A novel clinical tool to predict cancer-specific survival in patients with primary pelvic sarcomas: A large population-based retrospective cohort study

一种预测原发性盆腔肉瘤患者癌症特异性生存率的新型临床工具:一项基于大型人群的回顾性队列研究

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Abstract

BACKGROUND: Primary osseous sarcoma of the pelvis is rare and has a particularly sinister outcome. This study aims to identify independent prognostic factors of cancer-specific survival (CSS) in patients with primary pelvic sarcoma (PS) and develop a nomogram to predict 3-, 5-, and 10-year probability of CSS in these patients. METHODS: The Surveillance, Epidemiology, and End Results (SEER) database was used to identify 416 patients with primary PS, who were divided into two groups: a training cohort and a validation cohort. Univariate and multivariate Cox analyses were used to screen independent prognostic factors in patients with primary PS. Based on these independent prognostic factors, a prognostic nomogram was developed to predict 3-, 5-, and 10-year probability of CSS. The nomogram's predictive performance and clinical value were evaluated using the calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Finally, a mortality risk stratification system was developed. RESULTS: Tumor size, tumor stage, histological type, surgery, and chemotherapy were identified as independent prognostic factors for the CSS of primary PS patients. Based on these factors, a nomogram was created to predict the 3-, 5-, and 10-year probability of CSS in these patients. The calibration curve, ROC, and DCA indicated that the nomogram performed well and was appropriate for clinical use, with 3-, 5-, and 10-year areas under ROC curve all higher than 0.800. Furthermore, the nomogram-based mortality risk stratification system could effectively divide these patients into three risk subgroups. CONCLUSIONS: The nomogram constructed in this study could accurately predict 3-, 5-, and 10-year probability of CSS in patients with primary PS. Clinicians can use the nomogram to categorize these patients into risk subgroups and provide personalized treatment plans.

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