Artificial intelligence in prognostication and treatment response modeling in advanced hepatocellular carcinoma

Table 1 Methodological taxonomy of artificial intelligence applications in advanced hepatocellular carcinoma

Axis	Categories	Typical application in advanced HCC	Key methodological risk
Learning paradigm	Supervised/unsupervised	Survival prediction, response modeling vs clustering phenotypes	Overfitting in supervised models
Clinical objective	Prognostic/predictive	OS estimation vs treatment benefit estimation	Confounding by indication
Outcome structure	Binary/time-to-event/competing risk	12-month mortality vs OS vs liver failure-specific death	Improper censoring handling
Data modality	Radiomics/deep learning imaging/pathomics/clinical ML/multimodal	CNN imaging models; LASSO radiomics; radiopathomics	Feature instability, dimensionality inflation

HCC: Hepatocellular carcinoma; OS: Overall survival; ML: Machine learning; CNN: Convolutional neural network; LASSO: Least absolute shrinkage and selection operator.

Table 2 Prognostic vs predictive modeling in advanced hepatocellular carcinoma

Feature	Prognostic model	Predictive model
Core question	What is the patient’s outcome risk?	Does the patient benefit more from treatment A vs B?
Treatment consideration	Ignored or uniform	Central to model structure
Typical dataset	Single-treatment cohort	Multi-treatment or counterfactual framework
Key bias risk	Overfitting	Confounding by indication
Statistical requirement	Discrimination and calibration	Causal inference methods (propensity modeling, counterfactual ML, uplift models)
Clinical utility	Risk stratification	Therapy selection guidance

ML: Machine learning.

Table 3 Discrimination gains of artificial intelligence vs conventional models

Clinical context	AI model type	AI performance	Comparator	Comparator performance	Absolute gain	Validation type
Atezo-Bev (advanced HCC)	Radiomic + clinical ML	AUC 0.89 (derivation), 0.75 (external)	BCLC/ALBI	0.61/0.48	+0.14-0.41	Multicenter external
TACE	Radiomic + clinical	AUC approximately 0.79-0.81	Clinical-only	Approximately 0.60	+0.19-0.21	Internal validation

Values represent reported discrimination metrics from individual retrospective studies and are not pooled estimates. Variability reflects differences in study design, patient populations, and validation strategies across cohorts. AI: Artificial intelligence; HCC: Hepatocellular carcinoma; Atezo-Bev: Atezolizumab plus bevacizumab; ML: Machine learning; AUC: Area under the receiver operating characteristic curve; ALBI: Albumin-bilirubin; BCLC: Barcelona Clinic Liver Cancer; TACE: Transarterial chemoembolization.

Table 4 What artificial intelligence adds beyond conventional staging

Capability	Conventional staging	AI-based modeling
Feature handling	Limited predefined variables	High-dimensional nonlinear integration
Interaction modeling	Limited	Complex nonlinear interactions
Spatial imaging patterns	Qualitative	Quantitative radiomic features
Multimodal fusion	Rare	Integrated imaging + pathology + clinical
Intra-stage stratification	Limited	Fine-grained risk clustering

AI: Artificial intelligence.

Table 5 Major methodological limitations in current artificial intelligence-hepatocellular carcinoma studies

Domain	Specific limitation	Consequence
Statistical design	High-dimensional feature space with low event count	Model instability, optimism bias
Validation	Limited external and temporal validation	Poor transportability
Causal modeling	Lack of treatment-effect estimation frameworks	Prognostic misinterpreted as predictive
Radiomics	Scanner variability, segmentation inconsistency	Reduced reproducibility
Calibration	Rarely reported	Poor absolute risk estimation
Clinical utility	No decision curve or impact analysis	Uncertain real-world benefit

Table 6 Barriers and potential solutions for clinical translation of artificial intelligence in advanced hepatocellular carcinoma

Limitation	Impact	Potential solution
Overfitting/low EPV	Model instability	Regularization, larger datasets
Limited external validation	Poor generalizability	Multicenter validation
Lack of causal modeling	Misinterpretation of treatment benefit	Counterfactual ML, uplift modeling
Poor calibration	Inaccurate risk estimation	Calibration plots, Brier score
Radiomic variability	Reduced reproducibility	Standardization, ComBat harmonization
Black-box models	Low clinical trust	Explainable AI
Lack of prospective validation	Limited clinical adoption	Prospective trials

EPV: Events per variable; ML: Machine learning; AI: Artificial intelligence; ComBat: Empirical Bayes-based batch effect harmonization method.

Table 7 Roadmap for clinical translation of artificial intelligence in advanced hepatocellular carcinoma

Priority area	Required action	Goal
Validation	Multicenter, temporal, prospective evaluation	Transportability
Causal modeling	Incorporate counterfactual ML/uplift modeling	True treatment guidance
Reporting	Adherence to TRIPOD-AI/CONSORT-AI	Transparency
Calibration	Brier score, calibration plots	Reliable absolute risk
Clinical impact	Decision curve analysis, outcome trials	Demonstrate net benefit
Infrastructure	Federated learning, harmonization protocols	Data diversity and robustness

ML: Machine learning; TRIPOD-AI: Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis-Artificial Intelligence; CONSORT-AI: Consolidated Standards of Reporting Trials-Artificial Intelligence.