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

Table 5 Performance evaluation of the logistic regression models on the training set and the two test sets

Set	Model	AUC (95%CI)	Accuracy	Sensitivity	Specificity	PPV	NPV
Training
	Intratumoral radiomics	0.772 (0.684-0.860)	0.689	0.736	0.642	0.673	0.708
	Peritumoral radiomics	0.823 (0.745-0.901)	0.745	0.774	0.717	0.732	0.760
	Combined radiomics	0.825 (0.747-0.903)	0.726	0.736	0.717	0.722	0.731
Internal test
	Intratumoral radiomics	0.768 (0.628-0.908)	0.638	0.696	0.583	0.615	0.667
	Peritumoral radiomics	0.757 (0.615-0.899)	0.702	0.783	0.625	0.750	0.667
	Combined radiomics	0.788 (0.649-0.927)	0.745	0.783	0.708	0.720	0.773
External test
	Intratumoral radiomics	0.673 (0.495-0.851)	0.568	0.556	0.579	0.556	0.579
	Peritumoral radiomics	0.605 (0.418-0.792)	0.568	0.389	0.737	0.560	0.583
	Combined radiomics	0.680 (0.498-0.862)	0.676	0.500	0.842	0.750	0.640

CI: Confidence interval; AUC: Area under the curve; PPV: Positive predictive value; NPV: Negative predictive value.