Multivariable Quantitative US Parameters for Assessing Hepatic Steatosis

Published Online:https://doi.org/10.1148/radiol.230341

A model built using the quantitative US parameters (US-guided attenuation parameter, integrated backscatter coefficient, and signal-to-noise ratio) showed good performance for discriminating at least 5% steatosis in patients with chronic liver disease.

Background

Because of the global increase in the incidence of nonalcoholic fatty liver disease, the development of noninvasive, widely available, and highly accurate methods for assessing hepatic steatosis is necessary.

Purpose

To evaluate the performance of models with different combinations of quantitative US parameters for their ability to predict at least 5% steatosis in patients with chronic liver disease (CLD) as defined using MRI proton density fat fraction (PDFF).

Materials and Methods

Patients with CLD were enrolled in this prospective multicenter study between February 2020 and April 2021. Integrated backscatter coefficient (IBSC), signal-to-noise ratio (SNR), and US-guided attenuation parameter (UGAP) were measured in all participants. Participant MRI PDFF value was used to define at least 5% steatosis. Four models based on different combinations of US parameters were created: model 1 (UGAP alone), model 2 (UGAP with IBSC), model 3 (UGAP with SNR), and model 4 (UGAP with IBSC and SNR). Diagnostic performance of all models was assessed using area under the receiver operating characteristic curve (AUC). The model was internally validated using 1000 bootstrap samples.

Results

A total of 582 participants were included in this study (median age, 64 years; IQR, 52–72 years; 274 female participants). There were 364 participants in the steatosis group and 218 in the nonsteatosis group. The AUC values for steatosis diagnosis in models 1–4 were 0.92, 0.93, 0.95, and 0.96, respectively. The C-indexes of models adjusted by the bootstrap method were 0.92, 0.93, 0.95, and 0.96, respectively. Compared with other models, models 3 and 4 demonstrated improved discrimination of at least 5% steatosis (P < .01).

Conclusion

A model built using the quantitative US parameters UGAP, IBSC, and SNR could accurately discriminate at least 5% steatosis in patients with CLD.

© RSNA, 2023

Supplemental material is available for this article.

See also the editorial by Han in this issue.

