Construction and efficacy validation of a multi-parametric contrast-enhanced ultrasound nomogram model for discriminating hepatic inflammatory lesions from malignancies

Table 3 Subgroup analysis by cirrhosis status (test set)

Model	Subgroup	n	AUC (95%CI)	Sensitivity	Specificity	Accuracy
Logistic	Cirrhosis	47	0.979 (0.947-1.000)	0.886	1.000	0.915
	Non-cirrhosis	77	0.943 (0.876-1.000)	0.875	0.981	0.948
DT	Cirrhosis	47	0.937 (0.843-1.000)	0.914	0.917	0.915
	Non-cirrhosis	77	0.774 (0.662-0.885)	0.750	0.830	0.805
RF	Cirrhosis	47	0.993 (0.977-1.000)	0.971	1.000	0.979
	Non-cirrhosis	77	0.938 (0.878-0.997)	0.750	1.000	0.922
XGBoost	Cirrhosis	47	0.974 (0.932-1.000)	0.971	0.917	0.957
	Non-cirrhosis	77	0.972 (0.943-1.000)	0.958	0.887	0.909
SVM	Cirrhosis	47	0.979 (0.944-1.000)	0.971	0.917	0.957
	Non-cirrhosis	77	0.940 (0.868-1.000)	0.875	0.981	0.948

AUC: Area under the curve; SVM: Support vector machine; RF: Random forest; DT: Decision tree.