information system test environment. After a washout period of 13 weeks, pathologists reported the same clinical workload using whole slide image integrated within the laboratory information system. Intraobserver equivalency metrics included top-line diagnosis, margin status, lymphovascular and/or perineural invasion, pathology stage, and the need to order ancillary testing (i.e., recuts, immunohistochemistry). Turnaround time (efficiency) evaluation was defined by the start of each case when opened in the laboratory information system and when the case was completed for that day (i.e., case sent to signout queue or pending ancillary studies). Eight pathologists participated from the following subspecialties: bone and soft tissue, genitourinary, gastrointestinal, breast, gynecologic, and dermatopathology. Glass slides signouts comprised of 204 cases, encompassing 2091 glass slides; and digital signouts comprised of 199 cases, encompassing 2073 whole slide images. The median whole slide image file size was 1.54 GB; scan time/slide, 6 min 24 s; and scan area 32.1 × 18.52 mm. Overall diagnostic equivalency (e.g., top-line diagnosis) was 99.3% between digital and glass slide signout; however, signout using whole slide images showed a median overall 19% decrease in efficiency per case. No significant difference by reader, subspecialty, or specimen type was identified. Our experience is the most comprehensive study to date and shows high intraobserver whole slide image to glass slide equivalence in reporting of true clinical workflows and workloads. Efficiency needs to improve for digital pathology to gain more traction among pathologists.
DA - 2019/02/18/
PY - 2019
DO - 10.1038/s41379-019-0205-0
DP - www.nature.com
VL - 32
SP - 916
EP - 928
LA - En
SN - 1530-0285
ST - Whole slide imaging equivalency and efficiency study
UR - https://www.nature.com/articles/s41379-019-0205-0
Y2 - 2019/02/21/21:15:25
ER -
Whole slide imaging is Food and Drug Administration-approved for primary diagnosis in the United States of America; however, relatively few pathology departments in the country have fully implemented an enterprise wide digital pathology system enabled for primary diagnosis. Digital pathology has significant potential to transform pathology practice with several published studies documenting some level of diagnostic equivalence between digital and conventional systems. However, whole slide imaging also has significant potential to disrupt pathology practice, due to the differences in efficiency of manipulating digital images vis-à-vis glass slides, and studies on the efficiency of actual digital pathology workload are lacking. Our randomized, equivalency and efficiency study aimed to replicate clinical workflow, comparing conventional microscopy to a complete digital pathology signout using whole slide images, evaluating the equivalency and efficiency of glass slide to whole slide image reporting, reflective of true pathology practice workloads in the clinical setting. All glass slides representing an entire day’s routine clinical signout workload for six different anatomic pathology subspecialties at Memorial Sloan Kettering Cancer Center were scanned on Leica Aperio AT2 at ×40 (0.25 µm/pixel). Integration of whole slide images for each accessioned case is through an interface between the Leica eSlide manager database and the laboratory information system, Cerner CoPathPlus. Pathologists utilized a standard institution computer workstation and viewed whole slide images through an internally developed, vendor agnostic whole slide image viewer, named the “MSK Slide Viewer”. Subspecialized pathologists first reported on glass slides from surgical pathology cases using routine clinical workflow. Glass slides were de-identified, scanned, and re-accessioned in the laboratory information system test environment. After a washout period of 13 weeks, pathologists reported the same clinical workload using whole slide image integrated within the laboratory information system. Intraobserver equivalency metrics included top-line diagnosis, margin status, lymphovascular and/or perineural invasion, pathology stage, and the need to order ancillary testing (i.e., recuts, immunohistochemistry). Turnaround time (efficiency) evaluation was defined by the start of each case when opened in the laboratory information system and when the case was completed for that day (i.e., case sent to signout queue or pending ancillary studies). Eight pathologists participated from the following subspecialties: bone and soft tissue, genitourinary, gastrointestinal, breast, gynecologic, and dermatopathology. Glass slides signouts comprised of 204 cases, encompassing 2091 glass slides; and digital signouts comprised of 199 cases, encompassing 2073 whole slide images. The median whole slide image file size was 1.54 GB; scan time/slide, 6 min 24 s; and scan area 32.1 × 18.52 mm. Overall diagnostic equivalency (e.g., top-line diagnosis) was 99.3% between digital and glass slide signout; however, signout using whole slide images showed a median overall 19% decrease in efficiency per case. No significant difference by reader, subspecialty, or specimen type was identified. Our experience is the most comprehensive study to date and shows high intraobserver whole slide image to glass slide equivalence in reporting of true clinical workflows and workloads. Efficiency needs to improve for digital pathology to gain more traction among pathologists.
Carl A. J. Puylaert, Jeroen A. W. Tielbeek, Peter J. Schüffler, C. Yung Nio, Karin Horsthuis, Banafsche Mearadji, Cyriel Y. Ponsioen, Frans M. Vos and Jaap Stoker.
Comparison of contrast-enhanced and diffusion-weighted MRI in assessment of the terminal ileum in Crohn’s disease patients.
Abdominal Radiology, vol. 44, p. 398–405, 2018-8-14 doi: 10.1007/s00261-018-1734-6
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@article{puylaert_comparison_2018,
    title = {Comparison of contrast-enhanced and diffusion-weighted {MRI} in assessment of the terminal ileum in {Crohn}’s disease patients},
    volume = {44},
    issn = {2366-004X, 2366-0058},
    url = {http://link.springer.com/10.1007/s00261-018-1734-6},
    doi = {10.1007/s00261-018-1734-6},
    language = {en},
    urldate = {2018-09-04},
    journal = {Abdominal Radiology},
    author = {Puylaert, Carl A. J. and Tielbeek, Jeroen A. W. and Sch\"uffler, Peter J. and Nio, C. Yung and Horsthuis, Karin and Mearadji, Banafsche and Ponsioen, Cyriel Y. and Vos, Frans M. and Stoker, Jaap},
    month = aug,
    year = {2018},
    pages = {398--405},
}
Download Endnote/RIS citation
TY - JOUR
TI - Comparison of contrast-enhanced and diffusion-weighted MRI in assessment of the terminal ileum in Crohn’s disease patients
AU - Puylaert, Carl A. J.
AU - Tielbeek, Jeroen A. W.
AU - Schüffler, Peter J.
AU - Nio, C. Yung
AU - Horsthuis, Karin
AU - Mearadji, Banafsche
AU - Ponsioen, Cyriel Y.
AU - Vos, Frans M.
AU - Stoker, Jaap
T2 - Abdominal Radiology
DA - 2018/08/14/
PY - 2018
DO - 10.1007/s00261-018-1734-6
DP - Crossref
VL - 44
SP - 398
EP - 405
LA - en
SN - 2366-004X, 2366-0058
UR - http://link.springer.com/10.1007/s00261-018-1734-6
Y2 - 2018/09/04/23:18:09
ER -
Christian D. Fankhauser, Peter J. Schüffler, Silke Gillessen, Aurelius Omlin, Niels J. Rupp, Jan H. Rueschoff, Thomas Hermanns, Cedric Poyet, Tullio Sulser, Holger Moch and Peter J. Wild.
Comprehensive immunohistochemical analysis of PD-L1 shows scarce expression in castration-resistant prostate cancer.
Oncotarget, vol. 9, 12, 2018-02-13 doi: 10.18632/oncotarget.22888
PDF   URL   BibTeX   Endnote / RIS   Abstract
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@article{fankhauser_comprehensive_2018,
    title = {Comprehensive immunohistochemical analysis of {PD}-{L1} shows scarce expression in castration-resistant prostate cancer},
    volume = {9},
    issn = {1949-2553},
    url = {http://www.oncotarget.com/fulltext/22888},
    doi = {10.18632/oncotarget.22888},
    language = {en},
    number = {12},
    urldate = {2018-05-31},
    journal = {Oncotarget},
    author = {Fankhauser, Christian D. and Sch\"uffler, Peter J. and Gillessen, Silke and Omlin, Aurelius and Rupp, Niels J. and Rueschoff, Jan H. and Hermanns, Thomas and Poyet, Cedric and Sulser, Tullio and Moch, Holger and Wild, Peter J.},
    month = feb,
    year = {2018},
}
Download Endnote/RIS citation
TY - JOUR
TI - Comprehensive immunohistochemical analysis of PD-L1 shows scarce expression in castration-resistant prostate cancer
AU - Fankhauser, Christian D.
AU - Schüffler, Peter J.
AU - Gillessen, Silke
AU - Omlin, Aurelius
AU - Rupp, Niels J.
AU - Rueschoff, Jan H.
AU - Hermanns, Thomas
AU - Poyet, Cedric
AU - Sulser, Tullio
AU - Moch, Holger
AU - Wild, Peter J.
T2 - Oncotarget
DA - 2018/02/13/
PY - 2018
DO - 10.18632/oncotarget.22888
DP - Crossref
VL - 9
IS - 12
LA - en
SN - 1949-2553
UR - http://www.oncotarget.com/fulltext/22888
Y2 - 2018/05/31/17:41:01
ER -
Gabriele Campanella, Arjun R. Rajanna, Lorraine Corsale, Peter J. Schüffler, Yukako Yagi and Thomas J. Fuchs.
Towards machine learned quality control: A benchmark for sharpness quantification in digital pathology.
Computerized Medical Imaging and Graphics, vol. 65, p. 142-151, 04/2018 doi: 10.1016/j.compmedimag.2017.09.001
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@article{campanella_towards_2018,
    title = {Towards machine learned quality control: {A} benchmark for sharpness quantification in digital pathology},
    volume = {65},
    issn = {08956111},
    shorttitle = {Towards machine learned quality control},
    url = {https://linkinghub.elsevier.com/retrieve/pii/S0895611117300800},
    doi = {10.1016/j.compmedimag.2017.09.001},
    language = {en},
    urldate = {2019-11-26},
    journal = {Computerized Medical Imaging and Graphics},
    author = {Campanella, Gabriele and Rajanna, Arjun R. and Corsale, Lorraine and Sch\"uffler, Peter J. and Yagi, Yukako and Fuchs, Thomas J.},
    month = apr,
    year = {2018},
    pages = {142--151},
}
Download Endnote/RIS citation
TY - JOUR
TI - Towards machine learned quality control: A benchmark for sharpness quantification in digital pathology
AU - Campanella, Gabriele
AU - Rajanna, Arjun R.
AU - Corsale, Lorraine
AU - Schüffler, Peter J.
AU - Yagi, Yukako
AU - Fuchs, Thomas J.
T2 - Computerized Medical Imaging and Graphics
DA - 2018/04//
PY - 2018
DO - 10.1016/j.compmedimag.2017.09.001
DP - DOI.org (Crossref)
VL - 65
SP - 142
EP - 151
J2 - Computerized Medical Imaging and Graphics
LA - en
SN - 08956111
ST - Towards machine learned quality control
UR - https://linkinghub.elsevier.com/retrieve/pii/S0895611117300800
Y2 - 2019/11/26/18:28:10
ER -
Carl A.J. Puylaert, Peter J. Schüffler, Robiel E. Naziroglu, Jeroen A.W. Tielbeek, Zhang Li, Jesica C. Makanyanga, Charlotte J. Tutein Nolthenius, C. Yung Nio, Douglas A. Pendsé, Alex Menys, Cyriel Y. Ponsioen, David Atkinson, Alastair Forbes, Joachim M. Buhmann, Thomas J. Fuchs, Haralambos Hatzakis, Lucas J. van Vliet, Jaap Stoker, Stuart A. Taylor and Frans M. Vos.
Semiautomatic Assessment of the Terminal Ileum and Colon in Patients with Crohn Disease Using MRI (the VIGOR++ Project).
Academic Radiology, vol. 25, 8, p. 1038-1045, 2/2018 doi: 10.1016/j.acra.2017.12.024
PDF   URL   BibTeX   Endnote / RIS   Abstract
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@article{puylaert_semiautomatic_2018,
    title = {Semiautomatic {Assessment} of the {Terminal} {Ileum} and {Colon} in {Patients} with {Crohn} {Disease} {Using} {MRI} (the {VIGOR}++ {Project})},
    volume = {25},
    issn = {10766332},
    url = {http://linkinghub.elsevier.com/retrieve/pii/S1076633218300060},
    doi = {10.1016/j.acra.2017.12.024},
    language = {en},
    number = {8},
    urldate = {2018-05-21},
    journal = {Academic Radiology},
    author = {Puylaert, Carl A.J. and Sch\"uffler, Peter J. and Naziroglu, Robiel E. and Tielbeek, Jeroen A.W. and Li, Zhang and Makanyanga, Jesica C. and Tutein Nolthenius, Charlotte J. and Nio, C. Yung and Pends\'e, Douglas A. and Menys, Alex and Ponsioen, Cyriel Y. and Atkinson, David and Forbes, Alastair and Buhmann, Joachim M. and Fuchs, Thomas J. and Hatzakis, Haralambos and van Vliet, Lucas J. and Stoker, Jaap and Taylor, Stuart A. and Vos, Frans M.},
    month = feb,
    year = {2018},
    pages = {1038--1045},
}
Download Endnote/RIS citation
TY - JOUR
TI - Semiautomatic Assessment of the Terminal Ileum and Colon in Patients with Crohn Disease Using MRI (the VIGOR++ Project)
AU - Puylaert, Carl A.J.
AU - Schüffler, Peter J.
AU - Naziroglu, Robiel E.
AU - Tielbeek, Jeroen A.W.
AU - Li, Zhang
AU - Makanyanga, Jesica C.
AU - Tutein Nolthenius, Charlotte J.
AU - Nio, C. Yung
AU - Pendsé, Douglas A.
AU - Menys, Alex
AU - Ponsioen, Cyriel Y.
AU - Atkinson, David
AU - Forbes, Alastair
AU - Buhmann, Joachim M.
AU - Fuchs, Thomas J.
AU - Hatzakis, Haralambos
AU - van Vliet, Lucas J.
AU - Stoker, Jaap
AU - Taylor, Stuart A.
AU - Vos, Frans M.
