Machine learning-based clinical models for prediction of urinary incontinence after robot-assisted laparoscopic radical prostatectomy

作者：Xuejing Wang, Yuan Yuan, Ping Zhou, Bin Gu, X P Jiang, Yan Sha, Na Li, Aiping Gan, Qingli Chen · 发表于：BMC Surgery · 年份：2026 · DOI：10.1186/s12893-026-04053-1 · 研究领域：Prostate Cancer Diagnosis and Treatment、Urinary Tract Infections Management、Pelvic floor disorders treatments

OBJECTIVE: To investigate risk factors for urinary incontinence (UI) after robot-assisted laparoscopic radical prostatectomy (RARP) using interpretable machine learning methods, establish and validate a predictive model. METHODS: Clinicopathological data of 464 localized prostate cancer patients undergoing RARP at our institution (June 2022-June 2024) were retrospectively analyzed. UI status was assessed at 24-48 h, 1, 3, and 6 months post-catheter removal, with early UI defined as requiring ≥ 1 pad/day at 3 months. Patients were randomly split 7:3 into training/validation sets, patients from three external centers were used as the test set. Between-group comparisons used t-tests, Mann-Whitney U tests, or chi-square tests. Multivariate logistic regression identified risk factors in the training set, followed by construction of logistic regression and five machine learning models. Model performance was evaluated via ROC curves. The optimal model was interpreted using SHAP (Shapley additive explanations). Statistical significance was set at P < 0.05. RESULTS: Early UI occurred in 54 (training set), 28 (validation set) patients and 19 (test set) patients, with no intergroup baseline differences (P > 0.05). Univariate analysis linked total PSA level, PI-RADS score, ISUP grade, IPSS score, underlying disease, length of membranous urethra, prostate volume, nerve sparing procedure and urinary leakage preoperative total PSA, ISUP grade, dysuria, comorbidities, urinary tract infection...