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

Table 4 Subgroup analysis by lesion size (test set)

Model	Subgroup (cm)	n	AUC (95%CI)	Sensitivity	Specificity	Accuracy
Logistic	Large (≥ 4.0)	62	0.983 (0.955-1.000)	0.906	1.000	0.952
	Small (< 4.0)	62	0.950 (0.891-1.000)	0.889	0.971	0.936
DT	Large (≥ 4.0)	62	0.887 (0.805-0.969)	0.875	0.833	0.855
	Small (< 4.0)	62	0.882 (0.795-0.968)	0.815	0.857	0.839
RF	Large (≥ 4.0)	62	0.984 (0.963-1.000)	0.938	0.967	0.952
	Small (< 4.0)	62	0.956 (0.906-1.000)	0.815	1.000	0.919
XGBoost	Large (≥ 4.0)	62	0.988 (0.968-1.000)	0.969	0.967	0.968
	Small (< 4.0)	62	0.971 (0.940-1.000)	0.963	0.886	0.919
SVM	Large (≥ 4.0)	62	0.983 (0.959-1.000)	0.938	0.967	0.952
	Small (< 4.0)	62	0.950 (0.886-1.000)	0.889	0.971	0.936

Median lesion size = 4.0 cm was used as the cut-off value. AUC: Area under the curve; SVM: Support vector machine; RF: Random forest; DT: Decision tree.