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European Radiology

, Volume 27, Issue 2, pp 578–588 | Cite as

Grading diffuse gliomas without intense contrast enhancement by amide proton transfer MR imaging: comparisons with diffusion- and perfusion-weighted imaging

  • Osamu Togao
  • Akio HiwatashiEmail author
  • Koji Yamashita
  • Kazufumi Kikuchi
  • Jochen Keupp
  • Koji Yoshimoto
  • Daisuke Kuga
  • Masami Yoneyama
  • Satoshi O. Suzuki
  • Toru Iwaki
  • Masaya Takahashi
  • Koji Iihara
  • Hiroshi Honda
Magnetic Resonance

Abstract

Objectives

To investigate whether amide proton transfer (APT) MR imaging can differentiate high-grade gliomas (HGGs) from low-grade gliomas (LGGs) among gliomas without intense contrast enhancement (CE).

Methods

This retrospective study evaluated 34 patients (22 males, 12 females; age 36.0 ± 11.3 years) including 20 with LGGs and 14 with HGGs, all scanned on a 3T MR scanner. Only tumours without intense CE were included. Two neuroradiologists independently performed histogram analyses to measure the 90th-percentile (APT90) and mean (APTmean) of the tumours’ APT signals. The apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) were also measured. The parameters were compared between the groups with Student’s t-test. Diagnostic performance was evaluated with receiver operating characteristic (ROC) analysis.

Results

The APT90 (2.80 ± 0.59 % in LGGs, 3.72 ± 0.89 in HGGs, P = 0.001) and APTmean (1.87 ± 0.49 % in LGGs, 2.70 ± 0.58 in HGGs, P = 0.0001) were significantly larger in the HGGs compared to the LGGs. The ADC and rCBV values were not significantly different between the groups. Both the APT90 and APTmean showed medium diagnostic performance in this discrimination.

Conclusions

APT imaging is useful in discriminating HGGs from LGGs among diffuse gliomas without intense CE.

Key Points

Amide proton transfer (APT) imaging helps in grading non-enhancing gliomas

High-grade gliomas showed higher APT signal than low-grade gliomas

APT imaging showed better diagnostic performance than diffusion- and perfusion-weighted imaging

Keywords

Amide proton transfer imaging Chemical exchange saturation transfer MR imaging Brain tumour Glioma 

Abbreviations

APT

Amide proton transfer

LGG

Low-grade gliomas

HGG

High-grade glioma

CE

Contrast enhancement

ADC

Apparent diffusion coefficient

rCBV

relative cerebral blood volume

ROC

Receiver operating characteristic

DSC

Dynamic susceptibility contrast

PW

Perfusion-weighted

DW

Diffusion-weighted

CEST

Chemical exchange saturation transfer

WHO

World Health Organization

GBM

Glioblastoma multiforme

RF

Radiofrequency

TR

Repetition time

TE

Echo time

FOV

Field of view

2D

Two dimensional

NAWM

Normal-appearing white matter

FLAIR

Fluid attenuation inversion recovery

MTRasym

Asymmetry of the magnetization transfer ratio

ROI

Region-of-interest

ICC

Intra-class correlation coefficient

AUC

Area under the curve

3D

Three-dimensional

Notes

Acknowledgments

The scientific guarantor of this publication is Hiroshi Honda.

The authors of this manuscript declare relationships with the following companies: Jochen Keupp is an employee of Philips Reseach Europe, and Masami Yoneyama is an employees of Philips Electronics Japan. This study has received funding by the Japanese Society of Neuroradiology, Japanese Radiological Society, the Fukuoka Foundation for Sound Health Cancer Research Fund, and JSPS KAKENHI Grants-in-Aid for Scientific Research nos. 26461827, 26293278, 26670564 and 22591340. No complex statistical methods were necessary for this paper. Institutional Review Board approval was obtained. Written informed consent was waived by the Institutional Review Board.

Methodology: retrospective, diagnostic or prognostic study, performed at one institution.

