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.

Table 2 Artificial intelligence for early detection during surveillance

Ref.	Design, region	Population, aetiology and evidence category (n)	Endpoint	Approach	Validation	Performance	Calibration and explainability	Key limitation
Chaiteerakij et al[23]	Retrospective image cohort, Thailand	Patients undergoing hepatic ultrasonography; general HCC (26288 images, 5444 patients)	Focal lesion detection and malignancy	YOLOv5 object detection	Internal	Detection 84.8%; sensitivity for malignancy 97.0%; maintained at lesions ≤ 1 cm	Not reported	Image-level rather than outcome-level endpoint
Rhyou and Yoo[24]	Retrospective, single centre	Ultrasound dataset; general HCC	HCC classification	Wavelet-transform deep network (HCC-Net)	Internal	Accuracy: 96.2%; sensitivity: 97.6%; specificity 98.1%	Not reported	Single-centre dataset; no external validation
Brehar et al[25]	Retrospective	Ultrasound images; general HCC	Tumour vs cirrhotic parenchyma	CNN vs texture-based ML	Internal	CNN superior to conventional ML	Not reported	Small dataset; no clinical endpoint
Dana et al[26]	Retrospective multicentre	Cirrhosis under surveillance; mixed-aetiology	Risk stratification and early HCC detection	Ultrasound-based deep learning	Internal and multicentre	Improved stratification and detection of early HCC	Not reported in detail	Retrospective; no prospective outcome data
Schotten et al[27]	Retrospective, Europe	Chronic hepatitis B and C; mixed-aetiology	Early-stage HCC detection	GALAD (logistic regression; conventional comparator)	Internal	AUC around 0.95 in selected cohorts	Not applicable	Not an AI model; comparator instrument
Marsh et al[28]	Phase 3 biomarker validation, United States	Cirrhosis; mixed-aetiology	HCC detection within 12 months of diagnosis	GALAD (conventional comparator)	Prospective phase 3 design	Improved detection in the 12 months preceding clinical diagnosis	Not applicable	Not an AI model; comparator instrument
Johnson et al[29]	Retrospective, surveillance setting	Surveillance cohort; mixed-aetiology	HCC detection	Random forest applied to GALAD biomarkers	Internal	Outperformed original logistic GALAD in discrimination and F1 score	Not reported	Internal validation only
Tayob et al[30]	Community-based cohort, United States	Cirrhosis of multiple aetiologies; mixed-aetiology	Early HCC detection	HES V2.0 algorithm	External validation in community cohort	Validated in community-based cirrhosis cohort	Not reported in detail	Aetiology heterogeneous; limited HBV representation
Sun et al[31]	Case-control, China	Healthy, HBV-infected, cirrhotic and HCC participants; mixed-aetiology with HBV subgroup (374)	All-stage and early-stage HCC detection	Targeted bisulfite sequencing with ML	Internal	All-stage sensitivity 96.7% at specificity 93.7%; early-stage sensitivity 90.0%	Not reported	Case-control design including healthy controls; spectrum bias inflates apparent sensitivity
Xu et al[32]	Case-control, China	HCC patients and normal controls; general HCC (1098 HCC, 835 controls)	Diagnosis and prognosis	ctDNA methylation markers with prediction modelling	Internal	Diagnostic and prognostic models correlating with tumour burden, stage and treatment response	Not reported	Healthy comparator group; not a surveillance population

HCC: Hepatocellular carcinoma; YOLOv5: You only look once, version 5; CNN: Convolutional neural network; ML: Machine learning; GALAD: Gender, age, alpha-fetoprotein-L3 fraction, alpha-fetoprotein and des-gamma-carboxyprothrombin score; AUC: Area under the receiver operating characteristic curve; AI: Artificial intelligence; HES: Hepatocellular carcinoma early detection screening algorithm; HBV: Hepatitis B virus; ctDNA: Circulating tumour DNA.

Table 3 Artificial intelligence for diagnostic characterisation and prognosis

Ref.	Design, region	Population, aetiology and evidence category (n)	Endpoint	Approach	Validation	Performance	Calibration and explainability	Key limitation
Wei et al[33]	Retrospective, China	Resected HCC; recurrence-related (434)	Early recurrence after treatment	MRI radiomics with 3D U-net automated segmentation	Internal validation cohort	C-index 0.706; exceeded BCLC and CNLC staging	Not reported	Post-diagnosis endpoint; single-country
Ji et al[34]	Retrospective, multi-institutional, China	Resected HCC; recurrence-related	Post-resection recurrence	Machine learning on contrast-enhanced CT radiomics	Multi-institutional	Predicted recurrence across institutions	Not reported	Retrospective; feature instability across scanners
Zhao et al[35]	Retrospective, China	HCC after curative resection; recurrence-related	Early recurrence	Deep-learning radiomics on contrast-enhanced MRI	Internal	Hybrid models outperformed single-stream inputs	Not reported	Single-centre; no external validation
Chaudhary et al[36]	Retrospective, public cohorts	Liver cancer; prognostic	Survival and molecular subgroup	Deep-learning multi-omics integration	External replication in independent cohorts	Reproducible prognostic molecular subgroups	Not reported	Omics assays costly and unavailable in high-burden settings
Kucukakcali et al[37]	Case-control, open-access gene expression data	HBV-related HCC vs HBV infection alone; HBV-specific (17 vs 36)	Discrimination of HBV-HCC from HBV alone	Extreme gradient boosting with 10-fold cross-validation	Internal cross-validation	18 genes selected; candidate biomarkers nominated	Not reported	Very small sample; reported accuracy not an estimate of real-world performance

