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 1,226 slides, and evaluated on an
independent validation dataset of 331 slides, where the reference standard was
established by genitourinary specialist pathologists. On the validation
dataset, the mean accuracy among 29 general pathologists was 0.61. 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.

本研究开发了一个深度学习系统，利用细胞病理医生注释的图像样本对前列腺癌组织学图像进行 Gleason 评分，精度达 0.70，较普通病理医生有较大改善，有望提高患者预后评估准确性，并且可以更细致地描述和量化肿瘤形态学。