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

Table 6 Diagnostic performance of the clinical-radiological feature, combined radiomics, and radiomics nomogram models

Set	Model	AUC (95%CI)	Accuracy	Sensitivity	Specificity	PPV	NPV
Training
	Clinical-radiological feature	0.833 (0.753-0.913)	0.792	0.830	0.754	0.737	0.776
	Combined radiomics	0.825 (0.747-0.903)	0.726	0.736	0.717	0.722	0.731
	Radiomics nomogram	0.859 (0.787-0.931)	0.792	0.830	0.754	0.772	0.816
Internal test
	Clinical-radiological feature	0.781 (0.644-0.918)	0.744	0.782	0.708	0.720	0.773
	Combined radiomics	0.788 (0.649-0.927)	0.745	0.783	0.709	0.720	0.773
	Radiomics nomogram	0.848 (0.726-0.970)	0.787	0.826	0.750	0.760	0.818
External test
	Clinical-radiological feature	0.684 (0.498-0.862)	0.676	0.500	0.842	0.750	0.64
	Combined radiomics	0.680 (0.502-0.866)	0.676	0.500	0.842	0.750	0.640
	Radiomics nomogram	0.757 (0.592-0.922)	0.729	0.611	0.842	0.750	0.783

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