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

  • Daniel PaechEmail author
  • Constantin Dreher
  • Sebastian Regnery
  • Jan-Eric Meissner
  • Steffen Goerke
  • Johannes Windschuh
  • Johanna Oberhollenzer
  • Miriam Schultheiss
  • Katerina Deike-Hofmann
  • Sebastian Bickelhaupt
  • Alexander Radbruch
  • Moritz Zaiss
  • Andreas Unterberg
  • Wolfgang Wick
  • Martin Bendszus
  • Peter Bachert
  • Mark E. Ladd
  • Heinz-Peter Schlemmer



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.


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.


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).


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.


Magnetic resonance imaging Glioma Glioblastoma Survival Biomarkers, cancer 



Amide proton transfer


APT contrast calculated with the AREX metric


APT contrast calculated with the LD metric


Apparent exchange-dependent relaxation


Chemical exchange saturation transfer


Downfield relayed nuclear Overhauser effect suppressed APT


Fluid-attenuated inversion recovery


Field of view


Gadolinium-based contrast agents


Glioblastoma multiforme


T1-weighted gadolinium contrast-enhanced MRI


Gradient echo


High-grade glioma


Isocitrate dehydrogenase


Interquartile range


Karnofsky performance scale


Lorentzian difference


O6-Methylguanine-DNA methyltransferase


Medical Imaging Interaction Toolkit


Magnetization-prepared rapid gradient echo


Magnetization transfer ratio asymmetry


Magnetization transfer Lorentzian difference


Nuclear Overhauser effect


NOE contrast calculated with the AREX metric


NOE contrast calculated with the LD metric


Overall survival


Progression-free survival


Response assessment in neuro-oncology


Relative cerebral blood volume




Relayed nuclear Overhauser effect






Echo time


Repetition time


Turbo spin echo


Simultaneous mapping of water shift and B1


World Health Organization


Label Z-spectrum


Reference Z-spectrum



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.


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

Compliance with ethical standards


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.


• prospective

• diagnostic or prognostic

• performed at one institution

Supplementary material

330_2019_6066_MOESM1_ESM.docx (2.7 mb)
ESM 1 (DOCX 2813 kb)


