Advertisement

European Radiology

, Volume 29, Issue 12, pp 6611–6619 | Cite as

Prediction of esophageal varices by liver and spleen MR elastography

  • Hayato Abe
  • Yutaka MidorikawaEmail author
  • Naoki Matsumoto
  • Mitsuhiko Moriyama
  • Kazu Shibutani
  • Masahiro Okada
  • Seiichi Udagawa
  • Shingo Tsuji
  • Tadatoshi Takayama
Magnetic Resonance

Abstract

Objectives

We aimed to assess the diagnostic performance of MR elastography (MRE) in predicting esophageal varices (EVs) in patients with chronic liver disease.

Methods

We prospectively performed liver (LSM) and spleen stiffness measurements (SSM) using MRE and endoscopic screening for EVs to determine if patients with hepatocellular carcinoma were eligible for resection. We investigated whether LSM, SSM, and other non-invasive preoperative parameters were associated with the presence of EVs. In order to predict EVs, 211 patients were divided into training (n = 140) and test (n = 71) groups. A nomogram was built using independent factors based on logistic regression analysis in the training group and its accuracy was validated using an independent cohort.

Results

Forty-six patients (21.8%) were diagnosed as having EVs (mild, n = 36; severe, n = 10). According to multiple regression analysis, LSM (odds ratio, 2.362; 95% confidence interval [CI], 1.341–4.923; p = 0.001) and SSM (1.489; 1.095–2.235; p = 0.010) were independent predictors of EVs in the training group. The nomogram showed good discrimination, with a C-index of 0.942 (95% CI, 0.892–0.974) through internal validation, and good calibration. Application of the nomogram in the test group still gave good discrimination (C-index, 0.948; 95% CI, 0.868–0.995).

Conclusions

The combination of LSM and SSM using MRE is an accurate tool to identify patients at risk for EVs.

Key Points

Performance of MR elastography can estimate the presence of esophageal varices non-invasively.

Liver and spleen stiffness measurements are independent predictors for esophageal varices.

The nomogram using a combination of liver and spleen stiffness measurements allows for the risk of esophageal varices.

Keywords

Esophageal varices Liver MR elastography Spleen 

Abbreviations

APRI

Aspartate aminotransferase-to-platelet ratio index

AST

Aspartate aminotransferase

AUC

Area under the curve

CI

Confidence interval

C-index

Concordance index

CLD

Chronic liver disease

EVs

Esophageal varices

HCC

Hepatocellular carcinoma

ICGR15

Indocyanine green clearance rate at 15 min

LSM

Liver stiffness measurement

MRE

MR elastography

OR

Odds ratio

ROC

Receiver operating characteristic

SSM

Spleen stiffness measurement

Notes

Acknowledgements

This research was supported by Japan Agency for Medical Research and Development (AMED) under Grant Number 18hk0102049s0301 and a grant-in-aid of the 106th Annual Congress of JSS Memorial Surgical Research Fund, Tokyo, Japan.

Funding

This research was supported by Japan Agency for Medical Research and Development (AMED) under Grant Number 18hk0102049s0301 and a grant-in-aid of the 106th Annual Congress of JSS Memorial Surgical Research Fund, Tokyo, Japan.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Tadatoshi Takayama, Department of Digestive Surgery, Nihon University School of Medicine.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• prospective

