CT Liver Fat Fraction Quantification

Non-Alcoholic Fatty Liver Disease Clinical Trial

Official title: AI-based Quantification of Liver Fat Fraction Using Two-energy Ultra-low Dose CT Compared to MRI

Status Enrolling by invitation

Clinical Trial Summary

Our aim is to develop an AI based tool to use ultra-low dose CT in two separate energy levels using a single-energy CT machine to quantify liver fat in individuals at risk for having non-alcoholic fatty liver disease (NAFLD), compared to MRI which serves as the standard of reference.

Secondary aim of our study is to validate the developed artificial intelligence (AI)-based model on a second group of participants ("external validation").

Clinical Trial Description

Non-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease. It affects 25% of the global population, with a higher proportion in Middle Eastern countries, especially in individuals with type 2 diabetes mellitus (T2DM), in whom NAFLD may be seen in up to 70% of patients. Studies have shown that in coming years, the disease is likely to become more prevalent, with increasing number of patients presenting with the more advanced disease form, nonalcoholic steatohepatitis (NASH), the latter gradually becoming a leading cause for liver transplantation, alongside viral hepatitis. NAFLD is characterized by excessive fat deposition (steatosis) in liver cells and can appear in both obese and non-obese individuals. Among individuals diagnosed with NAFLD, an estimated 12-14% have NASH, which can lead to liver fibrosis, cirrhosis and hepatocellular carcinoma (HCC). As NAFLD is associated with cardiometabolic disorders, including obesity, insulin resistance, T2DM, hypertension and atherogenic dyslipidemia, it increases the risk of cardiovascular events and death. When discovered early, NAFLD can be treated by both lifestyle modification and various drugs. Although the gold standard for detecting NAFLD and quantifying the fat contents in liver cells is a non-targeted liver biopsy, blood tests and non-invasive imaging can assist in early diagnosis of patients at risk for developing NASH and for follow-up after treatment. Ultrasound for detection and assessment of hepatic steatosis is limited by subjective assessment; and variable sensitivity and specificity (53-76% and 76-93%, respectively). US may fail in obese patients or those with ascites, and is highly operator- and platform-dependent (inter- and intra-reader agreement ~50%). Moreover, it has limited utility in fat fraction quantification. Novel US methods are being constantly developed for accurate quantification, but are yet to be agreed upon and used in daily clinical routine.

The most commonly used method for quantifying the amount of fat in the liver is MRI, and specifically chemical shift imaging sequences. However, MRI has limitations, including the cost of scans, limited availability worldwide and patient-specific limitations, including claustrophobia and implanted electronic devices which may be unsafe in the MRI magnetic field. Currently, single and dual energy CT have shown limited utility in diagnosing and quantifying liver steatosis, and although CT is readily available worldwide, currently CT cannot be used for liver fat fraction quantification or for early NAFLD diagnosis. Attempts to utilize dual-energy CT, with and without use of artificial intelligence (AI) has shown limited success. Moreover, dual-energy CT is not readily available in most medical centers.

A prior study the investigators performed has already shown that ultra-low dose chest CT can diagnose liver steatosis, but the investigators have not yet assessed its capabilities in quantifying the amount of liver fat. Therefore, the investigators' aim is to develop a novel methodology in which ultra-low dose abdominal CT could be used for both diagnosing NAFLD and quantifying liver fat contents. ;

Related Conditions & MeSH terms

• Fatty Liver
• Liver Diseases
• Non-alcoholic Fatty Liver Disease
• Steatosis of Liver

• NCT number: NCT05676177
• Study type: Interventional
• Source: Sheba Medical Center
• Status: Enrolling by invitation
• Phase: N/A
• Start date: January 29, 2023
• Completion date: December 31, 2025