Shan Liping, Xu Hanfeng, Piao Chengri, Liu Zhen, Xie Shuang
Department of Urology, Shengjing Hospital of China Medical University, Shenyang, People's Republic of China.
Department of Urology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, People's Republic of China.
Ann Surg Oncol. 2025 Jan;32(1):589-597. doi: 10.1245/s10434-024-16429-9. Epub 2024 Oct 28.
No validated instrument currently exists to predict neoadjuvant chemotherapy (NAC) response in muscle-invasive bladder cancer (MIBC) patients. We aim to develop and validate a nomogram based on clinicopathological factors for predicting who would benefit most from NAC.
Between January 2016 and April 2023, 361 consecutive MIBC patients treated with NAC were enrolled in the study. Two hundred sixty patients at the Hu Nan institution comprised the development cohort. The validation cohort (91 patients) was from the Xiang Hua center. Patient clinicopathologic information was documented. Using regression coefficients, a predictive model was constructed using multivariate logistic regression. The likelihood ratio test with Akaike's information criterion was then used as the ending rule for backward stepwise selection. This predictive model's efficacy was evaluated for discrimination, calibration, and clinical utility.
Predictors of this model included the origin of MIBC, pathological tumor type, clinical tumor stage, and tumor size. In the validation cohort, the model demonstrated good discrimination with an AUROC of 0.7221 (P < 0.001) and calibration (Unreliability test, P = 0.580). In addition, decision curve analysis revealed that the model was clinically beneficial.
This study indicated that primary MIBC, pure UC pathological type, lower clinical tumor stage, and maximum tumor diameter <3 cm were significant predictors of ypCR in MIBC patients after NAC. This nomogram may contribute to the precious administration of NAC and the avoidance of chemotherapy toxicity and delayed RC.
目前尚无经过验证的工具可用于预测肌肉浸润性膀胱癌（MIBC）患者的新辅助化疗（NAC）反应。我们旨在开发并验证一种基于临床病理因素的列线图，以预测哪些患者将从NAC中获益最大。
2016年1月至2023年4月期间，连续纳入361例接受NAC治疗的MIBC患者。湖南机构的260例患者组成了开发队列。验证队列（91例患者）来自湘华中心。记录患者的临床病理信息。使用回归系数，通过多变量逻辑回归构建预测模型。然后使用带有赤池信息准则的似然比检验作为向后逐步选择的结束规则。对该预测模型的判别力、校准度和临床实用性进行评估。
该模型的预测因素包括MIBC的起源、病理肿瘤类型、临床肿瘤分期和肿瘤大小。在验证队列中，该模型显示出良好的判别力，曲线下面积（AUROC）为0.7221（P < 0.001），校准度良好（不可靠性检验，P = 0.580）。此外，决策曲线分析表明该模型具有临床益处。
本研究表明，原发性MIBC、纯尿路上皮癌病理类型、较低的临床肿瘤分期以及最大肿瘤直径<3 cm是MIBC患者NAC后达到ypCR的重要预测因素。该列线图可能有助于NAC的精准管理，避免化疗毒性和延迟根治性膀胱切除术（RC）。
