Ultrasound Entropy Imaging of Nonalcoholic Fatty Liver Disease: Association with Metabolic Syndrome

Nonalcoholic fatty liver disease (NAFLD) is the leading cause of advanced liver diseases. Fat accumulation in the liver changes the hepatic microstructure and the corresponding statistics of ultrasound backscattered signals. Acoustic structure quantification (ASQ) is a typical model-based method for analyzing backscattered statistics. Shannon entropy, initially proposed in information theory, has been demonstrated as a more flexible solution for imaging and describing backscattered statistics without considering data distribution. NAFLD is a hepatic manifestation of metabolic syndrome (MetS). Therefore, we investigated the association between ultrasound entropy imaging of NAFLD and MetS for comparison with that obtained from ASQ. A total of 394 participants were recruited to undergo physical examinations and blood tests to diagnose MetS. Then, abdominal ultrasound screening of the liver was performed to calculate the ultrasonographic fatty liver indicator (US-FLI) as a measure of NAFLD severity. The ASQ analysis and ultrasound entropy parametric imaging were further constructed using the raw image data to calculate the focal disturbance (FD) ratio and entropy value, respectively. Tertiles were used to split the data of the FD ratio and entropy into three groups for statistical analysis. The correlation coefficient r, probability value p, and odds ratio (OR) were calculated. With an increase in the US-FLI, the entropy value increased (r = 0.713; p < 0.0001) and the FD ratio decreased (r = –0.630; p < 0.0001). In addition, the entropy value and FD ratio correlated with metabolic indices (p < 0.0001). After adjustment for confounding factors, entropy imaging (OR = 7.91, 95% confidence interval (CI): 0.96–65.18 for the second tertile; OR = 20.47, 95% CI: 2.48–168.67 for the third tertile; p = 0.0021) still provided a more significant link to the risk of MetS than did the FD ratio obtained from ASQ (OR = 0.55, 95% CI: 0.27–1.14 for the second tertile; OR = 0.42, 95% CI: 0.15–1.17 for the third tertile; p = 0.13). Thus, ultrasound entropy imaging can provide information on hepatic steatosis. In particular, ultrasound entropy imaging can describe the risk of MetS for individuals with NAFLD and is superior to the conventional ASQ technique.

[1]  Hsiang-Yang Ma,et al.  Effects of fatty infiltration in human livers on the backscattered statistics of ultrasound imaging , 2015, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[2]  Kebin Jia,et al.  Empirical Mode Decomposition of Ultrasound Imagingfor Gain-Independent Measurement on Tissue Echogenicity: A Feasibility Study , 2017 .

[3]  R. Bloigu,et al.  Fatty liver predicts the risk for cardiovascular events in middle-aged population: a population-based cohort study , 2014, BMJ Open.

[4]  R. Turner,et al.  Homeostasis model assessment: insulin resistance and β-cell function from fasting plasma glucose and insulin concentrations in man , 1985, Diabetologia.

[5]  Tiansong Zhang,et al.  Controlled attenuation parameter for assessment of hepatic steatosis grades: a diagnostic meta-analysis. , 2015, International journal of clinical and experimental medicine.

[6]  E. Paulssen,et al.  Hepatic steatosis, detected by hepatorenal index in ultrasonography, as a predictor of insulin resistance in obese subjects , 2016, BMC Obesity.

[7]  Jae Young Lee,et al.  Hepatic Steatosis: Assessment with Acoustic Structure Quantification of US Imaging. , 2016, Radiology.

[8]  Che-Chou Shen,et al.  Using high-frequency ultrasound statistical scattering model to assess Nonalcoholic Fatty Liver Disease (NAFLD) in mice , 2016, 2016 39th International Conference on Telecommunications and Signal Processing (TSP).

[9]  G. Marchesini,et al.  Insulin resistance in nonalcoholic fatty liver disease. , 2010, Current pharmaceutical design.

[10]  A. Pollett,et al.  Controlled Attenuation Parameter (CAP): a noninvasive method for the detection of hepatic steatosis based on transient elastography , 2012, Liver international : official journal of the International Association for the Study of the Liver.

[11]  M. Abraham,et al.  Association between diabetes, family history of diabetes, and risk of nonalcoholic steatohepatitis and fibrosis , 2012, Hepatology.

[12]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[13]  Chih-Kuang Yeh,et al.  Hepatic Steatosis Assessment with Ultrasound Small-Window Entropy Imaging. , 2018, Ultrasound in medicine & biology.

[14]  F. Schaffner,et al.  Nonalcoholic fatty liver disease. , 1986, Progress in liver diseases.

