Artificial intelligence to predict hepatocellular carcinoma risk in cirrhosis

Table 1 Application of artificial intelligence for the prediction of hepatocellular carcinoma in patients with cirrhosis

Ref.	Cohort	Data source	AI-based machine learning algorithms	Input	Results
Xu et al[24]	6980 patients; training cohort: 20%; validation cohort: 80%	Hospital of Nanchang University (patients with HBV-related cirrhosis)	XGBoost; LR; RF; AdaBoost; MLP	Clinical and biological data	XGBoost was the most efficient model (AUC: 0.829, 95%CI: 0.804-0.852) in the test set; AUC: 0.832, 95%CI: 0.807-0.857 in the validation set)
Zou et al[25]	400 patients with HCV-related cirrhosis who achieved SVR with direct-acting antivirals	Chronic Hepatitis C Research Program of Jiangsu (China)	RF	Clinical and biological data	AUC for the longitudinal models were 0.9507 (0.8838-0.9997), 0.8767 (0.6972-0.9918), and 0.8307 (0.6941-0.9993) for 1-year, 2-year, and 3-year risk prediction, respectively
Nam et al[26]	424 patients with HBV-related cirrhosis	Two tertiary hospitals (Republic of Korea)	DL-based model	Clinical and biological data	Better performance of DL-based model compared with previously reported risk models
Ioannou et al[27]	48151 patients with HCV-related cirrhosis; no external validation	National Veterans Health Administration	LR with cross-sectional inputs (cross-sectional LR); LR with longitudinal inputs (longitudinal LR); DL model with longitudinal inputs	Baseline and longitudinal predictors	DL models outperformed conventional LR models: [mean (SD) AUC: 0.806 (0.025); mean (SD) Brier score: 0.117 (0.007)]
Audureau et al[28]	836 patients with compensated biopsy-proven hepatitis C virus-cirrhosis	French ANRS CO12 CirVir Cohort	Three prognostic models for HCC occurrence: (1) Fine-Gray regression as a benchmark; (2) DT; and (3) RF	Parameters before and after SVR	Externally validated C-indexes before/after SVR were 0.64/0.64 (Fine-Gray), 0.60/62 (DT), and 0.71/0.70 (RF)
Singal et al[29]	442 patients with Child-Pugh A or B cirrhosis	University of Michigan cohort and HALT-C cohort (for independent validation)	RF	Clinical and biological data	Machine learning algorithm had significantly better diagnostic accuracy, a net reclassification improvement (P < 0.001), and an integrated discrimination improvement (P = 0.04)

AdaBoost: Adaptive boosting; AI: Artificial intelligence; AUC: Area under the curve; DL: Deep learning; DT: Decision tree; HALT-C: Hepatitis C Antiviral Long-Term Treatment Against Cirrhosis; HBV: Hepatitis B virus; HCV: Hepatitis C virus; LR: Logistic regression; MLP: Multilayer perceptron; RF: Random forest; SD: Standard deviation; SVR: Sustained virological response; XGBoost: Extreme gradient boosting.