Table 3.

The performance of the clinical, imaging, radiomics model and the nomogram for predicting MVI

Models	Classifier	Training cohort (n = 205)			Validation cohort (n = 106)			Cutoff
		Sen	Spe	AUC (95% CI)	Sen	Spe	AUC (95% CI)
Clinical	RF	0.72	0.83	0.798 (0.739–0.857)	0.73	0.59	0.725 (0.647–0.803)	0.25
	LR	0.73	0.72	0.779 (0.719–0.837)	0.70	0.55	0.668 (0.570–0.766)	0.17
Imaging	RF	0.83	0.88	0.919 (0.880–0.958)	0.77	0.87	0.876 (0.816–0.934)	0.31
	LR	0.82	0.84	0.894 (0.855–0.933)	0.83	0.67	0.792 (0.713–0.869)	0.13
Radiomics a	RF	1.00	0.97	0.999 (0.999–0.999)	0.96	0.86	0.918 (0.859–0.977)	0.26
	LR	0.70	0.69	0.773 (0.714–0.832)	0.63	0.88	0.809 (0.731–0.887)	0.27
Nomogram	RF	0.87	0.94	0.960 (0.940–0.980)	0.93	0.85	0.920 (0.861–0.979)	0.23
	LR	0.92	0.84	0.934 (0.895–0.973)	0.93	0.75	0.879 (0.820–0.938)	0.19

Abbreviations: RF, random forest; LR, logistic regression; Sen, sensitivity; Spe, specificity; AUC, area under the curve; CI, confidence interval

Radiomics ^a: the final radiomics model based on the multi-parametric (arterial phase, portal venous phase, hepatobiliary phase T1-weighted image, and diffusion-weighted imaging) fusion in VOI_{tumor + 10mm + liver}