Artificial intelligence unravels interpretable malignancy grades of prostate cancer on histology images.

ABSTRACT: Malignancy grading of prostate cancer (PCa) is fundamental for risk stratification, patient counseling, and treatment decision-making. Deep learning has shown potential to improve the expert consensus for tumor grading, which relies on the Gleason score/grade grouping. However, the core problem of interobserver variability for the Gleason grading system remains unresolved. We developed a novel grading system for PCa and utilized artificial intelligence (AI) and multi-institutional international datasets from 2647 PCa patients treated with radical prostatectomy with a long follow-up of ≥10 years for biochemical recurrence and cancer-specific death. Through survival analyses, we evaluated the novel grading system and showed that AI could develop a tumor grading system with four risk groups i

SUBMITTER: Eminaga O 

PROVIDER: S-EPMC12118760 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature