The Chinese University of Hong Kong, Prince of Wale Hospital
Hong Kong, Shatin
NCT Number: NCT05168982
Chronic liver disease is a major healthcare problem in Hong Kong and worldwide. The diagnosis of liver fibrosis and inflammation in patients with chronic liver disease has important prognostic and therapeutic implications. The current gold standard to evaluate and stage the severity of liver fibrosis and inflammation is based on liver biopsies, which are invasive and impractical for screening and monitoring the disease. The existing non-invasive methods still have significant limitations to meet the challenge.
Magnetic resonance effect can be used to obtain the molecular-level information on the biochemical properties of human tissues. The investigators will develop non-invasive quantitative MRI technologies to evaluate and stage liver fibrosis and inflammation. Our approaches are based on the endogenous contrast mechanism and thus do not need to inject an MRI contrast agent. Our approaches can be implemented on a regular MRI scanner and do not need any extra hardware. To enable the technology for routine clinical use, the investigators will develop fully automated post-processing techniques for the proposed MRI acquisition approaches. The investigators will perform multi-center clinical studies in Hong Kong and mainland China to validate our imaging measurements by histopathologic results from liver biopsies on patient cohorts.
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Notify Me18 year–65 year
All sexes
Observational
Hong Kong, Shatin
Healthy volunteers accepted: Yes
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Time frame: one year
T-Test or one-way analysis of variance (ANOVA) will be performed for continuous variables with normally distribution, along with Kruskal-Wallis test or Mann-Whitney U test for continuous variables with skewed or unknown distribution, while χ2 or Fisher exact test will be carried out for categorical variables.
Chinese University of Hong Kong
Other
Development of Fully Automated Non-Invasive MRI Applications for Diagnosis and Staging of Liver Fibrosis and Inflammation
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