Table 2:

Summary of top 3 performing models in the external data set for each feature set

Feature Set	Processing	Algorithm	Feature Selection	mAUC	LogLoss	Brier Score
CE_ET and F_PTR	SD/none	SVM-P	ICC	0.833	0.871	0.521
	SD/ComBat	GBRM	LinearComb	0.832	0.860	0.507
	SD/none	SVM-RBF	Corr	0.831	0.835	0.519
CE_ET and T2_PTR	None/ComBat	ENET	None	0.841	0.922	0.492
	None/ComBat	SVM-P	LASSO	0.840	0.896	0.505
	None/ComBat	SVM-P	linearComb	0.839	0.915	0.509
CE, ET, A, ET and F, PTR	SD/none	SVM-P	ICC	0.886	0.712	0.414
	SD/none	SVM-P	PCA	0.874	0.699	0.398
	None/none	SVM-P	ICC	0.873	0.764	0.433
CE_ET	SD/none	SVM-P	ICC	0.859	0.789	0.472
	SD/none	SVM-P	None	0.856	0.800	0.499
	SD/none	SVM-P	LASSO	0.856	0.786	0.494

Note:—ENET indicates multinomial elastic net; GBRM, generalized boosted regression mode; LASSO, least absolute shrinkage and selection operator; PCA, principal component analysis; SVM-P, support vector machine-polynomial kernel; SVM-RBF, support vector machine-Gaussian kernel; LinearComb, Linear combination filter; A, ADC; F, FLAIR; Corr, Correlation filter.