Hepatocellular Carcinoma Subtype Characterization and Imaging Techniques

Hepatocellular carcinoma (HCC) is a biologically and clinically heterogeneous liver malignancy. Subtypes such as proliferative, macrotrabecular-massive, vessels encapsulating tumour clusters and steatohepatitic variants exhibit distinct molecular alterations, histopathological architectures and prognostic trajectories. Traditional imaging with contrast-enhanced CT and MRI has been complemented by quantitative and computational methods. Radiomics harnesses automated feature extraction to link image phenotypes with underlying genomics and tumour microenvironment, while nomograms integrate radiomics signatures with clinical data to predict subtype and outcome. Three-dimensional multifrequency magnetic resonance elastography quantifies tissue stiffness and fluidity, offering non-invasive biomarkers of proliferative phenotypes. Deep learning radiomics models leverage multiphase imaging and clinical variables to forecast subtype classification and survival after interventions such as hepatic arterial infusion chemotherapy. Advanced MRI techniques, including proton density fat fraction mapping, enable precise grading of steatotic HCC. Together, these innovations facilitate personalised treatment planning, risk stratification and surveillance, addressing the global burden of HCC by improving early detection and guiding therapeutic decisions.

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Recent studies have demonstrated the power of imaging-based models to characterise HCC subtypes and anticipate clinical outcomes. A computed tomography-based radiomics nomogram was developed to predict the proliferative subtype, integrating quantitative texture features with clinicoradiological variables. This tool stratified patients into risk groups for early recurrence and treatment response, and transcriptomic and pathomic analyses linked the radiomics signature to immune infiltration, TP53 mutation and metabolic pathways. Three-dimensional multifrequency magnetic resonance elastography has emerged as a complementary modality, measuring shear wave speed and loss angle to capture viscoelastic signatures of proliferative HCC. Incorporation of these elastography parameters into conventional MRI nomograms significantly improved diagnostic accuracy for aggressive phenotypes. In parallel, a multitask deep learning radiomics model was constructed to identify the macrotrabecular-massive subtype and predict prognosis after hepatic arterial infusion chemotherapy. By extracting deep learning features from dual-phase contrast-enhanced CT and fusing them with clinical indicators, the model achieved high performance in subtype classification and postoperative survival risk stratification, providing a decision support tool for personalised management.

Hepatocellular Carcinoma Subtype Characterization and Imaging Techniques publication trend

The graph below shows the total number of articles in hepatocellular carcinoma subtype characterization and imaging techniques across all publications each year (not limited to Nature Index journals).

Technical terms

Radiomics: High-throughput extraction and analysis of quantitative features from medical images to reveal tumour phenotypes.

Nomogram: A graphical prediction tool that integrates multiple variables to estimate individualized risk or prognosis.

Macrotrabecular-massive subtype: An aggressive HCC variant characterised by thick, trabecular tumour cell plates and poor prognosis.

Proliferative subtype: A molecularly defined HCC group with high cell proliferation, genomic instability and adverse outcomes.

Magnetic resonance elastography (MRE): An MRI technique that quantifies tissue stiffness and fluidity by measuring shear wave propagation.

Deep learning radiomics: The use of neural networks to automatically learn imaging features for prediction of tumour characteristics and outcomes.

References

1. CT-based radiomics nomogram to predict proliferative hepatocellular carcinoma and explore the tumor microenvironment. Journal of Translational Medicine (2024).
2. Preoperative CT for Characterization of Aggressive Macrotrabecular-Massive Subtype and Vessels That Encapsulate Tumor Clusters Pattern in Hepatocellular Carcinoma. Radiology (2021).
3. Three-dimensional multifrequency magnetic resonance elastography improves preoperative assessment of proliferative hepatocellular carcinoma. Insights into Imaging (2023).
4. A multitask deep learning radiomics model for predicting the macrotrabecular-massive subtype and prognosis of hepatocellular carcinoma after hepatic arterial infusion chemotherapy. La radiologia medica (2023).
5. MRI proton density fat fraction for estimation of tumor grade in steatotic hepatocellular carcinoma. European Radiology (2023).
6. Advances in Histological and Molecular Classification of Hepatocellular Carcinoma. Biomedicines (2023).