Statistical Analysis
REGRESSIONS
All of the cases were randomly divided into either the training or validation cohort (the split ratio was 8:2). The training cohort was used to establish the prediction model and to construct the nomogram and risk stratification system, while the validation cohort was used to validate the model.Univariate and multivariate Cox regressions were performed to identify the significant variables. Those variables identified were applied to establish the nomogram to predict CSS. To evaluate the predictive accuracy of nomogram, the C-index and the calibration plots were performed (bootstraps with 300 resample). Time-dependent receiver operating characteristic curves and the area under the curve (AUC) were used to evaluate the discriminative power of the nomogram. In addition, decision curve analysis (DCA) for nomogram was also performed. Furtherly, patients were divided into different risk-stratification groups based on the cut-off value of the total point calculated by the nomogram. Survival analysis between 2 groups was conducted using the Kaplan–Meier method and log-rank test. All the statistical analyses were performed by R software version 4.1.3. The cut-off value of the total point calculated by the nomogram was identified by X-tile (Version 3.6.1). A 2-sided 