Xu Hao, Zhang Yu, Zhang Zhe, Wang Jian, Shen Chong, Wu Zhouliang, Qie Yunkai, Tian Dawei, Liu Shenglai, Hu Hailong, Wu Changli
Department of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, No. 23, Pingjiang Road, Tianjin, 300211, China.
Department of Urology, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300193, China.
Sci Rep. 2025 Mar 26;15(1):10431. doi: 10.1038/s41598-025-95684-6.
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.
为了确定膀胱癌患者前列腺尿道受累(PUI)的危险因素,并建立准确的列线图预测模型。我们回顾性分析了295例接受经尿道前列腺活检的男性膀胱尿路上皮癌患者。通过单因素和多因素逻辑回归分析评估膀胱癌患者PUI的危险因素。基于PUI的独立危险因素构建预测临床结局的列线图模型。通过“留一法”交叉验证(LOOCV)和校准曲线对模型性能进行内部验证。应用决策曲线分析(DCA)评估临床实用性。使用相同方法评估非肌层浸润性膀胱癌(NMIBC)背景下PUI及相关危险因素。多因素分析显示,肿瘤多灶性(OR = 2.44,95%CI 1.17 - 5.26,P = 0.019)、三角区/颈部肿瘤位置(OR = 7.42,95%CI 4.00 - 14.24,P < 0.001)、高级别肿瘤(OR = 5.17,95%CI 1.52 - 21.95,P = 0.014)和复发性癌(OR = 4.39,95%CI 2.32 - 8.63,P < 0.001)被确定为膀胱癌患者PUI的独立危险因素(所有P < 0.05)。基于这四个独立危险因素建立了最终的预测列线图。经LOOCV内部验证后,列线图显示出较强的区分能力(曲线下面积,AUC = 0.8,95%CI 0.749 - 0.851)和良好的校准。DCA进一步证实了该模型在广泛风险阈值范围内的临床实用性。NMIBC亚组分析得出了一致的结果(AUC = 0.819,95%CI 0.764 - 0.874)。该列线图为分层膀胱癌患者PUI风险提供了一个强大的工具,指导选择性前列腺活检和个性化管理。将其纳入临床工作流程可能会减少分期不足并优化治疗结果。有必要进行进一步的外部验证。