Fig. 3. Heatmap of AUC values.

Heat map of AUC values from machine learning classifier to predict grade (A) in entire LGG group in internal validation for institutional test set (n = 136) after training on institutional training set (n = 68) and entire LGG group in external validation for TCGA validation set (n = 99) after training on entire institutional cohort (n = 204); and (B) in nonenhancing LGG subgroup in internal validation for institutional test set (n = 73) after training on institutional training set (n = 37) and nonenhancing LGG subgroup in external validation on TCGA cohort (n = 37) after training on entire nonenhancing institutional cohort (n = 110). AUC = area under curve, GBM = gradient boosting machine, LDA = linear discriminant analysis, RF = random forest, RFE = recursive feature elimination, ROSE = random over-sampling examples, SMOTE = synthetic minority over-sampling technique