Skip to main content
SpringerLink
Log in

Metabolic-Oncological MR Imaging of Diffuse Low-Grade Glioma: A Dynamic Approach

  • Chapter
  • First Online:
Book cover Diffuse Low-Grade Gliomas in Adults

  • 1284 Accesses

Abstract

Today, magnetic resonance imaging is the “gold standard” of brain tumor imaging, but remains widely used only under its conventional aspect. Recent advances on MRI sequences development and use provided a new conceptual approach of diagnosis and follow-up of WHO II glioma based on multiparametrical and dynamic study of their metabolism allowed by spectroscopy (even multinuclear) and perfusion-weighted imaging, namely, oncological biometabolic imaging. We discuss in this chapter the different aspects and methodological issues and address some practical consequences on MRI clinical practice.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 159.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bonavita S, Di Salle F, Tedeschi G. Proton MRS in neurological disorders. Eur J Radiol. 1999;30(2):125–31.

    Article  PubMed  CAS  Google Scholar 

  2. Smith I, Stewart L. Magnetic resonance spectroscopy in medicine: clinical impact. Prog Nucl Mag Reson Spectrosc. 2002;40(1):1–34.

    Article  CAS  Google Scholar 

  3. Stenger VA, Boada FE, Noll DC. Three-dimensional tailored RF pulses for the reduction of susceptibility artifacts in T(*)(2)-weighted functional MRI. Magn Reson Med. 2000;44(4):525–31.

    Article  PubMed  CAS  Google Scholar 

  4. Cavaliere R, Lopes MB, Schiff D. Low-grade gliomas: an update on pathology and therapy. Lancet Neurol. 2005;4(11):760–70.

    Article  PubMed  Google Scholar 

  5. Wessels PH, Weber WE, Raven G, Ramaekers FC, Hopman AH, Twijnstra A. Supratentorial grade II astrocytoma: biological features and clinical course. Lancet Neurol. 2003;2(7):395–403.

    Article  PubMed  Google Scholar 

  6. Daumas-Duport C, Koziak M, Miquel C, Nataf F, Jouvet A, Varlet P. Reappraisal of the Sainte-Anne Hospital classification of oligodendrogliomas in view of retrospective studies. Neurochirurgie. 2005;51(3–4 Pt 2):247–53.

    Article  PubMed  CAS  Google Scholar 

  7. Kreth FW, Faist M, Grau S, Ostertag CB. Interstitial 125I radiosurgery of supratentorial de novo WHO Grade 2 astrocytoma and oligoastrocytoma in adults: long-term results and prognostic factors. Cancer. 2006;106(6):1372–81.

    Article  PubMed  Google Scholar 

  8. Pallud J, Capelle L, Taillandier L, Fontaine D, Mandonnet E, Guillevin R, et al. Prognostic significance of imaging contrast enhancement for WHO grade II gliomas. Neuro Oncol. 2009;11(2):176–82.

    Article  PubMed  Google Scholar 

  9. Kato Y, Higano S, Tamura H, Mugikura S, Umetsu A, Murata T, et al. Usefulness of contrast-enhanced T1-weighted sampling perfection with application-optimized contrasts by using different flip angle evolutions in detection of small brain metastasis at 3T MR imaging: comparison with magnetization-prepared rapid acquisition of gradient echo imaging. AJNR Am J Neuroradiol. 2009;30(5):923–9.

    Article  PubMed  CAS  Google Scholar 

  10. Nagao E, Yoshiura T, Hiwatashi A, Obara M, Yamashita K, Kamano H, et al. 3D turbo spin-echo sequence with motion-sensitized driven-equilibrium preparation for detection of brain metastases on 3T MR imaging. AJNR Am J Neuroradiol. 2011;32(4):664–70.

    Article  PubMed  CAS  Google Scholar 

  11. Sener RN. Diffusion MRI: apparent diffusion coefficient (ADC) values in the normal brain and a classification of brain disorders based on ADC values. Comput Med Imaging Graph. 2001;25(4):299–326.

    Article  PubMed  CAS  Google Scholar 

  12. Bian W, Khayal IS, Lupo JM, McGue C, Vandenberg S, Lamborn KR, et al. Multiparametric characterization of grade 2 glioma subtypes using magnetic resonance spectroscopic, perfusion, and diffusion imaging. Transl Oncol. 2009;2(4):271–80.

