Clinical value of artificial intelligence in hepatocellular carcinoma: Current status and prospect

Table 1 Recent developments in artificial intelligence assisted diagnosis

AI category	Data adopted	Advantages	Control	Ref.
ANN	Preoperative serum AFP, tumor number, size and volume	The ANN showed higher AUCs in identifying tumor grade (0.94) and MVI (0.92)	LR model (0.85 and 0.85)	[20]
CNN	Enhanced MRI	The CNN showed comparable accuracy (90%)	Traditional multiphase MRI (89%)	[24,25]
Open-source framework “caffe” based CNN model	DWI	CNN trained with three sets of b-values found better grading accuracy (80%)	CNN trained with different b-values (65%, 68%, 70%)	[26]
CNN	Nonenhanced MRI	The deeply supervised and pretrained CNN model performed better in characterizing HCC (accuracy 77.00 ± 1.00%)	CNN-based method pretrained by ImageNet (65.00 ± 1.58%)	[27]
DL-based segmentation model	Contrast-enhanced CT	The model with a combination of 2D multiphase strategy showed higher ability of segmenting active part from the tumors	Traditional CT estimation	[28-30]
RF based ML model	HE-stained histopathological images	The classifying model showed an AUC of 0.988 in the test set and 0.886 in the external validation set	-	[31]
1D CNN	Hyperspectral and HE-stained images	The models had a higher average AUC of 0.950	RF (0.939) and SVM (0.930) models	[33]
Shiny and Caret packages-based prediction model	Clinical and laboratorial information	The optimal model had an AUC of 0.943	Single factor-based predictors (0.766, 0.644 and 0.683)	[34]

1D: One-dimensional; 2D: Two-dimensional; AFP: α-fetoprotein; AI: Artificial intelligence; ANN: Artificial neural network; AUC: Area under the curve; CNN: Convolutional neural network; CT: Computed tomography; DL: Deep learning; DWI: Diffusion-weighted imaging; HE: Hematoxylin and eosin; LR: Logistic regression; ML: Machine learning; MRI: Magnetic resonance imaging; MVI: Microvascular invasion; SVM: Support vector machine; RF: Random forest.