TY  - JOUR
A1  - Arvaniti, Eirini
A1  - Fricker, Kim S.
A1  - Moret, Michael
A1  - Rupp, Niels Jan Felix
A1  - Hermanns, Thomas
A1  - Fankhauser, Christian D.
A1  - Wey, Norbert
A1  - Wild, Peter Johannes
A1  - Rüschoff, Jan Hendrik
A1  - Claassen, Manfred
T1  - Automated Gleason grading of prostate cancer tissue microarrays via deep learning
T2  - Scientific reports
N2  - The Gleason grading system remains the most powerful prognostic predictor for patients with prostate cancer since the 1960s. Its application requires highly-trained pathologists, is tedious and yet suffers from limited inter-pathologist reproducibility, especially for the intermediate Gleason score 7. Automated annotation procedures constitute a viable solution to remedy these limitations. In this study, we present a deep learning approach for automated Gleason grading of prostate cancer tissue microarrays with Hematoxylin and Eosin (H&E) staining. Our system was trained using detailed Gleason annotations on a discovery cohort of 641 patients and was then evaluated on an independent test cohort of 245 patients annotated by two pathologists. On the test cohort, the inter-annotator agreements between the model and each pathologist, quantified via Cohen’s quadratic kappa statistic, were 0.75 and 0.71 respectively, comparable with the inter-pathologist agreement (kappa = 0.71). Furthermore, the model’s Gleason score assignments achieved pathology expert-level stratification of patients into prognostically distinct groups, on the basis of disease-specific survival data available for the test cohort. Overall, our study shows promising results regarding the applicability of deep learning-based solutions towards more objective and reproducible prostate cancer grading, especially for cases with heterogeneous Gleason patterns.
KW  - Machine learning
KW  - Medical imaging
KW  - Pathology
KW  - Prostate cancer
Y1  - 2018
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/50328
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-503283
SN  - 2045-2322
N1  - Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
N1  - Correction erschienen in: Scientific reports, 9.2019, Nr. 1, Art. 7668, doi:10.1038/s41598-019-43989-8
VL  - 8
IS  - 1, Art. 12054
SP  - 1
EP  - 11
PB  - Macmillan Publishers Limited, part of Springer Nature
CY  - [London]
ER  -