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

TLDR

The Gleason score is a key prognostic factor for prostate cancer but is subjectively determined and has poor reproducibility. The study introduces a deep learning system to automate Gleason scoring of prostatectomy whole‑slide images. The DLS was trained on 112 million annotated patches from 1,226 slides and validated on 331 independent slides. The DLS achieved a higher diagnostic accuracy of 0.70 versus 0.61 for general pathologists (p = 0.002), trended toward better risk stratification, and offers finer tumor morphology quantification that could refine Gleason scoring and improve treatment decisions, especially where specialist expertise is lacking.

Abstract

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.