The Clinical Characteristics, Risk Classification System, and Web-Based Nomogram for Primary Spinal Ewing Sarcoma: A Large Population-Based Cohort Study

Abstract

Background The goal of this study was to determine the clinical characteristics of patients with primary spinal Ewing sarcoma (PSES) and to create a prognostic nomogram. Methods Clinical information related to patients diagnosed with PSES between 2004 and 2015 was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Independent prognostic factors were identified using univariate and multivariate Cox analyses to construct nomograms predicting overall survival in patients with PSES. Calibration curves and receiver operating characteristic curves were used to assess the model’s prediction accuracy, while decision curve analysis was used to assess the model’s clinical utility. Results The overall number of 314 patients with PSES were screened from the SEER database between 2004 and 2015. Race, chemotherapy, age, and disease stage were found to be independent predictive factors for overall survival in both univariate and multivariate Cox analyses. The training and validation cohorts’ calibration curves, receiver operating characteristic curves, and decision curve analysis showed that the nomogram has strong discrimination and clinical value. Furthermore, a new risk classification system has been constructed that can divide all patients into 2 risk groups. Conclusions Based on a broad population, the research demonstrates statistical evidence for the clinical features and prognostic variables of patients with PSES. The constructed prognostic nomogram provides a more precise prediction of prognosis for PSES patients.