The clinical characteristics and nomograms for the prognosis of patients with primary pelvic malignancies: A SEER population-based analysis

Abstract

Abstract Background and Objective: Primary pelvic malignancies are infrequent, and they exhibit unique clinical characteristics. This study aimed to determine the risk factors and develop nomograms to predict cancer-specific survival (CSS) and overall survival (OS) in patients with primary pelvic malignancies. Methods: Patients with primary pelvic bone malignancies between 2000 and 2019 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Kaplan-Meier survival analysis and univariate and multivariate Cox regression analyses were applied to determine the independent prognostic factors. Nomograms were developed to predict the likelihood of CSS and OS. The receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) curves were utilized to evaluate the performance of the nomograms. Results: The clinical data of 2,231 patients with primary pelvic malignancies were retrieved from the SEER database. A total of 534 patients, all with complete survival and treatment data, were included in this study. Among the primary pelvic malignancies, chondrosarcoma was the most prevalent, comprising 316 cases, followed by osteosarcoma with 132 cases, Ewing sarcoma with 59 cases, chordoma with 23 cases, and giant cell tumor of bone with 4 cases. Independent prognostic factors for CSS and OS included age, tumor size, International Classification of Diseases for Oncology (ICD-O)-3 histology code, tumor extent, tumor differentiation grade, and surgery at the primary tumor site. Nomograms incorporating these prognostic factors were developed, demonstrating an area under the curve (AUC) of 0.785 for CSS and 0.808 for OS. Conclusion: Nomograms for the prediction of CSS and OS in patients with primary pelvic malignancies were developed, which may serve as a reliable reference for clinicians when making clinical decisions.