References

  1. 1.
    Daumas-Duport C, Scheithauer B, O'Fallon J, Kelly P (1988) Grading of astrocytomas. A simple and reproducible method. Cancer 62:2152–2165CrossRefPubMedGoogle Scholar
  2. 2.
    Law M, Yang S, Babb JS et al (2004) Comparison of cerebral blood volume and vascular permeability from dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade. AJNR Am J Neuroradiol 25:746–755PubMedGoogle Scholar
  3. 3.
    Hilario A, Sepulveda JM, Perez-Nunez A et al (2014) A prognostic model based on preoperative MRI predicts overall survival in patients with diffuse gliomas. AJNR Am J Neuroradiol 35:1096–1102CrossRefPubMedGoogle Scholar
  4. 4.
    Pierallini A, Bonamini M, Bozzao A et al (1997) Supratentorial diffuse astrocytic tumours: proposal of an MRI classification. Eur Radiol 7:395–399CrossRefPubMedGoogle Scholar
  5. 5.
    Mihara F, Numaguchi Y, Rothman M, Sato S, Fiandaca MS (1995) MR imaging of adult supratentorial astrocytomas: an attempt of semi-automatic grading. Radiat Med 13:5–9PubMedGoogle Scholar
  6. 6.
    Fan GG, Deng QL, Wu ZH, Guo QY (2006) Usefulness of diffusion/perfusion-weighted MRI in patients with non-enhancing supratentorial brain gliomas: a valuable tool to predict tumour grading? Br J Radiol 79:652–658CrossRefPubMedGoogle Scholar
  7. 7.
    McKnight TR, Lamborn KR, Love TD et al (2007) Correlation of magnetic resonance spectroscopic and growth characteristics within Grades II and III gliomas. J Neurosurg 106:660–666CrossRefPubMedGoogle Scholar
  8. 8.
    Lee EJ, Lee SK, Agid R, Bae JM, Keller A, Terbrugge K (2008) Preoperative grading of presumptive low-grade astrocytomas on MR imaging: diagnostic value of minimum apparent diffusion coefficient. AJNR Am J Neuroradiol 29:1872–1877CrossRefPubMedGoogle Scholar
  9. 9.
    Falk A, Fahlstrom M, Rostrup E et al (2014) Discrimination between glioma grades II and III in suspected low-grade gliomas using dynamic contrast-enhanced and dynamic susceptibility contrast perfusion MR imaging: a histogram analysis approach. Neuroradiology 56:1031–1038CrossRefPubMedGoogle Scholar
  10. 10.
    Zhou J, Payen JF, Wilson DA, Traystman RJ, van Zijl PC (2003) Using the amide proton signals of intracellular proteins and peptides to detect pH effects in MRI. Nat Med 9:1085–1090CrossRefPubMedGoogle Scholar
  11. 11.
    Zhou J, Lal B, Wilson DA, Laterra J, van Zijl PC (2003) Amide proton transfer (APT) contrast for imaging of brain tumors. Magn Reson Med 50:1120–1126CrossRefPubMedGoogle Scholar
  12. 12.
    Zhou J, Blakeley JO, Hua J et al (2008) Practical data acquisition method for human brain tumor amide proton transfer (APT) imaging. Magn Reson Med 60:842–849CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Togao O, Yoshiura T, Keupp J et al (2014) Amide proton transfer imaging of adult diffuse gliomas: correlation with histopathological grades. Neuro Oncol 16:441–448CrossRefPubMedGoogle Scholar
  14. 14.
    Sakata A, Okada T, Yamamoto A et al (2015) Grading glial tumors with amide proton transfer MR imaging: different analytical approaches. J Neurooncol 122:339–348CrossRefPubMedGoogle Scholar
  15. 15.
    Keupp J, Baltes C, Harvey P, Van den Brink J (2011) Parallel RF transmission based MRI technique for highly sensitive detection of amide proton transfer in the human brain at 3T. Proc Int Soc Magn Reson Med 19:710Google Scholar
  16. 16.
