RESUMO
BACKGROUND: Retroperitoneal soft tissue sarcoma (RPS) is a quite rare disease,and new nomograms need to be constructed to predict the overall survival (OS) and cancer specific survival (CSS) of RPS patients. METHODS: The clinical data of patients with RPS were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and authors' hospital. The LASSO method and COX proportional hazard regression were used to screen independent risk factors which were used to develop nomograms. Nomograms was evaluated in terms of discrimination by consistency index (C-index) and calibration curve. Decision curve analysis (DCA) and integrated discrimination index (IDI) were performed to assess net benefit and the improvement of model, respectively. Kaplan-Meier method and log-rank test were applied to compare the survival difference between groups. RESULTS: A total of 1164 cases were enrolled which were divided into training cohort (n = 814) and internal validation cohort (n = 350) at a 7:3 ratio. The discrimination of nomograms were good with C-index of 0.728 (95%CI = 0.704-0.752, R2 = 0.270), 0.754 (95%CI = 0.729-0.779, R2 = 0.281) for OS and CSS, respectively. Calibration curve showed good predictive accuracy of nomograms both in internal and external validation cohort, and IDI indicated that nomograms perform well than AJCC stage. Kaplan Meier curve and log-rank test uncovered statistically significance survival difference between high- and low-risk groups with P < 0.001 in terms of OS and CSS. CONCLUSION: Novel nomograms predicting the OS and CSS of RPS patients perform well in discrimination, calibration, clinical benefit and IDI. These nomograms may facilitate risk stratification and making clinical decision.