Table 1.

ML-based prognostic model characteristics of HCC patients after TACE

Author	ML Algorithms	Prediction Targets	Key Predictors	Main Results/Performance Indicators	Model Validation and Interpretability	Other Important Findings
Muller 2022 [32]	CNN	OS, PFS, TTUP	SV	Significant correlation between SV and survival rates	Internal validation, S⊘rensen Dice score, Bland-Altman plot	Spleen volume significantly correlates with risk of liver dysfunction after TACE
Bartnik 2024 [33]	DL, RSF, COX	OS, PFS	Tumor VOI and non-tumor VOI	OS: C-index range 0.616 to 0.640. PFS: C-index 0.713	Cross-validation, XAI	Multiple VOI features extracted from CT images, overcoming manual segmentation limitations
Bernatz 2023 [34]	RF	TACE response, OS	Radiomic features and clinical mHAP-II score	Lesion-level AUC 0.70, Accuracy 0.72; Patient-level AUC 0.62; C-index 0.67	Reliability and redundancy analysis	Supports the potential of lipid deposition as an imaging biomarker
Dong 2021 [35]	XGBoost, Decision Tree, SVM, RF, KNN, ANN	Early treatment response post first cTACE	Portal vein tumor thrombus type, Albumin level, Tumor distribution in liver	RF model performed best, AUC 0.802, Accuracy 0.784, Sensitivity 0.904, Specificity 0.480	5-fold cross-validation	Portal vein tumor thrombus type is the most important factor for predicting response to first cTACE treatment
Ma 2023 [36]	CART, AdaBoost, XGBoost, RF, SVM	Response to combination therapy (lenvatinib + TACE)	K, LDL, D-D, Red blood cells, ALT, ALB, Mono, Tumor size, TG, and Age	RF model AUC 0.91, SVM and RF performed best	SHAP algorithm enhanced model interpretability	Lower serum K, older age, higher BMI, and larger tumor size correlate with better efficacy of combination therapy
Peng 2021 [37]	Linear model, LR, SVM, GBM, RF, DL	TACE treatment response	Tumor size	DL model AUC 0.972, Integrated model AUC 0.994	Multicenter data validated model robustness	Tumor size significantly correlates with initial treatment response, while AFP levels do not
Zhang 2022 [38]	ResNet18 and Multilayer Perceptron	TACE treatment response	DSA video information, Demographics, and liver function parameters	Accuracy rates on internal and external validation sets were 78.2% and 75.1% respectively	Internal and external validation	Predictive model performance using segmentation results as input is slightly lower than using true segmentation results, but not significantly

ML: Machine Learning; CNN: Convolutional Neural Network; OS: Overall Survival; PFS: Progression-Free Survival; TTUP: Time to Tumor Progression; SV: Segmentation Volume; TACE: Transarterial Chemoembolization; VOI: Volume of Interest; DL: Deep Learning; RSF: Random Survival Forest; COX: Cox Proportional Hazards Model; RF: Random Forest; AUC: Area Under the Curve; mHAP-II: Modified Hepatoma Arterial Embolization Prognostic Score; SVM: Support Vector Machine; KNN: k-Nearest Neighbors; GBM: Gradient Boosting Machine; LR: Logistic Regression; DL: Deep Learning (used in the context of the algorithm name); AFP: Alpha-Fetoprotein; ALT: Alanine Aminotransferase; ALB: Albumin; Mono: Monocytes; TG: Triglyceride; BMI: Body Mass Index; DSA: Digital Subtraction Angiography; AUC: Area Under the Receiver Operating Characteristic Curve; XAI: Explainable Artificial Intelligence; SHAP: SHapley Additive exPlanations.