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

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 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.