Non-contrast computed tomography radiomics model to predict benign and malignant thyroid nodules with lobe segmentation: A dual-center study

Table 5 DeLong test for different models and human radiologists in the validation cohort

Models	DT	LR	RF	XGB	SVM	KNN	LGBM	Senior radiologist	Junior radiologist
DT	-	0.011	0.118	< 0.001	< 0.001	0.022	0.309	0.235	0.179
LR	0.011	-	0.137	0.615	0.833	0.537	0.093	< 0.001	< 0.001
RF	0.118	0.137	-	0.038	0.016	0.100	0.950	0.031	0.017
XGB	< 0.001	0.615	0.038	-	0.469	0.825	0.239	< 0.001	< 0.001
SVM	< 0.001	0.833	0.016	0.469	-	0.535	0.159	< 0.001	< 0.001
KNN	0.022	0.537	0.100	0.825	0.535	-	0.321	0.002	< 0.001
LGBM	0.309	0.093	0.950	0.239	0.159	0.321	-	0.017	0.008
Senior radiologist	0.235	< 0.001	0.031	< 0.001	< 0.001	0.002	0.017	-	0.810
Junior radiologist	0.179	< 0.001	0.017	< 0.001	< 0.001	< 0.001	0.008	0.810	-

DT: Decision trees; KNM: K-nearest neighbors; LR: Logistic regression; RF: Random forests; LGBM: Light gradient boosting machines; SVM: Support vector machines; XGB: Extreme gradient boosting.