Application of artificial intelligence in preoperative imaging of hepatocellular carcinoma: Current status and future perspectives

Table 1 Summary of studies on differential diagnosis of hepatocellular carcinoma

Ref.	Aim of the study	Modality	Patients	Method	AUC of the final model
Nie et al[7]	Differentiation between HCC and HCA	CT	131	ML	T: 0.96, V: 0.94
Nie et al[8]	Differentiation between HCC and FNH	CT	156	ML	T: 0.979, V: 0.917
Mokrane et al[9]	Differentiation between HCC and non-HCC nodules	CT	178	ML	T: 0.70, V: 0.66
Ponnoprat et al[10]	Differentiation between HCC and ICC	CT	257	ML	NA
Shi et al[11]	Differentiation of HCC from other FLLs	CT	342	DL	0.925
Yasaka et al[12]	Liver mass classification	CT	560	DL	0.92
Cao et al[13]	FLL classification	CT	375	DL	V: 0.88-0.99
Jiang et al[14]	Comparing the diagnostic accuracies of EASL (v2018), LI-RADS criteria, and radiomics models for HCC	MRI	211	ML	T: 0.861, V: 0.810
Zhen et al[15]	Classification of liver tumors	MRI	1411	DL	0.963-0.998
Liu et al[16]	Differentiation of cHCC-CC from CC and HCC	MRI	85	ML	0.77
Huang et al[17]	Diagnosis of DPHCC	MRI	100	ML	0.784
Jian et al[18]	Characterization of HCC	MRI	112	DL	NA
Wu et al[19]	Classification of HCC and hepatic hemangioma	MRI	369	ML	T: 0.86, V: 0.89

T: Training cohort; V: Validation cohort; NA: Not available; AUC: The area under the receiver operating characteristic curve; ML: Machine learning; DL: Deep learning; HCC: Hepatocellular carcinoma; HCA: Hepatocellular adenoma; FNH: Focal nodular hyperplasia; ICC: Intrahepatic cholangiocarcinoma; cHCC-CC: Combined hepatocellular cholangiocarcinoma; CC: Cholangiocarcinoma; DPHCC: Dual-phenotype hepatocellular carcinoma; FLL: Focal liver lesion.