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For prostate cancer patients, the Gleason score is one of the most importantprognostic factors, potentially determining treatment independent of the stage.However, Gleason scoring is based on subjective microscopic examination oftumor morphology and suffers from poor reproducibility. Here we present a deeplearning system (DLS) for Gleason scoring whole-slide images ofprostatectomies. Our system was developed using 112 millionpathologist-annotated image patches from 1,226 slides, and evaluated on anindependent validation dataset of 331 slides, where the reference standard wasestablished by genitourinary specialist pathologists. On the validationdataset, the mean accuracy among 29 general pathologists was 0.61. The DLSachieved a significantly higher diagnostic accuracy of 0.70 (p=0.002) andtrended towards better patient risk stratification in correlations to clinicalfollow-up data. Our approach could improve the accuracy of Gleason scoring andsubsequent therapy decisions, particularly where specialist expertise isunavailable. The DLS also goes beyond the current Gleason system to more finelycharacterize and quantitate tumor morphology, providing opportunities forrefinement of the Gleason system itself.

Development and Validation of a Deep Learning Algorithm for Improving Gleason Scoring of Prostate Cancer