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Intravoxel incoherent motion assessment of liver fibrosis staging in MASLD

  • Hepatobiliary
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Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

Partial correlation analysis was performed to account for the interference of steatosis changes and inflammatory factors, to determine the true correlation between fibrosis and IVIM parameters (Dfast, Dslow, and F), and to evaluate the diagnostic efficacy of IVIM for liver fibrosis.

Methods

A total of 106 patients with metabolic dysfunction-associated steatotic liver disease (MASLD) examined by IVIM from November 2016 to November 2023 at our hospital were retrospectively included. Preliminary analysis of each IVIM parameter and correlations with pathological findings were performed using Spearman correlation analysis, and partial correlation analysis was used to exclude the interference of other pathological factors, thus yielding the true correlations between IVIM parameters (Dfast, Dslow, and F) and pathology. The diagnostic efficacy of IVIM parameters for diagnosing MASLD was assessed via receiver operating characteristic (ROC) curve analysis.

Results

Spearman correlation analysis of all the IVIM parameters revealed correlations with steatosis, lobular inflammation, and ballooning. Partial correlation analysis indicated that Dfast was correlated with the pathological fibrosis stage (r = − 0.593, P < 0.001), Dslow was correlated with the pathological steatosis score (r = − 0.313, P < 0.05), and F was correlated with the pathological fibrosis stage and steatosis score (r = − 0.456 and 0.255, P < 0.001 and P < 0.05). In the diagnosis of hepatic fibrosis, significant hepatic fibrosis, advanced liver fibrosis and cirrhosis, Dfast achieved areas under the ROC curve of 0.763, 0.801, 0.853, and 0.897, respectively. The threshold values for diagnosing different fibrosis stages using Dfast (10–3 mm2/s) were 57.613, 54.587, 52.714, and 51.978, respectively.

Conclusion

According to our partial correlation analysis, there was a moderate correlation between Dfast and F according to fibrosis stage, and Dfast was not influenced by inflammation or steatosis when diagnosing fibrosis in MASLD patients. A relatively close Dfast threshold is insufficient for accurately and noninvasively assessing various stages of MASLD fibrosis. In clinical practice, this approach can be considered an alternative method for the preliminary assessment of fibrosis in MASLD patients.

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References

  1. Rinella ME, Lazarus JV, Ratziu V, et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature. Ann Hepatol. Published online June 24, 2023. https://doi.org/10.1016/j.aohep.2023.101133

  2. Eslam M, Sanyal AJ, George J (2020) MAFLD: A consensus-driven proposed nomenclature for metabolic associated fatty liver disease. Gastroenterology 158:1999-2014.e1. https://doi.org/10.1053/j.gastro.2019.11.312

    Article  PubMed  CAS  Google Scholar 

  3. Zhou J, Zhou F, Wang W, Zhang XJ, Ji YX, Zhang P, She ZG, Zhu L, Cai J, Li H (2020) Epidemiological features of NAFLD from 1999 to 2018 in China. Hepatology 71:1851-1864. https://doi.org/10.1002/hep.31150

    Article  PubMed  Google Scholar 

  4. Sheka AC, Adeyi O, Thompson J, Hameed B, Crawford PA, Ikramuddin S (2020) Nonalcoholic steatohepatitis: A review. JAMA 323:1175-1183. https://doi.org/10.1001/jama.2020.2298

    Article  PubMed  CAS  Google Scholar 

  5. Chalasani N, Younossi Z, Lavine JE, Charlton M, Cusi K, Rinella M, Harrison SA, Brunt EM, Sanyal AJ (2018) The diagnosis and management of nonalcoholic fatty liver disease: Practice guidance from the American association for the study of liver diseases. Hepatology 67:328-357. https://doi.org/10.1002/hep.29367

    Article  PubMed  Google Scholar 

  6. Pepin KM, Welle CL, Guglielmo FF, Dillman JR, Venkatesh SK. Magnetic resonance elastography of the liver: everything you need to know to get started. Abdom Radiol (NY). 2022;47(1):94-114. https://doi.org/10.1007/s00261-021-03324-0

    Article  PubMed  Google Scholar 

  7. Chen BB, Hsu CY, Yu CW, et al. Dynamic contrast-enhanced magnetic resonance imaging with Gd-EOB-DTPA for the evaluation of liver fibrosis in chronic hepatitis patients. Eur Radiol. 2012;22(1):171-180. https://doi.org/10.1007/s00330-011-2249-5

