Prediction of biochemical prostate cancer recurrence from any Gleason score using robust tissue structure and clinically available information.

ABSTRACT: Biopsy information and protein Prostate-Specific Antigen (PSA) levels are the most robust information available to oncologists worldwide to diagnose and decide therapies for prostate cancer patients. However, prostate cancer presents a high risk of recurrence, and the technologies used to evaluate it demand more complex resources. This paper aims to predict Biochemical Recurrence (BCR) based on Whole Slide Images (WSI) of biopsies, Gleason scores, and PSA levels. A U-net model was used to segment phenotypic features and trained on images from the Prostate Cancer Grade Assessment (PANDA) database to segment tumorous regions from pre-processed and scored WSI of biopsies. Then, the model was tested on data from publicly available repositories achieving an Intersection over Union of 87%. Tissu

SUBMITTER: Marin LE 

PROVIDER: S-EPMC11805747 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature