Prediction models and individualized nomogram development and evaluation
lung cancer
LUNG CANCER, REGRESSION, METASTASIS
The models for predicting the hypermetabolic mediastinal–hilar LNs status in lung cancer were developed by the multivariable regression with the Akaike’s information criterion (AIC), with prediction scores (pre-scores) of each model calculated for each patient by the linear fusion of the selected non-zero features weighted by their coefficients. The performance and clinical utility of these models were evaluated and compared by the receiver-operator characteristic curve (ROC) analysis, DeLong test, and decision curve analysis (DCA) in both the training and test sets. The area under the curve (AUC) with 95% confidence interval (CI), sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), FPR, and false negative rate (FNR) were calculated for each model.For models with similar overall AUC and accuracy, a lower FPR is more clinically relevant for this study. Thus, we developed an individualized nomogram to visually quantify the risk of hypermetabolic mediastinal–hilar LNs metastasis on the basis of prediction model corresponded to this rule. Calibration curves were plotted to assess the agreement between the actual probability and predicted probability of the nomogram by bootstrapping (1000 bootstrap resamples) in both the training and test sets.