    Article  PubMed  Google Scholar 

  8. Huh J, Ham SJ, Cho YC, et al. Gadoxetate-enhanced dynamic contrast-enhanced MRI for evaluation of liver function and liver fibrosis in preclinical trials. BMC Med Imaging. 2019;19(1):89. Published 2019 Nov 15. https://doi.org/10.1186/s12880-019-0378-5

  9. Wang Q, Yu G, Qiu J, Lu W. Application of Intravoxel Incoherent Motion in Clinical Liver Imaging: A Literature Review. J Magn Reson Imaging. Published online November 1, 2023. https://doi.org/10.1002/jmri.29086

  10. Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology. 1988;168(2):497-505. https://doi.org/10.1148/radiology.168.2.3393671

    Article  PubMed  Google Scholar 

  11. Joo I, Lee JM, Yoon JH, Jang JJ, Han JK, Choi BI. Nonalcoholic fatty liver disease: intravoxel incoherent motion diffusion-weighted MR imaging-an experimental study in a rabbit model. Radiology. 2014;270(1):131-140. https://doi.org/10.1148/radiol.13122506

    Article  PubMed  Google Scholar 

  12. Lu PX, Huang H, Yuan J, et al. Decreases in molecular diffusion, perfusion fraction and perfusion-related diffusion in fibrotic livers: a prospective clinical intravoxel incoherent motion MR imaging study. PLoS One. 2014;9(12):e113846. Published 2014 Dec 1. https://doi.org/10.1371/journal.pone.0113846

  13. Yoon JH, Lee JM, Lee KB, Kim D, Kabasawa H, Han JK. Comparison of monoexponential, intravoxel incoherent motion diffusion-weighted imaging and diffusion kurtosis imaging for assessment of hepatic fibrosis. Acta Radiol. 2019;60(12):1593-1601. https://doi.org/10.1177/0284185119840219

    Article  PubMed  Google Scholar 

  14. Tosun M, Onal T, Uslu H, Alparslan B, Çetin Akhan S. Intravoxel incoherent motion imaging for diagnosing and staging the liver fibrosis and inflammation. Abdom Radiol (NY). 2020;45(1):15-23. https://doi.org/10.1007/s00261-019-02300-z

    Article  PubMed  Google Scholar 

  15. França M, Martí-Bonmatí L, Alberich-Bayarri Á, Oliveira P, Guimaraes S, Oliveira J, Amorim J, Gonzalez JS, Vizcaíno JR, Miranda HP (2017) Evaluation of fibrosis and inflammation in diffuse liver diseases using intravoxel incoherent motion diffusion-weighted MR imaging. Abdom Radiol (NY) 42:468-477. https://doi.org/10.1007/s00261-016-0899-0

    Article  PubMed  Google Scholar 

  16. Lefebvre T, Hébert M, Bilodeau L, Sebastiani G, Cerny M, Olivié D, Gao ZH, Sylvestre MP, Cloutier G, Nguyen BN, Gilbert G, Tang A (2021) Intravoxel incoherent motion diffusion-weighted MRI for the characterization of inflammation in chronic liver disease. Eur Radiol 31:1347-1358. https://doi.org/10.1007/s00330-020-07203-y

    Article  PubMed  Google Scholar 

  17. Kleiner DE, Brunt EM, Van Natta M et al (2005) Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology 41:1313-1321. https://doi.org/10.1002/hep.20701

    Article  PubMed  Google Scholar 

  18. Diehl AM, Day C (2017). Cause, pathogenesis, and treatment of nonalcoholic steatohepatitis. N Engl J Med 377:2063-2072. https://doi.org/10.1056/NEJMra1503519

    Article  PubMed  CAS  Google Scholar 

  19. Yang M, Yan Y, Wang H (2019) IMAge/enGINE: A freely available software for rapid computation of high-dimensional quantification. Quant Imaging Med Surg 9:210–218. https://doi.org/10.21037/qims.2018.12.03

  20. Chan YH. Biostatistics 104: correlational analysis. Singapore Med J. 2003;44(12):614-619.

    PubMed  CAS  Google Scholar 

  21. Shin HJ, Yoon H, Kim MJ, Han SJ, Koh H, Kim S, Lee MJ (2018) Liver intravoxel incoherent motion diffusion-weighted imaging for the assessment of hepatic steatosis and fibrosis in children. World J Gastroenterol 24:3013-3020. https://doi.org/10.3748/wjg.v24.i27.3013

