Preoperatively predicting vessels encapsulating tumor clusters in hepatocellular carcinoma: Machine learning model based on contrast-enhanced computed tomography

Table 4 Performance of logistic regression, support vector machine, decision tree, and random forest in the combined radiomics for predicting vessels encapsulating tumor clusters

Set	ML model	AUC (95%CI)	Accuracy	Sensitivity	Specificity	PPV	NPV
Training
	LR	0.825 (0.747-0.903)	0.726	0.736	0.717	0.722	0.731
	SVM	0.874 (0.805-0.943)	0.764	0.792	0.736	0.745	0.765
	DT	0.862 (0.794-0.930)	0.820	0.811	0.830	0.827	0.815
	RF	1 (1.000-1.000)	1	1	1	1	1
Internal test
	LR	0.788 (0.649-0.927)	0.745	0.783	0.708	0.720	0.773
	SVM	0.766 (0.629-0.903)	0.681	0.739	0.625	0.654	0.714
	DT	0.698 (0.556-0.840)	0.659	0.696	0.625	0.640	0.682
	RF	0.723 (0.577-0.869)	0.702	0.739	0.667	0.667	0.696
External test
	LR	0.680 (0.498-0.862)	0.676	0.500	0.842	0.750	0.640
	SVM	0.632 (0.438-0.826)	0.676	0.500	0.842	0.75	0.640
	DT	0.667 (0.482-0.852)	0.676	0.500	0.842	0.750	0.640
	RF	0.614 (0.428-0.800)	0.568	0.444	0.684	0.571	0.565

LR: Logistic regression; SVM: Support vector machine; DT: Decision tree; RF: Random forest; CI: Confidence interval; AUC: Area under the curve; PPV: Positive predictive value; NPV: Negative predictive value.