Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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UNKNOWN
200 participants
OBSERVATIONAL
2020-10-05
2023-12-22
Brief Summary
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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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Detailed Description
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GOAL: The long-term reaching goal is to develop US biomarkers of focal liver lesions and strategies to improve diagnostic sensitivity to HCC while maintaining a high specificity. This would constitute a major breakthrough because HCC diagnosis currently requires a combination of US for screening and confirmation using MRI, CT and less often biopsy.
OBJECTIVES: 1) Develop a machine learning model based on QUS for classification of solid hepatocellular carcinomas identified at US and diagnosed with MRI (or biopsy if required); 2) Determine if QUS maps can improve visual detection of suspected lesions at US; 3) Compare performance of QUS- versus MRI-based viscoelastography for lesion characterization.
Hypothesis: 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.
METHODOLOGY - Study design: This will be a clinical study with two sequential cohorts: 1) a training cohort of 100 patients at risk for HCC to optimize QUS biomarkers for classification of solid liver lesions using MRI and/or biopsy as gold standard clinical references; and 2) a validation cohort of 100 patients to confirm diagnostic performance.
Data analysis: Random forests machine learning to develop QUS classification models. Sensitivity and specificity to assess diagnostic accuracy, according to MRI and/or biopsy, with bootstrapping to obtain confidence intervals with training set. Confirmation of accuracy on test set. Inter-observer assessment of lesion detectability on clinical B-mode US versus QUS maps. Comparison of US- and MRI-based elasticity and viscosity according to diagnostic results.
Conditions
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Study Design
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OTHER
CROSS_SECTIONAL
Study Groups
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Training cohort
All patients enrolled will undergo:
* Magnetic Resonance Viscoelastography
* Quantitative ultrasound (QUS)
Magnetic Resonance Viscoelastography
Magnetic Resonance Viscoelastography
QUS
QUS
Validation cohort
All patients enrolled will undergo:
• Quantitative ultrasound (QUS)
QUS
QUS
Interventions
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Magnetic Resonance Viscoelastography
Magnetic Resonance Viscoelastography
QUS
QUS
Eligibility Criteria
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Inclusion Criteria
* 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 Criteria
* 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);
* Have chronic kidney disease preventing the injection of gadolinium-based contrast agent.
18 Years
ALL
No
Sponsors
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Siemens Corporation, Corporate Technology
INDUSTRY
Fonds de la Recherche en Santé du Québec
OTHER_GOV
Centre de Recherche du Centre Hospitalier de l'Université de Montréal
OTHER
Centre hospitalier de l'Université de Montréal (CHUM)
OTHER
Responsible Party
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Principal Investigators
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Guy Cloutier, PhD
Role: PRINCIPAL_INVESTIGATOR
Centre hospitalier de l'Université de Montréal (CHUM)
An Tang, MD, MSc
Role: PRINCIPAL_INVESTIGATOR
Centre hospitalier de l'Université de Montréal (CHUM)
Locations
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Centre hospitalier de l'Université de Montréal (CHUM)
Montreal, Quebec, Canada
Countries
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Central Contacts
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Facility Contacts
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Other Identifiers
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2021-8561
Identifier Type: -
Identifier Source: org_study_id
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