Nomogram Predicting All-cause Mortality and Cancer-Specific Mortality after TURBT for Non-Muscle-Invasive Bladder Cancer: A Retrospective Study Based on SEER Data

Abstract Objective The study aims to develop two nomograms that predicts the ACM and CSM in patients with NMIBC using the Surveillance, Epidemiology, and End Results (SEER) database. Methods We extracted clinical data from 2004 to 2017 from the SEER database about NMIBC patients who had undergone transurethral resection of bladder tumor (TURBT) treatment. All patients were randomly divided into training cohort and validation cohort in the ratio of 7:3. We conducted univariate and multivariate Cox regression analyses and constructed nomograms for ACM and CSM using independent influencing factors. Nomogram predictive performance and clinical utility was evaluated by the consistency index (C-index), the time-dependent ROC curves, the calibration curve, and decision curve. Results Multivariate Cox regression analysis showed that age at diagnosis, race, etc. were independent risk factors for ACM and CSM. Based on the multivariate Cox regression results, we constructed nomograms of ACM and CSM. In the training cohort, The C-index values for the ACM nomogram was 0.742 and the CSM nomogram was 0.784. In the validation cohort, the C-index values for the ACM nomogram was 0.745, while the CSM nomogram was 0.790. Our nomograms have better prediction than the nomograms based on AJCC stage T. And the calibration curves of the nomograms showed good consistency between the predicted and actual 5- and 10-year ACM and CSM rates. Conclusion The nomograms can assist clinicians in identifying high-risk populations and devising more individualized treatment strategies for NMIBC patients.