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Prospective comparison of diffusion-weighted MRI and dynamic Gd-EOB-DTPA-enhanced MRI for detection and staging of hepatic fibrosis in primary sclerosing cholangitis

  • S. Keller
  • J. Sedlacik
  • T. Schuler
  • R. Buchert
  • M. Avanesov
  • R. Zenouzi
  • A. W. Lohse
  • H. Kooijman
  • J. Fiehler
  • C. Schramm
  • J. Yamamura
Magnetic Resonance
  • 191 Downloads

Abstract

Purpose

To assess the diagnostic value of multiparametric magnetic resonance imaging (MRI) including dynamic Gd-EOB-DTPA-enhanced (DCE) and diffusion-weighted (DW) imaging for diagnosis and staging of hepatic fibrosis in primary sclerosing cholangitis (PSC) using transient elastography as a standard reference.

Material and methods

Multiparametric MRI was prospectively performed on a 3.0-Tesla scanner in 47 patients (age 43.9±14.3 years). Transient elastography derived liver stiffness measurements (LSM), DCE-MRI derived parameters (hepatocellular uptake rate (Ki), arterial (Fa), portal venous (Fv) and total (Ft) blood flow, mean transit time (MTT), and extracellular volume (Ve)) and the apparent diffusion coefficient (ADC) were calculated. Correlation and univariate analysis of variance with post hoc pairwise comparison were applied to test for differences between LSM derived fibrosis stages (F0/F1, F2/3, F4). ROC curve analysis was used as a performance measure.

Results

Both ADC and Ki correlated significantly with LSM (r= -0.614; p<0.001 and r= -0.368; p=0.01). The ADC significantly discriminated fibrosis stages F0/1 from F2/3 and F4 (p<0.001). Discrimination of F0/1 from F2/3 and F4 reached a sensitivity/specificity of 0.917/0.821 and 0.8/0.929, respectively. Despite significant inter-subject effect for classification of fibrosis stages, post hoc pairwise comparison was not significant for Ki (p>0.096 for F0/1 from F2/3 and F4). LSM, ADC and Ki were significantly associated with serum-based liver functional tests, disease duration and spleen volume.

Conclusion

DW-MRI provides a higher diagnostic performance for detection of hepatic fibrosis and cirrhosis in PSC patients in comparison to Gd-EOB-DTPA-enhanced DCE-MRI.

Key Points

• Both ADC and hepatocellular uptake rate (Ki) correlate significantly with liver stiffness (r= -0.614; p<0.001 and r= -0.368; p=0.01).

• The DCE-imaging derived quantitative parameter hepatocellular uptake rate (Ki) fails to discriminate pairwise intergroup differences of hepatic fibrosis (p>0.09).

• DWI is preferable to DCE-imaging for discrimination of fibrosis stages F0/1 to F2/3 (p<0.001) and F4 (p<0.001).

Keywords

Magnetic resonance imaging Primary sclerosing cholangitis Liver fibrosis Diffusion magnetic resonance imaging Gadolinium ethoxybenzyl DTPA 

Abbreviations

ADC

Apparent diffusion coefficient

ALT

Alanine amino transferase

AP

Alkaline phosphatase

AST

Aspartate amino transferase

DCE

Dynamic contrast-enhanced

DWI

Diffusion-weighted imaging

EASL

European Association for the Study of the Liver

Fa

Arterial flow

Fi

Hepatic uptake fraction

FOV

Field-of-view

Fv

Portal venous flow

Gd-EOB-DTPA

Gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid

GFR

Glomerular filtration rate

GGT

Gamma-glutamyl-transferase

IgG

Immunoglobulin G

Ki

Hepatocellular uptake rate

LSM

Liver stiffness measurements

METAVIR

Meta-analysis of histological data in viral hepatitis

MRI

Magnetic resonance imaging

MTT

Mean transit time

NEX

Number of excitations

PSC

Primary sclerosing cholangitis

ROC

Receiver operating characteristic

ROI

Region of interest

SD

Standard deviation

SPIR

Spectral inversion recovery

TE

Echo time

TR

Repetition time

TSE

Turbo spin echo

Ve

Extracellular volume

Notes

Funding

A.W. Lohse and C. Schramm were funded by the Deutsche Forschungsgemeinschaft (DFG) (SFB841 and KFO306).

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is J. Yamamura.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: Philips Healthcare (H. Kooijman).

Statistics and biometry

One of the authors has significant statistical expertise (R. Buchert).

