European Radiology

, Volume 27, Issue 5, pp 2086–2094 | Cite as

Dynamic contrast enhanced MR imaging for evaluation of angiogenesis of hepatocellular nodules in liver cirrhosis in N-nitrosodiethylamine induced rat model

Magnetic Resonance

Abstract

Purpose

To investigate whether dynamic contrast -enhanced MRI (DCE-MRI) can distinguish the type of liver nodules in a rat model with N-nitrosodiethylamine- induced cirrhosis.

Methods

Liver nodules in cirrhosis were induced in 60 male Wistar rats via 0.01 % N-nitrosodiethylamine in the drinking water for 35-100 days. The nodules were divided into three groups: regenerative nodule (RN), dysplastic nodule (DN), and hepatocellular carcinoma (HCC). DCE-MRI was performed, and parameters including transfer constant (Ktrans), rate constant (Kep), extravascular extracellular space volume fraction (Ve), and initial area under the contrast concentration versus time curve (iAUC) were measured and compared.

Results

The highest Ktrans and iAUC values were seen in HCC, followed by DN and RN (all P < 0.05). The area under the receiver operating characteristic curve (AUROC) for DN and HCC were 0.738 and 0.728 for Ktrans and iAUC, respectively. The AUROC for HCC were 0.850 and 0.840 for Ktrans and iAUC, respectively. Ordinal logistic regression analysis showed that Ktrans had a high goodness of fit (0.970, 95 % confidence interval, 13.751-24.958).

Conclusion

DCE-MRI is a promising method to differentiate of liver nodules. Elevated Ktrans suggested that the nodules may be transformed into HCC.

Key points

DCE-MRI is promising for differentiating among RN, DN, and HCC

K trans and iAUC positively correlated with malignancy degree of liver nodules

Elevated K trans suggests that the nodules may be transformed into HCC

Keywords

Dynamic contrast -enhanced -MRI (DCE-MRI) Hepatocellular nodules in cirrhosis Hepatocellular carcinoma Angiogenesis Ktrans 

Abbreviations

DCE-MRI

dynamic contrast- enhanced MRI

Ktrans

transfer constant

Kep

rate constant

Ve

extravascular extracellular volume fraction

iAUC

initial area under the gadolinium concentration-time curve

AUROC

area under the receiver operating characteristic curve

RN

regenerative nodule

DN

dysplastic nodule

HCC

hepatocellular carcinoma

Notes

Acknowledgments

The scientific guarantor of this publication is Wei Huang. 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. This study has received funding by National Natural Science Foundation of China (grant No. 81171313 to L.J.Z.), and the Program for New Century Excellent Talents in the University (NCET-12-0260 to L.J.Z.). No complex statistical methods were necessary for this paper.

Institutional Review Board approval was obtained. Approval from the institutional animal care committee was obtained. No study subjects or cohorts have been previously reported. Methodology: prospective, experimental, performed at one institution.

Supplementary material

330_2016_4505_Fig6_ESM.gif (313 kb)
Supplementary Fig. 1

The type of RE-time curves of nodules. The RE-time curves of nodules were divided into four types:constant slow rise (A), slow rise and fall (B), rapid rise, but slow fall (C), and rapid rise and fall (D). (GIF 313 kb)

330_2016_4505_MOESM1_ESM.tif (4.2 mb)
High Resolution Image (TIF 4289 kb)

