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

Abstract

Abstract Background Primary pulmonary sarcoma (PPS) is a rare and poor prognostic malignancy. This study aimed to investigate the prognostic factors of PPS and to construct a predictive nomogram to predict the overall survival (OS) rate of PPS. Methods We extracted data on patients diagnosed with pulmonary sarcoma from 2010–2019 in the SEER database. We used univariate and multivariate COX regression analysis to identify independent influences affecting the prognosis of patients with PPS. Then, we constructed a predictive nomogram based on the above prognostic factors. Finally, we assessed the validity of the predictive nomogram by receiver operating characteristic (ROC) curves, calibration curves, and decision analysis curves (DCA). Results Univariate and multivariate COX regression analyses showed that age, pathological grade, liver metastasis, surgical intervention, and chemotherapy were independent influences on the prognosis of PPS patients. The results of ROC curves, calibration curves, and DCA plots confirmed that the nomogram obtained in this study to predict the prognosis of PPS have good discrimination, accuracy, and clinical practice efficacy. Conclusion The study explores the factors affecting the prognosis of PPS. Moreover, we established a novel prognostic nomogram to predict the OS rate of PPS patients, which can help to make proper clinical decisions.