Abstract:

Objective ·To develop a nomogram prediction model and an online risk calculator, and to predict the continence of patients after robot-assisted laparoscopic radical prostatectomy (RARP). Methods ·A total of 604 prostate cancer patients who underwent RARP and had preoperative prostate magnetic resonance imaging at the Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine from September 2022 to December 2024 were analyzed and included. All patients were randomly resampled and divided into a training set (n=420) and a validation set (n=184) at a ratio of 7∶3. The patients' continence was followed up every month from the first month after the operation. The least absolute shrinkage and selection operator (LASSO) model was applied to screen the features. A Logistic multivariate regression analysis was used to establish a prediction model integrating the features selected from the LASSO analysis. The receiver operator characteristic (ROC) curve was drawn to predict the recovery of continence in patients after RARP, and the areas under the curve were compared by the DeLong test to evaluate the discrimination of the model. Calibration curves and decision curve analysis (DCA) were used to evaluate the calibration and clinical utility the model. Results ·According to the postoperative continence follow-up data of the patients, the continence rate of the patients at 3 months after the operation was 58.28% (352/604). The length of the membranous urethra, the thickness of the right levator ani muscle, and blood loss were identified as independent predictors of early postoperative (3-month) incontinence by Logistic multivariate regression analysis of the training set. The area under the ROC curve was calculated as 0.976 (0.954, 0.998) for the training set and 0.977 (0.945, 1.000) for the validation set, demonstrating good discrimination of this model. No significant difference between the ROC curves of the training set and the validation set was confirmed by the DeLong test (P=0.949). A good goodness of fit of this model was demonstrated by the Hosmer-Lemeshow test (P=0.179). The clinical utility of the nomogram prediction model was indicated by the DCA plot. This nomogram prediction model was incorporated into an online calculator (https://yitingd.shinyapps.io/DynNomapp). Conclusion ·This study developed and validated a nomogram prediction model that can effectively predict the early continence of patients after RARP. The length of the membranous urethra, the thickness of the right levator ani muscle, and the intraoperative blood loss are significant independent predictors of early postoperative incontinence.

Key words: prostate cancer, urinary incontinence, robot-assisted laparoscopic radical prostatectomy (RARP), predictive factor, nomogram