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

Table 1 Characteristics of deep learning networks for diagnosis of hepatocellular carcinoma from computed tomography images, mean ± SD

Ref.	Method	Base network	Type of CT	Train set	Test set	Validation set	ACC	Sen	Spe	AUC	DICE
Wang et al[21]	HCCNet and NoduleNet	ResNet	NCCT/CECT	7512	385/556		0.81/0.81	0.78/0.89	0.84/0.74	0.89/0.88
Ling et al[22]	3D ResNet	ResNet	CECT	480 or 481	120 or 121		0.92 (0.87, 0.96)	0.95 (0.90, 1)	0.88 (0.82, 0.91)	0.96 (0.93, 0.98)
Guo et al[23]	ALARM	3D ResNet50 and nnUNet	CECT	924		231/703		0.92/0.94	0.89/0.93	0.90/0.92
Kim et al[24]	MASK R-NN	ResNet101 and UNet and FPN and RPN	CECT	568	589			0.85			0.96
Shan et al[25]	3D ResUNet	ResUNet	CECT								0.88
Zossou et al[26]	RA-UNet	UNet	CT	4536 slices	315 slices	1134 slices	0.94	0.94			0.88
Chen et al[27]	SED	UNet and DenseUNet	CT	4000 slices	300 slices	800 slices	0.99			0.95	0.75
Gao et al[28]	STIC	CNN and gated RNN	CECT	499	113/111		0.93 ± 0.04	0.93 ± 0.10	0.94 ± 0.04	0.99 ± 0.01
Rocha et al[29]	CNN	CNN	CECT	317	79		0.95	0.92 ± 0.01		0.99 ± 0.00
Khan et al[30]	Multi-modal deep neural network	AlexNet	CECT	248	75	75	0.96	> 0.94	> 0.98	0.83
Balagourouchetty et al[31]	FCNet	GoogleNet	CECT	444	190		0.97			0.995

CT: Computed tomography; AUC: Area under curve; ACC: Accuracy; Sen: Sensitivity; Spe: Specificity; DICE: Dice coefficient; ResNet: Residual network; 3D: Three-dimensional; ALARM: Alarm; MASK R-NN: Mask region-based convolutional neural network; ResUNet: Residual U shaped network; RA-UNet: Residual attention U shaped network; SED: Successive encoder-decoder; CNN: Convolutional neural network; FCNet: Fully connected neural network; RNN: Recurrent neural network; UNet: U shaped network; FPN: Feature pyramid network; RPN: Region proposal network; nnUNet: Neural network U shaped network; NCCT: Non-contrast computed tomography; CECT: Contrast enhanced computed tomography; STIC: Spatial extractor temporal encoder integration classifier.

Table 2 Characteristics of deep learning networks for segmentation of hepatocellular carcinoma from computed tomography images, mean ± SD

Ref.	Method	Combine	Type of CT	Train set	Test set	Validation set	VOE (%)	RVD (%)	ASD (mm)	RMSD (mm)	DICE (%)
Nakai et al[32]	CNN		CECT	493	62	62
Shah et al[33]	MDL-CNN	Cascade	CECT	2948 slice	1264 slice		9.8	8.2			95.7
Ouhmich et al[34]	UNet	Cascade	CECT	Cross-validation (1:6)							68.1 ± 23.2
Khan et al[35]	RMS-UNet	Residual multi-scale	CT	101	21	21	14.95 ± 9.40	-0.7 ± 1.3	3.06 ± 3.13	1.60 ± 0.72	91.92 ± 0.05
Gong et al[36]	UNet-DRLSEIC	DRLSEIC	CT	110	40						95.2 ± 1.7
Chen et al[37]	RDA-UNet	ResNet DenseNet and UNet	CT	15611 slice		3903 slice					87.03
Li et al[38]	H-DenseUnNet	DenseNet and UNet	CECT	131	70	20	11.68 ± 4.33	-0.01 ± 0.05	0.58 ± 0.46	1.87 ± 2.33	93.7 ± 2
Wang et al[39]	MAD-Unet	Multi-scale attention and deep supervision	CECT	116	15		6.83 ± 2.31	0.34 ± 0.19	1.03 ± 0.37	3.74 ± 3.58	97.27 ± 1.22
Lee et al[40]	HFS-Net	DenseUNet and UNet	DNCT	298	179	118					82.8
Ou et al[41]	ResTransUNet	Transformer and UNet	CECT				8.04 ± 6.8	-0.07 ± 9.5			95.35 ± 4.5
Jiang et al[42]	Swin-UNet	SFTB and LCAB	CECT	104	26		37.38	-0.1577	5.1433		76.14
Clinton Atabansi et al[43]	ICT-Net	Transformer and convolution	CECT	1789	225	223					90.91
d’Albenzi et al[44]	DEDC-Net	ResNet and VGG-19	CECT	101	15	15			12.17 ± 12.67		46.1 ± 27.4
Singh et al[45]	FasNet	ResNet-50 and VGG-16	CECT								87.66
Guo et al[46]	FCN and ACM	FCN and ACM	CT	42	16	19			1.6 ± 0.5	3.5 ± 1.2	95.8 ± 1.4
Zhang et al[47]	DeepRecS	RMP-Net and CGBS-Net	CECT	139	46	46	15.88 ± 3.79	0.32 ± 6.45	0.47 ± 0.45	1.57 ± 1.46	91.32 ± 2.30
Balasubramanian et al[48]	APESTNet and Mask R-CNN	APESTNet and Mask R-CNN	CECT	121	10	20	5.37 ± 3.27	-1.08 ± 2.06	1.85 ± 0.30		97.31 ± 1.49
Liu et al[49]	S2DANet	FSMF and MAHA and GMCA	CECT	92	26	13	43.95	0.3861	10.80		69.51

