Computed tomography-based deep learning and multi-instance learning for predicting microvascular invasion and prognosis in hepatocellular carcinoma

Table 1 Scanning parameters of different computed tomography devices

Devices	Revolution aca (GE)	Ingenuity core 64 (Philips)	Revolution (GE)
Layer thickness (mm)	5	5	5
Layer interval (mm)	5	5	5
Tube voltage (kV)	120	120	120
Tube current (mA)	50	30	50
Matrix	512 × 512	512 × 512	512 × 512
Threshold of ROI (HU)	100	150	120

HU: Hounsfield units; ROI: Region of interest.

Table 2 Slice-level prediction results for various deep learning model

Dataset	Model	Accuracy	AUC (95%CI)	Sensitivity	Specificity
Training	Resnet18	0.771	0.841 (0.820-0.861)	0.701	0.814
Validation	Resnet18	0.757	0.777 (0.739-0.814)	0.705	0.777
Training	VGG19	0.695	0.770 (0.745-0.794)	0.708	0.688
Validation	VGG19	0.790	0.749 (0.708-0.791)	0.472	0.911
Training	Densenet121	0.750	0.845 (0.825-0.866)	0.809	0.714
Validation	Densenet121	0.704	0.645 (0.596-0.694)	0.437	0.805

AUC: Area under the curve; CI: Confidence interval; VGG19: Visual Geometry Group 19.

Table 3 Evaluation results of different models on the 2.5-dimensional deep learning-based multi-instance learning method

Dataset	Model	Accuracy	AUC (95%CI)	Sensitivity	Specificity
Training	ExtraTrees	0.821	0.917 (0.874-0.961)	0.784	0.843
Validation	ExtraTrees	0.776	0.861 (0.756-0.966)	0.882	0.732
Training	LightGBM	0.821	0.880 (0.821-0.940)	0.824	0.819
Validation	LightGBM	0.793	0.877 (0.784-0.970)	0.882	0.756
Training	MLP	0.836	0.914 (0.868-0.959)	0.824	0.843
Validation	MLP	0.776	0.841 (0.727-0.956)	0.882	0.732

AUC: Area under the curve; CI: Confidence interval; MLP: Multi-layer perception.

Table 4 Predictive metrics of various signatures

Dataset	Signature	Accuracy	AUC (95%CI)	Sensitivity	Specificity
Training	Clinical	0.530	0.655 (0.559-0.751)	0.882	0.313
Validation	Clinical	0.500	0.631 (0.470-0.793)	0.882	0.341
Training	Radiomics	0.687	0.770 (0.691-0.849)	0.765	0.639
Validation	Radiomics	0.672	0.727 (0.597-0.857)	0.882	0.585
Training	MIL	0.821	0.880 (0.821-0.940)	0.824	0.819
Validation	MIL	0.793	0.877 (0.784-0.970)	0.882	0.756

AUC: Area under the curve; CI: Confidence interval; MIL: Multi-instance learning.