. Author manuscript; available in PMC: 2022 Feb 1.

Published in final edited form as: Med Image Anal. 2020 Nov 16;68:101903. doi: 10.1016/j.media.2020.101903

Table 4:

Performance of three different classifiers (LDA, QDA, and RF) with three different feature selection methods (WRST, TT, and MRMR) in the ES-NSCLC training cohort L_train, n=434, under a 10-fold CV scheme.

Classifier	Feature selection	AUC	Accuracy	Specificity	Sensitivity
LDA	WRST	0.66±0.02	0.68±0.05	0.65±0.05	0.69±0.05
	MRMR	0.68±0.03	0.67±0.06	0.71±0.10	0.65±0.09
	TT	0.65±0.03	0.70±0.07	0.57±0.04	0.76±0.03
QDA	WRST	0.63±0.04	0.69±0.09	0.57±0.17	0.74±0.16
	MRMR	0.67±0.04	0.71±0.05	0.63±0.09	0.75±0.17
	TT	0.63±0.07	0.68±0.08	0.66±0.21	0.68±0.13
RF	WRST	0.67±0.02	0.66±0.06	0.77±0.14	0.61±0.16
	MRMR	0.67±0.03	0.68±0.07	0.70±0.14	0.66±0.13
	TT	0.65±0.03	0.66±0.07	0.75±0.17	0.62±0.09