Multiparametric magnetic resonance imaging of deep learning-based super-resolution reconstruction for predicting histopathologic grade in hepatocellular carcinoma

Table 2 Predictive performance of different radiomics models based on XGBoost

Models	Cohorts	Original NR MRI				Deep learning-based SR MRI
		AUC (95%CI)	Accuracy	Sensitivity	Specificity	AUC (95%CI)	Accuracy	Sensitivity	Specificity
T2WI	Training	0.782 (0.732-0.832)	0.741	0.708	0.749	0.813 (0.765-0.861)	0.717	0.854	0.684
	Validation	0.721 (0.613-0.828)	0.745	0.600	0.771	0.738 (0.636-0.840)	0.755	0.633	0.777
	Test	0.685 (0.585-0.785)	0.637	0.722	0.615	0.721 (0.620-0.820)	0.637	0.833	0.585
DWI	Training	0.785 (0.732-0.834)	0.678	0.742	0.662	0.770 (0.716-0.825)	0.715	0.708	0.716
	Validation	0.697 (0.595-0.800)	0.653	0.733	0.639	0.721 (0.614-0.827)	0.801	0.500	0.855
	Test	0.695 (0.595-0.795)	0.550	0.861	0.467	0.694 (0.586-0.802)	0.696	0.639	0.711
PVP	Training	0.816 (0.765-0.866)	0.778	0.685	0.800	0.834 (0.791-0.877)	0.741	0.764	0.735
	Validation	0.727 (0.610-0.844)	0.801	0.567	0.843	0.762 (0.664-0.859)	0.816	0.567	0.861
	Test	0.713 (0.620-0.805)	0.678	0.611	0.696	0.752 (0.659-0.845)	0.743	0.667	0.763
All-sequences1	Training	0.890 (0.854-0.925)	0.793	0.876	0.773	0.884 (0.847-0.920)	0.815	0.809	0.816
	Validation	0.792 (0.700-0.883)	0.842	0.533	0.898	0.832 (0.748-0.915)	0.735	0.800	0.723
	Test	0.779 (0.695-0.862)	0.667	0.778	0.637	0.798 (0.720-0.875)	0.766	0.695	0.785

¹Including tree sequences (T2-weighted imaging, diffusion-weighted imaging, portal venous phase).

NR: Normal-resolution; SR: Super-resolution; MRI: Magnetic resonance imaging; AUC: Area under the curve; T2WI: T2-weighted imaging; DWI: Diffusion-weighted imaging; PVP: Portal venous phase.