Skip to main content

Advertisement

SpringerLink
Log in

Relaxation-compensated amide proton transfer (APT) MRI signal intensity is associated with survival and progression in high-grade glioma patients

  • Neuro
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

The purpose of this study was to investigate the association of relaxation-compensated chemical exchange saturation transfer (CEST) MRI with overall survival (OS) and progression-free survival (PFS) in newly diagnosed high-grade glioma (HGG) patients.

Methods

Twenty-six patients with newly diagnosed high-grade glioma (WHO grades III–IV) were included in this prospective IRB-approved study. CEST MRI was performed on a 7.0-T whole-body scanner. Association of patient OS/PFS with relaxation-compensated CEST MRI (amide proton transfer (APT), relayed nuclear Overhauser effect (rNOE)/NOE, downfield-rNOE-suppressed APT (dns-APT)) and diffusion-weighted imaging (apparent diffusion coefficient) were assessed using the univariate Cox proportional hazards regression model. Hazard ratios (HRs) and corresponding 95% confidence intervals were calculated. Furthermore, OS/PFS association with clinical parameters (age, gender, O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation status, and therapy: biopsy + radio-chemotherapy vs. debulking surgery + radio-chemotherapy) were tested accordingly.

Results

Relaxation-compensated APT MRI was significantly correlated with patient OS (HR = 3.15, p = 0.02) and PFS (HR = 1.83, p = 0.009). The strongest association with PFS was found for the dns-APT metric (HR = 2.61, p = 0.002). These results still stand for the relaxation-compensated APT contrasts in a homogenous subcohort of n = 22 glioblastoma patients with isocitrate dehydrogenase (IDH) wild-type status. Among the tested clinical parameters, patient age (HR = 1.1, p = 0.001) and therapy (HR = 3.68, p = 0.026) were significant for OS; age additionally for PFS (HR = 1.04, p = 0.048).

Conclusion

Relaxation-compensated APT MRI signal intensity is associated with overall survival and progression-free survival in newly diagnosed, previously untreated glioma patients and may, therefore, help to customize treatment and response monitoring in the future.

Key Points

• Amide proton transfer (APT) MRI signal intensity is associated with overall survival and progression in glioma patients.

• Relaxation compensation enhances the information value of APT MRI in tumors.

• Chemical exchange saturation transfer (CEST) MRI may serve as a non-invasive biomarker to predict prognosis and customize treatment.

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

Similar content being viewed by others

Abbreviations

APT:

Amide proton transfer

APTAREX :

APT contrast calculated with the AREX metric

APTLD :

APT contrast calculated with the LD metric

AREX:

Apparent exchange-dependent relaxation

CEST:

Chemical exchange saturation transfer

dns-APT:

Downfield relayed nuclear Overhauser effect suppressed APT

FLAIR:

Fluid-attenuated inversion recovery

FoV:

Field of view

GBCA:

Gadolinium-based contrast agents

GBM:

Glioblastoma multiforme

gdce-T1:

T1-weighted gadolinium contrast-enhanced MRI

GRE:

Gradient echo

HGG:

High-grade glioma

IDH:

Isocitrate dehydrogenase

IQR:

Interquartile range

KPS:

Karnofsky performance scale

LD:

Lorentzian difference

MGMT:

O6-Methylguanine-DNA methyltransferase

MITK:

Medical Imaging Interaction Toolkit

MPRAGE:

Magnetization-prepared rapid gradient echo

MTRasym :

Magnetization transfer ratio asymmetry

MTRLD :

Magnetization transfer Lorentzian difference

NOE:

Nuclear Overhauser effect

NOEAREX :

NOE contrast calculated with the AREX metric

NOELD :

NOE contrast calculated with the LD metric

OS:

Overall survival

PFS:

Progression-free survival

RANO:

Response assessment in neuro-oncology

rCBV:

Relative cerebral blood volume

RCT:

Radio-chemotherapy

rNOE:

Relayed nuclear Overhauser effect

T1-w:

T1-weighted

T2-w:

T2-weighted

TE:

Echo time

TR:

Repetition time

TSE:

Turbo spin echo

WASABI:

Simultaneous mapping of water shift and B1

WHO:

World Health Organization

Zlab :

Label Z-spectrum

Zref :

Reference Z-spectrum

References

  1. Kim KB (2014) PFS as a surrogate for overall survival in metastatic melanoma. Lancet Oncol 15:246–248

    Article  PubMed  Google Scholar 

  2. Porter KR, McCarthy BJ, Freels S, Kim Y, Davis FG (2010) Prevalence estimates for primary brain tumors in the United States by age, gender, behavior, and histology. Neuro Oncol 12:520–527