References

  • 1. Loomba R, Sanyal AJ. The global NAFLD epidemic. Nat Rev Gastroenterol Hepatol 2013;10(11):686–690. Crossref, MedlineGoogle Scholar
  • 2. Younossi ZM, Golabi P, de Avila L, et al. The global epidemiology of NAFLD and NASH in patients with type 2 diabetes: A systematic review and meta-analysis. J Hepatol 2019;71(4):793–801. Crossref, MedlineGoogle Scholar
  • 3. Cusi K, Isaacs S, Barb D, et al. American Association of clinical endocrinology clinical practice guideline for the diagnosis and management of nonalcoholic fatty liver disease in primary care and endocrinology clinical settings: co-sponsored by the American Association for the Study of Liver Diseases (AASLD). Endocr Pract 2022;28(5):528–562. Crossref, MedlineGoogle Scholar
  • 4. McPherson S, Armstrong MJ, Cobbold JF, et al. Quality standards for the management of non-alcoholic fatty liver disease (NAFLD): consensus recommendations from the British Association for the Study of the Liver and British Society of Gastroenterology NAFLD Special Interest Group. Lancet Gastroenterol Hepatol 2022;7(8):755–769. Crossref, MedlineGoogle Scholar
  • 5. Adinolfi LE, Gambardella M, Andreana A, Tripodi MF, Utili R, Ruggiero G. Steatosis accelerates the progression of liver damage of chronic hepatitis C patients and correlates with specific HCV genotype and visceral obesity. Hepatology 2001;33(6):1358–1364. Crossref, MedlineGoogle Scholar
  • 6. Berger D, Desai V, Janardhan S. Con: Liver biopsy remains the gold standard to evaluate fibrosis in patients with nonalcoholic fatty liver disease. Clin Liver Dis (Hoboken) 2019;13(4):114–116. Crossref, MedlineGoogle Scholar
  • 7. Imajo K, Kessoku T, Honda Y, et al. Magnetic resonance imaging more accurately classifies steatosis and fibrosis in patients with nonalcoholic fatty liver disease than transient elastography. Gastroenterology 2016;150(3):626–637.e7. Crossref, MedlineGoogle Scholar
  • 8. Le TA, Chen J, Changchien C, et al. Effect of colesevelam on liver fat quantified by magnetic resonance in nonalcoholic steatohepatitis: a randomized controlled trial. Hepatology 2012;56(3):922–932. Crossref, MedlineGoogle Scholar
  • 9. Saadeh S, Younossi ZM, Remer EM, et al. The utility of radiological imaging in nonalcoholic fatty liver disease. Gastroenterology 2002;123(3):745–750. Crossref, MedlineGoogle Scholar
  • 10. Dasarathy S, Dasarathy J, Khiyami A, Joseph R, Lopez R, McCullough AJ. Validity of real time ultrasound in the diagnosis of hepatic steatosis: a prospective study. J Hepatol 2009;51(6):1061–1067. Crossref, MedlineGoogle Scholar
  • 11. Chan WK, Nik ustapha MNR, Mahadeva S. Controlled attenuation parameter for the detection and quantification of hepatic steatosis in nonalcoholic fatty liver disease. J Gastroenterol Hepatol 2014;29(7):1470–1476. Crossref, MedlineGoogle Scholar
  • 12. Karlas T, Petroff D, Sasso M, et al. Individual patient data meta-analysis of controlled attenuation parameter (CAP) technology for assessing steatosis. J Hepatol 2017;66(5):1022–1030. Crossref, MedlineGoogle Scholar
  • 13. Fujiwara Y, Kuroda H, Abe T, et al. The B-mode image-guided ultrasound attenuation parameter accurately detects hepatic steatosis in chronic liver disease. Ultrasound Med Biol 2018;44(11):2223–2232. Crossref, MedlineGoogle Scholar
  • 14. Kumada T, Ogawa S, Goto T, et al. Intra-individual comparisons of the ultrasound-guided attenuation parameter and the magnetic resonance imaging-based proton density fat fraction using bias and precision statistics. Ultrasound Med Biol 2022;48(8):1537–1546. Crossref, MedlineGoogle Scholar
  • 15. Imajo K, Toyoda H, Yasuda S, et al. Utility of ultrasound-guided attenuation parameter for grading steatosis with reference to MRI-PDFF in a large cohort. Clin Gastroenterol Hepatol 2022;20(11):2533–2541.e7. Crossref, MedlineGoogle Scholar
  • 16. Kuroda H, Abe T, Fujiwara Y, Nagasawa T, Takikawa Y. Diagnostic accuracy of ultrasound-guided attenuation parameter as a noninvasive test for steatosis in non-alcoholic fatty liver disease. J Med Ultrason 2021;48(4):471–480. Crossref, MedlineGoogle Scholar
  • 17. Yao LX, Zagzebski JA, Madsen EL. Backscatter coefficient measurements using a reference phantom to extract depth-dependent instrumentation factors. Ultrason Imaging 1990;12(1):58–70. Crossref, MedlineGoogle Scholar
  • 18. Han A, Andre MP, Erdman JW Jr, Loomba R, Sirlin CB, O’Brien WD Jr. Repeatability and reproducibility of a clinically based QUS phantom study and methodologies. IEEE Trans Ultrason Ferroelectr Freq Control 2017;64(1):218–231. Crossref, MedlineGoogle Scholar
  • 19. Han A, Zhang YN, Boehringer AS, et al. Inter-platform reproducibility of ultrasonic attenuation and backscatter coefficients in assessing NAFLD. Eur Radiol 2019;29(9):4699–4708. Crossref, MedlineGoogle Scholar
  • 20. Liao YY, Yang KC, Lee MJ, Huang KC, Chen JD, Yeh CK. Multifeature analysis of an ultrasound quantitative diagnostic index for classifying nonalcoholic fatty liver disease. Sci Rep 2016;6(1):35083. Crossref, MedlineGoogle Scholar
  • 21. Mohana Shankar P. A general statistical model for ultrasonic backscattering from tissues. IEEE Trans Ultrason Ferroelectr Freq Control 2000;47(3):727–736. Crossref, MedlineGoogle Scholar
  • 22. Liao YY, Yeh CK, Huang KC, Tsui PH, Yang KC. Metabolic characteristics of a novel ultrasound quantitative diagnostic index for nonalcoholic fatty liver disease. Sci Rep 2019;9(1):7922. Crossref, MedlineGoogle Scholar
  • 23. Kuroda H, Kakisaka K, Kamiyama N, et al. Non-invasive determination of hepatic steatosis by acoustic structure quantification from ultrasound echo amplitude. World J Gastroenterol 2012;18(29):3889–3895. Crossref, MedlineGoogle Scholar
  • 24. Kuntz E, Kuntz HD. Hepatology: principles and practice: history, morphology, biochemistry, diagnostics, clinic, therapy. 2nd ed. Heidelberg, Germany: Springer, 2006. CrossrefGoogle Scholar
  • 25. Glantz SA, Slinker BK. Primer of applied regression and analysis of variance. New York, NY: McGraw-Hill, 1990; 181–238. Google Scholar
  • 26. Obuchowski NA. Receiver operating characteristic curves and their use in radiology. Radiology 2003;229(1):3–8. LinkGoogle Scholar
  • 27. Steyerberg EW. Clinical prediction models: a practical approach to development, validation, and updating. New York, NY: Springer, 2008. Google Scholar
  • 28. Kanda Y. Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics. Bone Marrow Transplant 2013;48(3):452–458. Crossref, MedlineGoogle Scholar
  • 29. Lu ZF, Zagzebski JA, Lee FT. Ultrasound backscatter and attenuation in human liver with diffuse disease. Ultrasound Med Biol 1999;25(7):1047–1054. Crossref, MedlineGoogle Scholar
  • 30. Lin SC, Heba E, Wolfson T, et al. Noninvasive diagnosis of nonalcoholic fatty liver disease and quantification of liver fat using a new quantitative ultrasound technique. Clin Gastroenterol Hepatol 2015;13(7):1337–1345.e6. Crossref, MedlineGoogle Scholar
  • 31. Paige JS, Bernstein GS, Heba E, 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. AJR Am J Roentgenol 2017;208(5):W168–W177. Crossref, MedlineGoogle Scholar
  • 32. Han A, Zhang YN, Boehringer AS, et al. Assessment of hepatic steatosis in nonalcoholic fatty liver disease by using quantitative US. Radiology 2020;295(1):106–113. LinkGoogle Scholar
  • 33. Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 2007;115(7):928–935. Crossref, MedlineGoogle Scholar
  • 34. Pencina MJ, D’Agostino RB, Pencina KM, Janssens AC, Greenland P. Interpreting incremental value of markers added to risk prediction models. Am J Epidemiol 2012;176(6):473–481. Crossref, MedlineGoogle Scholar
  • 35. Pirmoazen AM, Khurana A, Loening AM, et al. Diagnostic performance of 9 quantitative ultrasound parameters for detection and classification of hepatic steatosis in nonalcoholic fatty liver disease. Invest Radiol 2022;57(1):23–32. Crossref, MedlineGoogle Scholar

Article History

Received: Feb 23 2023
Revision requested: Apr 14 2023
Revision received: July 25 2023
Accepted: Aug 29 2023
Published online: Oct 03 2023