3.3. Nomogram comparison and evaluation
Once the nomograms were established, we used a series of indicators to evaluate the performance of the new prediction models. The consistency index for OS (CSS) prediction of the training and validation cohort were 0.74 (0.778) and 0.734 (0.775), which were higher than that of the Ann Arbor staging system (training cohort: 0.576 [0.611], validation cohort: 0.565 [0.598]). We further compared receiver operating characteristics curve curves for the OS (CSS) nomogram, and the 3-, 5-, 8-year AUC values in the training cohort were 0.789 (0.820), 0.801 (0.826), 0.802 (0.835), and 0.761 (0.798), 0.76 (0.795), 0.788 (0.757) in the validation cohort, which were higher than those of the old model too (Fig. The area under the ROC curve (AUC) for (A) OS probability and (B) CSS probability. (A–C) The 3-, 5-, 8-year results of the training cohort. (D–F) The 3-, 5-, 8-year results of the validation cohort. CSS = cancer-specific survival, OS = overall survival, ROC = receiver operating characteristics curve.Calibration curves for (A) OS and (B) CSS probability depict the consistency between our calibration curves and the ideal lines. (A–C) The 3-, 5-, 8-year results of the training cohort. (D–F) The 3-, 5-, 8-year result of the validation cohort. CSS = cancer-specific survival, OS = overall survival.Decision curve analysis curves (DCA) for (A) OS and (B) CSS probability. (A–C) The 3-, 5-, 8-year results of the training cohort. (D–F) The 3-, 5-, 8-year results of the validation cohort. CSS = cancer-specific survival, OS = overall survival.