Prognostic visualization model for primary pulmonary sarcoma: a SEER-based study

Abstract

Abstract Primary pulmonary sarcoma (PPS) is a rare and poor prognostic malignancy that results from current clinical studies are lacking. Our study aimed to investigate the prognostic factors of PPS and to construct a predictive nomogram that predict the overall survival (OS) rate. We extracted data on patients diagnosed with PPS from 2010 to 2019 in the SEER database. A total of 169 patients were included after screening by inclusion and exclusion criteria. Univariate and multivariate COX regression analyses showed that age, pathological grade, liver metastasis, surgical intervention, and chemotherapy influenced the prognosis. We constructed the prediction model nomogram based on these factors. Moreover, the results of the internal and external ROC curves, calibration curves, and DCA plots confirmed that the model has good discrimination, accuracy, and clinical practice efficacy. The present study is the first population-based study to explore the factors affecting the prognosis of PPS. We established a novel prognostic nomogram to predict the OS rate, which can help to make proper clinical decisions.