Locoregional Therapy Response Evaluation in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) ranks among the most prevalent and deadly primary liver malignancies worldwide. For patients ineligible for surgical resection or transplantation, a range of locoregional therapies—such as transarterial chemoembolization (TACE), radiofrequency ablation, stereotactic body radiotherapy (SBRT) and radioembolisation—has become the cornerstone of management. Precise assessment of treatment response underpins optimal patient selection, timely retreatment decisions and prognostic stratification. Conventional anatomic criteria, including RECIST and WHO size-based measures, have been supplemented by enhancement-based criteria (EASL, mRECIST) and structured reporting systems (LI-RADS Treatment Response). Advances in quantitative imaging and computational analysis are now enhancing objectivity, harnessing multiphase CT and MRI data to extract imaging biomarkers that reflect tissue perfusion, tumour viability and microstructure. Emerging radiomics and machine-learning approaches integrate clinical and histopathological parameters to predict response patterns, offering the promise of personalised therapy pathways and improved survival outcomes.

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Locoregional Therapy Response Evaluation in Hepatocellular Carcinoma publication trend

The graph below shows the total number of articles in locoregional therapy response evaluation in hepatocellular carcinoma across all publications each year (not limited to Nature Index journals).

Technical terms

Locoregional therapy: Targeted treatment delivered directly to liver tumours, such as TACE, ablation or SBRT, to maximise local tumour control while sparing healthy tissue.

RECIST (Response Evaluation Criteria in Solid Tumours): Standardised size-based criteria for assessing changes in tumour diameter on imaging.

mRECIST (modified RECIST): Refinement of RECIST that evaluates viable tumour based on arterial enhancement rather than size alone, improving assessment after embolic or ablative therapies.

Radiomics: Extraction of large-scale quantitative features from medical images to characterise tumour phenotypes and predict clinical outcomes.

Machine learning: Computational methods that identify patterns in data to build predictive models, often combining imaging features with clinical variables.

LI-RADS Treatment Response (LR-TR): An algorithmic framework for categorising post-treatment imaging findings as non-viable, equivocal or viable to standardise reporting in HCC follow-up.

References

1. Multimodality annotated hepatocellular carcinoma data set including pre- and post-TACE with imaging segmentation. Scientific Data (2023).
2. Prediction of initial objective response to drug-eluting beads transcatheter arterial chemoembolization for hepatocellular carcinoma using CT radiomics-based machine learning model. Frontiers in Pharmacology (2024).
3. Multi-algorithms analysis for pre-treatment prediction of response to transarterial chemoembolization in hepatocellular carcinoma on multiphase MRI. Insights into Imaging (2023).
4. Radiological‐pathological analysis of WHO, RECIST, EASL, mRECIST and DWI: Imaging analysis from a prospective randomized trial of Y90 ± sorafenib. Hepatology (2013).
5. Assessment of hepatocellular carcinoma treatment response with LI-RADS: a pictorial review. Insights into Imaging (2019).