  3. Stupp R, Roila F (2009) Malignant glioma: ESMO clinical recommendations for diagnosis, treatment and follow-up. Ann Oncol 20(Suppl 4):126–128

    PubMed  Google Scholar 

  4. Yan H, Parsons DW, Jin G et al (2009) IDH1 and IDH2 mutations in gliomas. N Engl J Med 360:765–773

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Hegi ME, Diserens AC, Gorlia T et al (2005) MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med 352:997–1003

    Article  CAS  PubMed  Google Scholar 

  6. Wen PY, Kesari S (2008) Malignant gliomas in adults. N Engl J Med 359:492–507

    Article  CAS  PubMed  Google Scholar 

  7. Cerqua R, Balestrini S, Perozzi C et al (2016) Diagnostic delay and prognosis in primary central nervous system lymphoma compared with glioblastoma multiforme. Neurol Sci 37:23–29

    Article  CAS  PubMed  Google Scholar 

  8. Henson JW, Gaviani P, Gonzalez RG (2005) MRI in treatment of adult gliomas. Lancet Oncol 6:167–175

    Article  PubMed  Google Scholar 

  9. Ellingson BM, Chung C, Pope WB, Boxerman JL, Kaufmann TJ (2017) Pseudoprogression, radionecrosis, inflammation or true tumor progression? Challenges associated with glioblastoma response assessment in an evolving therapeutic landscape. J Neurooncol 134(3):495–504

  10. Pope WB, Qiao XJ, Kim HJ et al (2012) Apparent diffusion coefficient histogram analysis stratifies progression-free and overall survival in patients with recurrent GBM treated with bevacizumab: a multi-center study. J Neurooncol 108:491–498

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Oh J, Henry RG, Pirzkall A et al (2004) Survival analysis in patients with glioblastoma multiforme: predictive value of choline-to-n-acetylaspartate index, apparent diffusion coefficient, and relative cerebral blood volume. J Magn Reson Imaging 19:546–554

    Article  PubMed  Google Scholar 

  12. Ellingson BM, Cloughesy TF, Lai A et al (2011) Graded functional diffusion map–defined characteristics of apparent diffusion coefficients predict overall survival in recurrent glioblastoma treated with bevacizumab. Neuro Oncol 13:1151–1161

  13. Higano S, Yun X, Kumabe T et al (2006) Malignant astrocytic tumors: clinical importance of apparent diffusion coefficient in prediction of grade and prognosis. Radiology 241:839–846

    Article  PubMed  Google Scholar 

  14. Law M, Young RJ, Babb JS et al (2008) Gliomas: predicting time to progression or survival with cerebral blood volume measurements at dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology 247:490–498

    Article  PubMed  Google Scholar 

  15. Hamstra DA, Chenevert TL, Moffat BA et al (2005) Evaluation of the functional diffusion map as an early biomarker of time-to-progression and overall survival in high-grade glioma. Proc Natl Acad Sci U S A 102:16759–16764

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Bonekamp D, Deike K, Wiestler B et al (2015) Association of overall survival in patients with newly diagnosed glioblastoma with contrast-enhanced perfusion MRI: comparison of intraindividually matched T1 - and T2 (*) -based bolus techniques. J Magn Reson Imaging 42:87–96

    Article  PubMed  Google Scholar 

  17. Burth S, Kickingereder P, Eidel O et al (2016) Clinical parameters outweigh diffusion- and perfusion-derived MRI parameters in predicting survival in newly diagnosed glioblastoma. Neuro Oncol 18:1673–1679

  18. Wiestler B, Kluge A, Lukas M et al (2016) Multiparametric MRI-based differentiation of WHO grade II/III glioma and WHO grade IV glioblastoma. Sci Rep 6:35142

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Kickingereder P, Götz M, Muschelli J et al (2016) Large-scale radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-angiogenic treatment response. Clin Cancer Res 22:5765–5771

  20. Lao J, Chen Y, Li ZC et al (2017) A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme. Sci Rep 7:10353

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Jones CK, Huang A, Xu J et al (2013) Nuclear Overhauser enhancement (NOE) imaging in the human brain at 7T. Neuroimage 77:114–124

    Article  PubMed  Google Scholar 

  22. Jin T, Wang P, Zong X, Kim SG (2013) MR imaging of the amide-proton transfer effect and the pH-insensitive nuclear overhauser effect at 9.4 T. Magn Reson Med 69:760–770

    Article  CAS  PubMed  Google Scholar 

  23. Zaiss M, Kunz P, Goerke S, Radbruch A, Bachert P (2013) MR imaging of protein folding in vitro employing nuclear-Overhauser-mediated saturation transfer. NMR Biomed 26:1815–1822

