Table 5.

ML-based prognostic model characteristics of HCC patients after MWA

Author	ML Algorithms	Prediction Target	Key Predictive Factors	Main Results/Performance Metrics	Model Validation & Interpretability	Other Important Findings
Ren 2023 [59]	CatBoost, SVM, RF, LR	LTP	Number of tumors, Albumin, AFP, Tumor size, Age, INR	Best performance by CatBoost model, AUC of 0.898	Internal and external validation	NA
An 2022 [60]	LR, RF, SVM, XGBoost	ER	Tumor number, Platelets, AFP, Comorbidity score, WBC, ChE, PT, Neutrophils, Etiology	Best performance by XGBoost model, AUC 0.74 (internal) and 0.76 (external)	Internal and external validation, SHAP and LIME algorithms for model explanation	NA
Shahveranova 2023 [61]	LR	LTP	Preoperative extrahepatic metastasis, Tumor size, CA 19-9	Combined Model 2 (clinical data and Phase 2 radiomic features) has the highest discriminative performance for LTP prediction (AUC 0.981)	NA	LTP group patients have significantly higher radiomic scores in both MRI phases (Phase 1 and Phase 2)

CatBoost: A machine learning algorithm based on decision trees; LTP: Local Tumor Progression; INR: International Normalized Ratio; ER: Early Recurrence; ChE: Cholinesterase; PT: Prothrombin Time; WBC: White Blood Cell Count; CRLM: Colorectal Liver Metastasis; CA 19-9: A tumor marker for gastrointestinal malignancies; LIME: Local Interpretable Model-agnostic Explanations; AFP: Alpha-Fetoprotein.