T2 - Academic Radiology
DA - 2018/02//
PY - 2018
DO - 10.1016/j.acra.2017.12.024
DP - Crossref
VL - 25
IS - 8
SP - 1038
EP - 1045
LA - en
SN - 10766332
UR - http://linkinghub.elsevier.com/retrieve/pii/S1076633218300060
Y2 - 2018/05/21/12:24:37
ER -
Peter J. Schüffler, Qing Zhong, Peter J. Wild and Thomas J. Fuchs.
Computational Pathology.
In: Johannes Haybäck (ed.) Mechanisms of Molecular Carcinogenesis - Volume 2, 1st ed. 2017 edition, Springer, ISBN 3-319-53660-5, 21. Jun. 2017 
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@incollection{schuffler_computational_2017,
    edition = {1st ed. 2017 edition},
    title = {Computational {Pathology}},
    isbn = {3-319-53660-5},
    url = {http://www.springer.com/de/book/9783319536606},
    booktitle = {Mechanisms of {Molecular} {Carcinogenesis} - {Volume} 2},
    publisher = {Springer},
    author = {Sch\"uffler, Peter J. and Zhong, Qing and Wild, Peter J. and Fuchs, Thomas J.},
    editor = {Hayb\"ack, Johannes},
    month = jun,
    year = {2017},
}
Download Endnote/RIS citation
TY - CHAP
TI - Computational Pathology
AU - Schüffler, Peter J.
AU - Zhong, Qing
AU - Wild, Peter J.
AU - Fuchs, Thomas J.
T2 - Mechanisms of Molecular Carcinogenesis - Volume 2
A2 - Haybäck, Johannes
DA - 2017/06/21/
PY - 2017
ET - 1st ed. 2017 edition
PB - Springer
SN - 3-319-53660-5
UR - http://www.springer.com/de/book/9783319536606
ER -
Gabriele Abbati, Stefan Bauer, Sebastian Winklhofer, Peter J. Schüffler, Ulrike Held, Jakob M. Burgstaller, Johann Steurer and Joachim M. Buhmann.
MRI-Based Surgical Planning for Lumbar Spinal Stenosis.
In: Maxime Descoteaux, Lena Maier-Hein, Alfred Franz, Pierre Jannin, D. Louis Collins and Simon Duchesne (eds.) Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017, vol. 10435, p. 116-124, Lecture Notes in Computer Science, Springer, ISBN 978-3-319-66179-7, 2017 
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@incollection{descoteaux_mri-based_2017,
    address = {Cham},
    series = {Lecture {Notes} in {Computer} {Science}},
    title = {{MRI}-{Based} {Surgical} {Planning} for {Lumbar} {Spinal} {Stenosis}},
    volume = {10435},
    isbn = {978-3-319-66179-7},
    url = {http://link.springer.com/10.1007/978-3-319-66179-7_14},
    urldate = {2017-09-18},
    booktitle = {Medical {Image} {Computing} and {Computer}-{Assisted} {Intervention} − {MICCAI} 2017},
    publisher = {Springer},
    author = {Abbati, Gabriele and Bauer, Stefan and Winklhofer, Sebastian and Sch\"uffler, Peter J. and Held, Ulrike and Burgstaller, Jakob M. and Steurer, Johann and Buhmann, Joachim M.},
    editor = {Descoteaux, Maxime and Maier-Hein, Lena and Franz, Alfred and Jannin, Pierre and Collins, D. Louis and Duchesne, Simon},
    year = {2017},
    doi = {10.1007/978-3-319-66179-7_14},
    pages = {116--124},
}
Download Endnote/RIS citation
TY - CHAP
TI - MRI-Based Surgical Planning for Lumbar Spinal Stenosis
AU - Abbati, Gabriele
AU - Bauer, Stefan
AU - Winklhofer, Sebastian
AU - Schüffler, Peter J.
AU - Held, Ulrike
AU - Burgstaller, Jakob M.
AU - Steurer, Johann
AU - Buhmann, Joachim M.
T2 - Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017
A2 - Descoteaux, Maxime
A2 - Maier-Hein, Lena
A2 - Franz, Alfred
A2 - Jannin, Pierre
A2 - Collins, D. Louis
A2 - Duchesne, Simon
T3 - Lecture Notes in Computer Science
CY - Cham
DA - 2017///
PY - 2017
DP - CrossRef
VL - 10435
SP - 116
EP - 124
PB - Springer
SN - 978-3-319-66179-7
UR - http://link.springer.com/10.1007/978-3-319-66179-7_14
Y2 - 2017/09/18/12:44:49
ER -
Peter J. Schüffler, Judy Sarungbam, Hassan Muhammad, Ed Reznik, Satish K. Tickoo and Thomas J. Fuchs.
Mitochondria-based Renal Cell Carcinoma Subtyping: Learning from Deep vs. Flat Feature Representations.
Proceedings of the 1st Machine Learning for Healthcare Conference, Machine Learning for Healthcare, vol. 56, p. 191-208, Proceedings of Machine Learning Research, PMLR, 18 Aug 2016 
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@inproceedings{schuffler_mitochondria-based_2016,
    address = {Los Angeles},
    series = {Proceedings of {Machine} {Learning} {Research}},
    title = {Mitochondria-based {Renal} {Cell} {Carcinoma} {Subtyping}: {Learning} from {Deep} vs. {Flat} {Feature} {Representations}},
    volume = {56},
    url = {http://proceedings.mlr.press/v56/Schuffler16.html},
    abstract = {Accurate subtyping of renal cell carcinoma (RCC) is of crucial importance for understanding disease progression and for making informed treatment decisions. New discoveries of significant mitochondria alterations between subtypes makes immunohistochemical (IHC) staining based image classification an imperative.
    Until now, accurate quantification and subtyping was made impossible by huge IHC variations, the absence of cell membrane staining for cytoplasm segmentation as well as the complete lack of systems for robust and reproducible image based classification.
    In this paper we present a comprehensive classification framework to overcome these challenges for tissue microarrays (TMA) of RCCs. We compare and evaluate models based on domain specific hand-crafted "flat"-features versus "deep" feature representations from various layers of a pre-trained convolutional neural network (CNN).
    The best model reaches a cross-validation accuracy of 89\%, which demonstrates for the first time, that robust mitochondria-based subtyping of renal cancer is feasible.},
    language = {English},
    booktitle = {Proceedings of the 1st {Machine} {Learning} for {Healthcare} {Conference}},
    publisher = {PMLR},
    author = {Sch\"uffler, Peter J. and Sarungbam, Judy and Muhammad, Hassan and Reznik, Ed and Tickoo, Satish K. and Fuchs, Thomas J.},
    editor = {Finale, Doshi-Valez and Fackler, Jim and Kale, David and Wallace, Byron and Weins, Jenna},
    month = aug,
    year = {2016},
    pages = {191--208},
}
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TY - CONF
TI - Mitochondria-based Renal Cell Carcinoma Subtyping: Learning from Deep vs. Flat Feature Representations
AU - Schüffler, Peter J.
AU - Sarungbam, Judy
AU - Muhammad, Hassan
AU - Reznik, Ed
AU - Tickoo, Satish K.
AU - Fuchs, Thomas J.
T2 - Machine Learning for Healthcare
A2 - Finale, Doshi-Valez
A2 - Fackler, Jim
A2 - Kale, David
A2 - Wallace, Byron
A2 - Weins, Jenna
T3 - Proceedings of Machine Learning Research
AB - Accurate subtyping of renal cell carcinoma (RCC) is of crucial importance for understanding disease progression and for making informed treatment decisions. New discoveries of significant mitochondria alterations between subtypes makes immunohistochemical (IHC) staining based image classification an imperative.
Until now, accurate quantification and subtyping was made impossible by huge IHC variations, the absence of cell membrane staining for cytoplasm segmentation as well as the complete lack of systems for robust and reproducible image based classification.
In this paper we present a comprehensive classification framework to overcome these challenges for tissue microarrays (TMA) of RCCs. We compare and evaluate models based on domain specific hand-crafted "flat"-features versus "deep" feature representations from various layers of a pre-trained convolutional neural network (CNN).
The best model reaches a cross-validation accuracy of 89%, which demonstrates for the first time, that robust mitochondria-based subtyping of renal cancer is feasible.
C1 - Los Angeles
C3 - Proceedings of the 1st Machine Learning for Healthcare Conference
DA - 2016/08/18/
PY - 2016
VL - 56
SP - 191
EP - 208
LA - English
PB - PMLR
UR - http://proceedings.mlr.press/v56/Schuffler16.html
ER -
Accurate subtyping of renal cell carcinoma (RCC) is of crucial importance for understanding disease progression and for making informed treatment decisions. New discoveries of significant mitochondria alterations between subtypes makes immunohistochemical (IHC) staining based image classification an imperative. Until now, accurate quantification and subtyping was made impossible by huge IHC variations, the absence of cell membrane staining for cytoplasm segmentation as well as the complete lack of systems for robust and reproducible image based classification. In this paper we present a comprehensive classification framework to overcome these challenges for tissue microarrays (TMA) of RCCs. We compare and evaluate models based on domain specific hand-crafted "flat"-features versus "deep" feature representations from various layers of a pre-trained convolutional neural network (CNN). The best model reaches a cross-validation accuracy of 89%, which demonstrates for the first time, that robust mitochondria-based subtyping of renal cancer is feasible.
Qing Zhong, Jan H. Rüschoff, Tiannan Guo, Maria Gabrani, Peter J. Schüffler, Markus Rechsteiner, Yansheng Liu, Thomas J. Fuchs, Niels J. Rupp, Christian Fankhauser, Joachim M. Buhmann, Sven Perner, Cédric Poyet, Miriam Blattner, Davide Soldini, Holger Moch, Mark A. Rubin, Aurelia Noske, Josef Rüschoff, Michael C. Haffner, Wolfram Jochum and Peter J. Wild.
Image-Based Computational Quantification and Visualization of Genetic Alterations and Tumour Heterogeneity.
Scientific Reports, vol. 6, p. 24146, 2016 doi: 10.1038/srep24146
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@article{zhong_image-based_2016,
    title = {Image-{Based} {Computational} {Quantification} and {Visualization} of {Genetic} {Alterations} and {Tumour} {Heterogeneity}},
    volume = {6},
    issn = {2045-2322},
    url = {http://www.nature.com/articles/srep24146},
    doi = {10.1038/srep24146},
    urldate = {2016-04-12},
    journal = {Scientific Reports},
    author = {Zhong, Qing and R\"uschoff, Jan H. and Guo, Tiannan and Gabrani, Maria and Sch\"uffler, Peter J. and Rechsteiner, Markus and Liu, Yansheng and Fuchs, Thomas J. and Rupp, Niels J. and Fankhauser, Christian and Buhmann, Joachim M. and Perner, Sven and Poyet, C\'edric and Blattner, Miriam and Soldini, Davide and Moch, Holger and Rubin, Mark A. and Noske, Aurelia and R\"uschoff, Josef and Haffner, Michael C. and Jochum, Wolfram and Wild, Peter J.},
    year = {2016},
    pages = {24146},
}
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TY - JOUR
TI - Image-Based Computational Quantification and Visualization of Genetic Alterations and Tumour Heterogeneity
AU - Zhong, Qing
AU - Rüschoff, Jan H.
AU - Guo, Tiannan
AU - Gabrani, Maria
AU - Schüffler, Peter J.
AU - Rechsteiner, Markus
AU - Liu, Yansheng
AU - Fuchs, Thomas J.
AU - Rupp, Niels J.
AU - Fankhauser, Christian
AU - Buhmann, Joachim M.
AU - Perner, Sven
AU - Poyet, Cédric
AU - Blattner, Miriam
AU - Soldini, Davide
AU - Moch, Holger
AU - Rubin, Mark A.
AU - Noske, Aurelia
AU - Rüschoff, Josef
AU - Haffner, Michael C.
AU - Jochum, Wolfram
AU - Wild, Peter J.
T2 - Scientific Reports
DA - 2016///
PY - 2016
DO - 10.1038/srep24146
DP - CrossRef
VL - 6
SP - 24146
SN - 2045-2322
UR - http://www.nature.com/articles/srep24146
Y2 - 2016/04/12/01:49:30
ER -
Andrew J. Schaumberg, S. Joseph Sirintrapun, Hikmat A. Al-Ahmadie, Peter J. Schüffler and Thomas J. Fuchs.
DeepScope: Nonintrusive Whole Slide Saliency Annotation and Prediction from Pathologists at the Microscope.
13th International Conference on Computational Intelligence methods for Bioinformatics and Biostatistics, CIBB, 2016 
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@inproceedings{schaumberg_deepscope_2016,
    address = {Stirling, United Kingdom},
    title = {{DeepScope}: {Nonintrusive} {Whole} {Slide} {Saliency} {Annotation} and {Prediction} from {Pathologists} at the {Microscope}},
    shorttitle = {{DeepScope}},
    url = {http://www.cs.stir.ac.uk/events/cibb2016/},
    abstract = {Modern digital pathology departments have grown to produce whole-slide image data at petabyte scale, an unprecedented treasure chest for medical machine learning tasks.
    Unfortunately, most digital slides are not annotated at the image level, hindering large-scale application of supervised learning. Manual labeling is prohibitive, requiring pathologists with decades of training and outstanding clinical service responsibilities. This problem is further aggravated by the United States Food and Drug Administration's ruling that primary diagnosis must come from a glass slide rather than a digital image.
    We present the first end-to-end framework to overcome this problem, gathering annotations in a nonintrusive manner during a pathologist's routine clinical work: (i) microscope-specific 3D-printed commodity camera mounts are used to video record the glass-slide-based clinical diagnosis process; (ii) after routine scanning of the whole slide, the video frames are registered to the digital slide; (iii) motion and observing time are estimated to generate a spatial and temporal saliency map of the whole slide.
    Demonstrating the utility of these annotations, we train a convolutional neural network that detects diagnosis-relevant salient regions, then report accuracy of 85.15\% in bladder and 91.50\% in prostate, with 75.00\% accuracy when training on prostate but predicting in bladder, despite different pathologists examining the different tissues.},
    language = {English},
    booktitle = {13th {International} {Conference} on {Computational} {Intelligence} methods for {Bioinformatics} and {Biostatistics}},
    author = {Schaumberg, Andrew J. and Sirintrapun, S. Joseph and Al-Ahmadie, Hikmat A. and Sch\"uffler, Peter J. and Fuchs, Thomas J.},
    year = {2016},
}
Download Endnote/RIS citation
TY - CONF
TI - DeepScope: Nonintrusive Whole Slide Saliency Annotation and Prediction from Pathologists at the Microscope
AU - Schaumberg, Andrew J.