[15]  Jacek M. Zurada,et al.  An information-theoretic approach to estimating ultrasound backscatter characteristics , 2004, Comput. Biol. Medicine.

[16]  A. Lonardo,et al.  Ultrasonographic fatty liver indicator, a novel score which rules out NASH and is correlated with metabolic parameters in NAFLD , 2012, Liver international : official journal of the International Association for the Study of the Liver.

[17]  M. Braddock,et al.  Nonalcoholic Fatty Liver Disease , 2015, Medicine.

[18]  N. Chalasani,et al.  Nonalcoholic fatty liver disease and cardiovascular risk , 2009, Current gastroenterology reports.

[19]  Po-Hsiang Tsui,et al.  Effects of Fatty Infiltration of the Liver on the Shannon Entropy of Ultrasound Backscattered Signals , 2016, Entropy.

[20]  Mustafa Cengiz,et al.  Sonographic assessment of fatty liver: intraobserver and interobserver variability. , 2014, International journal of clinical and experimental medicine.

[21]  T. Yamanaka,et al.  Magnetic Resonance Imaging More Accurately Classifies Steatosis and Fibrosis in Patients With Nonalcoholic Fatty Liver Disease Than Transient Elastography. , 2016, Gastroenterology.

[22]  Erick M Remer,et al.  The utility of radiological imaging in nonalcoholic fatty liver disease. , 2002, Gastroenterology.

[23]  S. O. Lima,et al.  Association of the Nonalcoholic Hepatic Steatosis and Its Degrees With the Values of Liver Enzymes and Homeostasis Model Assessment-Insulin Resistance Index , 2015, Gastroenterology research.

[24]  H. Poustchi,et al.  Non-alcoholic fatty liver disease (NAFLD) and 10-year risk of cardiovascular diseases. , 2017, Clinics and research in hepatology and gastroenterology.

[25]  Y. Takikawa,et al.  Non-invasive determination of hepatic steatosis by acoustic structure quantification from ultrasound echo amplitude. , 2012, World journal of gastroenterology.

[26]  Michael S. Hughes,et al.  Analysis of digitized waveforms using Shannon entropy , 1993 .

[27]  J. Bruzzi,et al.  Liver biopsy. , 2001, The New England journal of medicine.

[28]  Hiroyuki Hachiya,et al.  B-mode ultrasound with algorithm based on statistical analysis of signals: evaluation of liver fibrosis in patients with chronic hepatitis C. , 2009, AJR. American journal of roentgenology.

[29]  Mari Mino-Kenudson,et al.  Imaging-based quantification of hepatic fat: methods and clinical applications. , 2009, Radiographics : a review publication of the Radiological Society of North America, Inc.

[30]  S. Klein,et al.  Obesity and nonalcoholic fatty liver disease: Biochemical, metabolic, and clinical implications , 2010, Hepatology.

[31]  Jae Young Lee,et al.  Evaluation of Hepatic Steatosis by Using Acoustic Structure Quantification US in a Rat Model: Comparison with Pathologic Examination and MR Spectroscopy. , 2017, Radiology.

[32]  P. Tsui,et al.  Ultrasound parametric imaging of hepatic steatosis using the homodyned‐K distribution: An animal study , 2018, Ultrasonics.

[33]  H. Brody.  Fatty liver disease , 2017, Nature.

[34]  Chih-Kuang Yeh,et al.  Multifeature analysis of an ultrasound quantitative diagnostic index for classifying nonalcoholic fatty liver disease , 2016, Scientific Reports.

[35]  Ying-Hsiu Lin,et al.  Effect of ultrasound frequency on the Nakagami statistics of human liver tissues , 2017, PloS one.

[36]  Jonathan Mamou,et al.  Quantitative Ultrasound in Soft Tissues , 2013, Springer Netherlands.

[37]  Y. Yakoshi.  Non-invasive quantification of liver damage by a novel application for statistical analysis of ultrasound signals , 2014 .

[38]  A. Shaaban,et al.  Patterns of Fatty Liver Disease , 2016, Current Radiology Reports.

[39]  G. Cloutier,et al.  A critical review and uniformized representation of statistical distributions modeling the ultrasound echo envelope. , 2010, Ultrasound in medicine & biology.

[40]  L. Katsnelson,et al.  Interobserver and intraobserver variability in the sonographic assessment of fatty liver. , 2007, AJR. American journal of roentgenology.

[41]  G. Marchesini,et al.  Insulin resistance: A metabolic pathway to chronic liver disease , 2005, Hepatology.

