Artificial intelligence-driven risk estimation, early detection, diagnostic characterisation and prognosis in hepatitis b-related hepatocellular carcinoma

Table 1 Artificial intelligence models for risk estimation in chronic hepatitis B

Model/Ref.	Design, region	Population, aetiology and evidence category (n)	Endpoint	Algorithm	Validation	Performance	Calibration and explainability	Key limitation
PLAN-B[17]	Retrospective, multinational (Korea, Europe)	Antiviral-treated CHB; HBV-specific (> 13000)	Incident HCC	Gradient boosting	External (independent Korean and Caucasian cohorts)	Superior stratification to legacy scores; minimal-risk group identified	Not reported in detail	Retrospective derivation; treated patients only
Nam et al[18]	Retrospective, Korea	HBV-related cirrhosis on entecavir; HBV-specific (424 derivation, 316 external)	Incident HCC	Deep neural network	Internal and external	C-index 0.719 internal, 0.782 external; six comparator scores all < 0.60 in same cohort	Explainability not reported	Small derivation cohort; single-country
Singal et al[16]	Retrospective cohort	Cirrhosis; general HCC/non-HBV-specific	Incident HCC	Multiple ML algorithms vs regression	Internal	ML superior to conventional regression	Not reported	Indirect evidence for HBV; early-generation methods
Ioannou et al[19]	Retrospective, United States (Veterans Affairs)	HCV-related cirrhosis; HCV-derived (48151)	3-year HCC incidence	Recurrent neural network	Internal	AUC: 0.759 overall; 0.806 in sustained virological response	Not reported	Aetiology not HBV; internal validation only
Phan et al[20]	Retrospective, Taiwan (national insurance)	Viral hepatitis; mixed-aetiology (approximately 1000000 records)	Incident liver cancer	Convolutional neural network	Internal	AUC: 0.886	Not reported	Administrative data; no laboratory granularity
Wu et al[15]	Case-control, China	CHB; HBV-specific	Incident HCC	Five algorithms with LASSO feature selection	Internal	AUC commonly > 0.80	SHAP-based interpretation reported	Case-control design; no external validation
Li et al[21]	Retrospective, single centre, China	HBV-related cACLD; HBV-specific (1051; 736 training, 315 validation)	Incident HCC (103 events, 9.8%)	RF, XGBoost, SVM, LR, NB	Internal (random split)	RF AUC: 0.979 training, 0.956 validation; LSM alone 0.745 and 0.826	Calibration plots reported; SHAP reported	Single centre; no external validation; authors attribute high AUC partly to RF overfitting
Kuaaroon et al[22]	Retrospective, Thailand	Chronic HBV; HBV-specific	Short-term HCC development	Extreme gradient boosting	Internal with independent cohort	Models developed to tailor surveillance interval	Not reported in detail	Limited external generalisability

CHB: Chronic hepatitis B; HBV: Hepatitis B virus; HCC: Hepatocellular carcinoma; C-index: Harrell concordance index; ML: Machine learning; HCV: Hepatitis C virus; AUC: Area under the receiver operating characteristic curve; LASSO: Least absolute shrinkage and selection operator; SHAP: Shapley additive explanations; cACLD: Compensated advanced chronic liver disease; RF: Random forest; XGBoost: Extreme gradient boosting; SVM: Support vector machine; LR: Logistic regression; NB: Naive Bayes; LSM: Liver stiffness measurement.