Informed consent

Written informed consent was obtained from all subjects in this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• prospective

• experimental study

• performed at one institution

Supplementary material

330_2018_5614_MOESM1_ESM.docx (104 kb)
ESM 1 (DOCX 103 kb)

References

  1. 1.
    Portmann B, Zen Y (2012) Inflammatory disease of the bile ducts-cholangiopathies: liver biopsy challenge and clinicopathological correlation. Histopathology 60:236–248CrossRefPubMedGoogle Scholar
  2. 2.
    Kovac JD, Weber MA (2016) Primary Biliary Cirrhosis and Primary Sclerosing Cholangitis: an Update on MR Imaging Findings with Recent Developments. J Gastrointestin Liver Dis 25:517–524PubMedGoogle Scholar
  3. 3.
    Ziol M, Handra-Luca A, Kettaneh A et al (2005) Noninvasive assessment of liver fibrosis by measurement of stiffness in patients with chronic hepatitis C. Hepatology 41:48–54CrossRefPubMedGoogle Scholar
  4. 4.
    Castera L, Vergniol J, Foucher J et al (2005) Prospective comparison of transient elastography, Fibrotest, APRI, and liver biopsy for the assessment of fibrosis in chronic hepatitis C. Gastroenterology 128:343–350CrossRefPubMedGoogle Scholar
  5. 5.
    Foucher J, Chanteloup E, Vergniol J et al (2006) Diagnosis of cirrhosis by transient elastography (FibroScan): a prospective study. Gut 55:403–408CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Bedossa P, Poynard T (1996) An algorithm for the grading of activity in chronic hepatitis C. The METAVIR Cooperative Study Group. Hepatology 24:289–293CrossRefPubMedGoogle Scholar
  7. 7.
    Corpechot C, Gaouar F, El Naggar A et al (2014) Baseline values and changes in liver stiffness measured by transient elastography are associated with severity of fibrosis and outcomes of patients with primary sclerosing cholangitis. Gastroenterology 146:970–979 quiz e915-976CrossRefPubMedGoogle Scholar
  8. 8.
    Ehlken H, Wroblewski R, Corpechot C et al (2016) Validation of Transient Elastography and Comparison with Spleen Length Measurement for Staging of Fibrosis and Clinical Prognosis in Primary Sclerosing Cholangitis. PLoS One 11:e0164224CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Taouli B, Tolia AJ, Losada M et al (2007) Diffusion-weighted MRI for quantification of liver fibrosis: preliminary experience. AJR Am J Roentgenol 189:799–806CrossRefPubMedGoogle Scholar
  10. 10.
    Taouli B, Chouli M, Martin AJ, Qayyum A, Coakley FV, Vilgrain V (2008) Chronic hepatitis: role of diffusion-weighted imaging and diffusion tensor imaging for the diagnosis of liver fibrosis and inflammation. J Magn Reson Imaging 28:89–95CrossRefPubMedGoogle Scholar
  11. 11.
    Lewin M, Poujol-Robert A, Boelle PY et al (2007) Diffusion-weighted magnetic resonance imaging for the assessment of fibrosis in chronic hepatitis C. Hepatology 46:658–665CrossRefPubMedGoogle Scholar
  12. 12.
    Faria SC, Ganesan K, Mwangi I et al (2009) MR imaging of liver fibrosis: current state of the art. Radiographics 29:1615–1635CrossRefPubMedGoogle Scholar
  13. 13.
    Li Z, Sun J, Chen L et al (2016) Assessment of liver fibrosis using pharmacokinetic parameters of dynamic contrast-enhanced magnetic resonance imaging. J Magn Reson Imaging 44:98–104CrossRefPubMedGoogle Scholar
  14. 14.
    Patel J, Sigmund EE, Rusinek H, Oei M, Babb JS, Taouli B (2010) Diagnosis of cirrhosis with intravoxel incoherent motion diffusion MRI and dynamic contrast-enhanced MRI alone and in combination: preliminary experience. J Magn Reson Imaging 31:589–600CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Dyvorne HA, Jajamovich GH, Bane O et al (2016) Prospective comparison of magnetic resonance imaging to transient elastography and serum markers for liver fibrosis detection. Liver Int 36:659–666CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Nilsson H, Blomqvist L, Douglas L et al (2013) Gd-EOB-DTPA-enhanced MRI for the assessment of liver function and volume in liver cirrhosis. Br J Radiol 86:20120653CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Juluru K, Talal AH, Yantiss RK et al (2016) Diagnostic accuracy of intracellular uptake rates calculated using dynamic Gd-EOB-DTPA-enhanced MRI for hepatic fibrosis stage. J Magn Reson Imaging.  https://doi.org/10.1002/jmri.25431
