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.

Table 2 Artificial intelligence models that can help in predicting therapy responses

AI	Data adopted	Advantages	Control	Ref.
ANN	Cox-identified risk factors	The ANN had the highest AUC (0.855)	Cox model, TNM 6th, BCLC and HPBA system (0.826, 0.639, 0.612, 0.711)	[35]
CART model	Clinical and laboratorial parameters	The model successfully identified pre- and postoperative prognosis predictive factors	-	[36]
Weka-based ANNs	Cox-identified risk factors (15 factors for DFS and 21 for OS)	The ANNs showed higher abilities of predicting DFS and OS	LR and decision tree model	[37,38]
Radiomics-based DL CEUS model	Contrast-enhanced ultrasound	The model showed an AUC of 0.93 in predicting therapy response to TACE	Radiomics-based time-intensity curve of CEUS model (0.80) and radiomics-based B-Mode images model (0.81)	[40]
Pretrained CNN "ResNet50"	Manually segmented CT images	The model showed AUCs for predicting CR, PR, SD and PD in training (0.97, 0.96, 0.95, 0.96) and validation (0.98, 0.96, 0.95, 0.94) cohorts	-	[41]
Automatic predictive CNN model	Quantitative CT and BCLC stage	The model had a better prediction accuracy of 74.2%	ML model based on BCLC stage (62.9%)	[42]
ANN	Clinical features	The models showed higher AUCs in predicting 1- and 2-yr DFS (0.94, 0.88) after RFA	Model built with 8 features for 1-yr DFS (0.80), and model built with 6 features for 2-yr DFS (0.76)	[45]

AI: Artificial intelligence; ANN: Artificial neural network; AUC: Area under the curve; BCLC: Barcelona Clinic Liver Cancer; CART: Classification and Regression Tree; CEUS: Contrast-enhanced ultrasound; CNN: Convolutional neural network; CR: Complete response; CT: Computed tomography; DFS: Disease-free survival; DL: Deep learning; HPBA: HepatoPancreato-Biliary Association; LR: Logistic regression; ML: Machine learning; OS: Overall survival; PD: Progressive disease; PR: Partial response; RFA: Radiofrequency ablation; SD: Stable disease; TACE: Transarterial chemoembolization; TNM: Tumor, Node, Metastasis; Weka: Waikato Environment for Knowledge Analysis.

Table 3 Prognosis prediction models built with artificial intelligence algorithms

AI category	Data adopted	Advantages	Control	Ref.
DL algorithms CHOWDER and SCHMOWDER	Whole-slide digitized histological slide	C-indexes for survival prediction of SCHMOWDER and CHOWDER reached 0.78 and 0.75	Baseline factors and composite score	[49]
ML classifier	Previously determined relevant parameters and those identified by univariate analysis	The ML algorithm performed a c-statistic of 0.64 for HCC development prediction	Regression model (0.61) and the model built on the HALT-C cohort (0.60)	[50]
DL survival prediction model	RNA, miRNA and methylation data from TCGA	The DL model showed better potential in classifying HCC patients into two subgroups with different survival	PCA and the model built with manually inputted features	[51]
OS prediction model based on SVM-RFE algorithm	134 methylation sites identified using Cox regression and SVM-RFE algorithm	This algorithm showed a higher accuracy of classifying HCC patients	Traditionally set classifying methods based on DNA methylation	[54-56]
ANN	Mortality-related variables	The ANN showed higher AUCs (0.84 and 0.89) in predicting in-hospital and long-term mortality	LR model (0.76 and 0.77)	[57,58]

AI: Artificial intelligence; ANN: Artificial neural network; AUC: Area under the curve; DL: Deep learning; HALT-C: Hepatitis C antiviral long-term treatment against cirrhosis; HCC: Hepatocellular carcinoma; LR: Logistic regression; ML: Machine learning; OS: Overall survival; PCA: Principal component-based analysis; RFE: Recursive feature elimination; SVM: Support vector machine; TCGA: The Cancer Genome Atlas.