Clinical characterization and prognostic modeling of bladder cancer patients with a history of prior tumors: a SEER database analysis.

BACKGROUND

Bladder cancer is one of the most prevalent malignancies within the urinary system, with incidence and mortality rates showing a global upward trend. This study aims to examine the clinical characteristics of bladder cancer patients with a history of prior malignancies and to develop a prognostic model using extensive data from the Surveillance, Epidemiology, and End Results (SEER) database to inform clinical treatment strategies.

METHODS

Data from bladder cancer patients diagnosed between 2011 and 2015 were extracted using SEER*Stat software. Statistical analyses, including Kaplan-Meier survival curves, and Cox regression, were conducted using R software version 3.6.1 to develop a nomogram model. The predictive performance of the nomogram was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) and the concordance index (C-index).

RESULTS

A total of 12,260 bladder cancer patients were analyzed, including 8,959 individuals with no prior tumor history and 3,301 individuals with a history of previous tumors. The mean survival duration for patients with a prior tumor history was 56.04±39.96 months, significantly lower than the 70.28±39.36 months for patients without a prior tumor history (P<0.001). Significant differences were observed between the two groups across various clinical characteristics, such as age, race, gender, marital status, tumor location, tumor stage, and tumor grade. Multifactorial analysis identified age, race, gender, marital status, tumor grade, tumor stage, tumor histological type, surgical intervention, radiotherapy, chemotherapy, and prior tumor history as independent prognostic factors influencing survival. A nomogram was subsequently developed to predict overall mortality risk and 3- and 5-year survival rates, demonstrating robust predictive performance with a C-index and AUC exceeding 0.70.

CONCLUSIONS

Patients with a history of tumors exhibited lower survival rates and distinct clinical characteristics. The developed nomogram accurately predicts overall mortality and 3- and 5-year survival rates, offering potential for personalized prognostic assessments in clinical practice. Future research should validate the model's generalizability and include additional biological factors to enhance its predictive power.