References

  1. 1.
    Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM (2010) Estimates of worldwide burden of cancer in 2008. Int J Cancer 127:2893–2917CrossRefPubMedGoogle Scholar
  2. 2.
    Forner A, Llovet JM, Bruix J (2012) Hepatocellular carcinoma. Lancet 379:1245–1255CrossRefPubMedGoogle Scholar
  3. 3.
    Sherman M (2010) Hepatocellular carcinoma: epidemiology, surveillance, and diagnosis. Semin Liver Dis 30:3–16CrossRefPubMedGoogle Scholar
  4. 4.
    Alazawi W, Cunningham M, Dearden J, Foster GR (2010) Systematic review: outcome of compensated cirrhosis due to chronic hepatitis C infection. Aliment Pharmacol Ther 32:344–355CrossRefPubMedGoogle Scholar
  5. 5.
    International Consensus Group for Hepatocellular Neoplasia (2009) Pathologic diagnosis of early hepatocellular carcinoma: a report of the international consensus group for hepatocellular neoplasia. Hepatology 49:658–664CrossRefGoogle Scholar
  6. 6.
    Kim YS, Song JS, Lee HK, Han YM (2015) Hypovascular hypointense nodules on hepatobiliary phase without T2 hyperintensity on gadoxetic acid-enhanced MR images in patients with chronic liver disease: long-term outcomes and risk factors for hypervascular transformation. Eur RadiolGoogle Scholar
  7. 7.
    Song KD, Kim SH, Lim HK, Jung SH, Sohn I, Kim HS (2015) Subcentimeter hypervascular nodule with typical imaging findings of hepatocellular carcinoma in patients with history of hepatocellular carcinoma: natural course on serial gadoxetic acid-enhanced MRI and diffusion-weighted imaging. Eur Radiol 25:2789–2796CrossRefPubMedGoogle Scholar
  8. 8.
    Tajima T, Honda H, Taguchi K et al (2002) Sequential hemodynamic change in hepatocellular carcinoma and dysplastic nodules: CT angiography and pathologic correlation. AJR Am J Roentgenol 178:885–897CrossRefPubMedGoogle Scholar
  9. 9.
    Matsui O, Kobayashi S, Sanada J et al (2011) Hepatocelluar nodules in liver cirrhosis: hemodynamic evaluation (angiography-assisted CT) with special reference to multi-step hepatocarcinogenesis. Abdom Imaging 36:264–272CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Park YN, Yang CP, Fernandez GJ, Cubukcu O, Thung SN, Theise ND (1998) Neoangiogenesis and sinusoidal “capillarization” in dysplastic nodules of the liver. Am J Surg Pathol 22:656–662CrossRefPubMedGoogle Scholar
  11. 11.
    Hayashi M, Matsui O, Ueda K et al (1999) Correlation between the blood supply and grade of malignancy of hepatocellular nodules associated with liver cirrhosis: evaluation by CT during intraarterial injection of contrast medium. AIR Am J Roentgenol 172:969–976CrossRefGoogle Scholar
  12. 12.
    Boss MK, Muradyan N, Thrall DE (2013) DCE-MRI: a review and applications in veterinary oncology. Vet Comp Oncol 11:87–100CrossRefPubMedGoogle Scholar
  13. 13.
    Tofts PS, Brix G, Buckley DL et al (1999) Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging 10:223–232CrossRefPubMedGoogle Scholar
  14. 14.
    Leach MO, Morgan B, Tofts PS et al (2012) Imaging vascular function for early stage clinical trials using dynamic contrast-enhanced magnetic resonance imaging. Eur Radiol 22:1451–1464CrossRefPubMedGoogle Scholar
  15. 15.
    Türkbey B, Thomasson D, Pang Y, Bernardo M, Choyke PL (2010) The role of dynamic contrast-enhanced MRI in cancer diagnosis and treatment. Diagn Interv Radiol 16:186–192PubMedGoogle Scholar
  16. 16.
    Hötker AM, Mazaheri Y, Zheng J et al (2015) Prostate Cancer: assessing the effects of androgen-deprivation therapy using quantitative diffusion-weighted and dynamic contrast-enhanced MRI. Eur Radiol 25:2665–2672CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Miyazaki K, Jerome NP, Collins DJ et al (2015) Demonstration of the reproducibility of free-breathing diffusion-weighted MRI and dynamic contrast enhanced MRI in children with solid tumours: a pilot study. Eur Radiol 25:2641–2650CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Hsu CY, Shen YC, Yu CW et al (2011) Dynamic contrast-enhanced magnetic resonance imaging biomarkers predict survival and response in hepatocellular carcinoma patients treated with sorafenib and metronomic tegafur/uracil. J Hepatol 55:858–865CrossRefPubMedGoogle Scholar