AU - Sirintrapun, S. Joseph
AU - Al-Ahmadie, Hikmat A.
AU - Schüffler, Peter J.
AU - Fuchs, Thomas J.
T2 - CIBB
AB - Modern digital pathology departments have grown to produce whole-slide image data at petabyte scale, an unprecedented treasure chest for medical machine learning tasks.
Unfortunately, most digital slides are not annotated at the image level, hindering large-scale application of supervised learning. Manual labeling is prohibitive, requiring pathologists with decades of training and outstanding clinical service responsibilities. This problem is further aggravated by the United States Food and Drug Administration's ruling that primary diagnosis must come from a glass slide rather than a digital image.
We present the first end-to-end framework to overcome this problem, gathering annotations in a nonintrusive manner during a pathologist's routine clinical work: (i) microscope-specific 3D-printed commodity camera mounts are used to video record the glass-slide-based clinical diagnosis process; (ii) after routine scanning of the whole slide, the video frames are registered to the digital slide; (iii) motion and observing time are estimated to generate a spatial and temporal saliency map of the whole slide.
Demonstrating the utility of these annotations, we train a convolutional neural network that detects diagnosis-relevant salient regions, then report accuracy of 85.15% in bladder and 91.50% in prostate, with 75.00% accuracy when training on prostate but predicting in bladder, despite different pathologists examining the different tissues.
C1 - Stirling, United Kingdom
C3 - 13th International Conference on Computational Intelligence methods for Bioinformatics and Biostatistics
DA - 2016///
PY - 2016
LA - English
ST - DeepScope
UR - http://www.cs.stir.ac.uk/events/cibb2016/
ER -
Modern digital pathology departments have grown to produce whole-slide image data at petabyte scale, an unprecedented treasure chest for medical machine learning tasks. Unfortunately, most digital slides are not annotated at the image level, hindering large-scale application of supervised learning. Manual labeling is prohibitive, requiring pathologists with decades of training and outstanding clinical service responsibilities. This problem is further aggravated by the United States Food and Drug Administration's ruling that primary diagnosis must come from a glass slide rather than a digital image. We present the first end-to-end framework to overcome this problem, gathering annotations in a nonintrusive manner during a pathologist's routine clinical work: (i) microscope-specific 3D-printed commodity camera mounts are used to video record the glass-slide-based clinical diagnosis process; (ii) after routine scanning of the whole slide, the video frames are registered to the digital slide; (iii) motion and observing time are estimated to generate a spatial and temporal saliency map of the whole slide. Demonstrating the utility of these annotations, we train a convolutional neural network that detects diagnosis-relevant salient regions, then report accuracy of 85.15% in bladder and 91.50% in prostate, with 75.00% accuracy when training on prostate but predicting in bladder, despite different pathologists examining the different tissues.
Hassan Muhammad, Peter J. Schüffler, Judy Sarungbam, Satish K. Tickoo and Thomas Fuchs.
Classifying Renal Cell Carcinoma by Using Convolutional Neural Networks to Deconstruct Pathological Images.
Vincent du Vigneaud Memorial Research Symposium, 2016 
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@misc{muhammad_classifying_2016,
    address = {Weill Cornell Medicine},
    type = {Poster},
    title = {Classifying {Renal} {Cell} {Carcinoma} by {Using} {Convolutional} {Neural} {Networks} to {Deconstruct} {Pathological} {Images}.},
    author = {Muhammad, Hassan},
    collaborator = {Sch\"uffler, Peter J. and Sarungbam, Judy and Tickoo, Satish K. and Fuchs, Thomas},
    year = {2016},
}
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TY - SLIDE
TI - Classifying Renal Cell Carcinoma by Using Convolutional Neural Networks to Deconstruct Pathological Images.
T2 - Vincent du Vigneaud Memorial Research Symposium
A2 - Muhammad, Hassan
CY - Weill Cornell Medicine
DA - 2016///
PY - 2016
M3 - Poster
ER -
Jakob M. Burgstaller, Peter J. Schüffler, Joachim M. Buhmann, Gustav Andreisek, Sebastian Winklhofer, Filippo Del Grande, Michèle Mattle, Florian Brunner, Georgios Karakoumis, Johann Steurer and Ulrike Held.
Is There an Association Between Pain and Magnetic Resonance Imaging Parameters in Patients With Lumbar Spinal Stenosis?
Spine, vol. 41, 17, p. 1053-1062, 2016 doi: 10.1097/brs.0000000000001544
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@article{burgstaller_is_2016,
    title = {Is {There} an {Association} {Between} {Pain} and {Magnetic} {Resonance} {Imaging} {Parameters} in {Patients} {With} {Lumbar} {Spinal} {Stenosis}?},
    volume = {41},
    issn = {0362-2436},
    shorttitle = {Is {There} an {Association} {Between} {Pain} and {Magnetic} {Resonance} {Imaging} {Parameters} in {Patients} {With} {Lumbar} {Spinal} {Stenosis}?},
    url = {http://Insights.ovid.com/crossref?an=00007632-201609010-00015},
    doi = {10.1097/BRS.0000000000001544},
    abstract = {STUDY DESIGN:
    A prospective multicenter cohort study.
    OBJECTIVE:
    The aim of this study was to identify an association between pain and magnetic resonance imaging (MRI) parameters in patients with lumbar spinal stenosis (LSS).
    SUMMARY OF BACKGROUND DATA:
    At present, the relationship between abnormal MRI findings and pain in patients with LSS is still unclear.
    METHODS:
    First, we conducted a systematic literature search. We identified relationships of relevant MRI parameters and pain in patients with LSS. Second, we addressed the study question with a thorough descriptive and graphical analysis to establish a relationship between MRI parameters and pain using data of the LSS outcome study (LSOS).
    RESULTS:
    In the systematic review including four papers about the associations between radiological findings in the MRI and pain, the authors of two articles reported no association and two of them did. Of the latters, only one study found a moderate correlation between leg pain measured by Visual Analog Scale (VAS) and the degree of stenosis assessed by spine surgeons. In the data of the LSOS study, we could not identify a relevant association between any of the MRI parameters and buttock, leg, and back pain, quantified by the Spinal Stenosis Measure (SSM) and the Numeric Rating Scale (NRS). Even by restricting the analysis to the level of the lumbar spine with the most prominent radiological "stenosis," no relevant association could be shown.
    CONCLUSION:
    Despite a thorough analysis of the data, we were not able to prove any correlation between radiological findings (MRI) and the severity of pain. There is a need for innovative "methods/techniques" to learn more about the causal relationship between radiological findings and the patients' pain-related complaints.
    LEVEL OF EVIDENCE:
    2.},
    language = {en},
    number = {17},
    urldate = {2017-02-11},
    journal = {Spine},
    author = {Burgstaller, Jakob M. and Sch\"uffler, Peter J. and Buhmann, Joachim M. and Andreisek, Gustav and Winklhofer, Sebastian and Del Grande, Filippo and Mattle, Mich\`ele and Brunner, Florian and Karakoumis, Georgios and Steurer, Johann and Held, Ulrike},
    year = {2016},
    pages = {1053--1062},
}
Download Endnote/RIS citation
TY - JOUR
TI - Is There an Association Between Pain and Magnetic Resonance Imaging Parameters in Patients With Lumbar Spinal Stenosis?
AU - Burgstaller, Jakob M.
AU - Schüffler, Peter J.
AU - Buhmann, Joachim M.
AU - Andreisek, Gustav
AU - Winklhofer, Sebastian
AU - Del Grande, Filippo
AU - Mattle, Michèle
AU - Brunner, Florian
AU - Karakoumis, Georgios
AU - Steurer, Johann
AU - Held, Ulrike
T2 - Spine
AB - STUDY DESIGN:
A prospective multicenter cohort study.
OBJECTIVE:
The aim of this study was to identify an association between pain and magnetic resonance imaging (MRI) parameters in patients with lumbar spinal stenosis (LSS).
SUMMARY OF BACKGROUND DATA:
At present, the relationship between abnormal MRI findings and pain in patients with LSS is still unclear.
METHODS:
First, we conducted a systematic literature search. We identified relationships of relevant MRI parameters and pain in patients with LSS. Second, we addressed the study question with a thorough descriptive and graphical analysis to establish a relationship between MRI parameters and pain using data of the LSS outcome study (LSOS).
RESULTS:
In the systematic review including four papers about the associations between radiological findings in the MRI and pain, the authors of two articles reported no association and two of them did. Of the latters, only one study found a moderate correlation between leg pain measured by Visual Analog Scale (VAS) and the degree of stenosis assessed by spine surgeons. In the data of the LSOS study, we could not identify a relevant association between any of the MRI parameters and buttock, leg, and back pain, quantified by the Spinal Stenosis Measure (SSM) and the Numeric Rating Scale (NRS). Even by restricting the analysis to the level of the lumbar spine with the most prominent radiological "stenosis," no relevant association could be shown.
CONCLUSION:
Despite a thorough analysis of the data, we were not able to prove any correlation between radiological findings (MRI) and the severity of pain. There is a need for innovative "methods/techniques" to learn more about the causal relationship between radiological findings and the patients' pain-related complaints.
LEVEL OF EVIDENCE:
2.
DA - 2016///
PY - 2016
DO - 10.1097/BRS.0000000000001544
DP - CrossRef
VL - 41
IS - 17
SP - 1053
EP - 1062
LA - en
SN - 0362-2436
ST - Is There an Association Between Pain and Magnetic Resonance Imaging Parameters in Patients With Lumbar Spinal Stenosis?
UR - http://Insights.ovid.com/crossref?an=00007632-201609010-00015
Y2 - 2017/02/11/00:39:09
ER -
STUDY DESIGN: A prospective multicenter cohort study. OBJECTIVE: The aim of this study was to identify an association between pain and magnetic resonance imaging (MRI) parameters in patients with lumbar spinal stenosis (LSS). SUMMARY OF BACKGROUND DATA: At present, the relationship between abnormal MRI findings and pain in patients with LSS is still unclear. METHODS: First, we conducted a systematic literature search. We identified relationships of relevant MRI parameters and pain in patients with LSS. Second, we addressed the study question with a thorough descriptive and graphical analysis to establish a relationship between MRI parameters and pain using data of the LSS outcome study (LSOS). RESULTS: In the systematic review including four papers about the associations between radiological findings in the MRI and pain, the authors of two articles reported no association and two of them did. Of the latters, only one study found a moderate correlation between leg pain measured by Visual Analog Scale (VAS) and the degree of stenosis assessed by spine surgeons. In the data of the LSOS study, we could not identify a relevant association between any of the MRI parameters and buttock, leg, and back pain, quantified by the Spinal Stenosis Measure (SSM) and the Numeric Rating Scale (NRS). Even by restricting the analysis to the level of the lumbar spine with the most prominent radiological "stenosis," no relevant association could be shown. CONCLUSION: Despite a thorough analysis of the data, we were not able to prove any correlation between radiological findings (MRI) and the severity of pain. There is a need for innovative "methods/techniques" to learn more about the causal relationship between radiological findings and the patients' pain-related complaints. LEVEL OF EVIDENCE: 2.
Stefan Bauer, Nicolas Carion, Peter Schüffler, Thomas Fuchs, Peter Wild and Joachim M. Buhmann.
Multi-Organ Cancer Classification and Survival Analysis.
arXiv:1606.00897 [cs, q-bio, stat], 2016 
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@article{bauer_multi-organ_2016,
    title = {Multi-{Organ} {Cancer} {Classification} and {Survival} {Analysis}},
    url = {http://arxiv.org/abs/1606.00897},
    abstract = {Accurate and robust cell nuclei classification is the cornerstone for a wider range of tasks in digital and Computational Pathology. However, most machine learning systems require extensive labeling from expert pathologists for each individual problem at hand, with no or limited abilities for knowledge transfer between datasets and organ sites. In this paper we implement and evaluate a variety of deep neural network models and model ensembles for nuclei classification in renal cell cancer (RCC) and prostate cancer (PCa). We propose a convolutional neural network system based on residual learning which significantly improves over the state-of-the-art in cell nuclei classification. However, the main thrust of our work is to demonstrate for the first time the models ability to transfer the learned concepts from one organ type to another, with robust performance. Finally, we show that the combination of tissue types during training increases not only classification accuracy but also overall survival analysis. The best model, trained on combined data of RCC and PCa, exhibits optimal performance on PCa classification and better survival group stratification than an expert pathologist (\$p=0.006\$). All code, image data and expert labels are made publicly available to serve as benchmark for the community for future research into computational pathology.},
    urldate = {2016-06-16},
    journal = {arXiv:1606.00897 [cs, q-bio, stat]},
    author = {Bauer, Stefan and Carion, Nicolas and Sch\"uffler, Peter and Fuchs, Thomas and Wild, Peter and Buhmann, Joachim M.},
    year = {2016},
    keywords = {Computer Science - Learning, Quantitative Biology - Quantitative Methods, Quantitative Biology - Tissues and Organs, Statistics - Machine Learning},
}
Download Endnote/RIS citation
TY - JOUR
TI - Multi-Organ Cancer Classification and Survival Analysis
AU - Bauer, Stefan
AU - Carion, Nicolas
AU - Schüffler, Peter
AU - Fuchs, Thomas
AU - Wild, Peter
AU - Buhmann, Joachim M.
T2 - arXiv:1606.00897 [cs, q-bio, stat]
AB - Accurate and robust cell nuclei classification is the cornerstone for a wider range of tasks in digital and Computational Pathology. However, most machine learning systems require extensive labeling from expert pathologists for each individual problem at hand, with no or limited abilities for knowledge transfer between datasets and organ sites. In this paper we implement and evaluate a variety of deep neural network models and model ensembles for nuclei classification in renal cell cancer (RCC) and prostate cancer (PCa). We propose a convolutional neural network system based on residual learning which significantly improves over the state-of-the-art in cell nuclei classification. However, the main thrust of our work is to demonstrate for the first time the models ability to transfer the learned concepts from one organ type to another, with robust performance. Finally, we show that the combination of tissue types during training increases not only classification accuracy but also overall survival analysis. The best model, trained on combined data of RCC and PCa, exhibits optimal performance on PCa classification and better survival group stratification than an expert pathologist ($p=0.006$). All code, image data and expert labels are made publicly available to serve as benchmark for the community for future research into computational pathology.
