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

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.