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Background: An increasing volume of prostate biopsies and a world-wideshortage of uro-pathologists puts a strain on pathology departments.Additionally, the high intra- and inter-observer variability in grading canresult in over- and undertreatment of prostate cancer. Artificial intelligence(AI) methods may alleviate these problems by assisting pathologists to reduceworkload and harmonize grading.  Methods: We digitized 6,682 needle biopsies from 976 participants in thepopulation based STHLM3 diagnostic study to train deep neural networks forassessing prostate biopsies. The networks were evaluated by predicting thepresence, extent, and Gleason grade of malignant tissue for an independent testset comprising 1,631 biopsies from 245 men. We additionally evaluated gradingperformance on 87 biopsies individually graded by 23 experienced urologicalpathologists from the International Society of Urological Pathology. Weassessed discriminatory performance by receiver operating characteristics (ROC)and tumor extent predictions by correlating predicted millimeter cancer lengthagainst measurements by the reporting pathologist. We quantified theconcordance between grades assigned by the AI and the expert urologicalpathologists using Cohen's kappa.  Results: The performance of the AI to detect and grade cancer in prostateneedle biopsy samples was comparable to that of international experts inprostate pathology. The AI achieved an area under the ROC curve of 0.997 fordistinguishing between benign and malignant biopsy cores, and 0.999 fordistinguishing between men with or without prostate cancer. The correlationbetween millimeter cancer predicted by the AI and assigned by the reportingpathologist was 0.96. For assigning Gleason grades, the AI achieved an averagepairwise kappa of 0.62. This was within the range of the corresponding valuesfor the expert pathologists (0.60 to 0.73).

Pathologist-Level Grading of Prostate Biopsies with Artificial Intelligence