Cancer Medicine (Jan 2023)

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

  • Chao Huang,
  • Qiang Su,
  • Zichuan Ding,
  • Weinan Zeng,
  • Zongke Zhou

DOI
https://doi.org/10.1002/cam4.4998
Journal volume & issue
Vol. 12, no. 2
pp. 1279 – 1292

Abstract

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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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