MR Imaging–derived Oxygen Metabolism and Neovascularization Characterization for Grading and IDH Gene Mutation Detection of Gliomas

Published Online:https://doi.org/10.1148/radiol.2016161422

MR imaging–derived oxygen metabolism and neovascularization characterization may be useful for grading and isocitrate dehydrogenase mutation detection of gliomas and requires only 7 minutes of extra imaging time.

Purpose

To explore the diagnostic performance of physiological magnetic resonance (MR) imaging of oxygen metabolism and neovascularization activity for grading and characterization of isocitrate dehydrogenase (IDH) gene mutation status of gliomas.

Materials and Methods

This retrospective study had institutional review board approval; written informed consent was obtained from all patients. Eighty-three patients with histopathologically proven glioma (World Health Organization [WHO] grade II–IV) were examined with quantitative blood oxygen level–dependent imaging and vascular architecture mapping. Biomarker maps of neovascularization activity (microvessel radius, microvessel density, and microvessel type indicator [MTI]) and oxygen metabolism (oxygen extraction fraction [OEF] and cerebral metabolic rate of oxygen [CMRO2]) were calculated. Receiver operating characteristic analysis was used to determine diagnostic performance for grading and detection of IDH gene mutation status.

Results

Low-grade (WHO grade II) glioma showed areas with increased OEF (+18%, P < .001, n = 20), whereas anaplastic glioma (WHO grade III) and glioblastoma (WHO grade IV) showed decreased OEF when compared with normal brain tissue (−54% [P < .001, n = 21] and −49% [P < .001, n = 41], respectively). This allowed clear differentiation between low- and high-grade glioma (area under the receiver operating characteristic curve [AUC], 1) for the patient cohort. MTI had the highest diagnostic performance (AUC, 0.782) for differentiation between gliomas of grades III and IV among all biomarkers. CMRO2 was decreased (P = .037) in low-grade glioma with a mutated IDH gene, and MTI was significantly increased in glioma grade III with IDH mutation (P = .013) when compared with the IDH wild-type counterparts. CMRO2 showed the highest diagnostic performance for IDH gene mutation detection in low-grade glioma (AUC, 0.818) and MTI in high-grade glioma (AUC, 0.854) and for all WHO grades (AUC, 0.899) among all biomarkers.

Conclusion

MR imaging–derived oxygen metabolism and neovascularization characterization may be useful for grading and IDH mutation detection of gliomas and requires only 7 minutes of extra imaging time.

© RSNA, 2016

Online supplemental material is available for this article.

