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.