Methods of radiology in the diagnostics of chronic liver diseases

Background. Chronic liver disease is one of the most common diseases. In many countries, liver disease is among the top five causes of death. The liver is one of the main organs responsible for basic metabolic functions, protein and hormone synthesis, detoxification and waste elimination. In chronic liver disease, there is a continuous process of inflammation, destruction and regeneration, ultimately leading to severe dysfunction, causing the development of fibrosis and cirrhosis. The main task of the radiation diagnosis of chronic liver disease is the development and introduction into clinical practice of new noninvasive biomarkers for a comprehensive assessment of the structure of the liver parenchyma in order to choose further treatment tactics.

Aim a comprehensive analysis of the modern possibilities of radiation imaging methods in the diagnosis of chronic liver disease.

Materials and methods. The analysis of 107 modern publications of domestic and foreign literature devoted to the diagnosis of chronic liver disease  of various etiologies was carried out.

Conclusion. the review reflects the most common modern and promising methods of radiodiagnosis for chronic liver disease, which in most cases make it possible to avoid invasive interventions in the process of establishing a diagnosis and monitoring the response to treatment

1. 1 Li M., Wang Z.Q., Zhang L., Zheng H., Liu D.W., Zhou M.G. Burden of Cirrhosis and Other Chronic Liver Diseases Caused by Specific Etiologies in China, 1990-2016: Findings from the Global Burden of Disease Study 2016. Biomed Environ Sci. 2020;33(1):1-10. https://doi.org/10.3967/bes2020.001

2. 2 Kiseleva E.V., Demidova T.Yu. Nonalcoholic fatty liver disease and type 2 diabetes mellitus: the problem of conjugacy and stages of development. Obesity and metabolism. 2021;18(3):313-319. (In Russ).

3. 3 Xu X.Y., Wang W.S., Zhang Q.M. et al. Performance of common imaging techniques vs serum biomarkers in assessing fibrosis in patients with chronic hepatitis B: A systematic review and meta-analysis. World J Clin Cases. 2019;7 (15):2022–2037. https://doi.org/10.12998/wjcc.v7.i15.2022

4. 4 Lindenbraten L.D., Korolyuk L.D. Medical radiology and radiology (fundamentals of radiation diagnostics and radiation therapy. Moscow: Meditsina, 2000;667. (In Russ).

5. 5 Granov D.A., Polekhin A.S., Tarazov P.G., Rutkin I.O., Tileubergenov I.I., Borovik V.V. Chemoembolization of hepatic arteries in patients with hepatocellular cancer on the background of cirrhosis before liver transplantation: prognostic value of alphafetoprotein concentration. Bulletin of Transplantology and artificial organs. 2020;22(4):52-57. (In Russ). https://doi.org/10.15825/1995-1191-2020-4-52-57

6. 6 Ogurtsov P.P., Zykin B.I. Tarasova O.I., Kukhareva E.I., Krasnitskaya S. K., Mazurchik N.V., etc. Ultrasound shear elastometry and ultrasound steatometry of the liver. Methodological recommendations. Bulletin of Postgraduate medical education. 2019;1:137-148. (In Russ).

7. 7 Pirmoazen A.M., Khurana A., Kaffas A.E., Kamaya A. Quantitative ultrasound approaches for diagnosis and monitoring hepatic steatosis in nonalcoholic fatty liver disease. Theranostics. 2020;10(9):4277-4289. https://doi.org/10.7150/thno.40249

8. 8 Zykin B.I. Ultrasound shear elastography of the liver. Scientific and practical guide for doctors. Moscow: Real Time; 2022. (In Russ).

