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Updated: Sep 15, 2025
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Development and validation of nomograms for predicting prognosis in patients with resectable bladder urothelial
Junjie Ji1,2,3, Zengjin Wen4, Yu Yao1
1Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Purpose:
This study aimed to construct and validate nomograms for the prediction of overall survival (OS), cancer-specific survival (CSS), and disease-free survival (DFS) in patients with resectable bladder urothelial carcinoma (BUC) after radical cystectomy (RC).
Methods:
We retrospectively collected the demographic, pathological, imaging, and laboratory data from patients with BUC who underwent RC. The training cohort included patients from the Affiliated Hospital of Qingdao University from January 2018 to December 2021, while the test cohort included patients from the same hospital between January 2016 and December 2017, along with patients from Qilu Hospital of Shandong University. Univariate and multivariate Cox regression analyses were conducted to identify independent predictors of OS, CSS, and DFS. The performance of the nomograms was evaluated using Harrell's concordance index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), the corrected AUC following 1,000 bootstrap resamplings with calibration curves, and decision curve analysis in both cohort validations.
Results:
A total of 393 patients were included in the training cohort, while 156 patients comprised the test cohort. Multivariate analyses revealed that age, tumor size, lymph node metastasis (LNM), lymphovascular invasion (LVI), urea nitrogen, creatinine, and the albumin/fibrinogen ratio (AFR) were independent predictors for OS. For CSS, the independent predictors were tumor size, LNM, LVI, urea nitrogen, and AFR. LNM and LVI were the independent predictors for DFS. The nomograms for OS and CSS demonstrated high predictive accuracy with robust CC-indexes and ROC curves, along with reliable calibration curves with corrected AUCs and clinical utility in both cohorts. The DFS nomogram also showed high predictive accuracy with stable corrected AUCs in both cohorts.
Conclusion:
We constructed OS, CSS, and DFS nomograms to predict prognosis in patients with BUC treated with RC. These nomograms exhibited high accuracy, reliability, and clinical utility in predicting outcomes in both cohorts.
