Development and validation of a nomogram for predicting prostatic urethral involvement in bladder cancer - Hao Xu; Yu Zhang; Zhe Zhang; Jian Wang; Chong Shen; Zhouliang Wu; Yunkai Qie; Dawei Tian; Shenglai Liu; Hailong Hu; Changli Wu (Scientific Reports)
Abstract To identify risk factors for prostatic urethral involvement (PUI) in bladder cancer and develop an accurate nomogram prediction model. We retrospectively analyzed 295 male patients with bladder urothelial carcinoma undergoing transurethral prostatic biopsy. Risk factors of PUI in bladder cancer were assessed through univariate and multivariate logistic regression analyses. A nomogram model for predicting clinical outcomes was constructed based on the independent risk factors of PUI. The performance of the model was internally validated by ‘leave-one-out’ cross-validation (LOOCV) and calibration curve. The decision curve analysis (DCA) was applied to evaluate the clinical utility. Further evaluation of PUI and associated risk factors within the context of non-muscle-invasive bladder cancer (NMIBC) were assessed using the same methods. Multivariate analysis revealed that the tumor multiplicity (OR = 2.44, 95% CI 1.17–5.26, P = 0.019), trigonal/neck tumor location (OR = 7.42, 95% CI 4.00-14.24, P < 0.001), high-grade tumor (OR = 5.17, 95% CI 1.52–21.95, P = 0.014), and recurrent carcinoma (OR = 4.39, 95% CI 2.32–8.63, P < 0.001) were identified as independent risk factors for PUI in bladder cancer (all P < 0.05). A final prediction nomogram was established based on these four independent risk factors. After internally validated by LOOCV, the nomogram showed strong discrimination (area under the curve, AUC = 0.8, 95%CI 0.749–0.851) and excellent calibration. DCA further confirmed the model’s clinical utility across a wide range of risk thresholds. Subgroup analysis in NMIBC yielded consistent results (AUC = 0.819, 95%CI 0.764–0.874). This nomogram provides a robust tool to stratify PUI risk in bladder cancer, guiding selective prostatic biopsies and personalized management. Integration into clinical workflows may reduce understaging and optimize outcomes. Further external validation is warranted.