Identification of a clinical web-based nomogram to predict overall survival in elderly retroperitoneal sarcoma patients: A population-based study. Issue 39 (30th September 2022)

• Record Type: Journal Article
• Authors: Zheng, Honghong; Wei, Junqiang
• Is Part Of: Medicine. Volume 101:Issue 39(2022)
• Page Start: e30618
• Publication Date: 2022-09-30
• Subjects: elderly -- nomogram -- overall survival -- prognosis -- retroperitoneal sarcoma -- risk stratification model; Medicine -- Periodicals; Medicine -- Periodicals; Médecine -- Périodiques; Geneeskunde; Medicine; Periodicals; Periodicals; 610.5
• DOI: 10.1097/MD.0000000000030618
• Languages: English
• ISSNs: 0025-7974

Abstract

Abstract : The purpose of this study was to develop a web-based nomogram and risk stratification system to predict overall survival (OS) in elderly patients with retroperitoneal sarcoma (RPS). Elderly patients diagnosed with RPS between 2004 and 2015 were identified in the Surveillance, Epidemiology, and End Results (SEER) database. We used univariate and multivariate Cox analysis to identify independent prognostic factors. We plotted the nomogram for predicting the OS of elderly RPS patients at 1, 3, and 5 years by integrating independent prognostic factors. The nomograms were subsequently validated by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). By calculating the Nomogram score for each patient, we build a risk stratification model to evaluate the survival benefit of elderly RPS patients. A total of 722 elderly RPS patients were included in our study. The nomogram includes 5 clinicopathological variables as independent prognostic factors: age, histological subtype, grade, metastasis status, and surgery. Through the validation, we found that the nomogram has excellent prediction performance. Then web-based nomograms were established. We performed a web-based nomogram and a risk stratification model to assess the prognosis of elderly RPS patients, which are essential for prognostic clustering and decision-making about treatment.