Statistical analysis
REGRESSION
SPSS (26.0) and R (4.2.2) software were used for statistical analysis and graph plotting. The X‐tile software determined the optimal cutoff value, and continuous variables were transformed into categorical variables accordingly. Categorical variables were compared between groups using the Fisher's exact test or chi‐square test. Univariate Cox analysis was conducted for each variable, and variables with statistical significance (Based on the multivariate Cox regression analysis, a nomogram was constructed to predict patient CSS using the rms and survival packages in R software. The model's reliability was validated by internal and external validation cohorts. The model's discrimination was evaluated using the concordance index (C‐index), with a C‐index greater than 0.71 indicating excellent discrimination. The calibration curve measured the degree of closeness between the predicted and actual risk, with a closer curve indicating better predictive results. The time‐dependent receiver operating characteristic curve (ROC curve) evaluated the model's accuracy, with an area under the curve (AUC) greater than 0.71 indicating good predictive ability. Decision curve analysis (DCA) evaluated the model's clinical utility and quantified the net benefits at different threshold probabilities. Patients were divided into high‐risk and low‐risk groups based on the median risk score from the column chart. Kaplan–Meier analysis was used to plot the survival curves of high‐risk and low‐risk groups for predicting CSS, and the log‐rank test was used for survival analysis.