VOE: Volumetric overlap error; CT: Computed tomography; RVD: Relative volume difference; ASD: Average symmetric surface distance; RMSD: Root mean square symmetric surface distance; DICE: Dice coefficient; DNCT: Dynamic computed tomography; CECT: Contrast-enhanced computed tomography; MDL: Multi-channel deep learning; CNN: Convolutional neural network; UNet: U shaped network; RMS: Residual multi scale; DRLSEIC: Distance regularized level set evolution D constrained; RDA-Unet: Residual-dense-attention-U shaped network; H-DenseUnNet: Hybrid densely connected U shaped network; MAD: Multi-scale attention and deep supervision; HFS: Hierarchical fusion strategy; ResTransUnet: An effective network combined with transformer and U shaped network for liver segmentation in computed tomography scans; ICT-Net: Integrated convolution and transformer-based network; DEDC-Net: The dual-encoder double concatenation network; FCN: Fully convolutional network; ACM: Active contour model; DeepRecS: A novel deep learning based interactive method for liver tumor segmentation, which incorporated RECIST-marks as guiding signals; APESTNet: Enhanced Swin transformer network with adversarial propagation; Mask R-CNN: Mask region-based convolutional neural network; S2DANet: Spatial and spectral-learning double-branched aggregation network; ResNet: Residual network; DenseNet: Densely connected convolutional network; SFTB: Swin-neighborhood fusion transformer block; LCAB: Large-kernel convolutional attention block; VGG: Visual geometry group; RMP-Net: RECIST mark propagation network; CGBS-Net: Context-guided boundary-sensitive network; FSMF: Fourier spectral-learning multi-scale fusion; MAHA: Multi-axis aggregation Hadamard attention; GMCA: Group multi-head cross-attention aggregation.

Table 3 Characteristics of deep learning networks for treatment response of hepatocellular carcinoma from computed tomography images

Ref.	Therapy	Base network	Combine model			Type of CT	Train set	Test set	Validation set	CR and PR (%)	SD and PD (%)	AUC (%)	ACC (%)
Peng et al[50]	TACE	ResNet50			DL	CECT	562		89/138	41.58/42.03	58.42/57.97	96 (94-97); 97 (96-98)	85.1/82.8
Peng et al[51]	TACE	DL	R		DL	CECT	139		171	60.82	39.18	99.4 (98.7-100)
Sun et al[52]	TACE	ResNet18	R	C	DL	CECT	299		100	43	57	0.91 (0.85-0.97)
Lin et al[53]	TACE	ResNet50	ML (SVC)		DL	CECT	42	26				92 (90-94) (SVC)	81 (80-82)
Liao et al[54]	CLICI	ResNet18			DL	CECT	72		48	27.1	72.9	80.2 (78.0-82.4)	72.5
Lin et al[55]	ICI	ResNet18	R	C	DL	CECT	153	50		70	30	0.88 (0.77-0.99)
Yin et al[56]	TACE-HAIC and ICI and TKI	RseNet50	R	C	DL	CECT	92	30	50	24	76	0.85	79.1

CR: Complete response; CT: Computed tomography; PD: Progressive disease; PR: Partial response; SD: Stable disease; ACC: Accuracy; AUC: Area under the receiver operating characteristics curve; CECT: Contrast-enhanced computed tomography; C: Clinical data; DL: Deep learning; R: Radiomics; ML: Machine learning; SVC: Support vector classifier; ResNet: Residual network; CLICI: Combination therapy of lenvatinib and immune checkpoint inhibitor; TACE: Trans-arterial chemoembolization; HAIC: Hepatic arterial infusion chemotherapy; ICI: Immune checkpoint inhibitor; TKI: Tyrosine kinase inhibitor.

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.