AI-based Quantification of Liver Fat Fraction Using Two-energy Ultra-low Dose CT Compared to MRI
试验速览
- 阶段
- 不适用
- 状态
- Enrolling By Invitation
- 入组人数
- 150
- 试验地点
- 1
- 主要终点
- Developing AI model of liver fat fraction assessment on data obtained from ultra-low dose CT, using MRI data as a standard of reference
研究概览
简要总结
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").
详细描述
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —(Adult, Older Adult)
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Adult patients (age ≥18 years)
- •At risk for hepatic steatosis (defined as at least one of the followings: age >50 years, over weight (BMI>25), impaired fasting glucose or impaired glucose tolerance, T2DM, gestational diabetes, hyperlipidemia, hypertension, elevated liver enzymes, family history of steatosis or cirrhosis, increased liver span per medical examination, increased ferritin levels and the patatin-like phospholipase domain-containing 3 polymorphism), as decided by the treating endocrinologist in our institute's Medical screening department. 12-14
- •No history of malignancy involving the liver.
- •No known risk factors for hepatic iron deposition (multiple prior blood transfusions, known hemochromatosis).
- •Subjects able to understand study procedures and provide informed consent.
- •Subjects able to hold their breath during CT and MRI scans.
排除标准
- •Patients younger than 18 years.
- •Patients with risk factors from hepatic iron deposition (multiple prior blood transfusions, known hemochromatosis).
- •Patients with known malignancy that involves the liver.
- •Patients unable to hold their breath for both CT and MRI.
- •Patients with severe claustrophobia.
- •Patients with implanted devices of shrapnel.
- •Pregnant people
结局指标
主要结局
Developing AI model of liver fat fraction assessment on data obtained from ultra-low dose CT, using MRI data as a standard of reference
时间窗: Through study completion, up to 24 months
The investigators will 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 NAFLD, compared to MRI which serves as the standard of reference. The MRI data will be extracted from the dual-echo scan, which can produce an MRI-based liver fat-fraction, and this data will be then used to create an AI CT model. The AI model will be developed to be able to accurately produce an exact quantification of the liver fat fraction (exact percentage) on ultra-low dose CT.
次要结局
- External validation of the AI CT liver fat fraction model using a second participant group not included in the development of the AI-based CT model(Through study completion, up to 24 months)
研究者
Prof. Noam Tau
Staff Radiologist and Primary Investigator
Sheba Medical Center