DA - 2016///
PY - 2016
DP - arXiv.org
UR - http://arxiv.org/abs/1606.00897
Y2 - 2016/06/16/01:52:59
KW - Computer Science - Learning
KW - Quantitative Biology - Quantitative Methods
KW - Quantitative Biology - Tissues and Organs
KW - Statistics - Machine Learning
ER -
Accurate and robust cell nuclei classification is the cornerstone for a wider range of tasks in digital and Computational Pathology. However, most machine learning systems require extensive labeling from expert pathologists for each individual problem at hand, with no or limited abilities for knowledge transfer between datasets and organ sites. In this paper we implement and evaluate a variety of deep neural network models and model ensembles for nuclei classification in renal cell cancer (RCC) and prostate cancer (PCa). We propose a convolutional neural network system based on residual learning which significantly improves over the state-of-the-art in cell nuclei classification. However, the main thrust of our work is to demonstrate for the first time the models ability to transfer the learned concepts from one organ type to another, with robust performance. Finally, we show that the combination of tissue types during training increases not only classification accuracy but also overall survival analysis. The best model, trained on combined data of RCC and PCa, exhibits optimal performance on PCa classification and better survival group stratification than an expert pathologist ($p=0.006$). All code, image data and expert labels are made publicly available to serve as benchmark for the community for future research into computational pathology.
Peter J. Schüffler, Denis Schapiro, Charlotte Giesen, Hao A. O. Wang, Bernd Bodenmiller and Joachim M. Buhmann.
Automatic single cell segmentation on highly multiplexed tissue images.
Cytometry Part A, vol. 87, 10, p. 936-942, 10/2015 doi: 10.1002/cyto.a.22702
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@article{schuffler_automatic_2015,
    title = {Automatic single cell segmentation on highly multiplexed tissue images},
    volume = {87},
    issn = {15524922},
    shorttitle = {Automatic single cell segmentation on highly multiplexed tissue images},
    url = {http://doi.wiley.com/10.1002/cyto.a.22702},
    doi = {10.1002/cyto.a.22702},
    abstract = {The combination of mass cytometry and immunohistochemistry (IHC) enables new histopathological imaging methods in which dozens of proteins and protein modifications can be visualized simultaneously in a single tissue section. The power of multiplexing combined with spatial information and quantification was recently illustrated on breast cancer tissue and was described as next-generation IHC. Robust, accurate, and high-throughput cell segmentation is crucial for the analysis of this new generation of IHC data. To this end, we propose a watershed-based cell segmentation, which uses a nuclear marker and multiple membrane markers, the latter automatically selected based on their correlation. In comparison with the state-of-the-art segmentation pipelines, which are only using a single marker for object detection, we could show that the use of multiple markers can significantly increase the segmentation power, and thus, multiplexed information should be used and not ignored during the segmentation. Furthermore, we provide a novel, user-friendly open-source toolbox for the automatic segmentation of multiplexed histopathological images. (c) 2015 International Society for Advancement of Cytometry.},
    language = {en},
    number = {10},
    urldate = {2017-02-14},
    journal = {Cytometry Part A},
    author = {Sch\"uffler, Peter J. and Schapiro, Denis and Giesen, Charlotte and Wang, Hao A. O. and Bodenmiller, Bernd and Buhmann, Joachim M.},
    month = oct,
    year = {2015},
    pages = {936--942},
}
Download Endnote/RIS citation
TY - JOUR
TI - Automatic single cell segmentation on highly multiplexed tissue images
AU - Schüffler, Peter J.
AU - Schapiro, Denis
AU - Giesen, Charlotte
AU - Wang, Hao A. O.
AU - Bodenmiller, Bernd
AU - Buhmann, Joachim M.
T2 - Cytometry Part A
AB - The combination of mass cytometry and immunohistochemistry (IHC) enables new histopathological imaging methods in which dozens of proteins and protein modifications can be visualized simultaneously in a single tissue section. The power of multiplexing combined with spatial information and quantification was recently illustrated on breast cancer tissue and was described as next-generation IHC. Robust, accurate, and high-throughput cell segmentation is crucial for the analysis of this new generation of IHC data. To this end, we propose a watershed-based cell segmentation, which uses a nuclear marker and multiple membrane markers, the latter automatically selected based on their correlation. In comparison with the state-of-the-art segmentation pipelines, which are only using a single marker for object detection, we could show that the use of multiple markers can significantly increase the segmentation power, and thus, multiplexed information should be used and not ignored during the segmentation. Furthermore, we provide a novel, user-friendly open-source toolbox for the automatic segmentation of multiplexed histopathological images. (c) 2015 International Society for Advancement of Cytometry.
DA - 2015/10//
PY - 2015
DO - 10.1002/cyto.a.22702
DP - CrossRef
VL - 87
IS - 10
SP - 936
EP - 942
LA - en
SN - 15524922
ST - Automatic single cell segmentation on highly multiplexed tissue images
UR - http://doi.wiley.com/10.1002/cyto.a.22702
Y2 - 2017/02/14/19:42:24
ER -
The combination of mass cytometry and immunohistochemistry (IHC) enables new histopathological imaging methods in which dozens of proteins and protein modifications can be visualized simultaneously in a single tissue section. The power of multiplexing combined with spatial information and quantification was recently illustrated on breast cancer tissue and was described as next-generation IHC. Robust, accurate, and high-throughput cell segmentation is crucial for the analysis of this new generation of IHC data. To this end, we propose a watershed-based cell segmentation, which uses a nuclear marker and multiple membrane markers, the latter automatically selected based on their correlation. In comparison with the state-of-the-art segmentation pipelines, which are only using a single marker for object detection, we could show that the use of multiple markers can significantly increase the segmentation power, and thus, multiplexed information should be used and not ignored during the segmentation. Furthermore, we provide a novel, user-friendly open-source toolbox for the automatic segmentation of multiplexed histopathological images. (c) 2015 International Society for Advancement of Cytometry.
D. Mahapatra, P. J. Schüffler, F. M. Vos and J. M. Buhmann.
Crohn's Disease Segmentation from MRI Using Learned Image Priors.
Proceedings IEEE ISBI 2015, p. 625-628, 2015 
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@article{mahapatra_crohns_2015,
    title = {Crohn's {Disease} {Segmentation} from {MRI} {Using} {Learned} {Image} {Priors}},
    url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7163951},
    abstract = {We use a Field of Experts (FoE) model to segment abdominal regions from MRI affected with Crohns Disease (CD). FoE learns a prior model of diseased and normal bowel, and background non-bowel tissues from manually annotated training images. Unlike current approaches, FoE does not rely on hand designed features but learns the most discriminative features (in the form of filters) for different classes. FoE filter responses are integrated into a Random forest (RF) model that outputs probability maps for the test image and finally segments the diseased region. Experimental results show our method achieves significantly better performance than existing methods.},
    journal = {Proceedings IEEE ISBI 2015},
    author = {Mahapatra, D. and Sch\"uffler, P. J. and Vos, F. M. and Buhmann, J. M.},
    year = {2015},
    pages = {625--628},
}
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TY - JOUR
TI - Crohn's Disease Segmentation from MRI Using Learned Image Priors
AU - Mahapatra, D.
AU - Schüffler, P. J.
AU - Vos, F. M.
AU - Buhmann, J. M.
T2 - Proceedings IEEE ISBI 2015
AB - We use a Field of Experts (FoE) model to segment abdominal regions from MRI affected with Crohns Disease (CD). FoE learns a prior model of diseased and normal bowel, and background non-bowel tissues from manually annotated training images. Unlike current approaches, FoE does not rely on hand designed features but learns the most discriminative features (in the form of filters) for different classes. FoE filter responses are integrated into a Random forest (RF) model that outputs probability maps for the test image and finally segments the diseased region. Experimental results show our method achieves significantly better performance than existing methods.
DA - 2015///
PY - 2015
SP - 625
EP - 628
UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7163951
ER -
We use a Field of Experts (FoE) model to segment abdominal regions from MRI affected with Crohns Disease (CD). FoE learns a prior model of diseased and normal bowel, and background non-bowel tissues from manually annotated training images. Unlike current approaches, FoE does not rely on hand designed features but learns the most discriminative features (in the form of filters) for different classes. FoE filter responses are integrated into a Random forest (RF) model that outputs probability maps for the test image and finally segments the diseased region. Experimental results show our method achieves significantly better performance than existing methods.
Peter J. Schüffler, Dwarikanath Mahapatra, Franciscus M. Vos and Joachim M. Buhmann.
Computer Aided Crohn's Disease Severity Assessment in MRI.
VIGOR++ Workshop 2014 - Showcase of Research Outcomes and Future Outlook, 2014 
Best Poster Award
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@misc{schuffler_computer_2014,
    address = {London},
    type = {Poster},
    title = {Computer {Aided} {Crohn}'s {Disease} {Severity} {Assessment} in {MRI}},
    url = {https://www.researchgate.net/publication/262198303_Computer_Aided_Crohns_Disease_Severity_Assessment_in_MRI},
    author = {Sch\"uffler, Peter J. and Mahapatra, Dwarikanath and Vos, Franciscus M. and Buhmann, Joachim M.},
    year = {2014},
}
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TY - SLIDE
TI - Computer Aided Crohn's Disease Severity Assessment in MRI
T2 - VIGOR++ Workshop 2014 - Showcase of Research Outcomes and Future Outlook
A2 - Schüffler, Peter J.
A2 - Mahapatra, Dwarikanath
A2 - Vos, Franciscus M.
A2 - Buhmann, Joachim M.
CY - London
DA - 2014///
PY - 2014
M3 - Poster
UR - https://www.researchgate.net/publication/262198303_Computer_Aided_Crohns_Disease_Severity_Assessment_in_MRI
ER -
C. Giesen, H. A. Wang, D. Schapiro, N. Zivanovic, A. Jacobs, B. Hattendorf, P. J. Schüffler, D. Grolimund, J. M. Buhmann, S. Brandt, Z. Varga, P. J. Wild, D. Gunther and B. Bodenmiller.
Highly Multiplexed Imaging of Tumor Tissues with Subcellular Resolution by Mass Cytometry.
Nature methods, vol. 11, p. 417-22, 2014 doi: 10.1038/nmeth.2869
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@article{giesen_highly_2014,
    title = {Highly {Multiplexed} {Imaging} of {Tumor} {Tissues} with {Subcellular} {Resolution} by {Mass} {Cytometry}},
    volume = {11},
    issn = {1548-7105 (Electronic) 1548-7091 (Linking)},
    url = {http://www.nature.com/nmeth/journal/v11/n4/full/nmeth.2869.html},
    doi = {10.1038/nmeth.2869},
    abstract = {Mass cytometry enables high-dimensional, single-cell analysis of cell type and state. In mass cytometry, rare earth metals are used as reporters on antibodies. Analysis of metal abundances using the mass cytometer allows determination of marker expression in individual cells. Mass cytometry has previously been applied only to cell suspensions. To gain spatial information, we have coupled immunohistochemical and immunocytochemical methods with high-resolution laser ablation to CyTOF mass cytometry. This approach enables the simultaneous imaging of 32 proteins and protein modifications at subcellular resolution; with the availability of additional isotopes, measurement of over 100 markers will be possible. We applied imaging mass cytometry to human breast cancer samples, allowing delineation of cell subpopulations and cell-cell interactions and highlighting tumor heterogeneity. Imaging mass cytometry complements existing imaging approaches. It will enable basic studies of tissue heterogeneity and function and support the transition of medicine toward individualized molecularly targeted diagnosis and therapies.},
    journal = {Nat Methods},
    author = {Giesen, C. and Wang, H. A. and Schapiro, D. and Zivanovic, N. and Jacobs, A. and Hattendorf, B. and Sch\"uffler, P. J. and Grolimund, D. and Buhmann, J. M. and Brandt, S. and Varga, Z. and Wild, P. J. and Gunther, D. and Bodenmiller, B.},
    year = {2014},
    pages = {417--22},
}
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TY - JOUR
TI - Highly Multiplexed Imaging of Tumor Tissues with Subcellular Resolution by Mass Cytometry
AU - Giesen, C.
AU - Wang, H. A.
AU - Schapiro, D.
AU - Zivanovic, N.
AU - Jacobs, A.
AU - Hattendorf, B.
AU - Schüffler, P. J.
AU - Grolimund, D.
AU - Buhmann, J. M.
AU - Brandt, S.
AU - Varga, Z.
AU - Wild, P. J.
AU - Gunther, D.
AU - Bodenmiller, B.
T2 - Nat Methods
AB - Mass cytometry enables high-dimensional, single-cell analysis of cell type and state. In mass cytometry, rare earth metals are used as reporters on antibodies. Analysis of metal abundances using the mass cytometer allows determination of marker expression in individual cells. Mass cytometry has previously been applied only to cell suspensions. To gain spatial information, we have coupled immunohistochemical and immunocytochemical methods with high-resolution laser ablation to CyTOF mass cytometry. This approach enables the simultaneous imaging of 32 proteins and protein modifications at subcellular resolution; with the availability of additional isotopes, measurement of over 100 markers will be possible. We applied imaging mass cytometry to human breast cancer samples, allowing delineation of cell subpopulations and cell-cell interactions and highlighting tumor heterogeneity. Imaging mass cytometry complements existing imaging approaches. It will enable basic studies of tissue heterogeneity and function and support the transition of medicine toward individualized molecularly targeted diagnosis and therapies.
