Skip to main content
SpringerLink
Log in

Hepatocyte fraction: correlation with noninvasive liver functional biomarkers

  • Hepatobiliary
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

To evaluate the correlation between HeF obtained from gadoxetic acid-enhanced MR imaging and clinical biomarkers for the assessment of liver function.

Methods

This prospective study was approved by our Institutional Review Board, and written informed consent was obtained from the patients. We recruited 48 patients carrying a known or suspected liver disease to undergo gadoxetic acid-enhanced MR imaging. The new model of the HeF was calculated from ΔR1 values of the liver and spleen. The HeF, quantitative liver-to-spleen contrast ratio (Q-LSC), and ΔT1 value (the reduction rate of the T1 value between the pre- and post-contrast images) were compared with the Child–Pugh and end-stage liver disease (MELD) scores.

Results

Among 48 patients, 40 were in Child–Pugh class A and 8 were in class B. The median HeF (P = 0.0001), Q-LSC (P = 0.015), and ΔT1 value (P = 0.0023) in patients in Child–Pugh class A were significantly higher than those in class B. The sensitivities, specificities, and area under the receiver-operating-characteristic curves for differentiating Child–Pugh class A and B were 95.0%, 87.5%, and 0.93 in the HeF; 77.5%, 75.0%, and 0.78 in the Q-LSC; and 57.5%, 100.0%, and 0.84 in the ΔT1 value, respectively. The HeF was significantly correlated with Child–Pugh (r = − 0.58, P < 0.0001) and MELD score (r = − 0.57, P < 0.0001).

Conclusions

The HeF was well correlated with Child–Pugh and MELD score and could be a new biomarker to assess liver function.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Haimerl M, Verloh N, Zeman F, et al. Assessment of clinical signs of liver cirrhosis using T1 mapping on Gd-EOB-DTPA-enhanced 3T MRI. Plos One 2013; 8:e85658

    Article  Google Scholar 

  2. Yamada A, Hara T, Li F, et al. Quantitative evaluation of liver function with use of gadoxetate disodium-enhanced MR imaging. Radiology 2011; 260:727-733

    Article  Google Scholar 

  3. de Carli MA, de Carli LA, Correa MB, Junqueira G, Jr., Tovo CV, Coral GP. Performance of noninvasive scores for the diagnosis of advanced liver fibrosis in morbidly obese with nonalcoholic fatty liver disease. Eur J Gastroenterol Hepatol 2019;

  4. Ringe KI, Husarik DB, Sirlin CB, Merkle EM. Gadoxetate disodium-enhanced MRI of the liver: part 1, protocol optimization and lesion appearance in the noncirrhotic liver. AJR American journal of roentgenology 2010; 195:13-28

    Article  Google Scholar 

  5. Motosugi U, Ichikawa T, Sou H, et al. Liver parenchymal enhancement of hepatocyte-phase images in Gd-EOB-DTPA-enhanced MR imaging: which biological markers of the liver function affect the enhancement? J Magn Reson Imaging 2009; 30:1042-1046

    Article  Google Scholar 

  6. Noda Y, Goshima S, Kajita K, et al. Biliary tract enhancement in gadoxetic acid-enhanced MRI correlates with liver function biomarkers. Eur J Radiol 2016; 85:2001-2007

    Article  Google Scholar 

  7. Katsube T, Okada M, Kumano S, et al. Estimation of liver function using T1 mapping on Gd-EOB-DTPA-enhanced magnetic resonance imaging. Invest Radiol 2011; 46:277-283

    Article  Google Scholar 

  8. Yoon JH, Lee JM, Kim E, Okuaki T, Han JK. Quantitative Liver Function Analysis: Volumetric T1 Mapping with Fast Multisection B1 Inhomogeneity Correction in Hepatocyte-specific Contrast-enhanced Liver MR Imaging. Radiology 2017; 282:408-417

    Article  Google Scholar 

  9. Giraudeau C, Leporq B, Doblas S, et al. Gadoxetate-enhanced MR imaging and compartmental modelling to assess hepatocyte bidirectional transport function in rats with advanced liver fibrosis. Eur Radiol 2017; 27:1804-1811