    Article  PubMed  PubMed Central  Google Scholar 

  22. Parente DB, Paiva FF, Neto JAO, Machado-Silva L, Figueiredo FA, Lanzoni V, Campos CF, do Brasil PE, Gomes MDB, Perez RDM, Rodrigues RS (2015) Intravoxel incoherent motion diffusion weighted mr imaging at 3.0 T: Assessment of steatohepatitis and fibrosis compared with liver biopsy in type 2 diabetic patients. PLoS One 10:e0125653. https://doi.org/10.1371/journal.pone.0125653

  23. Zou L, Jiang J, Zhang H, Zhong W, Xiao M, Xin S, Wang Y, Xing W (2022) Comparing and combining MRE, T1ρ, SWI, IVIM, and DCE-MRI for the staging of liver fibrosis in rabbits: Assessment of a predictive model based on multiparametric MRI. Magn Reson Med 87:2424-2435. https://doi.org/10.1002/mrm.29126

    Article  PubMed  Google Scholar 

  24. Hu G, Chan Q, Quan X, Zhang X, Li Y, Zhong X, Lin X (2015) Intravoxel incoherent motion MRI evaluation for the staging of liver fibrosis in a rat model. J Magn Reson Imaging 42:331-339. https://doi.org/10.1002/jmri.24796

    Article  PubMed  Google Scholar 

  25. Tiniakos DG, Vos MB, Brunt EM (2010) Nonalcoholic fatty liver disease: Pathology and pathogenesis. Annu Rev Pathol 5:145-171. https://doi.org/10.1146/annurev-pathol-121808-102132

    Article  PubMed  CAS  Google Scholar 

  26. Hagiwara M, Rusinek H, Lee VS, Losada M, Bannan MA, Krinsky GA, Taouli B (2008) Advanced liver fibrosis: Diagnosis with 3D whole-liver perfusion MR imaging--initial experience. Radiology 246:926-934. https://doi.org/10.1148/radiol.2463070077

    Article  PubMed  Google Scholar 

  27. Li T, Che-Nordin N, Wáng YXJ, et al. Intravoxel incoherent motion derived liver perfusion/diffusion readouts can be reliable biomarker for the detection of viral hepatitis B induced liver fibrosis. Quant Imaging Med Surg. 2019;9(3):371-385. https://doi.org/10.21037/qims.2019.02.11

    Article  PubMed  PubMed Central  Google Scholar 

  28. Ichikawa S, Motosugi U, Morisaka H, et al. MRI-based staging of hepatic fibrosis: Comparison of intravoxel incoherent motion diffusion-weighted imaging with magnetic resonance elastography. J Magn Reson Imaging. 2015;42(1):204-210. https://doi.org/10.1002/jmri.24760

    Article  PubMed  Google Scholar 

  29. Wáng YXJ, Li YT, Chevallier O, Huang H, Leung JCS, Chen W, Lu PX (2019) Dependence of intravoxel incoherent motion diffusion MR threshold b-value selection for separating perfusion and diffusion compartments and liver fibrosis diagnostic performance. Acta Radiol 60:3-12. https://doi.org/10.1177/0284185118774913

    Article  PubMed  Google Scholar 

  30. Wáng YXJ (2019) Living tissue intravoxel incoherent motion (IVIM) diffusion MR analysis without b=0 image: An example for liver fibrosis evaluation. Quant Imaging Med Surg 9:127–133. https://doi.org/10.21037/qims.2019.01.07

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Acknowledgements

The authors would like to express our enormous appreciation and gratitude to all participants.

Funding

This study has received funding by the National Natural Science Foundation of China (Nos. 61871276, 82071876, 62171298), Beijing Natural Science Foundation (No. 7184199), Capital’s Funds for Health Improvement and Research (No. 2018-2-2023), and Beijing Hospitals Authority Clinical Medicine Development of Special Funding Support (ZYLX202101).

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Authors and Affiliations

  1. Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Yongan Road 95, West District, Beijing, 100050, China

    Hao Ren, Hui Xu, Dawei Yang, Zhenchang Wang & Zhenghan Yang

  2. Liver Research Center, Beijing Friendship Hospital, Capital Medical University, No. 95 Yongan Road, West District, Beijing, 100050, China

    Xiaofei Tong, Xinyan Zhao, Qianyi Wang, Yameng Sun, Xiaojuan Ou, Jidong Jia & Hong You

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  1. Hao Ren
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Ren, H., Xu, H., Yang, D. et al. Intravoxel incoherent motion assessment of liver fibrosis staging in MASLD. Abdom Radiol (2024). https://doi.org/10.1007/s00261-024-04207-w

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