  18. 18.
    Zhang W, Kong X, Wang ZJ, Luo S, Huang W, Zhang LJ (2015) Dynamic Contrast-Enhanced Magnetic Resonance Imaging with Gd-EOB-DTPA for the Evaluation of Liver Fibrosis Induced by Carbon Tetrachloride in Rats. PLoS One 10:e0129621CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Sourbron S, Sommer WH, Reiser MF, Zech CJ (2012) Combined quantification of liver perfusion and function with dynamic gadoxetic acid-enhanced MR imaging. Radiology 263:874–883CrossRefPubMedGoogle Scholar
  20. 20.
    Sandrin L, Fourquet B, Hasquenoph JM et al (2003) Transient elastography: a new noninvasive method for assessment of hepatic fibrosis. Ultrasound Med Biol 29:1705–1713CrossRefPubMedGoogle Scholar
  21. 21.
    Portney L, Watkins M (1999) Foundations of clinical research: application to practice. Prentice Hall, Upper Saddle RiverGoogle Scholar
  22. 22.
    Juluru K, Talal AH, Yantiss RK et al (2017) Diagnostic accuracy of intracellular uptake rates calculated using dynamic Gd-EOB-DTPA-enhanced MRI for hepatic fibrosis stage. J Magn Reson Imaging 45:1177–1185CrossRefPubMedGoogle Scholar
  23. 23.
    Ning J, Yang Z, Xie S, Sun Y, Yuan C, Chen H (2017) Hepatic function imaging using dynamic Gd-EOB-DTPA enhanced MRI and pharmacokinetic modeling. Magn Reson Med 78:1488–1495CrossRefPubMedGoogle Scholar
  24. 24.
    Bollow M, Taupitz M, Hamm B, Staks T, Wolf KJ, Weinmann HJ (1997) Gadolinium-ethoxybenzyl-DTPA as a hepatobiliary contrast agent for use in MR cholangiography: results of an in vivo phase-I clinical evaluation. Eur Radiol 7:126–132CrossRefPubMedGoogle Scholar
  25. 25.
    Tschirch FT, Struwe A, Petrowsky H, Kakales I, Marincek B, Weishaupt D (2008) Contrast-enhanced MR cholangiography with Gd-EOB-DTPA in patients with liver cirrhosis: visualization of the biliary ducts in comparison with patients with normal liver parenchyma. Eur Radiol 18:1577–1586CrossRefPubMedGoogle Scholar
  26. 26.
    Rohrer M, Bauer H, Mintorovitch J, Requardt M, Weinmann HJ (2005) Comparison of magnetic properties of MRI contrast media solutions at different magnetic field strengths. Invest Radiol 40:715–724CrossRefPubMedGoogle Scholar
  27. 27.
    Hennedige TP, Wang G, Leung FP et al (2017) Magnetic Resonance Elastography and Diffusion Weighted Imaging in the Evaluation of Hepatic Fibrosis in Chronic Hepatitis B. Gut Liver 11:401–408CrossRefPubMedGoogle Scholar
  28. 28.
    Feier D, Balassy C, Bastati N, Fragner R, Wrba F, Ba-Ssalamah A (2015) The diagnostic efficacy of quantitative liver MR imaging with diffusion-weighted, SWI, and hepato-specific contrast-enhanced sequences in staging liver fibrosis-a multiparametric approach. Eur Radiol.  https://doi.org/10.1007/s00330-015-3830-0
  29. 29.
    Kovac JD, Dakovic M, Stanisavljevic D et al (2012) Diffusion-weighted MRI versus transient elastography in quantification of liver fibrosis in patients with chronic cholestatic liver diseases. Eur J Radiol 81:2500–2506CrossRefPubMedGoogle Scholar
  30. 30.
    Wang QB, Zhu H, Liu HL, Zhang B (2012) Performance of magnetic resonance elastography and diffusion-weighted imaging for the staging of hepatic fibrosis: A meta-analysis. Hepatology 56:239–247CrossRefPubMedGoogle Scholar
  31. 31.
    Wang Y, Ganger DR, Levitsky J et al (2011) Assessment of chronic hepatitis and fibrosis: comparison of MR elastography and diffusion-weighted imaging. AJR Am J Roentgenol 196:553–561CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Taouli B, Koh DM (2010) Diffusion-weighted MR imaging of the liver. Radiology 254:47–66CrossRefPubMedGoogle Scholar
  33. 33.
    Luciani A, Vignaud A, Cavet M et al (2008) Liver cirrhosis: intravoxel incoherent motion MR imaging--pilot study. Radiology 249:891–899CrossRefPubMedGoogle Scholar
  34. 34.