• diagnostic or prognostic study

• performed at one institution

References

  1. 1.
    Yamazaki S, Takayama T, Nakamura M et al (2014) Prophylactic impact of endoscopic treatment for esophageal varices in liver resection: a prospective study. J Gastroenterol 49:917–922CrossRefGoogle Scholar
  2. 2.
    Garcia-Tsao G, Sanyal AJ, Grace ND, Carey W, Practice Guidelines Committee of the American Association for the Study of Liver D, Practice Parameters Committee of the American College of Gastroenterology (2007) Prevention and management of gastroesophageal varices and variceal hemorrhage in cirrhosis. Hepatology 46:922–938CrossRefGoogle Scholar
  3. 3.
    de Franchis R, Baveno VI Faculty (2015) Expanding consensus in portal hypertension: report of the Baveno VI Consensus Workshop: stratifying risk and individualizing care for portal hypertension. J Hepatol 63:743–752CrossRefGoogle Scholar
  4. 4.
    Morisaka H, Motosugi U, Ichikawa S, Sano K, Ichikawa T, Enomoto N (2015) Association of splenic MR elastographic findings with gastroesophageal varices in patients with chronic liver disease. J Magn Reson Imaging 41:117–124CrossRefGoogle Scholar
  5. 5.
    Berzigotti A, Seijo S, Arena U et al (2013) Elastography, spleen size, and platelet count identify portal hypertension in patients with compensated cirrhosis. Gastroenterology 144:102–111 e1CrossRefGoogle Scholar
  6. 6.
    Stefanescu H, Radu C, Procopet B et al (2015) Non-invasive menage a trois for the prediction of high-risk varices: stepwise algorithm using lok score, liver and spleen stiffness. Liver Int 35:317–325CrossRefGoogle Scholar
  7. 7.
    Ronot M, Lambert S, Elkrief L et al (2014) Assessment of portal hypertension and high-risk oesophageal varices with liver and spleen three-dimensional multifrequency MR elastography in liver cirrhosis. Eur Radiol 24:1394–1402PubMedGoogle Scholar
  8. 8.
    Morisaka H, Motosugi U, Ichikawa T et al (2013) MR-based measurements of portal vein flow and liver stiffness for predicting gastroesophageal varices. Magn Reson Med Sci 12:77–86CrossRefGoogle Scholar
  9. 9.
    Sun HY, Lee JM, Han JK, Choi BI (2014) Usefulness of MR elastography for predicting esophageal varices in cirrhotic patients. J Magn Reson Imaging 39:559–566CrossRefGoogle Scholar
  10. 10.
    Asrani SK, Talwalkar JA, Kamath PS et al (2014) Role of magnetic resonance elastography in compensated and decompensated liver disease. J Hepatol 60:934–939CrossRefGoogle Scholar
  11. 11.
    Bolognesi M, Merkel C, Sacerdoti D, Nava V, Gatta (2002) Role of spleen enlargement in cirrhosis with portal hypertension. Dig Liver Dis 34:144–150Google Scholar
  12. 12.
    Shin SU, Lee JM, Yu MH et al (2014) Prediction of esophageal varices in patients with cirrhosis: usefulness of three-dimensional MR elastography with echo-planar imaging technique. Radiology 272:143–153CrossRefGoogle Scholar
  13. 13.
    The Liver Cancer Study Group of Japan (2015) The general rules for the clinical and pathological study of primary liver cancer, 6th edn, revised version. Kanehara & Co., Ltd, TokyoGoogle Scholar
  14. 14.
    Wai CT, Greenson JK, Fontana RJ et al (2003) A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C. Hepatology 38:518–526CrossRefGoogle Scholar
  15. 15.
    Lin ZH, Xin YN, Dong QJ et al (2011) Performance of the aspartate aminotransferase-to-platelet ratio index for the staging of hepatitis C-related fibrosis: an updated meta-analysis. Hepatology 53:726–736CrossRefGoogle Scholar
  16. 16.
    Beppu K, Inokuchi K, Koyanagi N et al (1981) Prediction of variceal hemorrhage by esophageal endoscopy. Gastrointest Endosc 27:213–218CrossRefGoogle Scholar
  17. 17.
    Tajiri T, Yoshida H, Obara K et al (2010) General rules for recording endoscopic findings of esophagogastric varices (2nd edition). Dig Endosc 22:1–9CrossRefGoogle Scholar
