Computed Tomography in Nonalcoholic Fatty Liver Disease

Duman, Deniz; Çelikel, Çiğdem; Tüney, Davut; İmeryüz, Neşe; Avşar, Erol; Tözün, Nurdan

doi:10.1007/s10620-006-3136-9

Cited by 47 publications

(28 citation statements)

References 23 publications

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“…23, 24 However, imaging is expensive and not feasible on the population level, so a simple noninvasive index to help diagnosis hepatic steatosis is needed. A number of models already exist and include the Fatty Liver Index (FLI) 25 , the Fatty Liver Disease (FLD) Index 26 and the Hepatic Steatosis Index (HSI) 27 , derived from case-control datasets, and the index of Non-Alcoholic Steatohepatitis (NASH) (ION) 28 which was derived from a community-based sample.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of the Framingham Steatosis Index to Identify Persons With Hepatic Steatosis

Long

Pedley

Colantonio

et al. 2016

Clinical Gastroenterology and Hepatology

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Background & Aims Serum levels of aminotransferases are used as markers of non-alcoholic fatty liver disease in epidemiology research. However, it is not clear whether they can be to identify patients with fatty liver. We investigated the accuracy of serum levels of aminotransferases in detection of hepatic steatosis. Additionally, we derived a Framingham steatosis index (FSI), and tested its ability to identify patients with hepatic steatosis in an independent cohort. Methods We performed a cross-sectional study of 1181 members of the Framingham Third Generation Cohort (46.1% women, mean age 50.3±6.7 years). People with hepatic steatosis were identified by computed tomography, performed from 2008 through 2011. We compared the abilities of levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST), and the ratio of ALT:AST, to identify people with hepatic steatosis using c-statistic analyses. We performed a stepwise regression procedure to identify demographic and clinical factors that correlated with hepatic steatosis; we used these, along with biochemical factors associated with steatosis, to develop the FSI. We validated the FSI using data from the third National Health and Nutrition Examination Survey (NHANES III). Results The prevalence of hepatic steatosis in the Framingham Third Generation Cohort was 26.8%. The ratio of ALT:AST identified people with hepatic steatosis with the highest c-statistic value (0.728); the value for only ALT was 0.706 and the value for only AST was 0.589. We derived the FSI based on patient age, sex, body mass index, levels of triglycerides, hypertension, diabetes, and ratio of ALT:AST. The FSI identified patients with hepatic steatosis with a c-statistic value of 0.845. When it was applied to the NHANES III cohort, the FSI identified patients with steatosis with a c-statistic value of 0.760 and was well calibrated. Conclusions In an analysis of the Framingham Third Generation Cohort, we found the ratio of ALT:AST to identify people with hepatic steatosis more accurately than either ALT or AST alone. We used data from this cohort to develop and validate the FSI, which identifies patients with steatosis with a c-statistic value of about 0.8.

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Section: Discussionmentioning

confidence: 99%

Development and Validation of the Framingham Steatosis Index to Identify Persons With Hepatic Steatosis

Long

Pedley

Colantonio

et al. 2016

Clinical Gastroenterology and Hepatology

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“…The complexity of data acquisition and processing, as well as limited availability, are additional drawbacks of MR [67]. The use of US in LFQ is disputed due to prevalent concerns about inexact quantification methods and inter-observer and inter-equipment reproducibility of results [16], [61].…”

Section: Application Of MMD To Liver-fat Quantificationmentioning

confidence: 99%

A Flexible Method for Multi-Material Decomposition of Dual-Energy CT Images

Mendonça

Lamb

Sahani³

2014

IEEE Trans. Med. Imaging

177

202

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The ability of dual-energy computed-tomographic (CT) systems to determine the concentration of constituent materials in a mixture, known as material decomposition, is the basis for many of dual-energy CT's clinical applications. However, the complex composition of tissues and organs in the human body poses a challenge for many material decomposition methods, which assume the presence of only two, or at most three, materials in the mixture. We developed a flexible, model-based method that extends dual-energy CT's core material decomposition capability to handle more complex situations, in which it is necessary to disambiguate among and quantify the concentration of a larger number of materials. The proposed method, named multi-material decomposition (MMD), was used to develop two image analysis algorithms. The first was virtual unenhancement (VUE), which digitally removes the effect of contrast agents from contrast-enhanced dual-energy CT exams. VUE has the ability to reduce patient dose and improve clinical workflow, and can be used in a number of clinical applications such as CT urography and CT angiography. The second algorithm developed was liver-fat quantification (LFQ), which accurately quantifies the fat concentration in the liver from dual-energy CT exams. LFQ can form the basis of a clinical application targeting the diagnosis and treatment of fatty liver disease. Using image data collected from a cohort consisting of 50 patients and from phantoms, the application of MMD to VUE and LFQ yielded quantitatively accurate results when compared against gold standards. Furthermore, consistent results were obtained across all phases of imaging (contrast-free and contrast-enhanced). This is of particular importance since most clinical protocols for abdominal imaging with CT call for multi-phase imaging. We conclude that MMD can successfully form the basis of a number of dual-energy CT image analysis algorithms, and has the potential to improve the clinical utility of dual-energy CT in disease management.

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“…Results of MRS have shown to correlate closely with biochemical and histological assessments of liver triglyceride content (9,10), and to demonstrate a decrease in liver fat content following rosiglitazone therapy (11) or weight reduction (12,13). Other imaging modalities, such as ultrasonography and computed to-mography (CT), provide limited quantitative assessment (14,15). However, radiation exposure limits repeated measurements.…”

mentioning

confidence: 99%

Magnetic resonance imaging and spectroscopy for monitoring liver steatosis

Cowin

Jonsson

Bauer

et al. 2008

Magnetic Resonance Imaging

176

142

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Purpose:To compare noninvasive MRI and magnetic resonance spectroscopy (MRS) methods with liver biopsy to quantify liver fat content. Materials and Methods:Quantification of liver fat was compared by liver biopsy, proton MRS, and MRI using inphase/out-of-phase (IP/OP) and plus/minus fat saturation (ϮFS) techniques. The reproducibility of each MR measure was also determined. An additional group of overweight patients with steatosis underwent hepatic MRI and MRS before and after a six-month weight-loss program. Results:A close correlation was demonstrated between histological assessment of steatosis and measurement of intrahepatocellular lipid (IHCL) by MRS (r s ϭ 0.928, P Ͻ 0.0001) and MRI (IP/OP r s ϭ 0.942, P Ͻ 0.0001; FS r s ϭ 0.935, P Ͻ 0.0001). Following weight reduction, four of five patients with Ͼ5% weight loss had a decrease in IHCL of Ն50%. Conclusion:These findings suggest that standard MRI protocols provide a rapid, safe, and quantitative assessment of hepatic steatosis. This is important because MRS is not available on all clinical MRI systems. This will enable noninvasive monitoring of the effects of interventions such as weight loss or pharmacotherapy in patients with fatty liver diseases.

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Computed Tomography in Nonalcoholic Fatty Liver Disease

Cited by 47 publications

References 23 publications

Development and Validation of the Framingham Steatosis Index to Identify Persons With Hepatic Steatosis

Development and Validation of the Framingham Steatosis Index to Identify Persons With Hepatic Steatosis

A Flexible Method for Multi-Material Decomposition of Dual-Energy CT Images

Magnetic resonance imaging and spectroscopy for monitoring liver steatosis

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