DA - 2014///
PY - 2014
DO - 10.1038/nmeth.2869
VL - 11
SP - 417
EP - 22
J2 - Nature methods
SN - 1548-7105 (Electronic) 1548-7091 (Linking)
UR - http://www.nature.com/nmeth/journal/v11/n4/full/nmeth.2869.html
ER -
Mass cytometry enables high-dimensional, single-cell analysis of cell type and state. In mass cytometry, rare earth metals are used as reporters on antibodies. Analysis of metal abundances using the mass cytometer allows determination of marker expression in individual cells. Mass cytometry has previously been applied only to cell suspensions. To gain spatial information, we have coupled immunohistochemical and immunocytochemical methods with high-resolution laser ablation to CyTOF mass cytometry. This approach enables the simultaneous imaging of 32 proteins and protein modifications at subcellular resolution; with the availability of additional isotopes, measurement of over 100 markers will be possible. We applied imaging mass cytometry to human breast cancer samples, allowing delineation of cell subpopulations and cell-cell interactions and highlighting tumor heterogeneity. Imaging mass cytometry complements existing imaging approaches. It will enable basic studies of tissue heterogeneity and function and support the transition of medicine toward individualized molecularly targeted diagnosis and therapies.
P. J. Schüffler, D. Mahapatra, R. E. Naziroglu, Z. Li, C. A. J. Puylaert, R. Andriantsimiavona, F. M. Vos, D. A. Pendsé, C. Yung Nio, J. Stoker, S. A. Taylor and J. M. Buhmann.
Semi-Automatic Crohn's Disease Severity Estimation on MR Imaging.
6th MICCAI Workshop on Abdominal Imaging - Computational and Clinical Applications, 2014 
PDF   URL   BibTeX   Endnote / RIS   Abstract
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@inproceedings{schuffler_semi-automatic_2014,
    title = {Semi-{Automatic} {Crohn}'s {Disease} {Severity} {Estimation} on {MR} {Imaging}},
    url = {http://link.springer.com/chapter/10.1007%2F978-3-319-13692-9_12},
    abstract = {Crohn’s disease (CD) is a chronic inflammatory bowel disease which can be visualized by magnetic resonance imaging (MRI). For CD grading, several non-invasive MRI based severity scores are known, most prominent the MaRIA and AIS. As these scores rely on manual MRI readings for individual bowel segments by trained radiologists, automated MRI assessment has been more and more focused in recent research. We show on a dataset of 27 CD patients that semi-automatically measured bowel wall thickness (ABWT) and dynamic contrast enhancement (DCE) completely outperform manual scorings: the segmental correlation to the Crohn’s Disease Endoscopic Index of Severity (CDEIS) of ABWT and DCE is significantly higher (r = .78) than that of MaRIA (r = .45) or AIS (r = .51). Also on a per-patient basis, the models with ABWT and DCE show significantly higher correlation (r = .69) to global CDEIS than MaRIA (r = .46).},
    author = {Sch\"uffler, P. J. and Mahapatra, D. and Naziroglu, R. E. and Li, Z. and Puylaert, C. A. J. and Andriantsimiavona, R. and Vos, F. M. and Pends\'e, D. A. and Nio, C. Yung and Stoker, J. and Taylor, S. A. and Buhmann, J. M.},
    year = {2014},
}
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TY - CONF
TI - Semi-Automatic Crohn's Disease Severity Estimation on MR Imaging
AU - Schüffler, P. J.
AU - Mahapatra, D.
AU - Naziroglu, R. E.
AU - Li, Z.
AU - Puylaert, C. A. J.
AU - Andriantsimiavona, R.
AU - Vos, F. M.
AU - Pendsé, D. A.
AU - Nio, C. Yung
AU - Stoker, J.
AU - Taylor, S. A.
AU - Buhmann, J. M.
T2 - 6th MICCAI Workshop on Abdominal Imaging - Computational and Clinical Applications
AB - Crohn’s disease (CD) is a chronic inflammatory bowel disease which can be visualized by magnetic resonance imaging (MRI). For CD grading, several non-invasive MRI based severity scores are known, most prominent the MaRIA and AIS. As these scores rely on manual MRI readings for individual bowel segments by trained radiologists, automated MRI assessment has been more and more focused in recent research. We show on a dataset of 27 CD patients that semi-automatically measured bowel wall thickness (ABWT) and dynamic contrast enhancement (DCE) completely outperform manual scorings: the segmental correlation to the Crohn’s Disease Endoscopic Index of Severity (CDEIS) of ABWT and DCE is significantly higher (r = .78) than that of MaRIA (r = .45) or AIS (r = .51). Also on a per-patient basis, the models with ABWT and DCE show significantly higher correlation (r = .69) to global CDEIS than MaRIA (r = .46).
DA - 2014///
PY - 2014
UR - http://link.springer.com/chapter/10.1007%2F978-3-319-13692-9_12
ER -
Crohn’s disease (CD) is a chronic inflammatory bowel disease which can be visualized by magnetic resonance imaging (MRI). For CD grading, several non-invasive MRI based severity scores are known, most prominent the MaRIA and AIS. As these scores rely on manual MRI readings for individual bowel segments by trained radiologists, automated MRI assessment has been more and more focused in recent research. We show on a dataset of 27 CD patients that semi-automatically measured bowel wall thickness (ABWT) and dynamic contrast enhancement (DCE) completely outperform manual scorings: the segmental correlation to the Crohn’s Disease Endoscopic Index of Severity (CDEIS) of ABWT and DCE is significantly higher (r = .78) than that of MaRIA (r = .45) or AIS (r = .51). Also on a per-patient basis, the models with ABWT and DCE show significantly higher correlation (r = .69) to global CDEIS than MaRIA (r = .46).
Peter J. Schüffler, Thomas J. Fuchs, Cheng S. Ong, Peter Wild and Joachim M. Buhmann.
TMARKER: A Free Software Toolkit for Histopathological Cell Counting and Staining Estimation.
Journal of Pathology Informatics, vol. 4, 2, p. 2, 2013 doi: 10.4103/2153-3539.109804
PDF   URL   BibTeX   Endnote / RIS   Abstract
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@article{schuffler_tmarker_2013,
    title = {{TMARKER}: {A} {Free} {Software} {Toolkit} for {Histopathological} {Cell} {Counting} and {Staining} {Estimation}},
    volume = {4},
    url = {https://www.sciencedirect.com/science/article/pii/S2153353922006629?via%3Dihub},
    doi = {10.4103/2153-3539.109804},
    abstract = {Background: Histological tissue analysis often involves manual cell counting and staining estimation of cancerous cells. These assessments are extremely time consuming, highly subjective and prone to error, since immunohistochemically stained cancer tissues usually show high variability in cell sizes, morphological structures and staining quality. To facilitate reproducible analysis in clinical practice as well as for cancer research, objective computer assisted staining estimation is highly desirable. Methods: We employ machine learning algorithms as randomized decision trees and support vector machines for nucleus detection and classification. Superpixels as segmentation over the tissue image are classified into foreground and background and thereafter into malignant and benign, learning from the user's feedback. As a fast alternative without nucleus classification, the existing color deconvolution method is incorporated. Results: Our program TMARKER connects already available workflows for computational pathology and immunohistochemical tissue rating with modern active learning algorithms from machine learning and computer vision. On a test dataset of human renal clear cell carcinoma and prostate carcinoma, the performance of the used algorithms is equivalent to two independent pathologists for nucleus detection and classification. Conclusion: We present a novel, free and operating system independent software package for computational cell counting and staining estimation, supporting IHC stained tissue analysis in clinic and for research. Proprietary toolboxes for similar tasks are expensive, bound to specific commercial hardware (e.g. a microscope) and mostly not quantitatively validated in terms of performance and reproducibility. We are confident that the presented software package will proof valuable for the scientific community and we anticipate a broader application domain due to the possibility to interactively learn models for new image types.},
    number = {2},
    journal = {Journal of Pathology Informatics},
    author = {Sch\"uffler, Peter J. and Fuchs, Thomas J. and Ong, Cheng S. and Wild, Peter and Buhmann, Joachim M.},
    year = {2013},
    pages = {2},
}
Download Endnote/RIS citation
TY - JOUR
TI - TMARKER: A Free Software Toolkit for Histopathological Cell Counting and Staining Estimation
AU - Schüffler, Peter J.
AU - Fuchs, Thomas J.
AU - Ong, Cheng S.
AU - Wild, Peter
AU - Buhmann, Joachim M.
T2 - Journal of Pathology Informatics
AB - Background: Histological tissue analysis often involves manual cell counting and staining estimation of cancerous cells. These assessments are extremely time consuming, highly subjective and prone to error, since immunohistochemically stained cancer tissues usually show high variability in cell sizes, morphological structures and staining quality. To facilitate reproducible analysis in clinical practice as well as for cancer research, objective computer assisted staining estimation is highly desirable. Methods: We employ machine learning algorithms as randomized decision trees and support vector machines for nucleus detection and classification. Superpixels as segmentation over the tissue image are classified into foreground and background and thereafter into malignant and benign, learning from the user's feedback. As a fast alternative without nucleus classification, the existing color deconvolution method is incorporated. Results: Our program TMARKER connects already available workflows for computational pathology and immunohistochemical tissue rating with modern active learning algorithms from machine learning and computer vision. On a test dataset of human renal clear cell carcinoma and prostate carcinoma, the performance of the used algorithms is equivalent to two independent pathologists for nucleus detection and classification. Conclusion: We present a novel, free and operating system independent software package for computational cell counting and staining estimation, supporting IHC stained tissue analysis in clinic and for research. Proprietary toolboxes for similar tasks are expensive, bound to specific commercial hardware (e.g. a microscope) and mostly not quantitatively validated in terms of performance and reproducibility. We are confident that the presented software package will proof valuable for the scientific community and we anticipate a broader application domain due to the possibility to interactively learn models for new image types.
DA - 2013///
PY - 2013
DO - 10.4103/2153-3539.109804
VL - 4
IS - 2
SP - 2
UR - https://www.sciencedirect.com/science/article/pii/S2153353922006629?via%3Dihub
ER -
Background: Histological tissue analysis often involves manual cell counting and staining estimation of cancerous cells. These assessments are extremely time consuming, highly subjective and prone to error, since immunohistochemically stained cancer tissues usually show high variability in cell sizes, morphological structures and staining quality. To facilitate reproducible analysis in clinical practice as well as for cancer research, objective computer assisted staining estimation is highly desirable. Methods: We employ machine learning algorithms as randomized decision trees and support vector machines for nucleus detection and classification. Superpixels as segmentation over the tissue image are classified into foreground and background and thereafter into malignant and benign, learning from the user's feedback. As a fast alternative without nucleus classification, the existing color deconvolution method is incorporated. Results: Our program TMARKER connects already available workflows for computational pathology and immunohistochemical tissue rating with modern active learning algorithms from machine learning and computer vision. On a test dataset of human renal clear cell carcinoma and prostate carcinoma, the performance of the used algorithms is equivalent to two independent pathologists for nucleus detection and classification. Conclusion: We present a novel, free and operating system independent software package for computational cell counting and staining estimation, supporting IHC stained tissue analysis in clinic and for research. Proprietary toolboxes for similar tasks are expensive, bound to specific commercial hardware (e.g. a microscope) and mostly not quantitatively validated in terms of performance and reproducibility. We are confident that the presented software package will proof valuable for the scientific community and we anticipate a broader application domain due to the possibility to interactively learn models for new image types.
Peter J. Schüffler, Dwarikanath Mahapatra, Jeroen A. W. Tielbeek, Franciscus M. Vos, Jesica Makanyanga, Doug A. Pendsé, C. Yung Nio, Jaap Stoker, Stuart A. Taylor and Joachim M. Buhmann.
A Model Development Pipeline for Crohn's Disease Severity Assessment from Magnetic Resonance Images.
In: Hiroyuki Yoshida, Simon Warfield and Michael Vannier (eds.) Abdominal Imaging. Computation and Clinical Applications, vol. 8198, p. 1-10, Lecture Notes in Computer Science, Springer Berlin Heidelberg, ISBN 978-3-642-41082-6, 2013 
Outstanding Paper Award
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@incollection{schuffler_model_2013,
    series = {Lecture {Notes} in {Computer} {Science}},
    title = {A {Model} {Development} {Pipeline} for {Crohn}'s {Disease} {Severity} {Assessment} from {Magnetic} {Resonance} {Images}},
    volume = {8198},
    isbn = {978-3-642-41082-6},
    url = {http://link.springer.com/chapter/10.1007%2F978-3-642-41083-3_1},
    abstract = {Crohn's Disease affects the intestinal tract of a patient and can have varying severity which influences treatment strategy. The clinical severity score CDEIS (Crohn’s Disease Endoscopic Index of severity) ranges from 0 to 44 and is measured by endoscopy. In this paper we investigate the potential of non-invasive magnetic resonance imaging to assess this severity, together with the underlying question which features are most relevant for this estimation task. We propose a new general and modular pipeline that uses machine learning techniques to quantify disease severity from MR images and show its value on Crohn’s Disease severity assessment on 30 patients scored by 4 medical experts. With the pipeline, we can obtain a magnetic resonance imaging score which outperforms two existing reference scores MaRIA and AIS.},
    booktitle = {Abdominal {Imaging}. {Computation} and {Clinical} {Applications}},
    publisher = {Springer Berlin Heidelberg},
    author = {Sch\"uffler, Peter J. and Mahapatra, Dwarikanath and Tielbeek, Jeroen A. W. and Vos, Franciscus M. and Makanyanga, Jesica and Pends\'e, Doug A. and Nio, C. Yung and Stoker, Jaap and Taylor, Stuart A. and Buhmann, Joachim M.},
    editor = {Yoshida, Hiroyuki and Warfield, Simon and Vannier, Michael},
    year = {2013},
    keywords = {AIS, CDEIS, Crohn’s Disease, MaRIA, abdominal MRI},
    pages = {1--10},
}
Download Endnote/RIS citation
TY - CHAP
TI - A Model Development Pipeline for Crohn's Disease Severity Assessment from Magnetic Resonance Images
AU - Schüffler, Peter J.
AU - Mahapatra, Dwarikanath
AU - Tielbeek, Jeroen A. W.
AU - Vos, Franciscus M.
AU - Makanyanga, Jesica
AU - Pendsé, Doug A.
AU - Nio, C. Yung
AU - Stoker, Jaap
AU - Taylor, Stuart A.
AU - Buhmann, Joachim M.
