Decoding Treatment Response in Hepatocellular Carcinoma: The Era of Radiomics and Radiogenomics.

Area of Science:

• Oncology
• Radiology
• Immunotherapy

Background:

• Hepatocellular carcinoma (HCC) immunotherapy faces challenges with low response rates and limited predictive biomarkers.
• Radiomics offers a non-invasive method to extract detailed imaging features for improved patient management.

Purpose of the Study:

• To systematically review radiomics applications in HCC immunotherapy.
• To highlight radiomics' role in predicting treatment response, assessing the tumor immune microenvironment (TIME), and identifying molecular signatures.
• To discuss challenges and future directions for clinical translation.

Main Methods:

• Systematic review of multimodal imaging data in HCC immunotherapy.
• Analysis of radiomics feature extraction and model construction techniques.
• Evaluation of radiomics for predicting treatment outcomes and TIME characteristics.

Main Results:

• Radiomics shows promise in predicting HCC immunotherapy response and characterizing the TIME.
• Multimodal imaging data and advanced feature extraction enhance predictive capabilities.
• Key challenges include small sample sizes and limited model generalizability.

Conclusions:

• Radiomics is a valuable tool for enhancing HCC immunotherapy precision.
• Standardized multicenter studies and multi-omics integration are crucial for future development.
• Clinical translation of radiomics can optimize patient selection and treatment strategies for HCC.