Viscoelasticity Imaging to Assess Liver Cancer

Official Title

Added Value of Shear Wave Viscoelasticity Imaging, Homodyned-K Tissue Imaging and Acoustic Attenuation to Assess Liver Cancer at Ultrasound: a Multiparametric Learning Approach

Summary:

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 tumour 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).

Trial Description

Primary Outcome:

  • Liver viscoelastography determined by MRI
  • Liver viscoelastography determined by QUS
  • Detection of focal HCC lesions
RESEARCH QUESTION AND BACKGROUND: Primary liver cancer or hepatocellular carcinoma (HCC) is the fifth most common cancer in men and the seventh in women and is the second cause of cancer mortality worldwide. . In Canada, HCC is the only cancer for which mortality is increasing. More than 80% of HCC cases occur in individuals with advanced liver fibrosis (cirrhosis) due to viral hepatitis infection (B and C), non-alcoholic fatty liver disease (NAFLD), and alcoholic liver disease. Once cirrhosis is established, there is a significantly increased risk of developing HCC. Furthermore, HCC is observed in obese diabetic individuals without cirrhosis, increasing the population of patients at risk with a disease that has high fatality rate. HCC surveillance is associated with significantly prolonged survival. However, only 52% of patients undergoing surveillance have early HCCs that are eligible for curative treatment, whereas remainder of patients have intermediate- or advanced-stage disease eligible for bridge or palliative treatment only. HCC surveillance is also associated with significant improvements in early-stage detection, curative-treatment rates, and survival, even after adjusting for lead-time bias. North American guidelines recommend ultrasound (US) surveillance every 6 months in at-risk patients who are non-cirrhotic hepatitis B carriers and cirrhotic. However, a key challenge for US is the low sensitivity (60-78%) for identifying a lesion due to liver steatosis and cirrhosis. Once a suspicious malignant lesion is detected at US, current American Association for the Study of Liver Diseases (AASLD) guidelines recommend contrast-enhanced US, magnetic resonance imaging (MRI) or computed tomography (CT) scans to confirm suspicion. 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 tumour 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.

View this trial on ClinicalTrials.gov

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Resources

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