Evaluation of performance of the prediction model
tumor
TUMOR, RECURRENCE
The performance of the nomogram was evaluated using discrimination and calibration. The model's calibration was determined by combining two validation methods. One validation (bootstrap method with 1000 resamples) to obtain Harrell's concordance index (C‐index), and the validation cohort was used to further validate the prediction model. Calibration curves were plotted to calibrate the model. We also used decision curve analysis (DCA curves) to compare our model and single clinical variables. The scores of each patient were calculated by nomogram. After that, a receiver operating characteristic curve was constructed to determine the optional cutoff value of the scores. Patients were divided into low‐ and high‐risk groups using the cutoff value of score. The log‐rank test was used to compare RFS between the high‐ and low‐risk groups. After that, we compared recurrence difference in different subgroups (different surgery scope, different clinicopathological characteristics and different tumor stages) All statistical test levels were considered statistically significant at 