Deep learning techniques for using computed tomography imaging for hepatocellular carcinoma diagnosis, treatment and prognosis

Table 4 Characteristics of deep learning networks for prognosis of hepatocellular carcinoma from computed tomography images, mean ± SD

Ref.	Therapy	Predict aim	Base network	Combine model			Type of CT	Train set	Test set	Validation set	C-index	ACC	AUC
Wang et al[57]	SR	Recurrence	ResNet18	C		DL	CECT	Ten-fold cross-validation (167)				0.81 ± 0.01	0.87 ± 0.03
Lv et al[58]	SR	Recurrence	ResNet50	C	R	DL	CECT	156	68				0.83 (0.80-0.87)
Zhang et al[59]	SR	Recurrence	VGG19	C		DL	CECT	162		70/91		0.714	0.80
Gao et al[60]	LT or SR	Recurrence	DSViT			DL	CECT	5-fold cross validation (204)				0.76 ± 0.06	0.80 ± 0.04
Yao et al[61]	SR	Recurrence	DenseNet			DL	CECT	180		122	0.78 (0.76-0.79)		0.80 (2 years)
Hui et al[62]	SR	Recurrence	ResNet	C		DL	CECT	536	560	153			0.86 (0.78-0.92)
Sun et al[63]	TACE	Survival	ResNet101	C		DL	CECT	241		60	0.88 (0.80-0.96)	0.93	0.96 (0.88-1.00)
Dai et al[64]	TACE	Survival	ResNeXt	C	R	DL	CECT	115	165	30		0.80	0.89
Wei et al[65]	SBRT	Survival	CNN-survNet	C	R	DL	CECT	100	33	34	0.65 (0.64-0.68)
Chen et al[66]	SBRT	Survival	ResNet50	C	R	DL	CECT	Nested cross-validation					0.86 (0.80-0.93)
Ren et al[67]	TACE and TKI	Survival	Resnet50			DL	CECT	10-fold cross validation (103)				0.92	0.94
Xia et al[68]	IT	Survival	EfficientNet	C		DL	CECT	129	46	32	0.75 (0.66-0.84)		0.839
Xu et al[69]	IT	Survival	EfficientNet; Semisupervised; CNN-Transformer	C		DL	CECT	520	209	130	0.74 (0.70-0.78)		0.84 (0.80-0.88) (2 years)
Lee et al[70]	M	Survival	DenseNet121	C		DL	CECT	507	146		0.821 ± 0.022		0.89 ± 0.02

CT: Computed tomography; ACC: Accuracy; AUC: Area under the receiver operating characteristics curve; CECT: Contrast-enhanced computed tomography; DL: Deep learning; C: Clinical data; R: Radiomics; ResNet: Residual network; VGG: Visual geometry group; DSViT: Dual-style vision transformer; ResNet: Residual network; DenseNet: Densely connected convolutional network; CNN-survNet: Convolutional neural network-survival network; EfficientNet: Efficient network; CNN: Convolutional neural network; LT: Liver transplantation; SR: Surgical resection; TACE: Trans-arterial chemoembolization; SBRT: Stereotactic body radiotherapy; TKI: Tyrosine kinase inhibitor; IT: Immunotherapy; M: Multiple treatment modalities; C-index: Concordance index.