T2 - Abdominal Imaging. Computation and Clinical Applications
A2 - Yoshida, Hiroyuki
A2 - Warfield, Simon
A2 - Vannier, Michael
T3 - Lecture Notes in Computer Science
AB - Crohn's Disease affects the intestinal tract of a patient and can have varying severity which influences treatment strategy. The clinical severity score CDEIS (Crohn’s Disease Endoscopic Index of severity) ranges from 0 to 44 and is measured by endoscopy. In this paper we investigate the potential of non-invasive magnetic resonance imaging to assess this severity, together with the underlying question which features are most relevant for this estimation task. We propose a new general and modular pipeline that uses machine learning techniques to quantify disease severity from MR images and show its value on Crohn’s Disease severity assessment on 30 patients scored by 4 medical experts. With the pipeline, we can obtain a magnetic resonance imaging score which outperforms two existing reference scores MaRIA and AIS.
DA - 2013///
PY - 2013
VL - 8198
SP - 1
EP - 10
PB - Springer Berlin Heidelberg
SN - 978-3-642-41082-6
UR - http://link.springer.com/chapter/10.1007%2F978-3-642-41083-3_1
KW - AIS
KW - CDEIS
KW - Crohn’s Disease
KW - MaRIA
KW - abdominal MRI
ER -
Crohn's Disease affects the intestinal tract of a patient and can have varying severity which influences treatment strategy. The clinical severity score CDEIS (Crohn’s Disease Endoscopic Index of severity) ranges from 0 to 44 and is measured by endoscopy. In this paper we investigate the potential of non-invasive magnetic resonance imaging to assess this severity, together with the underlying question which features are most relevant for this estimation task. We propose a new general and modular pipeline that uses machine learning techniques to quantify disease severity from MR images and show its value on Crohn’s Disease severity assessment on 30 patients scored by 4 medical experts. With the pipeline, we can obtain a magnetic resonance imaging score which outperforms two existing reference scores MaRIA and AIS.
D. Mahapatra, P. J. Schüffler, J. A. W. Tielbeek, J. C. Makanyanga, J. Stoker, S. A. Taylor, F. M. Vos and J. M. Buhmann.
Automatic Detection and Segmentation of Crohn's Disease Tissues from Abdominal MRI.
IEEE Transactions on Medical Imaging, vol. 32, p. 2332-2347, 2013 doi: 10.1109/tmi.2013.2282124
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@article{mahapatra_automatic_2013,
    title = {Automatic {Detection} and {Segmentation} of {Crohn}'s {Disease} {Tissues} from {Abdominal} {MRI}},
    volume = {32},
    issn = {0278-0062},
    url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6600949},
    doi = {10.1109/TMI.2013.2282124},
    abstract = {We propose an information processing pipeline for segmenting parts of the bowel in abdominal magnetic resonance images that are affected with Crohn's disease. Given a magnetic resonance imaging test volume, it is first oversegmented into supervoxels and each supervoxel is analyzed to detect presence of Crohn's disease using random forest (RF) classifiers. The supervoxels identified as containing diseased tissues define the volume of interest (VOI). All voxels within the VOI are further investigated to segment the diseased region. Probability maps are generated for each voxel using a second set of RF classifiers which give the probabilities of each voxel being diseased, normal or background. The negative log-likelihood of these maps are used as penalty costs in a graph cut segmentation framework. Low level features like intensity statistics, texture anisotropy and curvature asymmetry, and high level context features are used at different stages. Smoothness constraints are imposed based on semantic information (importance of each feature to the classification task) derived from the second set of learned RF classifiers. Experimental results show that our method achieves high segmentation accuracy with Dice metric values of 0.90 ± 0.04 and Hausdorff distance of 7.3 ± 0.8 mm. Semantic information and context features are an integral part of our method and are robust to different levels of added noise.},
    journal = {IEEE Transactions on Medical Imaging},
    author = {Mahapatra, D. and Sch\"uffler, P. J. and Tielbeek, J. A. W. and Makanyanga, J. C. and Stoker, J. and Taylor, S. A. and Vos, F. M. and Buhmann, J. M.},
    year = {2013},
    keywords = {Anisotropic magnetoresistance, Context, Crohn\&\#x2019, Diseases, Entropy, Image segmentation, Radio frequency, content, graph cut, image features, probability maps, random forests, s disease, segmentation, semantic information, shape, supervoxels},
    pages = {2332--2347},
}
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TY - JOUR
TI - Automatic Detection and Segmentation of Crohn's Disease Tissues from Abdominal MRI
AU - Mahapatra, D.
AU - Schüffler, P. J.
AU - Tielbeek, J. A. W.
AU - Makanyanga, J. C.
AU - Stoker, J.
AU - Taylor, S. A.
AU - Vos, F. M.
AU - Buhmann, J. M.
T2 - IEEE Transactions on Medical Imaging
AB - We propose an information processing pipeline for segmenting parts of the bowel in abdominal magnetic resonance images that are affected with Crohn's disease. Given a magnetic resonance imaging test volume, it is first oversegmented into supervoxels and each supervoxel is analyzed to detect presence of Crohn's disease using random forest (RF) classifiers. The supervoxels identified as containing diseased tissues define the volume of interest (VOI). All voxels within the VOI are further investigated to segment the diseased region. Probability maps are generated for each voxel using a second set of RF classifiers which give the probabilities of each voxel being diseased, normal or background. The negative log-likelihood of these maps are used as penalty costs in a graph cut segmentation framework. Low level features like intensity statistics, texture anisotropy and curvature asymmetry, and high level context features are used at different stages. Smoothness constraints are imposed based on semantic information (importance of each feature to the classification task) derived from the second set of learned RF classifiers. Experimental results show that our method achieves high segmentation accuracy with Dice metric values of 0.90 ± 0.04 and Hausdorff distance of 7.3 ± 0.8 mm. Semantic information and context features are an integral part of our method and are robust to different levels of added noise.
DA - 2013///
PY - 2013
DO - 10.1109/TMI.2013.2282124
VL - 32
SP - 2332
EP - 2347
SN - 0278-0062
UR - http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6600949
KW - Anisotropic magnetoresistance
KW - Context
KW - Crohn’
KW - Diseases
KW - Entropy
KW - Image segmentation
KW - Radio frequency
KW - content
KW - graph cut
KW - image features
KW - probability maps
KW - random forests
KW - s disease
KW - segmentation
KW - semantic information
KW - shape
KW - supervoxels
ER -
We propose an information processing pipeline for segmenting parts of the bowel in abdominal magnetic resonance images that are affected with Crohn's disease. Given a magnetic resonance imaging test volume, it is first oversegmented into supervoxels and each supervoxel is analyzed to detect presence of Crohn's disease using random forest (RF) classifiers. The supervoxels identified as containing diseased tissues define the volume of interest (VOI). All voxels within the VOI are further investigated to segment the diseased region. Probability maps are generated for each voxel using a second set of RF classifiers which give the probabilities of each voxel being diseased, normal or background. The negative log-likelihood of these maps are used as penalty costs in a graph cut segmentation framework. Low level features like intensity statistics, texture anisotropy and curvature asymmetry, and high level context features are used at different stages. Smoothness constraints are imposed based on semantic information (importance of each feature to the classification task) derived from the second set of learned RF classifiers. Experimental results show that our method achieves high segmentation accuracy with Dice metric values of 0.90 ± 0.04 and Hausdorff distance of 7.3 ± 0.8 mm. Semantic information and context features are an integral part of our method and are robust to different levels of added noise.
Peter J. Schueffler, Niels Rupp, Cheng S. Ong, Joachim M. Buhmann, Thomas J. Fuchs and Peter J. Wild.
TMARKER: A Robust and Free Software Toolkit for Histopathological Cell Counting and Immunohistochemical Staining Estimations.
German Society of Pathology 97th Annual Meeting, 2013 
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@inproceedings{schueffler_tmarker_2013,
    title = {{TMARKER}: {A} {Robust} and {Free} {Software} {Toolkit} for {Histopathological} {Cell} {Counting} and {Immunohistochemical} {Staining} {Estimations}},
    url = {http://link.springer.com/article/10.1007%2Fs00292-013-1765-2},
    abstract = {Aims. Assessment of immunhistochemical staining intensity and percentage of positive cells generally suffers from high inter- and intra-observer variability. Besides, estimation of the intensity and percentage of immunostained cells often involves manual cell counting and is therefore time consuming.
    Methods. A novel, free, and open-source software toolkit was developed, connecting already available work flows for computational pathology and immunohistochemical tissue assessment with modern active learning algorithms from machine learning and computer vision.
    Results. A new and platform independent software program, called TMARKER, was developed and introduced. The validity and robustness of the used algorithms has been shown on a test dataset of human renal clear cell carcinomas and prostate carcinomas.
    Conclusions. This new software program together with a user-friendly Java-based graphical user interface enabled comprehensive computational assistance in pathological tissue rating. Routine use of this software toolkit may provide more reliable and robust biomarkers for treatment decision.},
    booktitle = {German {Society} of {Pathology} 97th {Annual} {Meeting}},
    author = {Schueffler, Peter J. and Rupp, Niels and Ong, Cheng S. and Buhmann, Joachim M. and Fuchs, Thomas J. and Wild, Peter J.},
    year = {2013},
}
Download Endnote/RIS citation
TY - CONF
TI - TMARKER: A Robust and Free Software Toolkit for Histopathological Cell Counting and Immunohistochemical Staining Estimations
AU - Schueffler, Peter J.
AU - Rupp, Niels
AU - Ong, Cheng S.
AU - Buhmann, Joachim M.
AU - Fuchs, Thomas J.
AU - Wild, Peter J.
AB - Aims. Assessment of immunhistochemical staining intensity and percentage of positive cells generally suffers from high inter- and intra-observer variability. Besides, estimation of the intensity and percentage of immunostained cells often involves manual cell counting and is therefore time consuming.
Methods. A novel, free, and open-source software toolkit was developed, connecting already available work flows for computational pathology and immunohistochemical tissue assessment with modern active learning algorithms from machine learning and computer vision.
Results. A new and platform independent software program, called TMARKER, was developed and introduced. The validity and robustness of the used algorithms has been shown on a test dataset of human renal clear cell carcinomas and prostate carcinomas.
Conclusions. This new software program together with a user-friendly Java-based graphical user interface enabled comprehensive computational assistance in pathological tissue rating. Routine use of this software toolkit may provide more reliable and robust biomarkers for treatment decision.
C3 - German Society of Pathology 97th Annual Meeting
DA - 2013///
PY - 2013
UR - http://link.springer.com/article/10.1007%2Fs00292-013-1765-2
ER -
Aims. Assessment of immunhistochemical staining intensity and percentage of positive cells generally suffers from high inter- and intra-observer variability. Besides, estimation of the intensity and percentage of immunostained cells often involves manual cell counting and is therefore time consuming. Methods. A novel, free, and open-source software toolkit was developed, connecting already available work flows for computational pathology and immunohistochemical tissue assessment with modern active learning algorithms from machine learning and computer vision. Results. A new and platform independent software program, called TMARKER, was developed and introduced. The validity and robustness of the used algorithms has been shown on a test dataset of human renal clear cell carcinomas and prostate carcinomas. Conclusions. This new software program together with a user-friendly Java-based graphical user interface enabled comprehensive computational assistance in pathological tissue rating. Routine use of this software toolkit may provide more reliable and robust biomarkers for treatment decision.
Peter J. Schueffler, Thomas J. Fuchs, Cheng Soon Ong, Volker Roth and Joachim M. Buhmann.
Automated Analysis of Tissue Micro-Array Images on the Example of Renal Cell Carcinoma.
In: Similarity-Based Pattern Analysis and Recognition, p. 219–246, Advances in Computer Vision and Pattern Recognition, Springer, ISBN 978-1-4471-5627-7, 2013 
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@incollection{schueffler_automated_2013,
    address = {London},
    series = {Advances in {Computer} {Vision} and {Pattern} {Recognition}},
    title = {Automated {Analysis} of {Tissue} {Micro}-{Array} {Images} on the {Example} of {Renal} {Cell} {Carcinoma}},
    isbn = {978-1-4471-5627-7},
    url = {https://www.springer.com/computer/image+processing/book/978-1-4471-5627-7},
    abstract = {Automated tissue micro-array analysis forms a challenging problem in computational pathology. The detection of cell nuclei, the classification into malignant and benign as well as the evaluation of their protein expression pattern by immunohistochemical staining are crucial routine steps for human cancer research and oncology. Computational assistance in this field can extremely accelerate the high throughput of the upcoming patient data as well as facilitate the reproducibility and objectivity of qualitative and quantitative measures. In this chapter, we describe an automated pipeline for staining estimation of tissue micro-array images, which comprises nucleus detection, nucleus segmentation, nucleus classification and staining estimation among cancerous nuclei. This pipeline is a practical example for the importance of non-metric effects in this kind of image analysis, e.g., the use of shape information and non-Euclidean kernels improve the nucleus classification performance significantly. The pipeline is explained and validated on a renal clear cell carcinoma dataset with MIB-1 stained tissue micro-array images and survival data of 133 patients. Further, the pipeline is implemented for medical use and research purpose in the free program TMARKER.},
    booktitle = {Similarity-{Based} {Pattern} {Analysis} and {Recognition}},
    publisher = {Springer},
    author = {Schueffler, Peter J. and Fuchs, Thomas J. and Ong, Cheng Soon and Roth, Volker and Buhmann, Joachim M.},
    year = {2013},
    pages = {219--246},
}
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TY - CHAP
TI - Automated Analysis of Tissue Micro-Array Images on the Example of Renal Cell Carcinoma
AU - Schueffler, Peter J.
AU - Fuchs, Thomas J.
AU - Ong, Cheng Soon
AU - Roth, Volker
AU - Buhmann, Joachim M.