    Article  Google Scholar 

  10. Malinchoc M, Kamath PS, Gordon FD, Peine CJ, Rank J, ter Borg PC. A model to predict poor survival in patients undergoing transjugular intrahepatic portosystemic shunts. Hepatology 2000; 31:864-871

    Article  CAS  Google Scholar 

  11. Goshima S, Kanematsu M, Watanabe H, et al. Gd-EOB-DTPA-enhanced MR imaging: prediction of hepatic fibrosis stages using liver contrast enhancement index and liver-to-spleen volumetric ratio. J Magn Reson Imaging 2012; 36:1148-1153

    Article  Google Scholar 

  12. Dahlqvist Leinhard O, Dahlstrom N, Kihlberg J, et al. Quantifying differences in hepatic uptake of the liver specific contrast agents Gd-EOB-DTPA and Gd-BOPTA: a pilot study. Eur Radiol 2012; 22:642-653

    Article  Google Scholar 

  13. Levitt DG. The pharmacokinetics of the interstitial space in humans. BMC Clin Pharmacol 2003; 3:3

  14. Hanley JA, McNeil BJ. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 1983; 148:839-843

    Article  CAS  Google Scholar 

  15. Razek AA, Abdalla A, Omran E, Fathy A, Zalata K. Diagnosis and quantification of hepatic fibrosis in children with diffusion weighted MR imaging. Eur J Radiol 2011; 78:129-134

    Article  Google Scholar 

  16. Besheer T, Elalfy H, Abd El-Maksoud M, et al. Diffusion-weighted magnetic resonance imaging and micro-RNA in the diagnosis of hepatic fibrosis in chronic hepatitis C virus. World J Gastroenterol 2019; 25:1366-1377

    Article  CAS  Google Scholar 

  17. Razek AA, Massoud SM, Azziz MR, El-Bendary MM, Zalata K, Motawea EM. Prediction of esophageal varices in cirrhotic patients with apparent diffusion coefficient of the spleen. Abdom Imaging 2015; 40:1465-1469

    Article  Google Scholar 

  18. Nishie A, Ushijima Y, Tajima T, et al. Quantitative analysis of liver function using superparamagnetic iron oxide- and Gd-EOB-DTPA-enhanced MRI: comparison with Technetium-99 m galactosyl serum albumin scintigraphy. Eur J Radiol 2012; 81:1100-1104

    Article  Google Scholar 

  19. Yoon JH, Lee JM, Kang HJ, et al. Quantitative Assessment of Liver Function by Using Gadoxetic Acid-enhanced MRI: Hepatocyte Uptake Ratio. Radiology 2019; 290:125-133

    Article  Google Scholar 

  20. Rockey DC. Hepatic fibrosis, stellate cells, and portal hypertension. Clin Liver Dis 2006; 10:459-479, vii-viii

    Article  Google Scholar 

  21. Cavalli G, Re G, Casali AM. Red pulp arterial terminals in congestive splenomegaly. A morphometric study. Pathology, research and practice 1984; 178:590-594

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan

    Yoshifumi Noda, Kimihiro Kajita, Nobuyuki Kawai, Hiroshi Kawada, Yukichi Tanahashi & Masayuki Matsuo

  2. Department of Diagnostic Radiology & Nuclear Medicine, Hamamatsu University, Hamamatsu, Japan

    Satoshi Goshima

  3. Philips Japan, Tokyo, Japan

    Tomoyuki Okuaki & Yuta Akamine

Authors
  1. Yoshifumi Noda
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Satoshi Goshima
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tomoyuki Okuaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuta Akamine
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kimihiro Kajita
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nobuyuki Kawai
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hiroshi Kawada
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yukichi Tanahashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Masayuki Matsuo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yoshifumi Noda.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Noda, Y., Goshima, S., Okuaki, T. et al. Hepatocyte fraction: correlation with noninvasive liver functional biomarkers. Abdom Radiol 45, 83–89 (2020). https://doi.org/10.1007/s00261-019-02238-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00261-019-02238-2

Keywords

Search

Navigation