A clinical tool to predict overall survival of elderly patients with soft tissue sarcoma after surgical resection.

With the aging world population, the incidence of soft tissue sarcoma (STS) in the elderly gradually increases and the prognosis is poor. The primary goal of this research was to analyze the relevant risk factors affecting the postoperative overall survival in elderly STS patients and to provide some guidance and assistance in clinical treatment. The study included 2,353 elderly STS patients from the Surveillance, Epidemiology, and End Results database. To find independent predictive variables, we employed the Cox proportional risk regression model. R software was used to develop and validate the nomogram model to predict postoperative overall survival. The performance and practical value of the nomogram were evaluated using calibration curves, the area under the curve, and decision curve analysis. Age, tumor primary site, disease stage, tumor size, tumor grade, N stage, and marital status, are the risk variables of postoperative overall survival, and the prognostic model was constructed on this basis. In the two sets, both calibration curves and receiver operating characteristic curves showed that the nomogram had high predictive accuracy and discriminative power, while decision curve analysis demonstrated that the model had good clinical usefulness. A predictive nomogram was designed and tested to evaluate postoperative overall survival in elderly STS patients. The nomogram allows clinical practitioners to more accurately evaluate the prognosis of individual patients, facilitates the progress of individualized treatment, and provides clinical guidance.