Viscoelasticity Imaging to Assess Liver Cancer
Key facts
- Where
- Montreal, CanadaSee the site
Where in Canada
- Centre Hospitalier de l'Universite de Montreal (CHUM)Montreal, CanadaTaking people
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- Centre hospitalier de l'Université de Montréal (CHUM)
- Trial code
- NCT04409340
- Record updated
- 17 September 2026
- Original description
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.Read full original descriptionShow less
Added Value of Shear Wave Viscoelasticity Imaging, Homodyned-K Tissue Imaging and Acoustic Attenuation to Assess Liver Cancer at Ultrasound: a Multiparametric Learning Approach
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).
Inclusion: Are at least 18 years old at screening; Able to comprehend and willingness to provide voluntary consent; Are able to have a MRI; Understand French or English; Patients enrolled in a monitoring program or referred for the characterization of a focal liver lesion; Focal liver lesion is visible during ultrasound screening in B-mode.
Exclusion: Have chronic kidney disease preventing the injection of gadolinium-based contrast agent; Are pregnant or trying to become pregnant; Have a weight or girth preventing from entering the MR magnet bore; Are unable to understand or unwilling to provide written informed consent for this study; Have a contraindication to MRI (pacemaker, insurmountable claustrophobia).