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Multi-parametric Magnetic Resonance Imaging for the Precise Diagnosis and Quantitative Study of Liver Steatosis, Inflammation, and Fibrosis in Chronic Liver Disease.

Recruiting
Conditions
Liver Stiffness
Registration Number
NCT06463366
Lead Sponsor
Shengjing Hospital
Brief Summary

To construct a novel, non-invasive, accurate, and convenient method to achieve the degree of liver damage is an important general problem in the management of patients with chronic liver disease. The investigators would like to develop non invasive advanced Magnetic Resonance Imaging (MRI) techniques (MR elastography, MRI-PDFF) to assess the degree of liver damage in patients with chronic liver disease. These techniques could reach high diagnostic performance for detection of liver fibrosis, inflammation and liver fat content; and could decrease the number of liver biopsies, which have risks and sample only a small portion of the liver.

Detailed Description

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 China.

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 elastography and MRI-PDFF techniques for the detection of fibrosis, cirrhosis and liver fat content 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 (storage modulus, loss modulus and damping ratio \[DR\]) by follow up every 6 month. 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 study is aimed to evaluate whether the change of liver stiffness assessed by MRE can predict treatment effectiveness in chronic liver disease treatment by follow up every 6 month.

Recruitment & Eligibility

Status
RECRUITING
Sex
All
Target Recruitment
100
Inclusion Criteria
  1. Chronic liver disease (including viral hepatitis, alcoholic hepatitis, non alcoholic steatohepatitis, primary biliary cirrhosis, primary sclerosing cholangitis, etc..)
  2. Age range of 18 to 75 years old
  3. Accept systematic antiviral therapy or hormone or ursodesoxycholic acid or supportive liver protection therapy
Exclusion Criteria
  1. Age less than 18 years
  2. Unable or unwilling to give informed consent
  3. Contra-indications to MRI
  4. Electrical implants such as cardiac pacemakers or perfusion pumps
  5. Ferromagnetic implants such as aneurysm clips, surgical clips, prostheses, artificial hearts, valves with steel parts, metal fragments, shrapnel, tattoos near the eye, or steel implants
  6. Ferromagnetic objects such as jewelry or metal clips in clothing
  7. Pregnant subjects
  8. Pre-existing medical conditions including a likelihood of developing seizures or claustrophobic reactions

Study & Design

Study Type
OBSERVATIONAL
Study Design
Not specified
Primary Outcome Measures
NameTimeMethod
the changes of liver stiffness assessed by 3D-MRE before and after treatment.12 months

Liver stiffness regression is defined as the change of 3D MRE shear stiffness larger than 19%

Secondary Outcome Measures
NameTimeMethod

Trial Locations

Locations (1)

Shengjing Hospital of China Medical University

🇨🇳

Shenyang, Liaoning, China

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