HCC: Hepatocellular carcinoma; MRI: Magnetic resonance imaging; U-net: Convolutional network architecture for biomedical image segmentation; C-index: Harrell concordance index; BCLC: Barcelona Clinic Liver Cancer; CNLC: China Liver Cancer staging system; CT: Computed tomography; HBV: Hepatitis B virus.

Table 4 Conventional risk scores for hepatocellular carcinoma in chronic hepatitis B, with development and validation populations

Score	Development population	Validation populations	Component variables	Reported discrimination
GAG-HCC	Untreated CHB, Chinese (Hong Kong)	Asian CHB cohorts, largely untreated	Age, sex, HBV DNA, core promoter mutation, cirrhosis	C-statistic up to approximately 0.89[7,10]
CU-HCC	CHB carriers, Chinese (Hong Kong)	Asian CHB cohorts; treated and untreated	Age, albumin, bilirubin, HBV DNA, cirrhosis	AUC approximately 0.73-0.85[8]
REACH-B	Untreated CHB, Taiwanese (REVEAL-HBV)	Asian cohorts; attenuated performance in treated patients	Sex, age, ALT, HBeAg, HBV DNA	AUC approximately 0.64-0.79[9]
LSM-HCC	CHB with transient elastography, Chinese (Hong Kong)	Asian CHB cohorts with elastography	Liver stiffness, age, albumin, HBV DNA	AUC approximately 0.77-0.83[10]
PAGE-B	Caucasian CHB on 5-year antiviral therapy (European)	European treated cohorts; reduced performance in Asian populations	Platelets, age, sex	AUC approximately 0.78-0.80[11]
mPAGE-B	Asian CHB on antiviral therapy (Korean)	Asian treated cohorts	PAGE-B variables plus albumin	AUC approximately 0.80-0.82[12]
aMAP	Mixed CHB and chronic hepatitis, multinational (Asian and Caucasian)	Multinational; validated in HBV cirrhosis on ETV or TDF	Age, sex, albumin-bilirubin, platelets	AUC/C-index approximately 0.72-0.87[13,14]

Scores were developed in populations differing by ethnicity, treatment status and stage of liver disease; reported discrimination values are therefore not directly comparable between rows. GAG-HCC: Guide with Age, Gender, Hepatitis B virus DNA, Core Promoter Mutations and cirrhosis-hepatocellular carcinoma; CHB: Chronic hepatitis B; HBV: Hepatitis B virus; CU-HCC: Chinese University hepatocellular carcinoma score; AUC: Area under the receiver operating characteristic curve; REVEAL-HBV: Risk Evaluation of Viral Load Elevation and Associated Liver Disease/Cancer-Hepatitis B Virus; REACH-B: Risk estimation for hepatocellular carcinoma in chronic hepatitis B; ALT: Alanine aminotransferase; HBeAg: Hepatitis B e antigen; LSM: Liver stiffness measurement; HCC: Hepatocellular carcinoma; PAGE-B: Platelets-age-gender-hepatitis B; mPAGE-B: Modified platelets-age-gender-hepatitis B; aMAP: Age-male-albumin-bilirubin-platelets; ETV: Entecavir; TDF: Tenofovir disoproxil fumarate.

Table 5 Present-day gaps and translational priorities

Domain	Current limitation	Recommended priority
Study design	Predominantly retrospective, single-centre	Prospective, multicentre, outcome-based designs
Aetiological specificity	HBV-specific evidence a minority of published work	Dedicated HBV cohorts rather than extrapolation from mixed or HCV populations
Validation	Internal validation dominant; external scarce	Mandatory external, temporal and geographic validation
Comparison	Superiority inferred across dissimilar studies	Head-to-head comparison against named scores within a single validation cohort
Calibration	Inconsistently reported	Routine calibration alongside discrimination
Explainability	Variable use of interpretability methods	Standardised Shapley-based explanation
Reporting	Uneven adherence to standards	TRIPOD+AI and PROBAST+AI as publication conditions
Equity	Cohorts skewed to East Asian and European data	Inclusion of African and South Asian HBV populations
Data scale	Siloed institutional datasets	Federated learning across centres

HBV: Hepatitis B virus; HCV: Hepatitis C virus; PROBAST+AI: Prediction model risk of bias assessment tool, artificial intelligence extension; TRIPOD+AI: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis, artificial intelligence extension.