CT and magnetic resonance imaging are becoming central to hepatocellular carcinoma treatment with transarterial radioembolisation. This therapy delivers yttrium-90-loaded microspheres through the hepatic artery to target liver tumours while limiting damage to normal tissue. A recent narrative review in Academic Radiology summarises evidence on CT and MRI biomarkers across treatment planning, response assessment and outcome prediction. The evidence points to growing use of functional imaging, body-composition measures, radiomics and machine learning, alongside a need for validation.
Imaging Supports Treatment Planning
CT and MRI have moved beyond simple anatomical mapping in hepatocellular carcinoma treated with transarterial radioembolisation. Before treatment, imaging can help assess tumour extent, liver volume, blood flow and liver function. These details are important because the therapy depends on delivering microspheres accurately to tumour tissue while limiting exposure to healthy liver and lungs.
One key planning step is lung shunt assessment. Excessive shunting can increase the risk of radiation reaching the lungs and may require dose reduction or treatment deferral. Dynamic contrast-enhanced MRI and contrast-enhanced CT features, including tumour size, tumour volume, blood flow patterns and enhancement features, are associated with elevated lung shunt fraction. These methods may help identify patients needing closer assessment, although nuclear medicine-based SPECT/CT remains the standard quantitative approach.
Imaging also supports dosimetry. Tumour-to-nontumour ratio helps estimate how selectively microspheres reach tumour tissue rather than surrounding liver. Cone-beam CT, angiography-CT and C-arm CT can help estimate perfused areas and dose distribution. These techniques show agreement with nuclear medicine methods and may improve boundary delineation and treatment personalisation. Functional MRI can also show changes in treated liver tissue after therapy. Artificial intelligence models are being tested for predicting microsphere distribution, liver growth after treatment and toxicity risk, but most evidence remains early and requires stronger validation.
Response Assessment Focuses on Viable Tumour
Imaging after transarterial radioembolisation is important because tumour response may not appear as simple shrinkage. Size-based criteria such as Response Evaluation Criteria in Solid Tumors 1.1 provide a standard framework but may underestimate the effects of treatment when therapy causes tumour necrosis without major size reduction.
Modified Response Evaluation Criteria in Solid Tumors focuses on viable tumour by assessing arterial-phase enhancement. This approach is more suited to locoregional therapies that affect tumour blood supply and viability. It has shown stronger links with overall survival and treatment response in hepatocellular carcinoma treated with transarterial radioembolisation.
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The Liver Imaging Reporting and Data System Treatment Response Algorithm provides a lesion-level method for assessing treated tumours. The 2024 version includes guidance specific to radiation-based therapies and adds a non-progressing category. This reflects the delayed response pattern that can occur after transarterial radioembolisation, where treated lesions may show stable or reduced enhancement before later becoming non-viable. In suitable cases, this category can support close surveillance rather than early retreatment.
More advanced imaging is also being explored. Gadoxetate-enhanced MRI can help assess tumour necrosis. CT perfusion and MRI measures of blood flow, stiffness and other tumour features have been linked with early response. Radiomics and machine learning models that combine imaging and clinical data show promising performance. However, wider use depends on consistent imaging timing, reproducible analysis methods, external validation and clear reporting standards.
Body Composition Adds Prognostic Value
Imaging biomarkers may also help predict outcomes after transarterial radioembolisation. Conventional imaging features already provide useful information. Hypoperfused tumours on SPECT/CT are associated with shorter progression-free and overall survival, as well as higher risks of intrahepatic recurrence and distant metastasis. On pre-treatment MRI, a higher enhancing tumour volume relative to total tumour volume is linked with more favourable outcomes.
The extent of liver involvement is also prognostic in infiltrative hepatocellular carcinoma. Imaging-based assessment of portal vein tumour thrombus has validated prognostic value in patients with tumour invasion of the portal vein. These features are available on routine imaging, but their use can still be limited by reader variation and lack of consistent thresholds.
Body-composition assessment adds another practical layer of risk stratification. CT and MRI can measure muscle and fat-related features from scans already performed as part of care. Sarcopenia is consistently associated with poorer survival in transarterial radioembolisation cohorts. MRI-based fat-free muscle area and muscle diffusion measures also correlate with outcomes. These markers may complement tumour-focused imaging, but standardised segmentation is needed for consistent use.
Radiomics and machine learning may further refine survival prediction. Models combining CT radiomics with clinical and dosimetry variables can stratify risk. Body-composition radiomics has also predicted one-year survival in the SORAMIC trial. Early machine learning work combining voxel-level dosimetry and clinical markers has identified predictors of overall and progression-free survival. These approaches remain investigational because most evidence is retrospective, single-centre and not yet validated across wider settings.
CT and MRI increasingly support treatment planning, response assessment and prognostic evaluation in hepatocellular carcinoma treated with transarterial radioembolisation. Enhancement-based assessment, radiation-specific response criteria and body-composition measures appear better aligned with the delayed effects of this therapy than size change alone. Functional imaging, radiomics and machine learning add further potential, but clinical use depends on harmonised imaging protocols, reproducible analysis and multi-centre prospective validation. Current practice is best supported by established response criteria, conventional enhancement patterns and practical body-composition measures.
Source: Academic Radiology
Image Credit: iStock
References:
Liu H, Pieterman K & Dwarkasing R (2026) Advances in CT and MRI for Yttrium-90 Radioembolization of Hepatocellular Carcinoma. Academic Radiology: In Press.
