Clinical Features, Risk Factors, and Prediction Nomogram for Primary Spinal Osteosarcoma: A Large-Cohort Retrospective Study

Abstract

Study Design Retrospective cohort study Objectives The goal of this study was to determine the clinical characteristics of patients with primary spinal osteosarcoma and to construct a practical clinical prediction model for patients to achieve an accurate prediction of overall survival. Methods This study included 230 patients diagnosed between 2004-2015 from the Surveillance, Epidemiology, and End Results database. Independent risk factors were screened in the training set using Cox regression algorithms, and a prognostic model was developed. Internal and external validation sets were used to test the nomogram model's calibration, discrimination, and clinical utility. A risk classification system based on the nomogram was developed and validated. Results Four independent prognostic factors were identified, and based on this a nomogram model was developed for predicting patient prognosis. The C-index of the training set was .737, while that of the validation set was .693. The time-varying area under the curve values was greater than .720 in both cohorts. The calibration curves proved that the prediction model has high prediction accuracy. The decision curve analysis showed that the nomogram is clinically useful. A risk classification system was established, which allows all patients to be divided into two different risk groups. Conclusions A nomogram and risk classification system was developed for patients with primary spinal osteosarcoma to accurately predict overall patient survival and achieve risk stratification of patient mortality. These tools are expected to play an important role in clinical practice, informing clinicians in making decisions.