References

  • 1. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 2011;144(5):646–674. Crossref, MedlineGoogle Scholar
  • 2. Alves TR, Lima FR, Kahn SA, et al. Glioblastoma cells: a heterogeneous and fatal tumor interacting with the parenchyma. Life Sci 2011;89(15-16):532–539. Crossref, MedlineGoogle Scholar
  • 3. Abdulrauf SI, Edvardsen K, Ho KL, Yang XY, Rock JP, Rosenblum ML. Vascular endothelial growth factor expression and vascular density as prognostic markers of survival in patients with low-grade astrocytoma. J Neurosurg 1998;88(3):513–520. Crossref, MedlineGoogle Scholar
  • 4. Hardee ME, Zagzag D. Mechanisms of glioma-associated neovascularization. Am J Pathol 2012;181(4):1126–1141. Crossref, MedlineGoogle Scholar
  • 5. Parsons DW, Jones S, Zhang X, et al. An integrated genomic analysis of human glioblastoma multiforme. Science 2008;321(5897):1807–1812. Crossref, MedlineGoogle Scholar
  • 6. Cohen AL, Holmen SL, Colman H. IDH1 and IDH2 mutations in gliomas. Curr Neurol Neurosci Rep 2013;13(5):345. Crossref, MedlineGoogle Scholar
  • 7. Turcan S, Rohle D, Goenka A, et al. IDH1 mutation is sufficient to establish the glioma hypermethylator phenotype. Nature 2012;483(7390):479–483. Crossref, MedlineGoogle Scholar
  • 8. Leu S, von Felten S, Frank S, et al. IDH/MGMT-driven molecular classification of low-grade glioma is a strong predictor for long-term survival. Neuro-oncol 2013;15(4):469–479. Crossref, MedlineGoogle Scholar
  • 9. Turkalp Z, Karamchandani J, Das S. IDH mutation in glioma: new insights and promises for the future. JAMA Neurol 2014;71(10):1319–1325. Crossref, MedlineGoogle Scholar
  • 10. Kickingereder P, Sahm F, Radbruch A, et al. IDH mutation status is associated with a distinct hypoxia/angiogenesis transcriptome signature which is non-invasively predictable with rCBV imaging in human glioma. Sci Rep 2015;5:16238. Crossref, MedlineGoogle Scholar
  • 11. Semenza GL. Targeting HIF-1 for cancer therapy. Nat Rev Cancer 2003;3(10):721–732. Crossref, MedlineGoogle Scholar
  • 12. Xu C, Kiselev VG, Möller HE, Fiebach JB. Dynamic hysteresis between gradient echo and spin echo attenuations in dynamic susceptibility contrast imaging. Magn Reson Med 2013;69(4):981–991. Crossref, MedlineGoogle Scholar
  • 13. Kiselev VG, Strecker R, Ziyeh S, Speck O, Hennig J. Vessel size imaging in humans. Magn Reson Med 2005;53(3):553–563. Crossref, MedlineGoogle Scholar
  • 14. Emblem KE, Mouridsen K, Bjornerud A, et al. Vessel architectural imaging identifies cancer patient responders to anti-angiogenic therapy. Nat Med 2013;19(9):1178–1183. Crossref, MedlineGoogle Scholar
  • 15. Hsu YY, Yang WS, Lim KE, Liu HL. Vessel size imaging using dual contrast agent injections. J Magn Reson Imaging 2009;30(5):1078–1084. Crossref, MedlineGoogle Scholar
  • 16. Christen T, Schmiedeskamp H, Straka M, Bammer R, Zaharchuk G. Measuring brain oxygenation in humans using a multiparametric quantitative blood oxygenation level dependent MRI approach. Magn Reson Med 2012;68(3):905–911. Crossref, MedlineGoogle Scholar
  • 17. Stadlbauer A, Zimmermann M, Heinz G, et al. Magnetic resonance imaging biomarkers for clinical routine assessment of microvascular architecture in glioma. J Cereb Blood Flow 2016 Jun 17. [Epub ahead of print] Crossref, MedlineGoogle Scholar
  • 18. Preibisch C, Volz S, Anti S, Deichmann R. Exponential excitation pulses for improved water content mapping in the presence of background gradients. Magn Reson Med 2008;60(4):908–916. Crossref, MedlineGoogle Scholar
  • 19. Prasloski T, Mädler B, Xiang QS, MacKay A, Jones C. Applications of stimulated echo correction to multicomponent T2 analysis. Magn Reson Med 2012;67(6):1803–1814. Crossref, MedlineGoogle Scholar
  • 20. Bjørnerud A, Emblem KE. A fully automated method for quantitative cerebral hemodynamic analysis using DSC-MRI. J Cereb Blood Flow Metab 2010;30(5):1066–1078. Crossref, MedlineGoogle Scholar