  18. 18.
    Huwart L, Sempoux C, Vicaut E et al (2008) Magnetic resonance elastography for the noninvasive staging of liver fibrosis. Gastroenterology 135:32–40CrossRefGoogle Scholar
  19. 19.
    Abe H, Midorikawa Y, Mitsuka Y et al (2017) Predicting postoperative outcomes of liver resection by magnetic resonance elastography. Surgery 162:248–255CrossRefGoogle Scholar
  20. 20.
    Venkatesh SK, Wang G, Lim SG, Wee A (2014) Magnetic resonance elastography for the detection and staging of liver fibrosis in chronic hepatitis B. Eur Radiol 24:70–78CrossRefGoogle Scholar
  21. 21.
    Ichikawa S, Motosugi U, Morisaka H et al (2015) Comparison of the diagnostic accuracies of magnetic resonance elastography and transient elastography for hepatic fibrosis. Magn Reson Imaging 33:26–30CrossRefGoogle Scholar
  22. 22.
    Dyvorne HA, Jajamovich GH, Besa C, Cooper N, Taouli B (2015) Simultaneous measurement of hepatic and splenic stiffness using MR elastography: preliminary experience. Abdom Imaging 40:803–809CrossRefGoogle Scholar
  23. 23.
    Huang YQ, Liang CH, He L et al (2016) Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol 34:2157–2164CrossRefGoogle Scholar
  24. 24.
    Sugihara T, Yasunaga H, Horiguchi H et al (2013) A nomogram predicting severe adverse events after ureteroscopic lithotripsy: 12 372 patients in a Japanese national series. BJU Int 111:459–466CrossRefGoogle Scholar
  25. 25.
    Paul P, Pennell ML, Lemeshow S (2013) Standardizing the power of the Hosmer-Lemeshow goodness of fit test in large data sets. Stat Med 32:67–80CrossRefGoogle Scholar
  26. 26.
    Harrell FE Jr, Lee KL, Mark DB (1996) Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 15:361–387CrossRefGoogle Scholar
  27. 27.
    R Development Core Team (2011) R: A language and environment for statistical computing. Available via http://www.R-project.org/. Accessed 10 Jan 2012
  28. 28.
    Abe H, Midorikawa Y, Okada M, Takayama T (2018) Clinical application of magnetic resonance elastography in chronic liver disease. Hepatol Res 48:780–787CrossRefGoogle Scholar
  29. 29.
    Singh S, Muir AJ, Dieterich DT, Falck-Ytter YT (2017) American Gastroenterological Association Institute technical review on the role of elastography in chronic liver diseases. Gastroenterology 152:1544–1577CrossRefGoogle Scholar
  30. 30.
    Forner A, Llovet JM, Bruix J (2012) Hepatocellular carcinoma. Lancet 379:1245–1255CrossRefGoogle Scholar
  31. 31.
    Mitsuka Y, Midorikawa Y, Abe H et al (2017) A prediction model for the grade of liver fibrosis using magnetic resonance elastography. BMC Gastroenterol 17:133CrossRefGoogle Scholar
  32. 32.
    Santambrogio R, Kluger MD, Costa M et al (2013) Hepatic resection for hepatocellular carcinoma in patients with Child-Pugh’s A cirrhosis: is clinical evidence of portal hypertension a contraindication? HPB (Oxford) 15:78–84CrossRefGoogle Scholar
  33. 33.
    Groszmann RJ, Wongcharatrawee S (2004) The hepatic venous pressure gradient: anything worth doing should be done right. Hepatology 39:280–282CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Hayato Abe
    • 1
  • Yutaka Midorikawa
    • 1
    Email author
  • Naoki Matsumoto
    • 2
  • Mitsuhiko Moriyama
    • 2
  • Kazu Shibutani
    • 3
  • Masahiro Okada
    • 3
  • Seiichi Udagawa
    • 4
  • Shingo Tsuji
    • 5
  • Tadatoshi Takayama
    • 1
  1. 1.Department of Digestive SurgeryNihon University School of MedicineTokyoJapan
  2. 2.Department of Gastroenterology and HepatologyNihon University School of MedicineTokyoJapan
  3. 3.Department of RadiologyNihon University School of MedicineTokyoJapan
  4. 4.Department of MathematicsNihon University School of MedicineTokyoJapan
  5. 5.Research Center of Advanced Science and Technology, Genome Science DivisionsUniversity of TokyoTokyoJapan

Personalised recommendations