    Article  CAS  PubMed  Google Scholar 

  24. Goerke S, Zaiss M, Kunz P et al (2015) Signature of protein unfolding in chemical exchange saturation transfer imaging. NMR Biomed 28:906–913

    Article  CAS  PubMed  Google Scholar 

  25. Longo DL, Di Gregorio E, Abategiovanni R et al (2014) Chemical exchange saturation transfer (CEST): an efficient tool for detecting molecular information on proteins’ behaviour. Analyst 139:2687–2690

    Article  CAS  PubMed  Google Scholar 

  26. 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 9:1085–1090

    Article  CAS  PubMed  Google Scholar 

  27. Sun PZ, Benner T, Copen WA, Sorensen AG (2010) Early experience of translating pH-weighted MRI to image human subjects at 3 Tesla. Stroke 41:S147–S151

    Article  PubMed  PubMed Central  Google Scholar 

  28. Zaiss M, Xu J, Goerke S et al (2014) Inverse Z-spectrum analysis for spillover-, MT-, and T1-corrected steady-state pulsed CEST-MRI--application to pH-weighted MRI of acute stroke. NMR Biomed 27:240–252

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 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 50:1120–1126

  30. 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 60:842–849

    Article  PubMed  PubMed Central  Google Scholar 

  31. Togao O, Yoshiura T, Keupp J et al (2014) Amide proton transfer imaging of adult diffuse gliomas: correlation with histopathological grades. Neuro Oncol 16:441–448

  32. Paech D, Windschuh J, Oberhollenzer J et al (2018) Assessing the predictability of IDH mutation and MGMT methylation status in glioma patients using relaxation-compensated multi-pool CEST MRI at 7.0 Tesla. Neuro Oncol 20(12):1661–1671

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Zaiss M, Windschuh J, Paech D et al (2015) Relaxation-compensated CEST-MRI of the human brain at 7 T: unbiased insight into NOE and amide signal changes in human glioblastoma. Neuroimage 112:180–188

  34. Louis DN, Perry A, Reifenberger G et al (2016) The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol 131:803–820

    Article  PubMed  Google Scholar 

  35. Regnery S, Adeberg S, Dreher C et al (2018) Chemical exchange saturation transfer MRI serves as predictor of early progression in glioblastoma patients. Oncotarget 9:28772–28783

    Article  PubMed  PubMed Central  Google Scholar 

  36. Zaiss M, Windschuh J, Goerke S et al (2017) Downfield-NOE-suppressed amide-CEST-MRI at 7 Tesla provides a unique contrast in human glioblastoma. Magn Reson Med 77:196–208

    Article  CAS  PubMed  Google Scholar 

  37. Wen PY, Macdonald DR, Reardon DA et al (2010) Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol 28:1963–1972

    Article  PubMed  Google Scholar 

  38. Zaiss M, Zu Z, Xu J et al (2015) A combined analytical solution for chemical exchange saturation transfer and semi-solid magnetization transfer. NMR Biomed 28:217–230

    Article  CAS  PubMed  Google Scholar 

  39. Schuenke P, Windschuh J, Roeloffs V, Ladd ME, Bachert P, Zaiss M (2017) Simultaneous mapping of water shift and B1 (WASABI)—application to field-inhomogeneity correction of CESTMRI data. Magn Reson Med 77:571–580

    Article  PubMed  Google Scholar 

  40. Windschuh J, Zaiss M, Meissner JE et al (2015) Correction of B1-inhomogeneities for relaxation-compensated CEST imaging at 7 T. NMR Biomed 28:529–537

    Article  PubMed  Google Scholar 

  41. Nolden M, Zelzer S, Seitel A et al (2013) The Medical Imaging Interaction Toolkit: challenges and advances: 10 years of open-source development. Int J Comput Assist Radiol Surg 8:607–620

    Article  PubMed  Google Scholar 

  42. Shanshan J, Tianyu Z, Eberhart GC et al (2017) Predicting IDH mutation status in grade II gliomas using amide proton transfer-weighted (APTw) MRI. Magn Reson Med 78:1100–1109

    Article  CAS  Google Scholar 

  43. Paech D, Zaiss M, Meissner JE et al (2014) Nuclear Overhauser enhancement mediated chemical exchange saturation transfer imaging at 7 tesla in glioblastoma patients. PLoS One 9:e104181

  44. Desmond KL, Mehrabian H, Chavez S et al (2017) Chemical exchange saturation transfer for predicting response to stereotactic radiosurgery in human brain metastasis. Magn Reson Med 78:1110–1120

    Article  CAS  PubMed  Google Scholar 

  45. Paech D, Burth S, Windschuh J et al (2015) Nuclear Overhauser enhancement imaging of glioblastoma at 7 Tesla: region specific correlation with apparent diffusion coefficient and histology. PLoS One 10:e0121220

