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Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer

Kunal Nagpal
Davis Foote
Cameron Chen
Fraser Tan
Niels Olson
Jenny Smith
Arash Mohtashamian
James H. Wren
Robert MacDonald
Lily Peng
Mahul Amin
Andrew Evans
Ankur Sangoi
Craig Mermel
Jason Hipp
Martin Stumpe
Nature Partner Journal (npj) Digital Medicine, vol. 2 (2019), pp. 48


For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage. However, Gleason scoring is based on subjective microscopic examination of tumor morphology and suffers from poor reproducibility. Here we present a deep learning system (DLS) for Gleason scoring whole-slide images of prostatectomies. Our system was developed using 112 million pathologist-annotated image patches from 1226 slides, and evaluated on an independent validation dataset of 331 slides. Compared to a reference standard provided by genitourinary pathology experts, the mean accuracy among 29 general pathologists was 0.61 on the validation set. The DLS achieved a significantly higher diagnostic accuracy of 0.70 (p = 0.002) and trended towards better patient risk stratification in correlations to clinical follow-up data. Our approach could improve the accuracy of Gleason scoring and subsequent therapy decisions, particularly where specialist expertise is unavailable. The DLS also goes beyond the current Gleason system to more finely characterize and quantitate tumor morphology, providing opportunities for refinement of the Gleason system itself.