Nomogram for the Pathological Complete Response After Neoadjuvant Chemotherapy in Muscle-Invasive Bladder Cancer Patients.

Background: 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.

Methods: 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.

Results:  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.

Conclusions: 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.