9. 9 Ozturk А., Olson M.C., Samir A.E., Venkatesh S.K. Liver fibrosis assessment: MR and US elastography. Abdominal Radiology. 2022;47:3037– 3050. https://doi.org/10.1007/s00261-021-03269-4

10. 10 Ferraioli G., Wai-Sun Wong V., Castera L., Berzigotti A., Sporea I., Dietrich C.F. et al. Liver Ultrasound Elastography: An Update to the World Federation for Ultrasound in Medicine and Biology Guidelines and Recommendations. Ultrasound Med Biol. 2018;44(12):2419-2440. https://doi.org/10.1016/j.ultrasmedbio.2018.07.008

11. 11 Byun J., Lee S.S., Sung Y.S., Shin Y., Yun J., Kim H.S. et al. CT indices for the diagnosis of hepatic steatosis using non-enhanced CT images: development and validation of diagnostic cut-off values in a large cohort with pathological reference standard. Eur Radiol. 2019;29(8):44274435. https://doi.org/10.1007/s00330-018-5905-1

12. 12 Wu Z.J., Hippe D.S., Zamora D.A., Briller N., Amin K.A., Kolokythas O. et al. Accuracy of Dual-Energy Computed Tomography Techniques for Fat Quantification in Comparison With Magnetic Resonance Proton Density Fat Fraction and Single-Energy Computed Tomography in an Anthropomorphic Phantom Environment. Comput Assist Tomogr. 2021;45(6):877-887.

13. 13 Furusato Hunt O.M., Lubner M.G., Ziemlewicz T.J., Del Rio A.M., Pickhardt P.J. The Liver Segmental Volume Ratio for Noninvasive Detection of Cirrhosis: Comparison With Established Linear and Volumetric Measures. J Comput Assist Tomogr. 2016;40(3):478-484. https://doi.org/10.1097/RCT.0000000000000389

14. 14 Kim S.W., Kim Y.R., Choi K.H.[et al. Staging of Liver Fibrosis by Means of Semiautomatic Measurement of Liver Surface Nodularity in MRI. AJR Am J Roentgenol. 2020;215(3):624-630. https://doi.org/10.2214/AJR.19.22041

15. 15 Thaiss W.M., Sannwald L., Kloth C., Ekert K., Hepp., Bösmüller H. et al. Quantification of Hemodynamic Changes in Chronic Liver Disease: Correlation of Perfusion-CT Data with Histopathologic Staging of Fibrosis. Acad Radiol. 2019;26(9): 1174-1180. https://doi.org/10.1016/j.acra.2018.11.009

16. 16 Ito E., Sato K., Yamamoto R., Sakamoto K., Urakawa H., Yoshimitsu K. Usefulness of iodine-blood material density images in estimating degree of liver fibrosis by calculating extracellular volume fraction obtained from routine dual-energy liver CT protocol equilibrium phase data: preliminary experience. Jpn J Radiol. 2020;38(4):365-373. https://doi.org/10.1007/s11604-019-00918-z

17. 17 Basso L., Baldi D., Mannelli L., Cavaliere C., Salvatore M., Brancato V. Investigating Dual-Energy CT Post-Contrast Phases for Liver Iron Quantification: A Preliminary Study. Dose Response. 2021;19(2). https://doi.org/10.1177/15593258211011359

18. 18 Chmelík M., Suchá S., Beneš J., Pátrovič L., Juskanič D. Photon-counting CT using multi-material decomposition algorithm enables fat quantification in the presence of iron deposits. Research Article. 2024;118. https://doi.org/10.1016/j.ejmp.2024.103210

19. 19 Okabayashi T., Shima Y., Morita S. et al. Liver function assessment using technetium 99m-Galactosyl single photon emission computed tomography/CT fusion imaging: a prospective trial. J Am Coll Surg. 2017;225(6):789–797. https://doi.org/10.1016/j.jamcollsurg.2017.08.021

20. 20 Al-Busafi S.A., Ghali P., Wong P. et al. The utility of Xenon-133 liver scan in the diagnosis and management of nonalcoholic fatty liver disease. Can J Gastroenterol. 2012;26:155-159. https://doi.org/10.1155/2012/796313