  1. 1.
    Kim KB (2014) PFS as a surrogate for overall survival in metastatic melanoma. Lancet Oncol 15:246–248CrossRefPubMedGoogle Scholar
  2. 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 12:520–527Google Scholar
  3. 3.
    Stupp R, Roila F (2009) Malignant glioma: ESMO clinical recommendations for diagnosis, treatment and follow-up. Ann Oncol 20(Suppl 4):126–128PubMedGoogle Scholar
  4. 4.
    Yan H, Parsons DW, Jin G et al (2009) IDH1 and IDH2 mutations in gliomas. N Engl J Med 360:765–773CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Hegi ME, Diserens AC, Gorlia T et al (2005) MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med 352:997–1003CrossRefPubMedGoogle Scholar
  6. 6.
    Wen PY, Kesari S (2008) Malignant gliomas in adults. N Engl J Med 359:492–507CrossRefPubMedGoogle Scholar
  7. 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 37:23–29CrossRefPubMedGoogle Scholar
  8. 8.
    Henson JW, Gaviani P, Gonzalez RG (2005) MRI in treatment of adult gliomas. Lancet Oncol 6:167–175CrossRefPubMedGoogle Scholar
  9. 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):495–504Google Scholar
  10. 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 108:491–498CrossRefPubMedPubMedCentralGoogle Scholar
  11. 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 19:546–554CrossRefPubMedGoogle Scholar
  12. 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 13:1151–1161Google Scholar
  13. 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 241:839–846CrossRefPubMedGoogle Scholar
  14. 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 247:490–498CrossRefPubMedPubMedCentralGoogle Scholar
  15. 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 102:16759–16764CrossRefPubMedPubMedCentralGoogle Scholar
  16. 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 42:87–96CrossRefPubMedGoogle Scholar
  17. 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 18:1673–1679Google Scholar
  18. 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 6:35142CrossRefPubMedPubMedCentralGoogle Scholar
  19. 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 22:5765–5771Google Scholar
  20. 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 7:10353CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Jones CK, Huang A, Xu J et al (2013) Nuclear Overhauser enhancement (NOE) imaging in the human brain at 7T. Neuroimage 77:114–124CrossRefPubMedGoogle Scholar
  22. 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 69:760–770CrossRefPubMedGoogle Scholar
  23. 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 26:1815–1822CrossRefPubMedGoogle Scholar
  24. 24.
    Goerke S, Zaiss M, Kunz P et al (2015) Signature of protein unfolding in chemical exchange saturation transfer imaging. NMR Biomed 28:906–913CrossRefPubMedGoogle Scholar
  25. 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 139:2687–2690CrossRefPubMedGoogle Scholar
  26. 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 9:1085–1090CrossRefPubMedGoogle Scholar
  27. 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–S151CrossRefPubMedPubMedCentralGoogle Scholar
  28. 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 27:240–252CrossRefPubMedPubMedCentralGoogle Scholar
  29. 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 50:1120–1126Google Scholar
  30. 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 60:842–849CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Togao O, Yoshiura T, Keupp J et al (2014) Amide proton transfer imaging of adult diffuse gliomas: correlation with histopathological grades. Neuro Oncol 16:441–448Google Scholar
  32. 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):1661–1671CrossRefPubMedGoogle Scholar
  33. 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 112:180–188Google Scholar
  34. 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 131:803–820CrossRefPubMedGoogle Scholar
  35. 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 9:28772–28783CrossRefPubMedPubMedCentralGoogle Scholar
  36. 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 77:196–208CrossRefPubMedGoogle Scholar
  37. 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 28:1963–1972CrossRefPubMedGoogle Scholar
  38. 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 28:217–230CrossRefPubMedGoogle Scholar
  39. 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 77:571–580CrossRefPubMedGoogle Scholar
  40. 40.
    Windschuh J, Zaiss M, Meissner JE et al (2015) Correction of B1-inhomogeneities for relaxation-compensated CEST imaging at 7 T. NMR Biomed 28:529–537CrossRefPubMedGoogle Scholar
  41. 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 8:607–620CrossRefPubMedGoogle Scholar
  42. 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 78:1100–1109CrossRefGoogle Scholar
  43. 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:e104181Google Scholar
  44. 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 78:1110–1120CrossRefPubMedGoogle Scholar
  45. 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:e0121220CrossRefPubMedPubMedCentralGoogle Scholar
  46. 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 27:3181–3189Google Scholar
  47. 47.
    Sakata A, Okada T, Yamamoto A et al (2015) Grading glial tumors with amide proton transfer MR imaging: different analytical approaches. J Neurooncol 122:339–348CrossRefPubMedGoogle Scholar
  48. 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):2115–2123CrossRefPubMedGoogle Scholar
  49. 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 75:1630–1639Google Scholar
  50. 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 71:1798–1812CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Daniel Paech
    • 1
    Email author
  • Constantin Dreher
    • 1
  • Sebastian Regnery
    • 1
    • 2
  • Jan-Eric Meissner
    • 3
  • Steffen Goerke
    • 3
  • Johannes Windschuh
    • 3
  • Johanna Oberhollenzer
    • 1
  • Miriam Schultheiss
    • 1
  • Katerina Deike-Hofmann
    • 1
  • Sebastian Bickelhaupt
    • 1
  • Alexander Radbruch
    • 1
    • 4
  • Moritz Zaiss
    • 5
  • Andreas Unterberg
    • 6
  • Wolfgang Wick
    • 7
  • Martin Bendszus
    • 8
  • Peter Bachert
    • 3
  • Mark E. Ladd
    • 3
    • 9
    • 10
  • Heinz-Peter Schlemmer
    • 1
  1. 1.Division of RadiologyGerman Cancer Research Center (DKFZ)HeidelbergGermany
  2. 2.Department of Radiation OncologyUniversity Hospital HeidelbergHeidelbergGermany
  3. 3.Division of Medical Physics in RadiologyGerman Cancer Research Center (DKFZ)HeidelbergGermany
  4. 4.Department of RadiologyUniversity Hospital EssenEssenGermany
  5. 5.Magnetic Resonance CenterMax-Planck-Institute for Biological CyberneticsTuebingenGermany
  6. 6.Department of NeurosurgeryUniversity Hospital HeidelbergHeidelbergGermany
  7. 7.Department of NeurologyUniversity Hospital HeidelbergHeidelbergGermany
  8. 8.Department of NeuroradiologyUniversity Hospital HeidelbergHeidelbergGermany
  9. 9.Faculty of Physics and AstronomyUniversity of HeidelbergHeidelbergGermany
  10. 10.Faculty of MedicineUniversity of HeidelbergHeidelbergGermany

Personalised recommendations