Shear-Wave Dispersion Slope (SWDS) is a new ultrasound parameter to detect liver inflammation
https://doi.org/10.24835/1607-0763-1513
Abstract
Background: one of the most pressing problems of ultrasound shear wave elastometry of the liver is the lack of distinct digital differential diagnostic criteria for the presence and severity of fibrosis in inflammation.
Purpose. To evaluate the possibility of a new ultrasonic parameter – the shear wave dispersion slope (SWDS) in solving this problem.
Materials and methods. 156 patients with various liver pathologies were examined. Depending on the absence or presence of fibrosis, as well as inflammatory changes in the liver (according to biopsy and serological blood tests), the examined patients were divided into three groups. The control group consisted of 10 healthy donors. Measurements of the shear wave velocity (SWS), shear wave dispersion slope (SWDS) and the assessment of ultrasound attenuation (ATI) in the liver were performed using the Canon Medical Aplio i800 ultrasound diagnostic system (Tokyo, Japan) using a standard convex transducer.
Results. The indicators of SWS, SWDS and ATI (Median and 95% CI) in the control group were Me 1.2 (95% CI 1.1–1.6) m/s, Me 10.1 (95% CI 9.7–14.3) m/s/kHz and Me 0.54 (95% CI 0.41–0.63) dB/cm/MHz, respectively. The study of the main group showed that there is a close correlation between SWS and SWDS (Spearman's rho = 0.74). At the same time, patients in the subgroup with inflammation had significantly (p < 0.01) higher SWDS values compared with the control group and the subgroup without inflammation – Me 16.4 (95% CI 15.3–17.9) m/s/kHz versus Me 10.1 (95% CI 9.7–14.3) m/s/kHz and Me 12.7 (95%CI 12.1–14.3) m/s/kHz. In patients of the subgroup with fibrosis, but without inflammation, there was also a significant (p < 0.01) increase in SWDS from Me 12,0 (95% CI 11.4–12.8) m/s/kHz at F0-1 to Me 16,5 (95% CI 12.9–20.3) m/s/kHz at F3–4 METAVIR.
Conclusion. The use of absolute SWDS values did not contribute to the achievement of the purpose of this study – the detection of criteria for the differential diagnosis of the presence and severity of liver fibrosis in patients with hepatitis. Nevertheless, the results of the study provide grounds for making a fairly confident conclusion that the assessment of the relationship between SWDS and SWS using the binary logistic regression formula (logit(p) = 0, 4152 SWDS (м/с/кГц) – 0,1344 SWS (м/с) – 6,5115) can become a valuable additional method for ultrasound diagnostics of inflammatory changes in the liver.
About the Authors
B. I. ZykinRussian Federation
Boris I. Zykin – Doct. of Sci. (Med.), professor, Department of radiology, Medical and Biological University of innovation and continuing education of Federal Medical Biophysical Center named after A.I. Burnazyan Russia's Federal Medical-Biological Agency, Moscow.
https://orcid.org/0000-0002-8871-1434
E. A. Ionova
Russian Federation
Elena A. Ionova – Doct. of Sci. (Med.), Head of Department of radiology, Medical and Biological University of innovation and continuing education of Federal Medical Biophysical Center named after A.I. Burnazyan Russia's Federal Medical-Biological Agency, Moscow. https://orcid.org/0000-0002-6084-2061
T. A. Anosova
Russian Federation
Tatyana A. Anosova – Cand. of Sci. (Med.), Assistant professor, Department of radiology, Medical and Biological University of innovation and continuing education of Federal Medical Biophysical Center named after A.I. Burnazyan Russia's Federal Medical-Biological Agency, Moscow.
https://orcid.org/0009-0000-9014-2165
References
1. Zubarev A.V., Gazhonova V.E., Gusakova E.V., Churkina S.O., Mironova E.V. New ultrasound technologies: shear wave dispersion and shear wave elastography in the diagnosis of post-Covid-19 liver injury. Kremlin Medicine Journal. 2022; 1: 16–20. http://doi.org/10.26269/4qvs-nz51 (In Russian)
2. Sugimoto K., Moriyasu F., Oshiro H. et al. Clinical utilization of shear wave dispersion imaging in diffuse liver disease. Ultrasonography. 2020; 39 (1): 3–10. http://doi.org/10.14366/usg.19031
3. Ferraioli G., Maiocchi L., Dellafiore C. et al. Performance and cutoffs for liver fibrosis staging of a two-dimensional shear wave elastography technique. Eur. J. Gastroenterol. Hepatol. 2021; 33 (1): 89–95. http://doi.org/10.1097/MEG.0000000000001702.