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Choi YS, Ahn SS, Lee SK et al (2017) Amide proton transfer imaging to discriminate between low- and high-grade gliomas: added value to apparent diffusion coefficient and relative cerebral blood volume. Eur Radiol 27:3181–3189

  47. Sakata A, Okada T, Yamamoto A et al (2015) Grading glial tumors with amide proton transfer MR imaging: different analytical approaches. J Neurooncol 122:339–348

    Article  CAS  PubMed  Google Scholar 

  48. Jiang S, Rui Q, Wang Y et al (2017) Discriminating MGMT promoter methylation status in patients with glioblastoma employing amide proton transfer-weighted MRI metrics. Eur Radiol 28(5):2115–2123

    Article  PubMed  PubMed Central  Google Scholar 

  49. Heo H-Y, Zhang Y, Jiang S, Lee DH, Zhou J (2016) Quantitative assessment of amide proton transfer (APT) and nuclear overhauser enhancement (NOE) imaging with extrapolated semisolid magnetization transfer reference (EMR) signals: II. Comparison of three EMR models and application to human brain glioma at 3 Tesla. Magn Reson Med 75:1630–1639

  50. Xu J, Yadav NN, Bar-Shir A et al (2014) Variable delay multi-pulse train for fast chemical exchange saturation transfer and relayed-nuclear overhauser enhancement MRI. Magn Reson Med 71:1798–1812

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Prof. Dr. Annette Kopp-Schneider for her invaluable help with the statistical analyses and Joseph Weygand, M.S., for carefully proof reading and reviewing of the manuscript.

Funding

The authors state that this work has not received any funding.

Author information

Authors and Affiliations

  1. Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany

    Daniel Paech, Constantin Dreher, Sebastian Regnery, Johanna Oberhollenzer, Miriam Schultheiss, Katerina Deike-Hofmann, Sebastian Bickelhaupt, Alexander Radbruch & Heinz-Peter Schlemmer

  2. Department of Radiation Oncology, University Hospital Heidelberg, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany

    Sebastian Regnery

  3. Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany

    Jan-Eric Meissner, Steffen Goerke, Johannes Windschuh, Peter Bachert & Mark E. Ladd

  4. Department of Radiology, University Hospital Essen, Essen, Germany

    Alexander Radbruch

  5. Magnetic Resonance Center, Max-Planck-Institute for Biological Cybernetics, Tuebingen, Germany

    Moritz Zaiss

  6. Department of Neurosurgery, University Hospital Heidelberg, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany

    Andreas Unterberg

  7. Department of Neurology, University Hospital Heidelberg, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany

    Wolfgang Wick

  8. Department of Neuroradiology, University Hospital Heidelberg, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany

    Martin Bendszus

  9. Faculty of Physics and Astronomy, University of Heidelberg, 69120, Heidelberg, Germany

    Mark E. Ladd

  10. Faculty of Medicine, University of Heidelberg, 69120, Heidelberg, Germany

    Mark E. Ladd

Authors
  1. Daniel Paech
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Constantin Dreher
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sebastian Regnery
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jan-Eric Meissner
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Steffen Goerke
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Johannes Windschuh
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Johanna Oberhollenzer
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Miriam Schultheiss
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Katerina Deike-Hofmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Sebastian Bickelhaupt
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Alexander Radbruch
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Moritz Zaiss
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Andreas Unterberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Wolfgang Wick
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Martin Bendszus
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Peter Bachert
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Mark E. Ladd
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Heinz-Peter Schlemmer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel Paech.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Dr. Daniel Paech.

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

Prof. Dr. Annette Kopp-Schneider (Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany) kindly provided statistical advice for this manuscript.

Informed consent

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

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

The study cohort has previously been reported (Paech et al. Neuro Oncol, 2018, noy073 and Regnery et al. Oncotarget, 2018, 9:28772–28783) and a subcohort of eleven patients has been included in methodical publications (Zaiss et al. Neuroimage, 2015, 112:180–188 and Zaiss et al. MRM, 2017, 77(1):196–208). However, no investigations of overall survival and progression-free survival have previously been performed.

Methodology

• prospective

• diagnostic or prognostic

• performed at one institution

Additional information

Publisher’s note

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

Electronic supplementary material

ESM 1

(DOCX 2813 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Paech, D., Dreher, C., Regnery, S. et al. Relaxation-compensated amide proton transfer (APT) MRI signal intensity is associated with survival and progression in high-grade glioma patients. Eur Radiol 29, 4957–4967 (2019). https://doi.org/10.1007/s00330-019-06066-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-019-06066-2

Keywords

Search

Navigation