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Overall and Cancer-Specific Survival in Patients With Renal Pelvic Transitional Cell Carcinoma: A Population-Based
1Department of Chemoradiation Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Abstract:
Background: Renal pelvic transitional cell carcinoma (TCC) is a relatively rare tumor. This study aimed to develop two prognostic nomograms to predict overall survival (OS) and cancer-specific survival (CSS) in renal pelvic TCC patients. Methods: Clinicopathological and follow-up data of renal pelvic TCC patients diagnosed between 2010 and 2015 were retrieved from the Surveillance, Epidemiology, and End Result (SEER) database. Univariate and multivariate Cox regression analyses were used to screen the independently prognostic factors. These independently prognostic factors were then utilized to build nomograms for predicting 3-, 4-, and 5- years OS and CSS of patients with renal pelvic TCC. The nomograms were assessed by calibration curve, receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Results: A total of 1,979 renal pelvic TCC patients were enrolled. Age, tumor size, histological type, American Joint Committee on Cancer (AJCC) stage, surgery, chemotherapy, bone metastasis and liver metastasis were confirmed as independently prognostic factors for both OS and CSS. The areas under the ROC curves (AUCs) of OS nomogram at 3-, 4- and 5-years in the training cohort were 0.797, 0.781, and 0.772, respectively, and the corresponding AUCs in the validation cohort were 0.813, 0.797, and 0.759, respectively. The corresponding AUCs of CSS nomogram were all higher than 0.800. The calibration curves and DCA indicated that both nomograms had favorable performance. Subgroup analyses showed that both nomograms perform in well and poorly differentiated patients. Conclusion: In conclusion, we successfully developed and validated two valuable nomograms to predict the OS and CSS for renal pelvic TCC patients. The nomograms incorporating various clinicopathological indicators can provide accurate prognostic assessment for patients and help clinicians to select appropriate treatment strategies.
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