Integrating radiomics and machine learning for the diagnosis and prognosis of hepatocellular carcinoma

Table 1 Summary of representative studies applying radiomics and machine learning in hepatocellular carcinoma

Ref.	Imaging modality	ML model/algorithm	Integrated features	Clinical application
Qi et al[2], 2024	CT	Logistic Regression with Radiomics	Texture features	Predict response to immunotherapy
Molostova et al[3], 2024	MRI	Radiomics + binary classification	Texture + Intensity	Differentiate early HCC from regenerative/dysplastic nodes
Wang et al[4], 2025	Multi-modal clinical data	Ensemble ML models	Clinical + Radiomics + Genomics	HCC diagnosis
Zhang et al[5], 2024	CT + clinical	XGBoost	Radiomics + clinical	Prognosis post-TACE
Şahin et al[6], 2025	CT	Deep learning (CNN)	Imaging only	Detect HCC from CT
Yin et al[7], 2025	CT	ResNet-based Deep learning	Imaging + clinical	Predict prognosis after combination therapy
Shen et al[9], 2024	Clinical + imaging	SHAP-integrated ML models	Multi-modal	Predict prognosis for advanced HCC
Cai et al[10], 2024	Radiomics + RNA-Seq	Survival analysis + ML	Radiomics + transcriptomics	Predict survival
Lou et al[11], 2024	Clinical + imaging	ML-based nomogram	Accessible clinical indicators	Predict prognosis

CT: Computed tomography; MRI: Magnetic resonance imaging; ML: Machine learning; HCC: Hepatocellular carcinoma; CNN: Convolutional neural network; ResNet: Residual convolutional networks; SHAP: SHapley Additive exPlanations.