Table 2.

ML-based prognostic model characteristics of HCC patients after HAIC

Author	ML Algorithms	Prediction Target	Key Predictive Factors	Main Results/Performance Metrics	Model Validation & Interpretability	Other Important Findings
Xu 2022 [39]	DL, XGBoost	OR	APE, RVI, R score, DL score	AUC in training set = 0.988, internal validation set AUC = 0.915, external validation set AUC = 0.896	Internal and external validation	Radiological parameters (APE and RVI) may predict the efficacy of HAIC better than clinical characteristics
Quan 2024 [40]	InceptionV4-CNN	HAIC response	MRI data, HAIC cycles, cancer thrombus, NLR	AUC in training cohort = 0.871, internal validation cohort AUC = 0.826	Cross-validation and independent validation, CAM used for visualization	Age, HAIC cycle number, tumor thrombus, extrahepatic spread, and AST level are independent predictors
Zhao 2023 [41]	LR	PFS	Radiomic score (Radscore) and ALBI score	Combined model AUC in training and validation sets are 0.79 and 0.75, respectively	Internal validation	NA
He 2023 [42]	MDLR	Post-HAIC patient prognosis	CECT radiomic features, portal vein cancer thrombus, HAIC response, HAIC cycles	AUC for survival prediction model in internal and external validation sets are 0.87 and 0.83	Internal and external validation	Tumor burden and distribution as well as tumor microenvironment features are associated with prognosis

XGBoost: Extreme Gradient Boosting; OR: Objective Response; APE: Asymmetry of Parenchymal Enhancement; RVI: Reduction in Viable Tumor on Initial; R score: Radiographic Response Score; DL score: Deep Learning Score; HAIC: Hepatic Arterial Infusion Chemotherapy; MRI: Magnetic Resonance Imaging; NLR: Neutrophil-to-Lymphocyte Ratio; CAM: Class Activation Mapping; PFS: Progression-Free Survival; Radscore: Radiomic Score; ALBI: Albumin-Bilirubin Grade; MDLR: Multitask Deep Learning Radiomics; CECT: Contrast-Enhanced Computed Tomography; AST: Aspartate Aminotransferase; NA: Not Available.