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Quantitative analysis of non-alcoholic fatty liver in rats via combining multiple ultrasound parameters

1 School of Biomedical Engineering, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong, P. R. China
2 Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, 510060, P. R. China

Special Issues: Advanced Computer Methods and Programs in Biomedicine

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease. The noninvasive and accurate classification of NAFLD is still a challenging problem. In this study we pro- posed a new quantitative ultrasound (QUS) technique, which combined multiple QUS parameters for distinguishing steatosis stages. NAFLD was induced in the livers of 57 rats by gavage feeding with a high fat emulsion, while 8 rats were given a standard diet to serve as controls. Ex vivo ultrasound mea- surement was conducted for capturing the radiofrequency signal. Six QUS parameters were extracted and selected for linear combination. The results show that the overall performance of the combined parameter is better than that of the single QUS parameter. The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) while using our proposed method to distinguish mild steatosis (stage S1) from the steatosis under stage S0 are 90.1%, 0.93, 0.88 and 0.97 respectively. In conclusion, the proposed method in this study can make up for the deficiency of single parameter and improve the quantitative staging ability of fatty liver, and thus could play an important role in the diagnosis of NAFLD.
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Keywords NAFLD; characteristic parameters; fusion; steatosis stage; quantitative analysis

Citation: Yuanyuan Shen, Yuncheng Xing, Haoming Lin, Siping Chen, Baiying Lei, Jianhua Zhou, Zhong Liu, Xin Chen. Quantitative analysis of non-alcoholic fatty liver in rats via combining multiple ultrasound parameters. Mathematical Biosciences and Engineering, 2019, 16(5): 4546-4558. doi: 10.3934/mbe.2019227

