A Novel Nomogram for Predicting Cancer-specific Survival in Women With Uterine Sarcoma: a Large Population-based Study

Abstract Background: To perform a comprehensive nomogram to predict the cancer-specific survival (CSS) for uterine sarcoma (US) based on the Surveillance, Epidemiology, and End Results (SEER) database.Methods: A total of 3861 patients with US between 2010 and 2015 were identified in this study. They were randomly divided into a training cohort (n = 2702) and a validation cohort (n = 1159) in a 7-to-3 ratio by R software. We performed multivariate Cox analysis to select predictive variables and identify independent prognostic factors. Then, the discrimination and calibration of the nomogram were evaluated by concordance index (C-index) and the area under the curve (AUC). Finally, the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), calibration plotting, and decision-curve analysis (DCA) were used to evaluate the benefits of the new prediction model.Results: We have established a nomogram to predict 1-, 3-, and 5-year CSS for US patients. In this nomogram, pathology grade has the highest risk on CSS in US, followed by age at diagnosis, then surgery status. Comparing to the AJCC staging system, the nomogram showed better predictive discrimination with higher C-index in both training and validation cohort (0.796 and 0.767 vs0.706 and 0.713, respectively) . Furthermore, AUC value, calibration plotting, NRI, IDI, and DCA also demonstrated better performance than the traditional system.Conclusion: Our study validated the first comprehensive nomogram for US which could provide more accurately and individualized survival predictions for US patients in clinical practice.