Abstract
An artificial intelligence (AI) algorithm for prostate cancer detection and grading was developed for clinical diagnostics on biopsies. The study cohort included 4221 scanned slides from 872 biopsy sessions at the HUS Helsinki University Hospital during 2016-2017 and a subcohort of 126 patients treated by robot-assisted radical prostatectomy (RALP) during 2016-2019. In the validation cohort (n = 391), the model detected cancer with a sensitivity of 98% and specificity of 98% (weighted kappa 0.96 compared with the pathologist's diagnosis). Algorithm-based detection of the grade area recapitulated the pathologist's grade group. The area of AI-detected cancer was associated with extra-prostatic extension (G5 OR: 48.52; 95% CI 1.11-8.33), seminal vesicle invasion (cribriform G4 OR: 2.46; 95% CI 0.15-1.7; G5 OR: 5.58; 95% CI 0.45-3.42), and lymph node involvement (cribriform G4 OR: 2.66; 95% CI 0.2-1.8; G5 OR: 4.09; 95% CI 0.22-3). Algorithm-detected grade group 3-5 prostate cancer depicted increased risk for biochemical recurrence compared with grade groups 1-2 (HR: 5.91; 95% CI 1.96-17.83). This study showed that a deep learning model not only can find and grade prostate cancer on biopsies comparably with pathologists but also can predict adverse staging and probability for recurrence after surgical treatment.