Oncological outcome and survival predictors of widowed patients with bladder cancer in special populations

Abstract This study aimed to identify prognostic indicators and develop a nomogram to predict the overall survival (OS) of widowed bladder cancer (WBCa) patients. WBCa patients between 2004 and 2015 were identified using the Surveillance, Epidemiology, and End Results (SEER) database. The patients w...

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Bibliographic Details
Main Authors: Fu-Zhen Sun, Yi-Xuan Liu, Qing-Le Xu, Liu-Xiong Guo, Pan-Ying Zhang, Liang Liu
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Scientific Reports
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Online Access:https://doi.org/10.1038/s41598-025-13033-z
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Summary:Abstract This study aimed to identify prognostic indicators and develop a nomogram to predict the overall survival (OS) of widowed bladder cancer (WBCa) patients. WBCa patients between 2004 and 2015 were identified using the Surveillance, Epidemiology, and End Results (SEER) database. The patients were randomly divided into the training and validation sets at a 7:3 ratio. Independent prognostic factors were determined using univariate and multivariate Cox analyses. We constructed a nomogram to predict 3- and 5-year for WBCa patients based on the results of multivariate Cox regression analysis. Consistency index (C-index), receiving operating characteristic (ROC), and calibration curve were used to assess the predictive accuracy of the nomogram. Gender, age at diagnosis, ethnicity, histologic type, histologic grade, tumor‑node metastasis (TNM) stage, and surgery were identified as independent predictors of OS. The C-index value of the nomogram for predicting OS was 0.704 and 0.701 in the training and validation cohorts, respectively. The ROC curves and calibration plots indicated that the model was relatively accurate. Independent prognostic factors for WBCa patients were identified, and a nomogram was constructed to predict the 3- and 5-year OS. The model enables clinicians to determine cancer patients’ survival prognosis and formulate personalized treatment plans.
ISSN:2045-2322