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

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.