References

  • 1. P. Angulo, Nonalcoholic Fatty Liver Disease, New Engl. J. Med., 346 (2002), 1221–1231.
  • 2. G. C. Farrell and C. Z. Larter, Nonalcoholic fatty liver disease: From steatosis to cirrhosis, Hepatology, 43 (2006), S99–S112.
  • 3. G. Targher, C. P.Day and E. Bonora, Risk ofCardiovascular Disease in Patientswith Nonalcoholic Fatty Liver Disease, New Engl. J. Med., 363 (2010), 1341–1350.
  • 4. Y. Takahashi and T. Fukusato, Histopathology of nonalcoholic fatty liver disease/nonalcoholic steatohepatitis, World J. Gastroenterol., 20 (2014), 15539–15548.
  • 5. N. Zamcheck and R. L. Sidman, Needle biopsy of the liver. I. Its use in clinical and investigative medicine, New Engl. J. Med., 249 (1953), 1020–1029.
  • 6. Y. Sumida, A. Nakajima and Y. Itoh, Limitations of liver biopsy and non-invasive diagnostic tests for the diagnosis of nonalcoholic fatty liver disease/nonalcoholic steatohepatitis, World J. Gastroenterol., 20 (2014), 475-485.
  • 7. N. F. Schwenzer, F. Springer, C. Schraml, et al., Non-invasive assessment and quantification of liver steatosis by ultrasound, computed tomography and magnetic resonance, J. Hepatol., 51 (2009), 433-445.
  • 8. S. B. Reeder, I. Cruite, G. Hamilton, et al., Quantitative Assessment of Liver Fat with Magnetic Resonance Imaging and Spectroscopy, J. Magn. Reson. Imaging, 34 (2011), 729–749.
  • 9. S. Saadeh, Z. M. Younossi, E. M. Remer, et al., The utility of radiological imaging in nonalcoholic fatty liver disease, Gastroenterology, 123 (2002), 745–750.
  • 10. Y. Guo, C. Dong, H. Lin, et al., Evaluation of Non-alcoholic Fatty Liver Disease Using Acoustic Radiation Force Impulse Imaging Elastography in Rat Models, Ultrasound Med. Biol., 43 (2017), 2619–2628.
  • 11. C. D. Williams, J. Stengel, M. I. Asike, et al., Prevalence of Nonalcoholic Fatty Liver Disease and Nonalcoholic Steatohepatitis Among a Largely Middle-Aged Population Utilizing Ultrasound and Liver Biopsy: A Prospective Study, Gastroenterology, 140 (2011), 124–131.
  • 12. A. E. Bohte, J. R. van Werven, S. Bipat, et al., The diagnostic accuracy of US, CT, MRI and 1 H-MRS for the evaluation of hepatic steatosis compared with liver biopsy: a meta-analysis, Eur. Radiol., 21 (2011), 87–97.
  • 13. A. Ozturk, J. R. Grajo, M. S. Gee, et al., Quantitative hepatic fat quantification in non-alcoholic fatty liver disease using ultrasound-based techniques: A review of literature and their diagnostic performance, Ultrasound Med. Biol., 44 (2018), 2461–2475.
  • 14. M. L. Oelze and J. Mamou, Review of Quantitative Ultrasound: Envelope Statistics and Backscat-ter Coefficient Imaging and Contributions to Diagnostic Ultrasound, IEEE Transa. Ultrason., Ferroelectr. Freq. Control, 63 (2016), 336–351.
  • 15. F. Destrempes and G. Cloutier, A critical review and uniformized representation of statistical distributions modeling the ultrasound echo envelope, Ultrasound Med. Biol., 36 (2010), 1037–1051.
  • 16. M. C. Ho, Y. H. Lee, Y. M. Jeng, et al., Relationship between Ultrasound Backscattered Statistics and the Concentration of Fatty Droplets in Livers: An Animal Study, PloS One, 8 (2013), e63543.
  • 17. P. H. Tsui and Y. L. Wan, Application of Ultrasound Nakagami Imaging for the Diagnosis of Fatty Liver, J. Med. Ultrasound, 24 (2016), 47–49.
  • 18. Y. L. Wan, D. I. Tai, H. Y. Ma, et al., Effects of fatty infiltration in human livers on the backscat-tered statistics of ultrasound imaging, Proc. Inst. Mech. Eng. Part H-J. Eng. Med., 229 (2015), 419–428.
  • 19. J. S. Paige, G. S. Bernstein, E. Heba, et al., A Pilot Comparative Study of Quantitative Ultrasound, Conventional Ultrasound, and MRI for Predicting Histology-Determined Steatosis Grade in Adult Nonalcoholic Fatty Liver Disease, Am. J. Roentgenol., 207 (2017), W168–W177.
  • 20. H. T. Yang, K. F. Chen, Q. Lu, et al., Ultrasonic integrated backscatter in assessing liver steatosis before and after liver transplantation, Hepatobiliary Pancreatic Dis. Int., 13 (2014), 402–408.
  • 21. S. C. Lin, E. Heba, T. Wolfson, et al., Noninvasive Diagnosis of Nonalcoholic Fatty Liver Disease and Quantification of Liver Fat Using a New Quantitative Ultrasound Technique, Clin. Gastroen-terol. Hepatol., 13 (2015), 1337–1345.
  • 22. G. Ghoshal, R. J. Lavarello, J. P. Kemmerer, et al., Ex vivo study of quantitative ultrasound parameters in fatty rabbit livers, Ultrasound Med. Biol., 38 (2012), 2238–2248.
  • 23. D. E. Kleiner, E. M. Brunt, M. Van Natta, et al., Design and validation of a histological scoring system for nonalcoholic fatty liver disease, Hepatology, 41 (2005), 1313–1321.
  • 24. J. J. Lin, J. Y. Cheng, L. F. Huang, et al., Detecting changes in ultrasound backscattered statistics by using Nakagami parameters: Comparisons of moment-based and maximum likelihood estima-tors, Ultrasonics, 77 (2017), 133–143.
  • 25. K. Samimi and T. Varghese, Ultrasonic attenuation imaging using spectral cross-correlation and the reference phantom method, 2011 IEEE Int. Ultrason. Symp., (2011), 53–55.
  • 26. V. Roberjot, S. L. Bridal, P. Laugier, et al., Absolute backscatter coefficient over a wide range of frequencies in a tissue mimicking phantom containing two populations of scatterers, IEEE Trans. Ultrason., Ferroelectr. Freq. Control, 43 (1996), 970–978.
  • 27. F. L. Lizzi, M. Astor, L. Tian, et al., Ultrasonic spectrum analysis for tissue assays and therapy evaluation, Int. J. Imaging Syst. Technol., 8 (1997), 3–10.
  • 28. J. Q. Su and J. S. Liu, Linear Combinations of Multiple Diagnostic Markers, Publ. Am. Stat. Assoc., 88 (1993), 1350–1355.
  • 29. Y. Liu, C. F. Dong, G. Yang, et al., Optimal linear combination of ARFI, transient elastography, and APRI for the assessment of fibrosis in chronic hepatitis B, Liver Int., 35 (2015), 816–825.
  • 30. S. Milić and D.Štimac, Nonalcoholic Fatty Liver Disease/Steatohepatitis: Epidemiology, Patho- genesis, Clinical Presentation and Treatment, Dig. Dis., 30 (2012), 158–162.
  • 31. C. D. Byrne and G. Targher, NAFLD: A multisystem disease, J. Hepatol., 62 (2015), S47–S64.
  • 32. R. J. Wong, M. Aguilar, P. Cheung, et al., Nonalcoholic Steatohepatitis Is the Second Leading Etiology of Liver Disease Among Adults Awaiting Liver Transplantation in the United States, Gastroenterology, 148 (2015), 547–555.
  • 33. R. Bouzitoune, M. Meziri, C. B. Machado, et al., Can early hepatic fibrosis stages be discriminated by combining ultrasonic parameters?, Ultrasonics, 68 (2016), 120–126.
  • 34. X. Chen, H. Wen, X. Zhang, et al., Development of a Simple Noninvasive Model to Predict Sig-nificant Fibrosis in Patients with Chronic Hepatitis B: Combination of Ultrasound Elastography, Serum Biomarkers, and Individual Characteristics, Clin. Transl. Gastroenterol., 8 (2017), e84.

 

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