Predictive model of positive surgical margins after radical prostatectomy based on Bayesian network analysis - Wang, Guipeng; Du, Haotian; Meng, Fanshuo; Jia, Yuefeng; Wang, Xinning; Yang, Xuecheng (Frontiers in Oncology, 2024)
Objective To analyze the independent risk factors for marginal positivity after radical prostatectomy, and to evaluate the clinical value of the predictive model based on Bayesian network analysis.We retrospectively analyzed clinical data from 238 patients who had undergone radical prostatectomy, between June 2018 and May 2022. The general clinical data, PSA-derived indicators, puncture factors, and magnetic resonance imaging (MRI) characteristics were included as predictive variables, and univariate and multivariate analyses were conducted. We established a nomogram model based on the independent predictors, and adopted BayesiaLab software to generate treeaugmented naive (TAN) and naive Bayesian models based on 15 predictor variables. Results Of the 238 patients included in the study, 103 exhibited positive surgical margins. Univariate analysis revealed that PSA density (PSAD) (P=0.02), Gleason scores for biopsied tissue (P=0.002) and the ratio of positive-biopsy cores (P < 0.001), preoperative T staging (P < 0.001), and location of abnormal signals (P=0.002) and the side of the abnormal signal (P=0.009) were all statistically significant. The AUC of the established nomogram model based on independent predictors was 73.80%, the AUC of the naive Bayesian model based on 15 predictors was 82.71%, and the AUC of the TAN Bayesian model was 80.80%. Conclusions The predictive model of positive resection margin after radical prostatectomy based on Bayesian network demonstrated high accuracy and usefulness.