21. 21 Shetty A.S., Sipe A.L., Zulfiqar M., Tsai R., Raptis D.A., Raptis C.A. et al. In-Phase and Opposed-Phase Imaging: Applications of Chemical Shift and Magnetic Susceptibility in the Chest and Abdomen. RadioGraphics. 2019;39(1):115–135. https://doi.org/10.1148/rg.2019180043

22. 22 Yokoo T., Serai S.D., Pirasteh A., Bashir M.R., Hamilton G., Hernando D. et al. Linearity, Bias, and Precision of Hepatic Proton Density Fat Fraction Measurements by Using MR Imaging: A Meta-Analysis. Radiology. 2018;286:486–498. https://doi.org/10.1148/radiol.2017170550

23. 23 Idilman I.S., Keskin O., Celik A., Savas B., Elhan A.H., Idilman R. et al. A comparison of liver fat content as determined by magnetic resonance imaging-proton density fat fraction and MRS versus liver histology in non-alcoholic fatty liver disease. Acta Radiol. 2016;57(3):271–278. https://doi.org/10.1177/0284185115580488

24. 24 Simchick G., Zhao R., Hamilton G. et al. Spectroscopy-Based Multi-Parametric Quantification in Subjects with Liver Iron Overload at 1.5T and 3T. Magn Reson Med. 2022;87(2):597–613. https://doi.org/10.1002/mrm.29021

25. 25 Tang A., Desai A., Hamilton G. et al. Accuracy of MR imaging-estimated proton density fat fraction for classification of dichotomized histologic steatosis grades in nonalcoholic fatty liver disease. Radiology. 2015;274:416–425. https://doi.org/10.1148/radiol.14140754

26. 26 Jones J.G. Non-Invasive Analysis of Human Liver Metabolism. Magnetic Resonance Spectroscopy Metabolites. 2021;11(11):751. https://doi.org/10.3390/metabo11110751

27. 27 Im W.H., Song J.S., Jang W. Noninvasive staging of liver fibrosis: review of current quantitative CT and MRI‑based techniques. Abdominal Radiology. 2022;47:3051–3067. https://doi.org/10.1007/s00261-021-03181-x

28. 28 Han M.A., Vipani A., Noureddin N., Ramirez K., Gornbein J., Saouaf R. et al. MR elastography-based liver fibrosis correlates with liver events in nonalcoholic fatty liver patients: A multicenter study. Liver Int. 2020;40(9):2242-2251. https://doi.org/10.1111/liv.14593

29. 29 Hoffman D.H., Ayoola A., Nickel D., Han F., Chandarana H., Shanbhogue K.P. T1 mapping, T2 mapping and MR elastography of the liver for detection and staging of liver fibrosis. Abdom Radiol. 2020;45(3):692–700. https://doi.org/10.1007/s00261-019-02382-9

30. 30 Mesropyan N., Kupczyk P., Isaak A., Endler C., Faron A., Dold L. et al. Synthetic extracellular volume fraction without hematocrit sampling for hepatic applications. Abdom Radiol (NY). 2021;46(10):4637–4646. https://doi.org/10.1007/s00261-021-03140-6

31. 31 Ren, H. Liu Y., Lu J. et al. Evaluating the clinical value of MRI multi-model diffusion-weighted imaging on liver fibrosis in chronic hepatitis B patients. Abdom Radiol. 2021;46(4):1552-1561. https://doi.org/10.1007/s00261-020-02806-x

32. 32 Amin K., Mileto A., Kolokythas O. MRI for Liver Iron Quantification: Concepts and Current Methods. Seminars in Ultrasound, CT and MRI. 2022;43(4): 364-370. https://doi.org/10.1053/j.sult.2022.03.006

33. 33 Bhimaniya S., Arora J., Sharma P., Zhang Z., Khanna G. Liver iron quantification in children and young adults: comparison of a volumetric multi-echo 3-D Dixon sequence with conventional 2-D T2* relaxometry. Pediatr Radiol. 2022;52(8):1476–1483. https://doi.org/10.1007/s00247-022-05352-4