What We’re Investigating
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Point of care (poc) liver assessment devices
The LIG is investigating the accuracy of a “point-of-care” liver assessment device that would make it easier for patients at risk to be screened for and diagnosed with fatty liver, and allow them to get treated earlier. Traditional MRI, the reference standard, is expensive and available only in large imaging centers or hospitals. These point-of-care devices could be conveniently used in offices or clinics, including use in rural, underserved, and third-world settings, which would make the evaluation of liver disease less expensive and more accessible. If validated as accurate and precise, this innovative point-of-care technology could provide affordable screening, diagnosis and monitoring of liver disease.
The LIG is also researching the use of point-of-care ultrasound in the screening and diagnosis of liver disease in children. In addition to expensive, traditional MRI is loud, can induce claustrophobia, and requires long breath-holds for imaging of the abdomen, which young children cannot always tolerate. Thus, a more accessible method of liver assessment must be validated for pediatric use.
Hepatocellular carninoma Screening
Hepatocellular carcinoma (HCC) screening is necessary to detect cancerous nodules in the liver when they are small and treatable. The LIG is investigating ways to make HCC screening more accessible, accurate, and up-to-date, including:
A novel, rapid, accurate, and potentially cost-effective MRI imaging protocol for HCC screening in high-risk individuals, which would be more accurate than the currently recommended guideline of screening with ultrasound.
An automated electronic medical record (EMR) alerting system for improving compliance with HCC surveillance
The continued implementation of the American College of Radiology (ACR) Liver Imaging Reporting and Data System (LI-RADS®), which was developed to standardize the interpretation, reporting and data collection of CT and MR imaging in patients with or at risk for HCC.
Learn more on our LI-RADS page or at the American College of Radiology LI-RADS page.
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AI in Medical Imaging
Artificial intelligence (AI)-based algorithms that incorporate imaging data and clinical input can be used to improve liver assessment imaging and automate time-consuming manual tasks. The LIG seeks to develop and validate automated analysis used for:
detecting hepatic steatosis & reducing interobserver variation in categorizing fatty liver on ultrasound
visualizing lesion behavior across phases through machine learning image augmentation that helps screen for HCC
automating liver segmentation including in CT and MRI which can be applied for liver volumetry
analyzing liver magnetic resonance elastography (MRE) & increasing extraction and analysis of complex imaging data from large populations
Biomarkers for MASld/Mash
Alternative imaging and blood-based biomarkers can be used to detect patients at risk of developing serious liver conditions, offering a non-invasive, lower risk alternative to liver biopsy. The LIG is evaluating the comparative performance and potential complementarity of two leading biomarkers: MRE-determined liver shear stiffness and corrected T1 (cT1). MRE, or tissue “stiffness”, is a biomarker for fibrosis, while cT1 correlates to inflammation and fibrosis in the liver.
The LIG is also investigating lesion “washout”, a feature of contrast-enhanced magnetic resonance imaging (MRI), by developing a numerically-based time intensity criteria for diagnosis of HCC in adults. Doing so may improve the accuracy of differentiating HCC from other benign and malignant lesions, which can be difficult to qualitatively distinguish when utilizing certain contrast agents (such as gadoxetate disodium), and could offer an alternative imaging biomarker for HCC.
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