4. Ferraioli G., Barr R.G., Berzigotti A. et al. WFUMB Guideline/Guidance on Liver Multiparametric Ultrasound: Pt 1. Update to 2018 Guidelines on Liver Ultrasound Elastography. Ultrasound Med. Biol. 2024; 50 (8): 1071–1087. http://doi.org/10.1016/j.ultrasmedbio.2024.03.013
5. Zheng Y., Chen X., Yao A. et al. Shear Wave Propagation in Soft Tissue and Ultrasound Vibrometry. In: Wave Propagation Theories and Applications / Ed. Zheng Y. InTechOpen, 2013. Ch. 1: 1–23. http://doi.org/10.5772/3393
6. Christensen R.М. Theory of Viscoelasticity. NY: Academic Press, 1974. 338 p.
7. Sandrin L., Jennifer O., Cécile B. et al. Non-Invasive Assessment of Liver Fibrosis by Vibration-Controlled Transient Elastography (Fibroscan). In: Liver Biopsy / Ed. Takahashi H. IntechOpen. 2011; Ch. 19: 293–314. http://doi.org/10.5772/811
8. Mueller S., Millonig G., Sarovska L. et al. Increased liver stiffness in alcoholic liver disease: Differentiating fibrosis from steatohepatitis. Wld J. Gastroenterol. 2010; 16 (8): 966–972.
9. Deffieux T., Montaldo G., Tanter M., Fink M. Shear wave spectroscopy for in vivo quantification of human soft tissues visco-elasticity. IEEE Trans. Med. Imaging. 2009; 28 (3): 313–322. http://doi.org/10.1109/TMI.2008.925077
10. Garcovich M., Paratore M., Ainora M.E. et al. Shear Wave Dispersion in Chronic Liver Disease: From Physical Principles to Clinical Usefulness. J. Pers. Med. 2023; 13 (6): 945. http://doi.org/10.3390/jpm13060945
11. Wang K., Yu D., Li G. et al. Comparison of the diagnostic performance of shear wave elastography with shear wave dispersion for pre-operative staging of hepatic fibrosis in patients with hepatocellular carcinoma. Eur. J. Radiol. 2022; 154: 110459. http://doi.org/10.1016/j.ejrad.2022.110459
12. Zhang X., Zheng R., Jin J. et al. US Shear-Wave Elastography Dispersion for Characterization of Chronic Liver Disease. Radiology. 2022; 305 (3): 597–605. http://doi.org/10.1148/radiol.212609
13. Lee D.H., Cho E.J., Bae J.S. et al. Accuracy of Two-Dimensional Shear Wave Elastography and Attenuation Imaging for Evaluation of Patients With Nonalcoholic Steatohepatitis. Clin. Gastroenterol. Hepatol. 2021; 19 (4): 797–805.e7. http://doi.org/10.1016/j.cgh.2020.05.034
14. Ormachea J., Parker K.J. Comprehensive Viscoelastic Characterization of Tissues and the Inter-relationship of Shear Wave (Group and Phase) Velocity, Attenuation and Dispersion. Ultrasound Med. Biol. 2020; 46 (12): 3448–3459. http://doi.org/10.1016/j.ultrasmedbio.2020.08.023
15. Ferraioli G., Barr R.G., Farrokh A. et al. How to perform shear wave elastography. Pt I. Med. Ultrason. 2022; 24 (1): 95–106. http://doi.org/10.11152/mu-3217
16. Jang J.K., Lee E.S., Seo J.W. et al. Two-dimensional Shear-Wave Elastography and US Attenuation Imaging for Nonalcoholic Steatohepatitis Diagnosis: A Cross-sectional, Multicenter Study. Radiology. 2022; 305 (1): 118–126. http://doi.org/10.1148/radiol.220220.
17. Gao J., Lee R., Trujillo M. Reliability of Performing Multiparametric Ultrasound in Adult Livers. J. Ultrasound Med. 2022; 41 (3): 699–711. http://doi.org/10.1002/jum.15751
18. Trout A., Xanthakos S., Bennett P., Dillman J. Liver Shear Wave Speed and Other Quantitative Ultrasound Measures of Liver Parenchyma: Prospective Evaluation in Healthy Children and Adults. Am. J. Roentgenol. 2020; 214 (3): 557–565. http://doi.org/10.2214/AJR.19.21796
19. Seyrek S., Ayyildiz H., Bulakci M. et al. Comparison of Fibroscan, Shear Wave Elastography, and Shear Wave Dispersion Measurements in Evaluating Fibrosis and Necroinflammation in Patients Who Underwent Liver Biopsy. Ultrasound Q. 2024; 40 (1): 74–81. http://doi.org/10.1097/RUQ.0000000000000677
20.
Review
For citations:
Zykin B.I., Ionova E.A., Anosova T.A. Shear-Wave Dispersion Slope (SWDS) is a new ultrasound parameter to detect liver inflammation. Medical Visualization. 2025;29(1):41-50. (In Russ.) https://doi.org/10.24835/1607-0763-1513