Predicting Positive Surgical Margins in Robot‐Assisted Prostatectomy Using Machine Learning Models

OBJECTIVE: To develop a machine learning (ML) model predicting positive surgical margins (PSM) after robot-assisted radical prostatectomy (RARP). METHODS: We conducted a single-centre retrospective analysis of 301 patients with RARP (211 for training and 90 for validation). Twenty-four features were reduced to five using the Boruta algorithm. Seven ML models were developed and evaluated, and the optimal model was interpreted using SHAP. RESULTS: PSM incidence was 42.0%. The artificial neural network (ANN) performed best (AUC 0.808, accuracy 0.811, sensitivity 0.789, F1 0.779). SHAP identified clinical T stage, creatinine, high-risk status, and positive biopsy percentage as risk factors, while neoadjuvant therapy was protective. CONCLUSION: The ANN model demonstrates potential in predicting PSM following RARP. Currently, it serves as an exploratory predictive tool for risk stratification, requiring further external validation prior to direct clinical application.