Spss27

Population data were presented in quantitative and percentage form. The study population was divided into a training set (n = 3238) and a validation set (n = 1390) in a ratio of 7:3. Differences in the baseline characteristics between the two groups were assessed using Chi-squared test. Clinical characteristics of patients with early-stage OEC and late-stage OEC were compared using Chi-squared test and Fisher test. Univariate and multivariate Cox regression analyses were performed to identify independent predictors of OS and CSS in OEC patients, and the results were presented as hazard ratio (HR) and 95% confidence interval (CI). OS and CSS curves were plotted using the Kaplan–Meier method. The predictor variables that were significant in the multivariate Cox analysis were used to build the nomogram. Nomogram plots predicting the OS of OEC patients at 3, 5, and 10 years were created, and calibrated in the training and validation sets, specifically using receiver operating characteristic (ROC) curve plots, C-index, calibration plots, and Decision curve analysis (DCA). All data and plots were produced using R version 4.2.0, SPSS27 software, and Graphpad prism 9. P values < 0.05 were considered indicative of statistical significance.