Computed tomography enterography-based radiomics for assessing mucosal healing in patients with small bowel Crohn's disease

Table 2 Diagnostic performance of the radiomic signature based on different machine learning models in the training and test cohorts

Model		AUC (95%CI)	ACC	SENS	SPEC	PPV	NPV
LR	Training	0.950 (0.893-1.000)	0.927	0.842	0.972	0.941	0.921
	Test	0.806 (0.598-1.000)	0.722	1.000	0.583	0.545	1.000
SVM	Training	0.959 (0.887-1.000)	0.964	0.947	0.972	0.947	0.972
	Test	0.944 (0.843-1.000)	0.889	0.833	0.917	0.833	0.917
ExtraTrees	Training	0.965 (0.915-1.000)	0.945	0.895	0.972	0.944	0.946
	Test	0.917 (0.783-1.000)	0.833	1.000	0.750	0.667	1.000
XGBoost	Training	0.971 (0.938-1.000)	0.909	0.947	0.889	0.818	0.970
	Test	0.882 (0.741-1.000)	0.778	1.000	0.667	0.600	1.000
LightGBM	Training	0.932 (0.863-1.000)	0.891	0.895	0.914	0.810	0.941
	Test	0.750 (0.506-0.994)	0.667	1.000	0.500	0.500	1.000

LR: Logistic regression; SVM: Support vector machine; ExtraTrees: Extremely randomized trees; XGBoost: Extreme gradient boosting; LightGBM: Light gradient boosting machine; ACC: Accuracy; AUC: Area under the receiver operating characteristic curve; 95%CI: 95% confidence interval; SENS: Sensitivity; SPEC: Specificity.