  19. 19.
    O’Connor JP, Jackson A, Parker GJ et al (2012) Dynamic contrast-enhanced MRI in clinical trials of antivascular therapies. Nat Rev Clin Oncol 9:167–177CrossRefPubMedGoogle Scholar
  20. 20.
    Barnes SL, Whisenant JG, Loveless ME et al (2012) Practical dynamic contrast enhanced MRI in small animal models of cancer: data acquisition, data analysis, and interpretation. Pharmaceutics 4:442–478CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Aryal MP, Nagaraja TN, Keenan KA et al (2014) Dynamic contrast enhanced MRI parameters and tumor cellularity in a rat model of cerebral glioma at 7 T. Magn Reson Med 71:2206–2214CrossRefPubMedGoogle Scholar
  22. 22.
    Nasui OC, Chan MW, Nathanael G et al (2014) Physiologic characterization of inflammatory arthritis in a rabbit model with BOLD and DCE MRI at 1.5 Tesla. Eur Radiol 24:2766–2778CrossRefPubMedGoogle Scholar
  23. 23.
    Thukral A, Thomasson DM, Chow CK et al (2007) Inflammatory breast cancer: dynamic contrast-enhanced MR in patients receiving bevacizumab--Initial experience. Radiology 244(3):727–735CrossRefPubMedGoogle Scholar
  24. 24.
    Mross K, Fasol U, Frost A et al (2009) DCE-MRI assessment of the effect of vandetanib on tumor vasculature in patients with advanced colorectal cancer and liver metastases: a randomized phase I study. J Angiogenes Res 1:5CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Hahn OM, Yang C, Medved M et al (2008) Dynamic contrast-enhanced magnetic resonance imaging pharmacodynamic biomarker study of sorafenib in metastatic renal carcinoma. J Clin Oncol 26:4572–4578CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Alonzi R, Padhani AR, Allen C (2007) Dynamic contrast enhanced MRI in prostate cancer. Eur J Radiol 63:335–350CrossRefPubMedGoogle Scholar
  27. 27.
    Jarnagin WR, Schwartz LH, Gultekin DH et al (2009) Regional chemotherapy for unresectable primary liver cancer: results of a phase II clinical trial and assessment of DCE-MRI as a biomarker of survival. Ann Oncol 20:1589–1595CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Hirashima Y, Yamada Y, Tateishi U et al (2012) Pharmacokinetic parameters from 3-Tesla DCE-MRI as surrogate biomarkers of antitumor effects of bevacizumab plus FOLFIRI in colorectal cancer with liver metastasis. Int J Cancer 130:2359–2365CrossRefPubMedGoogle Scholar
  29. 29.
    Xu H, Xie JX, Li X et al (2008) Perfusion-weighted MRI in evaluating the intranodular hemodynamic characteristics of dysplastic nodules and hepatocellular carcinomas in an experimental rat model. J Magn Reson Imaging 27:102–109CrossRefPubMedGoogle Scholar
  30. 30.
    Wang H, Van de Putte M, Chen F et al (2008) Murine liver implantation of radiation-induced fibrosarcoma: characterization with MR imaging, microangiography and histopathology. Eur Radiol 18:1422–1430CrossRefPubMedGoogle Scholar
  31. 31.
    Zhu AX, Sahani DV, Duda DG et al (2009) Efficacy, safety, and potential biomarkers of sunitinib monotherapy in advanced hepatocellular carcinoma: a phase II study. J Clin Oncol 27:3027–3035CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Yopp AC, Schwartz LH, Kemeny N et al (2011) Antiangiogenic therapy for primary liver cancer: correlation of changes in dynamic contrast-enhanced magnetic resonance imaging with tissue hypoxia markers and clinical response. Ann Surg Oncol 18:2192–2199CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Walker-Samuel S, Leach MO, Collins DJ (2006) Evaluation of response to treatment using DCE-MRI: the relationship between initial area under the gadolinium curve (IAUGC) and quantitative pharmacokinetic analysis. Phys Med Biol 51:3593–3602CrossRefPubMedGoogle Scholar
  34. 34.
    Koh TS, Thng CH, Hartono S et al (2011) A comparative study of dynamic contrast-enhanced MRI parameters as biomarkers for anti-angiogenic drug therapy. NMR Biomed 24:1169–1180CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2016

Authors and Affiliations

  1. 1.Department of Medical Imaging, Jinling HospitalMedical School of Nanjing UniversityNanjingChina
  2. 2.Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoUSA

Personalised recommendations