    Williamson KD, Chapman RW (2015) Editorial: further evidence for the role of serum alkaline phosphatase as a useful surrogate marker of prognosis in PSC. Aliment Pharmacol Ther 41:149–151CrossRefPubMedGoogle Scholar
  35. 35.
    Al Mamari S, Djordjevic J, Halliday JS, Chapman RW (2013) Improvement of serum alkaline phosphatase to <1.5 upper limit of normal predicts better outcome and reduced risk of cholangiocarcinoma in primary sclerosing cholangitis. J Hepatol 58:329–334CrossRefPubMedGoogle Scholar
  36. 36.
    Ponsioen CY, Chapman RW, Chazouilleres O et al (2016) Surrogate endpoints for clinical trials in primary sclerosing cholangitis: Review and results from an International PSC Study Group consensus process. Hepatology 63:1357–1367CrossRefPubMedGoogle Scholar
  37. 37.
    Ehlken H, Wroblewski R, Corpechot C et al (2016) Spleen size for the prediction of clinical outcome in patients with primary sclerosing cholangitis. Gut 65:1230–1232CrossRefPubMedGoogle Scholar
  38. 38.
    European Association for the Study of the L (2009) EASL Clinical Practice Guidelines: management of cholestatic liver diseases. J Hepatol 51:237–267CrossRefGoogle Scholar
  39. 39.
    Huwart L, Sempoux C, Vicaut E et al (2008) Magnetic resonance elastography for the noninvasive staging of liver fibrosis. Gastroenterology 135:32–40CrossRefPubMedGoogle Scholar
  40. 40.
    Lu PX, Huang H, Yuan J et al (2014) Decreases in molecular diffusion, perfusion fraction and perfusion-related diffusion in fibrotic livers: a prospective clinical intravoxel incoherent motion MR imaging study. PLoS One 9:e113846CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Yoon JH, Lee JM, Baek JH et al (2014) Evaluation of hepatic fibrosis using intravoxel incoherent motion in diffusion-weighted liver MRI. J Comput Assist Tomogr 38:110–116CrossRefPubMedGoogle Scholar
  42. 42.
    Haimerl M, Verloh N, Zeman F et al (2013) Assessment of clinical signs of liver cirrhosis using T1 mapping on Gd-EOB-DTPA-enhanced 3T MRI. PLoS One 8:e85658CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Heye T, Yang SR, Bock M et al (2012) MR relaxometry of the liver: significant elevation of T1 relaxation time in patients with liver cirrhosis. Eur Radiol 22:1224–1232CrossRefPubMedGoogle Scholar
  44. 44.
    Cassinotto C, Feldis M, Vergniol J et al (2015) MR relaxometry in chronic liver diseases: Comparison of T1 mapping, T2 mapping, and diffusion-weighted imaging for assessing cirrhosis diagnosis and severity. Eur J Radiol 84:1459–1465CrossRefPubMedGoogle Scholar
  45. 45.
    Li Z, Sun J, Hu X et al (2016) Assessment of liver fibrosis by variable flip angle T1 mapping at 3.0T. J Magn Reson Imaging 43:698–703CrossRefPubMedGoogle Scholar
  46. 46.
    Hinrichs H, Hinrichs JB, Gutberlet M et al (2016) Functional gadoxetate disodium-enhanced MRI in patients with primary sclerosing cholangitis (PSC). Eur Radiol 26:1116–1124CrossRefPubMedGoogle Scholar
  47. 47.
    Keller S, Aigner A, Zenouzi R et al (2018) Association of gadolinium-enhanced magnetic resonance imaging with hepatic fibrosis and inflammation in primary sclerosing cholangitis. PLoS One 13:e0193929CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Martin DR, Lauenstein T, Kalb B et al (2012) Liver MRI and histological correlates in chronic liver disease on multiphase gadolinium-enhanced 3D gradient echo imaging. J Magn Reson Imaging 36:422–429CrossRefPubMedGoogle Scholar
  49. 49.
    Saito H, Tada S, Nakamoto N et al (2004) Efficacy of non-invasive elastometry on staging of hepatic fibrosis. Hepatol Res 29:97–103CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of Diagnostic and Interventional Radiology and Nuclear MedicineUniversity Medical Center Hamburg-Eppendorf (UKE)HamburgGermany
  2. 2.Department of RadiologyCharitéBerlinGermany
  3. 3.Department of NeuroradiologyUniversity Medical Center Hamburg-Eppendorf (UKE)HamburgGermany
  4. 4.1st Department of MedicineUniversity Medical Center Hamburg-Eppendorf (UKE)HamburgGermany
  5. 5.Philips Medical Systems, MR Clinical ScienceHamburgGermany

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