A nomogram for predicting overall survival in patients with muscle-invasive bladder cancer undergoing radical cystectomy: a retrospective cohort study
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Updated: Sep 17, 2025

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Haiming Huang1,2, Han Hao3, Jialin Du1
1Department of Clinical Laboratory, Peking University First Hospital, Beijing, China.
Background And Aims:
Radical cystectomy (RC) remains the standard treatment for localized and regionally muscle-invasive bladder cancer (MIBC). However, only half of patients with MIBC survive more than 5 years after RC. We explored the factors associated with overall survival (OS) and constructed a prognostic nomogram for predicting 1-, 3-, and 5-year OS after RC.
Methods:
The data were sourced from the Surveillance, Epidemiology, and End Results (SEER) database and Peking University First Hospital (PKUFH). Univariate and multivariate Cox regression analyses were performed using the minimum value of the Akaike information criterion to select independent prognostic factors that significantly contributed to patient survival. A prognostic nomogram was designed to predict 1-, 3-, and 5-year OS.
Results:
Among the 16,949 patients with MIBC undergoing surgery, 31.15% survived for more than 5 years. The nomogram we created demonstrated satisfactory discriminative ability to predict the survival of MIBC patients with RC, with area under curve (AUC) of 0.939, 0.880 and 0.852 for 1-, 3- and 5-year OS in the testing set. Moreover, the nomogram still exhibited good performance in an externally independent dataset (1-year: AUC=0.970; 3-year: AUC=0.847; 5-year: AUC=0.790). Furthermore, decision curve analyses showed a modest net benefit for the use of the MIBC nomogram in the current cohort compared to the use of American Joint Committee on Cancer staging alone.
Conclusions:
A prognostic nomogram was developed and validated to help clinicians evaluate the prognosis of postoperative MIBC patients. The future integration of additional data will likely improve model performance and accuracy for personalized prognostics.
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