Establishment and Validation of a Machine Learning Model Predicting Post-Radical Prostatectomy Gleason grading group upgrading Author’s information

Abstract

Background Based on the 2014 International Society of Urological Pathology (ISUP) grading system, the study assesses the disparities in gleason grading group between preoperative needle biopsy pathology and post-radical prostatectomy (post-RP) specimens for prostate cancer (PCa). It investigates the risk factors for post-RP gleason grading group upgrading (GGU) and develops and validates a machine learning (ML) model for predicting post-RP GGU in PCa patients. Methods A retrospective analysis is conducted on demographic and clinicopathological variables of PCa patients from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2018. Five different ML algorithms, including logistic regression (LR), gradient boosting machine (GBM), neural network (NNET), random forest (RF), and XGBoost (XGB), are utilized. The patients with localized PCa who underwent radical prostatectomy (RP) at Zhongshan People's Hospital from January 2018 to December 2023 were selected as the external validation group. Model performance is evaluated using receiver operating characteristic (ROC) area under the curve (AUC), calibration curve, decision curve analysis (DCA), sensitivity (recall), and specificity. A web-based predictor is developed based on the best-performing model. Results This study included a total of 65,574 PCa patients from the SEER database and 98 patients from the external validation group. Among them, there were 11,931 in the training group, 5,112 in the internal validation group, and 24 in the external validation group who experienced post-RP GGU. Risk factors such as patient age, race, preoperative prostate-specific antigen (PSA) level, needle biopsy ISUP grading group, total number of biopsy cores, number of positive cores, and percentage of positive cores were significantly associated with GGU (P < 0.05). Five ML algorithms demonstrated relatively stable consistency, with their AUC values exceeding 0.7. A web-based predictor was developed using the XGB model, which showed the best predictive performance. Conclusion The study introduced a ML model and an online predictor designed to assess the risk of post-RP GGU in PCa patients, aiding physicians in customizing clinical decisions and treatment strategies.