Interpretable machine learning model predicts 1-year inguinal hernia risk after robot-assisted radical prostatectomy

摘要

Inguinal hernia represents a clinically significant yet underreported complication of robot-assisted radical prostatectomy (RARP) for localized prostate cancer, with a notably high incidence within the first postoperative year. Despite its adverse impact on quality of life and potential for severe sequelae, predictive tools for this outcome remain limited. To develop and validate the first machine learning (ML)-based clinical prediction model for inguinal hernia within 1 year after RARP, leveraging explainable artificial intelligence (AI) techniques for clinical interpretability. This retrospective study analyzed localized prostate cancer patients who underwent RARP between June 1, 2021 and May 1, 2023 at our center. Least absolute shrinkage and selection operator (LASSO) regression identified five key predictors from multiple clinical parameters. Five ML algorithms were developed and evaluated on a 70:30 training-test split. Model performance was assessed via area under the curve (AUC), accuracy, specificity, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) methodology provided interpretable feature attribution. The final analysis included 652 eligible patients. Extreme gradient boosting (XGBoost) demonstrated superior discriminative ability, with an AUC of 0.833 (95% CI: 0.770-0.895) in the validation set and 0.791 (95% CI: 0.734-0.848) in the test set. SHAP analysis identified five critical predictors ranked by impact: age, body mass index (BMI), preoperative albumin level, T stage, and history of abdominal surgery. This study established the first ML-driven predictive model for post-RARP inguinal hernia, with XGBoost demonstrating optimal performance. High-risk patients identified by the model warrant personalized proactive interventions.