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bioRxiv · 10.1101/280024

Automated Gleason grading of prostate cancer tissue microarrays via deep learning

Arvaniti, E.·Fricker, K. S.·Moret, M.·Rupp, N. J.·Hermanns, T.·Fankhauser, C.·Wey, N.·Wild, P. J.·Rueschoff, J. H.·Claassen, M.
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Abstract

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.\n\nIn 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 Cohens quadratic kappa statistic, were 0.75 and 0.71 respectively, comparable with the inter-pathologist agreement (kappa=0.71). Furthermore, the models 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.\n\nOverall, 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.

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Arvaniti, E., Fricker, K. S., Moret, M., Rupp, N. J., Hermanns, T., Fankhauser, C., Wey, N., Wild, P. J., Rueschoff, J. H., Claassen, M.. 2018-03-11. Automated Gleason grading of prostate cancer tissue microarrays via deep learning. https://doi.org/10.1101/280024

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