T2 - Similarity-Based Pattern Analysis and Recognition
T3 - Advances in Computer Vision and Pattern Recognition
AB - Automated tissue micro-array analysis forms a challenging problem in computational pathology. The detection of cell nuclei, the classification into malignant and benign as well as the evaluation of their protein expression pattern by immunohistochemical staining are crucial routine steps for human cancer research and oncology. Computational assistance in this field can extremely accelerate the high throughput of the upcoming patient data as well as facilitate the reproducibility and objectivity of qualitative and quantitative measures. In this chapter, we describe an automated pipeline for staining estimation of tissue micro-array images, which comprises nucleus detection, nucleus segmentation, nucleus classification and staining estimation among cancerous nuclei. This pipeline is a practical example for the importance of non-metric effects in this kind of image analysis, e.g., the use of shape information and non-Euclidean kernels improve the nucleus classification performance significantly. The pipeline is explained and validated on a renal clear cell carcinoma dataset with MIB-1 stained tissue micro-array images and survival data of 133 patients. Further, the pipeline is implemented for medical use and research purpose in the free program TMARKER.
CY - London
DA - 2013///
PY - 2013
SP - 219
EP - 246
PB - Springer
SN - 978-1-4471-5627-7
UR - https://www.springer.com/computer/image+processing/book/978-1-4471-5627-7
ER -
Automated tissue micro-array analysis forms a challenging problem in computational pathology. The detection of cell nuclei, the classification into malignant and benign as well as the evaluation of their protein expression pattern by immunohistochemical staining are crucial routine steps for human cancer research and oncology. Computational assistance in this field can extremely accelerate the high throughput of the upcoming patient data as well as facilitate the reproducibility and objectivity of qualitative and quantitative measures. In this chapter, we describe an automated pipeline for staining estimation of tissue micro-array images, which comprises nucleus detection, nucleus segmentation, nucleus classification and staining estimation among cancerous nuclei. This pipeline is a practical example for the importance of non-metric effects in this kind of image analysis, e.g., the use of shape information and non-Euclidean kernels improve the nucleus classification performance significantly. The pipeline is explained and validated on a renal clear cell carcinoma dataset with MIB-1 stained tissue micro-array images and survival data of 133 patients. Further, the pipeline is implemented for medical use and research purpose in the free program TMARKER.
Niels J. Rupp, Igor Cima, Ralph Schiess, Peter J. Schüffler, Thomas J. Fuchs, Niklaus Frankhauser, Martin Kälin, Silke Gillessen, Ruedi Aebersold, Wilhelm Krek, Mark A. Rubin, Holger Moch and Peter J. Wild.
Serum and Prostate Cancer Tissue Signatures of ERG Rearrangement Derived from Quantitative Analysis of the PTEN Conditional Knockout Mouse Proteome.
Symposium of the German Society for Pathology, 2012 
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@inproceedings{rupp_serum_2012,
    title = {Serum and {Prostate} {Cancer} {Tissue} {Signatures} of {ERG} {Rearrangement} {Derived} from {Quantitative} {Analysis} of the {PTEN} {Conditional} {Knockout} {Mouse} {Proteome}},
    abstract = {Aims: Applying a systems biology approach to assess serum and tissue signatures of ERG rearrangement in patients with prostate cancer with the goal of determining downstream molecular targets for gene fusion cancers.
    Methods: Prostate tissue from a conditional PTEN knockout mouse model of prostate cancer was investigated, using selective enrichment of N-glycopeptides and mass spectrometry-based label-free quantification. Mouse tissue signatures were validated in sera and tissue of mice (n=12) and humans (n=105) by selected reaction monitoring (SRM), ELISA, and immunohistochemistry. ERG rearrangement status was assessed using fluorescence in situ hybridization (FISH) on two independent tissue microarray based prostatectomy cohorts (n=41, n=348). A Random forest model was trained and validated to identify serum and tissue signatures of ERG rearrangement.
    Results: A comprehensive PTEN dependent protein catalogue representing over 700 glycoproteins was established. TMPRSS2-ERG gene fusions occured in 41\% (17/41) of analyzable prostate cancers of the training cohort. ERG dependent serum signatures could be found. Serum signatures were tested on prostate cancer tissue microarrays using immunohistochemistry (test cohort).
    Conclusions: This is the first study to demonstrate a proteomic signature of ERG rearrangement prostate cancer. Given the challenges of directly targeting ETS transcription factors, this study has potential clinical implications providing important insights into future targetable downstream pathways.},
    booktitle = {Symposium of the {German} {Society} for {Pathology}},
    author = {Rupp, Niels J. and Cima, Igor and Schiess, Ralph and Sch\"uffler, Peter J. and Fuchs, Thomas J. and Frankhauser, Niklaus and K\"alin, Martin and Gillessen, Silke and Aebersold, Ruedi and Krek, Wilhelm and Rubin, Mark A. and Moch, Holger and Wild, Peter J.},
    year = {2012},
}
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TY - CONF
TI - Serum and Prostate Cancer Tissue Signatures of ERG Rearrangement Derived from Quantitative Analysis of the PTEN Conditional Knockout Mouse Proteome
AU - Rupp, Niels J.
AU - Cima, Igor
AU - Schiess, Ralph
AU - Schüffler, Peter J.
AU - Fuchs, Thomas J.
AU - Frankhauser, Niklaus
AU - Kälin, Martin
AU - Gillessen, Silke
AU - Aebersold, Ruedi
AU - Krek, Wilhelm
AU - Rubin, Mark A.
AU - Moch, Holger
AU - Wild, Peter J.
AB - Aims: Applying a systems biology approach to assess serum and tissue signatures of ERG rearrangement in patients with prostate cancer with the goal of determining downstream molecular targets for gene fusion cancers.
Methods: Prostate tissue from a conditional PTEN knockout mouse model of prostate cancer was investigated, using selective enrichment of N-glycopeptides and mass spectrometry-based label-free quantification. Mouse tissue signatures were validated in sera and tissue of mice (n=12) and humans (n=105) by selected reaction monitoring (SRM), ELISA, and immunohistochemistry. ERG rearrangement status was assessed using fluorescence in situ hybridization (FISH) on two independent tissue microarray based prostatectomy cohorts (n=41, n=348). A Random forest model was trained and validated to identify serum and tissue signatures of ERG rearrangement.
Results: A comprehensive PTEN dependent protein catalogue representing over 700 glycoproteins was established. TMPRSS2-ERG gene fusions occured in 41% (17/41) of analyzable prostate cancers of the training cohort. ERG dependent serum signatures could be found. Serum signatures were tested on prostate cancer tissue microarrays using immunohistochemistry (test cohort).
Conclusions: This is the first study to demonstrate a proteomic signature of ERG rearrangement prostate cancer. Given the challenges of directly targeting ETS transcription factors, this study has potential clinical implications providing important insights into future targetable downstream pathways.
C3 - Symposium of the German Society for Pathology
DA - 2012///
PY - 2012
ER -
Aims: Applying a systems biology approach to assess serum and tissue signatures of ERG rearrangement in patients with prostate cancer with the goal of determining downstream molecular targets for gene fusion cancers. Methods: Prostate tissue from a conditional PTEN knockout mouse model of prostate cancer was investigated, using selective enrichment of N-glycopeptides and mass spectrometry-based label-free quantification. Mouse tissue signatures were validated in sera and tissue of mice (n=12) and humans (n=105) by selected reaction monitoring (SRM), ELISA, and immunohistochemistry. ERG rearrangement status was assessed using fluorescence in situ hybridization (FISH) on two independent tissue microarray based prostatectomy cohorts (n=41, n=348). A Random forest model was trained and validated to identify serum and tissue signatures of ERG rearrangement. Results: A comprehensive PTEN dependent protein catalogue representing over 700 glycoproteins was established. TMPRSS2-ERG gene fusions occured in 41% (17/41) of analyzable prostate cancers of the training cohort. ERG dependent serum signatures could be found. Serum signatures were tested on prostate cancer tissue microarrays using immunohistochemistry (test cohort). Conclusions: This is the first study to demonstrate a proteomic signature of ERG rearrangement prostate cancer. Given the challenges of directly targeting ETS transcription factors, this study has potential clinical implications providing important insights into future targetable downstream pathways.
Peter J. Schueffler, Thomas J. Fuchs, Cheng S. Ong, Peter Wild and Joachim M. Buhmann.
TMARKER: A User-Friendly Open-Source Assistance for Tma Grading and Cell Counting.
Histopathology Image Analysis (HIMA) Workshop at the 15th International Conference on Medical Image Computing and Computer Assisted Intervention MICCAI, 2012 
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@inproceedings{schueffler_tmarker_2012,
    title = {{TMARKER}: {A} {User}-{Friendly} {Open}-{Source} {Assistance} for {Tma} {Grading} and {Cell} {Counting}},
    booktitle = {Histopathology {Image} {Analysis} ({HIMA}) {Workshop} at the 15th {International} {Conference} on {Medical} {Image} {Computing} and {Computer} {Assisted} {Intervention} {MICCAI}},
    author = {Schueffler, Peter J. and Fuchs, Thomas J. and Ong, Cheng S. and Wild, Peter and Buhmann, Joachim M.},
    year = {2012},
}
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TY - CONF
TI - TMARKER: A User-Friendly Open-Source Assistance for Tma Grading and Cell Counting
AU - Schueffler, Peter J.
AU - Fuchs, Thomas J.
AU - Ong, Cheng S.
AU - Wild, Peter
AU - Buhmann, Joachim M.
C3 - Histopathology Image Analysis (HIMA) Workshop at the 15th International Conference on Medical Image Computing and Computer Assisted Intervention MICCAI
DA - 2012///
PY - 2012
ER -
Igor Cima, Ralph Schiess, Peter Wild, Martin Kaelin, Peter Schueffler, Vinzenz Lange, Paola Picotti, Reto Ossola, Arnoud Templeton, Olga Schubert, Thomas J. Fuchs, Thomas Leippold, Stephen Wyler, Jens Zehetner, Wolfram Jochum, Joachim Buhmann, Thomas Cerny, Holger Moch, Silke Gillessen, Ruedi Aebersold and Wilhelm Krek.
Cancer Genetics-Guided Discovery of Serum Biomarker Signatures for Diagnosis and Prognosis of Prostate Cancer.
PNAS: Proceedings of the National Academy of Sciences, vol. 108, 8, p. 3342-3347, 2011 doi: 10.1073/pnas.1013699108
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@article{cima_cancer_2011,
    title = {Cancer {Genetics}-{Guided} {Discovery} of {Serum} {Biomarker} {Signatures} for {Diagnosis} and {Prognosis} of {Prostate} {Cancer}},
    volume = {108},
    url = {http://www.pnas.org/content/108/8/3342.abstract},
    doi = {10.1073/pnas.1013699108},
    abstract = {A key barrier to the realization of personalized medicine for cancer is the identification of biomarkers. Here we describe a two-stage strategy for the discovery of serum biomarker signatures corresponding to specific cancer-causing mutations and its application to prostate cancer (PCa) in the context of the commonly occurring phosphatase and tensin homolog (PTEN) tumor-suppressor gene inactivation. In the first stage of our approach, we identified 775 N-linked glycoproteins from sera and prostate tissue of wild-type and Pten-null mice. Using label-free quantitative proteomics, we showed that Pten inactivation leads to measurable perturbations in the murine prostate and serum glycoproteome. Following bioinformatic prioritization, in a second stage we applied targeted proteomics to detect and quantify 39 human ortholog candidate biomarkers in the sera of PCa patients and control individuals. The resulting proteomic profiles were analyzed by machine learning to build predictive regression models for tissue PTEN status and diagnosis and grading of PCa. Our approach suggests a general path to rational cancer biomarker discovery and initial validation guided by cancer genetics and based on the integration of experimental mouse models, proteomics-based technologies, and computational modeling.},
    number = {8},
    journal = {PNAS: Proceedings of the National Academy of Sciences},
    author = {Cima, Igor and Schiess, Ralph and Wild, Peter and Kaelin, Martin and Schueffler, Peter and Lange, Vinzenz and Picotti, Paola and Ossola, Reto and Templeton, Arnoud and Schubert, Olga and Fuchs, Thomas J. and Leippold, Thomas and Wyler, Stephen and Zehetner, Jens and Jochum, Wolfram and Buhmann, Joachim and Cerny, Thomas and Moch, Holger and Gillessen, Silke and Aebersold, Ruedi and Krek, Wilhelm},
    year = {2011},
    pages = {3342--3347},
}
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TY - JOUR
TI - Cancer Genetics-Guided Discovery of Serum Biomarker Signatures for Diagnosis and Prognosis of Prostate Cancer
AU - Cima, Igor
AU - Schiess, Ralph
AU - Wild, Peter
AU - Kaelin, Martin
AU - Schueffler, Peter
AU - Lange, Vinzenz
AU - Picotti, Paola
AU - Ossola, Reto
AU - Templeton, Arnoud
AU - Schubert, Olga
AU - Fuchs, Thomas J.
AU - Leippold, Thomas
AU - Wyler, Stephen
AU - Zehetner, Jens
AU - Jochum, Wolfram
AU - Buhmann, Joachim
AU - Cerny, Thomas
AU - Moch, Holger
AU - Gillessen, Silke
AU - Aebersold, Ruedi
AU - Krek, Wilhelm
T2 - PNAS: Proceedings of the National Academy of Sciences
AB - A key barrier to the realization of personalized medicine for cancer is the identification of biomarkers. Here we describe a two-stage strategy for the discovery of serum biomarker signatures corresponding to specific cancer-causing mutations and its application to prostate cancer (PCa) in the context of the commonly occurring phosphatase and tensin homolog (PTEN) tumor-suppressor gene inactivation. In the first stage of our approach, we identified 775 N-linked glycoproteins from sera and prostate tissue of wild-type and Pten-null mice. Using label-free quantitative proteomics, we showed that Pten inactivation leads to measurable perturbations in the murine prostate and serum glycoproteome. Following bioinformatic prioritization, in a second stage we applied targeted proteomics to detect and quantify 39 human ortholog candidate biomarkers in the sera of PCa patients and control individuals. The resulting proteomic profiles were analyzed by machine learning to build predictive regression models for tissue PTEN status and diagnosis and grading of PCa. Our approach suggests a general path to rational cancer biomarker discovery and initial validation guided by cancer genetics and based on the integration of experimental mouse models, proteomics-based technologies, and computational modeling.
