Development and validation of a prognostic model based on metabolic risk score to predict overall survival of endometrial cancer in Chinese patients
- Author:
Xingchen LI
1
;
Xiao YANG
;
Yuan CHENG
;
Yangyang DONG
;
Jingyuan WANG
;
Jianliu WANG
Author Information
- Publication Type:Original Article
- From:Journal of Gynecologic Oncology 2023;34(6):e69-
- CountryRepublic of Korea
- Language:English
-
Abstract:
Objective:Metabolic syndrome (MetS) is closely related to the increased risk and poor prognosis of endometrial cancer (EC). The purpose of this study was to analyze the relationship between metabolic risk score (MRS) and EC, and establish a predictive model to predict the prognosis of EC.
Methods:A retrospective study was designed of 834 patients admitted between January 2004 to December 2019. Univariate and multivariate Cox analysis were performed to screen independent prognostic factors for overall survival (OS). A predictive nomogram is built based on independent risk factors for OS. Consistency index (C-index), calibration plots and receiver operating characteristic curve were used to evaluate the predictive accuracy of the nomogram.
Results:The patients were randomly divided into training cohort (n=556) and validation cohort (n=278). The MRS of EC patients, ranging from −8 to 15, was calculated. Univariate and multivariate Cox analysis indicated that age, MRS, FIGO stage, and tumor grade were independent risk factors for OS (p<0.05). The Kaplan–Meier analysis demonstrated that EC patients with low score showed a better prognosis in OS. Then, a nomogram was established and validated based on the above four variables. The C-index of nomogram were 0.819 and 0.829 in the training and validation cohorts, respectively. Patients with high-risk score had a worse OS according to the nomogram.
Conclusion:We constructed and validated a prognostic model based on MRS and clinical prognostic factors to predict the OS of EC patients accurately, which may help clinicians personalize prognostic assessments and effective clinical decisions.
