Abstract
The aim of this study was to develop a comprehensive and effective nomogram for predicting overall survival (OS) rates in postoperative patients with high-grade bladder urothelial carcinoma. Patients diagnosed with high-grade urothelial carcinoma of the bladder after radical cystectomy (RC) between 2004 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were enrolled. We randomly split (7:3) these patients into the primary cohort and the internal validation cohort. Two hundred eighteen patients from the First Affiliated Hospital of Nanchang University were collected as the external validation cohort. Univariate and multivariate Cox regression analyses were carried out to seek prognostic factors of postoperative patients with high-grade bladder cancer (HGBC). According to these significant prognostic factors, a simple-to-use nomogram was established for predicting OS. Their performances were evaluated using the concordance index (C-index), the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The study included 4,541 patients. Multivariate Cox regression analysis demonstrated that T stage, positive lymph nodes (PLNs), age, chemotherapy, regional lymph nodes examined (RLNE), and tumor size were correlated with OS. The C-index of the nomogram in the training cohort, internal validation cohort, and external validation cohort were 0.700, 0.717, and 0.681, respectively. In the training, internal validation, and external validation cohorts, the ROC curves showed that the 1-, 3-, and 5-year areas under the curve (AUCs) were higher than0.700, indicating that the nomogram had good reliability and accuracy. Theresults of calibration and DCA showed good concordance and clinical applicability. A nomogram was developed for the first time to predict personalized 1-, 3-, and 5-year OS in HGBC patients after RC. The internal and external validation confirmed the excellent discrimination and calibration ability of the nomogram. The nomogram can help clinicians design personalized treatment strategies and assist with clinical decisions.
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