[42]  Seong Ho Park,et al.  Non-invasive assessment of hepatic steatosis: prospective comparison of the accuracy of imaging examinations. , 2010, Journal of hepatology.

[43]  G. Salles,et al.  Prevalence and associated factors of non‐alcoholic fatty liver disease in patients with type‐2 diabetes mellitus , 2009, Liver international : official journal of the International Association for the Study of the Liver.

[44]  M. Kumar,et al.  Controlled attenuation parameter for non‐invasive assessment of hepatic steatosis: Does etiology affect performance? , 2013, Journal of gastroenterology and hepatology.

[45]  P. Mohana Shankar,et al.  A general statistical model for ultrasonic backscattering from tissues , 2000, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[46]  F. Jornayvaz,et al.  Metabolic syndrome and nonalcoholic fatty liver disease: Is insulin resistance the link? , 2015, Molecular and Cellular Endocrinology.

[47]  G. Targher,et al.  Factors associated with significant liver steatosis and fibrosis as assessed by transient elastography in patients with one or more components of the metabolic syndrome. , 2016, Journal of diabetes and its complications.

[48]  Z. Bloomgarden,et al.  Inflammation and insulin resistance. , 2003, Diabetes care.

[49]  J. Olefsky,et al.  Inflammation and insulin resistance , 2008, FEBS letters.

[50]  J. Vergniol,et al.  Non‐invasive diagnosis of liver steatosis using controlled attenuation parameter (CAP) and transient elastography , 2012, Liver international : official journal of the International Association for the Study of the Liver.

[51]  Jiang He,et al.  Metabolic syndrome and risk of cardiovascular disease: a meta-analysis. , 2006, The American journal of medicine.

[52]  Shuiping Zhao,et al.  Nonalcoholic fatty liver disease as a potential risk factor of cardiovascular disease , 2015, European journal of gastroenterology & hepatology.

[53]  E. Brunt,et al.  Role of liver biopsy in nonalcoholic fatty liver disease. , 2014, World journal of gastroenterology.

[54]  Yoshito Itoh,et al.  Limitations of liver biopsy and non-invasive diagnostic tests for the diagnosis of nonalcoholic fatty liver disease/nonalcoholic steatohepatitis. , 2014, World journal of gastroenterology.

[55]  J. Jessurun,et al.  Sources of fatty acids stored in liver and secreted via lipoproteins in patients with nonalcoholic fatty liver disease. , 2005, The Journal of clinical investigation.

[56]  Chia-Wen Lu,et al.  Association of Non-alcoholic Fatty Liver Disease with Metabolic Syndrome Independently of Central Obesity and Insulin Resistance , 2016, Scientific Reports.

[57]  V. de Lédinghen,et al.  Controlled attenuation parameter (CAP): a novel VCTE™ guided ultrasonic attenuation measurement for the evaluation of hepatic steatosis: preliminary study and validation in a cohort of patients with chronic liver disease from various causes. , 2010, Ultrasound in medicine & biology.

[58]  H. Busse,et al.  Estimating steatosis and fibrosis: Comparison of acoustic structure quantification with established techniques. , 2015, World journal of gastroenterology.

[59]  Po-Hsiang Tsui,et al.  Performance Evaluations on Using Entropy of Ultrasound Log-Compressed Envelope Images for Hepatic Steatosis Assessment: An In Vivo Animal Study , 2018, Entropy.

[60]  J. Féher,et al.  [Non-alcoholic fatty liver disease and cardiovascular risk]. , 2008, Orvosi hetilap.

[61]  Po-Hsiang Tsui,et al.  Ultrasound Detection of Scatterer Concentration by Weighted Entropy , 2015, Entropy.

[62]  S A Wickline,et al.  Properties of an entropy-based signal receiver with an application to ultrasonic molecular imaging. , 2007, The Journal of the Acoustical Society of America.

[63]  Thomas Seufferlein,et al.  Comparison of Acoustic Structure Quantification (ASQ), shearwave elastography and histology in patients with diffuse hepatopathies , 2015, BMC Medical Imaging.

[64]  D. S̆timac,et al.  Transient elastography (FibroScan(®)) with controlled attenuation parameter in the assessment of liver steatosis and fibrosis in patients with nonalcoholic fatty liver disease - Where do we stand? , 2016, World journal of gastroenterology.

[65]  Wen-Hung Kuo,et al.  Small-window parametric imaging based on information entropy for ultrasound tissue characterization , 2017, Scientific Reports.

[66]  Shyh-Hau Wang,et al.  Quantitative analysis of noise influence on the detection of scatterer concentration by Nakagami parameter , 2005 .