Viscoelasticity Imaging to Assess Liver Cancer

Ultrasound (US) used for hepatocellular carcinoma (HCC) surveillance suffers from low sensitivity (60-78%) due to fatty liver, obesity, and diffusely nodular appearance in cirrhosis. Once a suspicious malignant lesion is detected at US, guidelines recommend contrast-enhanced US, magnetic resonance imaging (MRI) or computed tomography (CT) scans to confirm suspicion. The investigators' team has developed innovative quantitative US (QUS) techniques that have a high potential to improve tissue characterization in terms of sensitivity and specificity. The investigators hypothesize that advanced QUS providing tumor viscoelasticity assessment, sub-resolution tissue structure characterization and US attenuation in the framework of a machine learning classification model can improve HCC diagnosis compared with standard US.

Early detection through systematic US surveillance translates into curative therapy in a higher proportion of patients and into improvements in survival rates. Thus, there is an urgent need to investigate innovative and cost-effective imaging techniques for improving detection and characterization of HCC. The proposed QUS methods are experimental and will be validated in this proof-of-concept clinical study. A major impact of this work, for patients and medical institutions, will be to improve early-stage detection and characterization of HCC, and offer alternatives in patients with negative or inconclusive conventional US. QUS are low-cost, non-invasive and non-irradiating imaging modalities available from a single exam (i.e., no additional imaging session is necessary).

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Centre hospitalier de l'Université de Montréal (CHUM)

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Study ID: NCT04409340