  • 21. Smith AM, Grandin CB, Duprez T, Mataigne F, Cosnard G. Whole brain quantitative CBF, CBV, and MTT measurements using MRI bolus tracking: implementation and application to data acquired from hyperacute stroke patients. J Magn Reson Imaging 2000;12(3):400–410. Crossref, MedlineGoogle Scholar
  • 22. Kennan RP, Zhong J, Gore JC. Intravascular susceptibility contrast mechanisms in tissues. Magn Reson Med 1994;31(1):9–21. Crossref, MedlineGoogle Scholar
  • 23. Ducreux D, Buvat I, Meder JF, et al. Perfusion-weighted MR imaging studies in brain hypervascular diseases: comparison of arterial input function extractions for perfusion measurement. AJNR Am J Neuroradiol 2006;27(5):1059–1069. MedlineGoogle Scholar
  • 24. Boxerman JL, Prah DE, Paulson ES, Machan JT, Bedekar D, Schmainda KM. The role of preload and leakage correction in gadolinium-based cerebral blood volume estimation determined by comparison with MION as a criterion standard. AJNR Am J Neuroradiol 2012;33(6):1081–1087. Crossref, MedlineGoogle Scholar
  • 25. Boxerman JL, Schmainda KM, Weisskoff RM. 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 2006;27(4):859–867. MedlineGoogle Scholar
  • 26. Jensen JH, Lu H, Inglese M. Microvessel density estimation in the human brain by means of dynamic contrast-enhanced echo-planar imaging. Magn Reson Med 2006;56(5):1145–1150. Crossref, MedlineGoogle Scholar
  • 27. Caulo M, Panara V, Tortora D, et al. Data-driven grading of brain gliomas: a multiparametric MR imaging study. Radiology 2014;272(2):494–503. LinkGoogle Scholar
  • 28. Tietze A, Mouridsen K, Lassen-Ramshad Y, Østergaard L. Perfusion MRI derived indices of microvascular shunting and flow control correlate with tumor grade and outcome in patients with cerebral glioma. PLoS One 2015;10(4):e0123044. Crossref, MedlineGoogle Scholar
  • 29. Jespersen SN, Østergaard L. The roles of cerebral blood flow, capillary transit time heterogeneity, and oxygen tension in brain oxygenation and metabolism. J Cereb Blood Flow Metab 2012;32(2):264–277. Crossref, MedlineGoogle Scholar
  • 30. Kang HY, Xiao HL, Chen JH, et al. Comparison of the effect of vessel size imaging and cerebral blood volume derived from perfusion MR imaging on glioma grading. AJNR Am J Neuroradiol 2016;37(1):51–57. Crossref, MedlineGoogle Scholar
  • 31. Lee S, Choi SH, Ryoo I, et al. Evaluation of the microenvironmental heterogeneity in high-grade gliomas with IDH1/2 gene mutation using histogram analysis of diffusion-weighted imaging and dynamic-susceptibility contrast perfusion imaging. J Neurooncol 2015;121(1):141–150. Crossref, MedlineGoogle Scholar
  • 32. Batchelor TT, Gerstner ER, Emblem KE, et al. Improved tumor oxygenation and survival in glioblastoma patients who show increased blood perfusion after cediranib and chemoradiation. Proc Natl Acad Sci U S A 2013;110(47):19059–19064. Crossref, MedlineGoogle Scholar
  • 33. Schmainda KM, Rand SD, Joseph AM, et al. Characterization of a first-pass gradient-echo spin-echo method to predict brain tumor grade and angiogenesis. AJNR Am J Neuroradiol 2004;25(9):1524–1532. MedlineGoogle Scholar
  • 34. Sorensen AG, Batchelor TT, Zhang WT, et al. A “vascular normalization index” as potential mechanistic biomarker to predict survival after a single dose of cediranib in recurrent glioblastoma patients. Cancer Res 2009;69(13):5296–5300. Crossref, MedlineGoogle Scholar
  • 35. Eichner C, Jafari-Khouzani K, Cauley S, et al. Slice accelerated gradient-echo spin-echo dynamic susceptibility contrast imaging with blipped CAIPI for increased slice coverage. Magn Reson Med 2014;72(3):770–778. Crossref, MedlineGoogle Scholar

Article History

Received June 30, 2016; revision requested September 6; revision received September 9; accepted September 29; final version accepted October 11.
Published online: Dec 13 2016
Published in print: June 2017