    Togao O, Hiwatashi A, Keupp J et al (2015) Scan-rescan reproducibility of parallel transmission based amide proton transfer imaging of brain tumors. J Magn Reson Imaging 42:1346–1353CrossRefPubMedGoogle Scholar
  17. 17.
    Boxerman JL, Schmainda KM, Weisskoff RM (2006) Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not. AJNR Am J Neuroradiol 27:859–867PubMedGoogle Scholar
  18. 18.
    Hu LS, Eschbacher JM, Heiserman JE et al (2012) Reevaluating the imaging definition of tumor progression: perfusion MRI quantifies recurrent glioblastoma tumor fraction, pseudoprogression, and radiation necrosis to predict survival. Neuro Oncol 14:919–930CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Paulson ES, Schmainda KM (2008) Comparison of dynamic susceptibility-weighted contrast-enhanced MR methods: recommendations for measuring relative cerebral blood volume in brain tumors. Radiology 249:601–613CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Thevenaz P, Ruttimann UE, Unser M (1998) A pyramid approach to subpixel registration based on intensity. IEEE Trans Image Process 7:27–41CrossRefPubMedGoogle Scholar
  21. 21.
    Shrout PE, Fleiss JL (1979) Intraclass correlations: uses in assessing rater reliability. Psychol Bull 86:420–428CrossRefPubMedGoogle Scholar
  22. 22.
    Wen Z, Hu S, Huang F et al (2010) MR imaging of high-grade brain tumors using endogenous protein and peptide-based contrast. Neuroimage 51:616–622CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Salhotra A, Lal B, Laterra J, Sun PZ, van Zijl PC, Zhou J (2008) Amide proton transfer imaging of 9L gliosarcoma and human glioblastoma xenografts. NMR Biomed 21:489–497CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Xu J, Zaiss M, Zu Z et al (2014) On the origins of chemical exchange saturation transfer (CEST) contrast in tumors at 9.4 T. NMR Biomed 27:406–416CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Heo HY, Zhang Y, Lee DH, Hong X, Zhou J (2015) Quantitative assessment of amide proton transfer (APT) and nuclear overhauser enhancement (NOE) imaging with extrapolated semi-solid magnetization transfer reference (EMR) signals: application to a rat glioma model at 4.7 tesla. Magn Reson Med. doi: 10.1002/mrm.25581 Google Scholar
  26. 26.
    Togao O, Yoshiura T, Keupp J et al (2012) Effect of saturation pulse length on parallel transmission based Amide Proton Transfer (APT) imaging of different brain tumor types. Proc Int Soc Magn Reson Med 21:744Google Scholar

Copyright information

© European Society of Radiology 2016

Authors and Affiliations

  • Osamu Togao
    • 1
  • Akio Hiwatashi
    • 1
    Email author
  • Koji Yamashita
    • 1
  • Kazufumi Kikuchi
    • 1
  • Jochen Keupp
    • 2
  • Koji Yoshimoto
    • 3
  • Daisuke Kuga
    • 3
  • Masami Yoneyama
    • 4
  • Satoshi O. Suzuki
    • 5
  • Toru Iwaki
    • 5
  • Masaya Takahashi
    • 6
  • Koji Iihara
    • 3
  • Hiroshi Honda
    • 1
  1. 1.Department of Clinical Radiology, Graduate School of Medical SciencesKyushu UniversityFukuokaJapan
  2. 2.Philips ResearchHamburgGermany
  3. 3.Department of Neurosurgery, Graduate School of Medical SciencesKyushu UniversityFukuokaJapan
  4. 4.Philips Electronics JapanTokyoJapan
  5. 5.Department of Neuropathology, Graduate School of Medical SciencesKyushu UniversityFukuokaJapan
  6. 6.Advanced Imaging Research Center, UT Southwestern Medical CenterDallasUSA

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