Artificial intelligence model for predicting early biochemical recurrence of prostate cancer after robotic-assisted radical prostatectomy.

ABSTRACT: Prostate cancer remains a significant public health concern, with a substantial proportion of patients experiencing biochemical recurrence (BCR) after radical prostatectomy (RP). Traditional risk models, such as CAPRA-S, have demonstrated moderate predictive performance, highlighting the need for more accurate tools. This study aimed to develop a machine learning (ML) model to predict BCR in patients undergoing robot-assisted laparoscopic RP (RALP). A retrospective cohort of 1024 (476 BCR+ and 548 BCR-) patients was analyzed, using a balanced dataset of 25 clinical and pathological variables. Five ML classifiers were evaluated, with XGBoost emerging as the best-performing model, achieving 84% accuracy and an AUC of 0.91. Model validation on an independent dataset of 96 patients confirmed i

SUBMITTER: Bergero MA 

PROVIDER: S-EPMC12370965 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature