Prospective Detection of Liver Fibrosis With MRI Compared to Fibroscan and Blood Tests
Patients with chronic hepatitis have increased risks of liver damage, including fibrosis and
cirrhosis, which may eventually lead to hepatocellular carcinoma and end-stage liver disease
requiring liver transplantation. These diseases are/will be the source of enormous health
care costs and morbidity/mortality in the US.
Most hepatologists still rely on liver biopsy findings in patients newly diagnosed with
chronic hepatitis, which enables the assessment of liver damage (fibrosis and inflammation).
Liver biopsy has limitations, including cost, invasiveness, poor patient acceptance, limited
sampling, inter-observer variability and is difficult to repeat.
Non invasive tests to capture the extent of liver damage at a larger scale are urgently
needed. These will gain more acceptance among patients and hepatologists.
In this proposal, the investigators would like to test and validate non invasive MRI methods
based on advanced MR diffusion, perfusion and elastography techniques for the detection of
fibrosis and cirrhosis in patients with chronic hepatitis. In order to improve the
diagnostic performance of MRI, the investigators would like to build and validate a
predictive model based on advanced functional MRI metrics (diffusion, perfusion and
elastography). If validated, this novel non invasive algorithm will not only decreases the
number of liver biopsies, but also enable earlier diagnosis of liver fibrosis when antiviral
treatment is more effective, and enable a comprehensive evaluation of the liver (to assess
for cirrhosis, portal hypertension and hepatocellular cancer).
This could significantly reduce the cost of care, could become a useful tool for testing new
antifibrogenic and antiviral drugs in chronic viral hepatitis, and could be used to follow
patients for detection of progression to cirrhosis.
Endpoint Classification: Efficacy Study, Intervention Model: Single Group Assignment, Masking: Open Label, Primary Purpose: Diagnostic
degree of liver fibrosis
Accuracy of prediction of degree of liver fibrosis using non invasive MRI methods
Bachir Taouli, MD
Mount Sinai School of Medicine
United States: Food and Drug Administration
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