Explainable artificial intelligence and ensemble learning for hepatocellular carcinoma classification: State of the art, performance, and clinical implications

Table 1 Explainable ensemble learning for hepatocellular carcinoma classification

Ref.	Ensemble learning method	Explainability technique	Dataset used (including size and type)	Performance metrics	Clinical relevance of top predictive features
[119]	Soft Voting Ensemble	SHAP	SEER program (n = 1897), External validation from two tertiary hospitals in China (n = 98)	AUC: 0.779 (internal), 0.764 (external); Brier score: 0.191 (internal), 0.195 (external)	Chemotherapy, radiation therapy, lung metastases (reflect metastatic burden and treatment history)
[112]	RF (best performing), NB, LR, KNN	LIME	NCBI GSE14520 microarray dataset (445 samples, 22268 genes)	Accuracy: 96.53%; Precision: 97.30%; AUC: 0.95	Gene expression profiles (e.g., TP53, CTNNB1 mutations linked to tumorigenesis)
[118]	Stacking Ensemble	LIME	Data from 1622 liver cancer patients (46 variables)	AUC: 0.9826 (training), 0.9675 (testing)	Tumor size, vascular invasion (key pathological determinants of staging)
[127]	AutoML (TPOT) leading to a Tree-based model (likely ensemble)	TreeSHAP	Publicly accessible metabolomics data of HCC patients and cirrhotic controls	AUC: 0.81	Metabolite ratios (e.g., glutamate/glutamine reflecting metabolic reprogramming in malignancy)
[67]	Stacking ensemble		Dataset from 165 HCC patients at Coimbra's Hospital and University Centre (49 features)	Accuracy: 0.9030; F1-score: 0.8857	AFP, child score
[126]	RF, RR, AdaBoost, DT, LR		Hospital Authority Data Collaboration Lab in Hong Kong (n = 124006 patients with chronic viral hepatitis)	AUC: 0.842 (RR, training), 0.844 (RR, validation); 0.992 (RF, training), 0.837 (RF, validation)	Viral load, platelet count (indicators of viral activity and portal hypertension)
[70]	GB (best performing), RT, RF, XGBoost	SHAP	Liver disease dataset	Accuracy, precision, recall, specificity, AUC (high values reported)	Bilirubin, albumin (liver function markers correlating with prognosis)
[6]	RF (best performing)	SHAP	Data from patients with HCC and HBV (training set: n = 361, Validation set: n = 155)	AUC: 0.996 (training), 0.993 (validation)	AFP, AST/ALT ratio, GGT (liver damage and tumor biomarkers validated against BCLC criteria)
[125]	XGBoost	SHAP	MRI data: 117 patients (training), 33 (external validation), 30 (prospective validation)	AUC: 0.835 (training), 0.830 (internal), 0.816 (external), 0.776 (prospective)	Arterial hyperenhancement, venous washout (LI-RADS imaging features for malignancy); Tumor margin irregularity (radiological indicator of invasiveness)

AdaBoost: Adaptive boosting; AFP: Alpha-fetoprotein; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; AUC: Area under the curve; BCLC: Barcelona Clinic Liver Cancer; DT: Decision tree; ET: Extra trees; HBV: Hepatitis B virus; HCC: Hepatocellular carcinoma; GB: Gradient boosting; GGT: Gamma-glutamyl transferase; KNN: K-nearest neighbor; LI-RADS: Liver imaging reporting and data system; LIME: Local interpretable model-agnostic explanations; LR: Logistic regression; MRI: Magnetic resonance imaging; NCBI: National Center for Biotechnology Information; NB: Naïve bayes classifier; RF: Random forest; RR: Ridge regression; SHAP: SHapley Additive exPlanations; TreeSHAP: Tree-based SHapley Additive exPlanations; XGBoost: Extreme gradient boosting.