Fig. 4

ROC curves of clinical and combined prediction models in both cohorts; decision curve analysis for the combined prediction model in the primary cohort, and calibration curve analysis for the combined prediction model in both cohorts. a ROC curves of clinical and combined prediction models in the primary cohort. b ROC curves of clinical and combined prediction models in the validation cohort. c Decision curve analysis for the nomogram. Nomogram for the combined prediction model in the primary cohort. To use this nomogram, first locate the CT reported LN status, then draw a line straight up to the points axis on the top to get the score associated with negative or positive. Repeat the process for the other covariates (pathological grade and radiomic signatures). Add the score of each covariate together and locate the total score on the total points axis. Next, draw a line straight down to the “probability of LN metastasis” axis at the bottom to obtain the probability. The y-axis measures the net benefit. The blue line represents the nomogram. The gray line represents the assumption that all patients have LN metastases. The thin black line represents the assumption that no patients have LN metastases. The decision curve showed that if the threshold probability of a patient and a doctor is 1 and 89%, respectively, using this nomogram to predict LN metastasis risk adds more benefit than the intervention-all-patients scheme or the intervention-none scheme. d Calibration curve analysis for the combined prediction model in the primary cohort and e validation cohort. The x-axis represents the predicted LN metastasis risk. The y-axis represents the actual diagnosed LN metastases. The diagonal dotted line represents a perfect prediction by an ideal model. The solid line represents the performance of the combined prediction model, of which a closer fit to the diagonal dotted line represents a better prediction