DA - 2011///
PY - 2011
DO - 10.1073/pnas.1013699108
VL - 108
IS - 8
SP - 3342
EP - 3347
UR - http://www.pnas.org/content/108/8/3342.abstract
ER -
A key barrier to the realization of personalized medicine for cancer is the identification of biomarkers. Here we describe a two-stage strategy for the discovery of serum biomarker signatures corresponding to specific cancer-causing mutations and its application to prostate cancer (PCa) in the context of the commonly occurring phosphatase and tensin homolog (PTEN) tumor-suppressor gene inactivation. In the first stage of our approach, we identified 775 N-linked glycoproteins from sera and prostate tissue of wild-type and Pten-null mice. Using label-free quantitative proteomics, we showed that Pten inactivation leads to measurable perturbations in the murine prostate and serum glycoproteome. Following bioinformatic prioritization, in a second stage we applied targeted proteomics to detect and quantify 39 human ortholog candidate biomarkers in the sera of PCa patients and control individuals. The resulting proteomic profiles were analyzed by machine learning to build predictive regression models for tissue PTEN status and diagnosis and grading of PCa. Our approach suggests a general path to rational cancer biomarker discovery and initial validation guided by cancer genetics and based on the integration of experimental mouse models, proteomics-based technologies, and computational modeling.
Peter J. Schueffler, Thomas J. Fuchs, Cheng Soon Ong, Volker Roth and Joachim M. Buhmann.
Computational TMA Analysis and Cell Nucleus Classification of Renal Cell Carcinoma.
Proceedings of the 32nd DAGM conference on Pattern recognition, p. 202–211, Springer-Verlag, Berlin, Heidelberg, ISBN 3-642-15985-0 978-3-642-15985-5, 2010 doi: 10.1007/978-3-642-15986-2_21
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@inproceedings{schueffler_computational_2010,
    address = {Darmstadt, Germany},
    title = {Computational {TMA} {Analysis} and {Cell} {Nucleus} {Classification} of {Renal} {Cell} {Carcinoma}},
    isbn = {3-642-15985-0 978-3-642-15985-5},
    url = {http://portal.acm.org/citation.cfm?id=1926258.1926281},
    doi = {10.1007/978-3-642-15986-2_21},
    abstract = {We consider an automated processing pipeline for tissue micro array analysis (TMA) of renal cell carcinoma. It consists of several consecutive tasks, which can be mapped to machine learning challenges. We investigate three of these tasks, namely nuclei segmentation, nuclei classification and staining estimation. We argue for a holistic view of the processing pipeline, as it is not obvious whether performance improvements at individual steps improve overall accuracy. The experimental results show that classification accuracy, which is comparable to trained human experts, can be achieved by using support vector machines (SVM) with appropriate kernels. Furthermore, we provide evidence that the shape of cell nuclei increases the classification performance. Most importantly, these improvements in classification accuracy result in corresponding improvements for the medically relevant estimation of immunohistochemical staining.},
    booktitle = {Proceedings of the 32nd {DAGM} conference on {Pattern} recognition},
    publisher = {Springer-Verlag, Berlin, Heidelberg},
    author = {Schueffler, Peter J. and Fuchs, Thomas J. and Ong, Cheng Soon and Roth, Volker and Buhmann, Joachim M.},
    year = {2010},
    pages = {202--211},
}
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TY - CONF
TI - Computational TMA Analysis and Cell Nucleus Classification of Renal Cell Carcinoma
AU - Schueffler, Peter J.
AU - Fuchs, Thomas J.
AU - Ong, Cheng Soon
AU - Roth, Volker
AU - Buhmann, Joachim M.
AB - We consider an automated processing pipeline for tissue micro array analysis (TMA) of renal cell carcinoma. It consists of several consecutive tasks, which can be mapped to machine learning challenges. We investigate three of these tasks, namely nuclei segmentation, nuclei classification and staining estimation. We argue for a holistic view of the processing pipeline, as it is not obvious whether performance improvements at individual steps improve overall accuracy. The experimental results show that classification accuracy, which is comparable to trained human experts, can be achieved by using support vector machines (SVM) with appropriate kernels. Furthermore, we provide evidence that the shape of cell nuclei increases the classification performance. Most importantly, these improvements in classification accuracy result in corresponding improvements for the medically relevant estimation of immunohistochemical staining.
C1 - Darmstadt, Germany
C3 - Proceedings of the 32nd DAGM conference on Pattern recognition
DA - 2010///
PY - 2010
DO - 10.1007/978-3-642-15986-2_21
SP - 202
EP - 211
PB - Springer-Verlag, Berlin, Heidelberg
SN - 3-642-15985-0 978-3-642-15985-5
UR - http://portal.acm.org/citation.cfm?id=1926258.1926281
ER -
We consider an automated processing pipeline for tissue micro array analysis (TMA) of renal cell carcinoma. It consists of several consecutive tasks, which can be mapped to machine learning challenges. We investigate three of these tasks, namely nuclei segmentation, nuclei classification and staining estimation. We argue for a holistic view of the processing pipeline, as it is not obvious whether performance improvements at individual steps improve overall accuracy. The experimental results show that classification accuracy, which is comparable to trained human experts, can be achieved by using support vector machines (SVM) with appropriate kernels. Furthermore, we provide evidence that the shape of cell nuclei increases the classification performance. Most importantly, these improvements in classification accuracy result in corresponding improvements for the medically relevant estimation of immunohistochemical staining.
Jurgen Veeck, Peter Wild, Thomas J. Fuchs, Peter Schueffler, Arndt Hartmann, Ruth Knuchel and Edgar Dahl.
Prognostic Relevance of Wnt-Inhibitory Factor-1 (WIF1) and Dickkopf-3 (DKK3) Promoter Methylation in Human Breast Cancer.
BMC Cancer, vol. 9, 1, p. 217, 2009 doi: 10.1186/1471-2407-9-217
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@article{veeck_prognostic_2009,
    title = {Prognostic {Relevance} of {Wnt}-{Inhibitory} {Factor}-1 ({WIF1}) and {Dickkopf}-3 ({DKK3}) {Promoter} {Methylation} in {Human} {Breast} {Cancer}},
    volume = {9},
    issn = {1471-2407},
    url = {http://www.biomedcentral.com/1471-2407/9/217},
    doi = {10.1186/1471-2407-9-217},
    abstract = {Background
    Secreted Wnt signaling antagonists have recently been described as frequent targets of epigenetic inactivation in human tumor entities. Since gene silencing of certain Wnt antagonists was found to be correlated with adverse patient survival in cancer, we aimed at investigating a potential prognostic impact of the two Wnt antagonizing molecules WIF1 and DKK3 in breast cancer, which are frequently silenced by promoter methylation in this disease.
    Methods
    WIF1 and DKK3 promoter methylation were assessed by methylation-specific PCR with bisulfite-converted DNA from 19 normal breast tissues and 150 primary breast carcinomas. Promoter methylation was interpreted in a qualitative, binary fashion. Statistical evaluations included two-sided Fisher's exact tests, univariate log-rank tests of Kaplan-Meier curves as well as multivariate Cox regression analyses.
    Results
    WIF1 and DKK3 promoter methylation were detected in 63.3\% (95/150) and 61.3\% (92/150) of breast carcinoma samples, respectively. In normal breast tissues, WIF1 methylation was present in 0\% (0/19) and DKK3 methylation in 5.3\% (1/19) of samples. In breast carcinomas, WIF1 methylation was significantly associated with methylation of DKK3 (p = 0.009). Methylation of either gene was not associated with clinicopathological parameters, except for DKK3 methylation being associated with patient age (p = 0.007). In univariate analysis, WIF1 methylation was not associated with clinical patient outcome. In contrast, DKK3 methylation was a prognostic factor in patient overall survival (OS) and disease-free survival (DFS). Estimated OS rates after 10 years were 54\% for patients with DKK3 -methylated tumors, in contrast to patients without DKK3 methylation in the tumor, who had a favorable 97\% OS after 10 years (p {\textless} 0.001). Likewise, DFS at 10 years for patients harboring DKK3 methylation in the tumor was 58\%, compared with 78\% for patients with unmethylated DKK3 (p = 0.037). Multivariate analyses revealed that DKK3 methylation was an independent prognostic factor predicting poor OS (hazard ratio (HR): 14.4; 95\% confidence interval (CI): 1.9–111.6; p = 0.011), and short DFS (HR: 2.5; 95\% CI: 1.0–6.0; p = 0.047) in breast cancer.
    Conclusion
    Although the Wnt antagonist genes WIF1 and DKK3 show a very similar frequency of promoter methylation in human breast cancer, only DKK3 methylation proves as a novel prognostic marker potentially useful in the clinical management of this disease.},
    number = {1},
    journal = {BMC Cancer},
    author = {Veeck, Jurgen and Wild, Peter and Fuchs, Thomas J. and Schueffler, Peter and Hartmann, Arndt and Knuchel, Ruth and Dahl, Edgar},
    year = {2009},
    pages = {217},
}
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TY - JOUR
TI - Prognostic Relevance of Wnt-Inhibitory Factor-1 (WIF1) and Dickkopf-3 (DKK3) Promoter Methylation in Human Breast Cancer
AU - Veeck, Jurgen
AU - Wild, Peter
AU - Fuchs, Thomas J.
AU - Schueffler, Peter
AU - Hartmann, Arndt
AU - Knuchel, Ruth
AU - Dahl, Edgar
T2 - BMC Cancer
AB - Background
Secreted Wnt signaling antagonists have recently been described as frequent targets of epigenetic inactivation in human tumor entities. Since gene silencing of certain Wnt antagonists was found to be correlated with adverse patient survival in cancer, we aimed at investigating a potential prognostic impact of the two Wnt antagonizing molecules WIF1 and DKK3 in breast cancer, which are frequently silenced by promoter methylation in this disease.
Methods
WIF1 and DKK3 promoter methylation were assessed by methylation-specific PCR with bisulfite-converted DNA from 19 normal breast tissues and 150 primary breast carcinomas. Promoter methylation was interpreted in a qualitative, binary fashion. Statistical evaluations included two-sided Fisher's exact tests, univariate log-rank tests of Kaplan-Meier curves as well as multivariate Cox regression analyses.
Results
WIF1 and DKK3 promoter methylation were detected in 63.3% (95/150) and 61.3% (92/150) of breast carcinoma samples, respectively. In normal breast tissues, WIF1 methylation was present in 0% (0/19) and DKK3 methylation in 5.3% (1/19) of samples. In breast carcinomas, WIF1 methylation was significantly associated with methylation of DKK3 (p = 0.009). Methylation of either gene was not associated with clinicopathological parameters, except for DKK3 methylation being associated with patient age (p = 0.007). In univariate analysis, WIF1 methylation was not associated with clinical patient outcome. In contrast, DKK3 methylation was a prognostic factor in patient overall survival (OS) and disease-free survival (DFS). Estimated OS rates after 10 years were 54% for patients with DKK3 -methylated tumors, in contrast to patients without DKK3 methylation in the tumor, who had a favorable 97% OS after 10 years (p < 0.001). Likewise, DFS at 10 years for patients harboring DKK3 methylation in the tumor was 58%, compared with 78% for patients with unmethylated DKK3 (p = 0.037). Multivariate analyses revealed that DKK3 methylation was an independent prognostic factor predicting poor OS (hazard ratio (HR): 14.4; 95% confidence interval (CI): 1.9–111.6; p = 0.011), and short DFS (HR: 2.5; 95% CI: 1.0–6.0; p = 0.047) in breast cancer.
Conclusion
Although the Wnt antagonist genes WIF1 and DKK3 show a very similar frequency of promoter methylation in human breast cancer, only DKK3 methylation proves as a novel prognostic marker potentially useful in the clinical management of this disease.
DA - 2009///
PY - 2009
DO - 10.1186/1471-2407-9-217
VL - 9
IS - 1
SP - 217
SN - 1471-2407
UR - http://www.biomedcentral.com/1471-2407/9/217
ER -
Background Secreted Wnt signaling antagonists have recently been described as frequent targets of epigenetic inactivation in human tumor entities. Since gene silencing of certain Wnt antagonists was found to be correlated with adverse patient survival in cancer, we aimed at investigating a potential prognostic impact of the two Wnt antagonizing molecules WIF1 and DKK3 in breast cancer, which are frequently silenced by promoter methylation in this disease. Methods WIF1 and DKK3 promoter methylation were assessed by methylation-specific PCR with bisulfite-converted DNA from 19 normal breast tissues and 150 primary breast carcinomas. Promoter methylation was interpreted in a qualitative, binary fashion. Statistical evaluations included two-sided Fisher's exact tests, univariate log-rank tests of Kaplan-Meier curves as well as multivariate Cox regression analyses. Results WIF1 and DKK3 promoter methylation were detected in 63.3% (95/150) and 61.3% (92/150) of breast carcinoma samples, respectively. In normal breast tissues, WIF1 methylation was present in 0% (0/19) and DKK3 methylation in 5.3% (1/19) of samples. In breast carcinomas, WIF1 methylation was significantly associated with methylation of DKK3 (p = 0.009). Methylation of either gene was not associated with clinicopathological parameters, except for DKK3 methylation being associated with patient age (p = 0.007). In univariate analysis, WIF1 methylation was not associated with clinical patient outcome. In contrast, DKK3 methylation was a prognostic factor in patient overall survival (OS) and disease-free survival (DFS). Estimated OS rates after 10 years were 54% for patients with DKK3 -methylated tumors, in contrast to patients without DKK3 methylation in the tumor, who had a favorable 97% OS after 10 years (p < 0.001). Likewise, DFS at 10 years for patients harboring DKK3 methylation in the tumor was 58%, compared with 78% for patients with unmethylated DKK3 (p = 0.037). Multivariate analyses revealed that DKK3 methylation was an independent prognostic factor predicting poor OS (hazard ratio (HR): 14.4; 95% confidence interval (CI): 1.9–111.6; p = 0.011), and short DFS (HR: 2.5; 95% CI: 1.0–6.0; p = 0.047) in breast cancer. Conclusion Although the Wnt antagonist genes WIF1 and DKK3 show a very similar frequency of promoter methylation in human breast cancer, only DKK3 methylation proves as a novel prognostic marker potentially useful in the clinical management of this disease.