    PubMed  Google Scholar 

  13. Khayal IS, McKnight TR, McGue C, Vandenberg S, Lamborn KR, Chang SM, et al. Apparent diffusion coefficient and fractional anisotropy of newly ­diagnosed grade II gliomas. NMR Biomed. 2009;22(4):449–55.

    Article  PubMed  Google Scholar 

  14. Khayal IS, Nelson SJ. Characterization of low-grade gliomas using RGB color maps derived from ADC histograms. J Magn Reson Imaging. 2009;30(1):209–13.

    Article  PubMed  Google Scholar 

  15. Brown RA, Frayne R. A comparison of texture quantification techniques based on the Fourier and S transforms. Med Phys. 2008;35(11):4998–5008.

    Article  PubMed  Google Scholar 

  16. Chawla S, Wang S, Wolf RL, Woo JH, Wang J, O’Rourke DM, et al. Arterial spin-labeling and MR spectroscopy in the differentiation of gliomas. AJNR Am J Neuroradiol. 2007;28(9):1683–9.

    Article  PubMed  CAS  Google Scholar 

  17. Folkman J. Tumor angiogenesis. Adv Cancer Res. 1974;19:331–58.

    Article  PubMed  CAS  Google Scholar 

  18. Czernicki Z, Horsztynski D, Jankowski W, Grieb P, Walecki J. Malignancy of brain tumors evaluated by proton magnetic resonance spectroscopy (1H-MRS) in vitro. Acta Neurochir Suppl. 2000;76:17–20.

    PubMed  CAS  Google Scholar 

  19. Isobe T, Matsumura A, Anno I, Yoshizawa T, Nagatomo Y, Itai Y, et al. Quantification of cerebral metabolites in glioma patients with proton MR spectroscopy using T2 relaxation time correction. Magn Reson Imaging. 2002;20(4):343–9.

    Article  PubMed  CAS  Google Scholar 

  20. Tedeschi G, Lundbom N, Raman R, Bonavita S, Duyn JH, Alger JR, et al. Increased choline signal coinciding with malignant degeneration of cerebral gliomas: a serial proton magnetic resonance spectroscopy imaging study. J Neurosurg. 1997;87(4):516–24.

    Article  PubMed  CAS  Google Scholar 

  21. Pallud J, Mandonnet E, Duffau H, Kujas M, Guillevin R, Galanaud D, et al. Prognostic value of initial magnetic resonance imaging growth rates for World Health Organization grade II gliomas. Ann Neurol. 2006;60(3):380–3.

    Article  PubMed  Google Scholar 

  22. Louis DN, Edgerton S, Thor AD, Hedley-Whyte ET. Proliferating cell nuclear antigen and Ki-67 immunohistochemistry in brain tumors: a comparative study. Acta Neuropathol (Berl). 1991;81(6):675–9.

    Article  CAS  Google Scholar 

  23. Onda K, Davis RL, Shibuya M, Wilson CB, Hoshino T. Correlation between the bromodeoxyuridine labeling index and the MIB-1 and Ki-67 proliferating cell indices in cerebral gliomas. Cancer. 1994;74(7):1921–6.

    Article  PubMed  CAS  Google Scholar 

  24. Matsumura A, Isobe T, Anno I, Takano S, Kawamura H. Correlation between choline and MIB-1 index in human gliomas. A quantitative in proton MR spectroscopy study. J Clin Neurosci. 2005;12(4):416–20.

    Article  PubMed  CAS  Google Scholar 

  25. Shimizu H, Kumabe T, Shirane R, Yoshimoto T. Correlation between choline level measured by proton MR spectroscopy and Ki-67 labeling index in gliomas. AJNR Am J Neuroradiol. 2000;21(4):659–65.

    PubMed  CAS  Google Scholar 

  26. Guillevin R, Menuel C, Duffau H, Kujas M, Capelle L, Aubert A, et al. Proton magnetic resonance spectroscopy predicts proliferative activity in diffuse low-grade gliomas. J Neurooncol. 2008;87(2):181–7.

    Article  PubMed  CAS  Google Scholar 

  27. Law M, Oh S, Johnson G, Babb JS, Zagzag D, Golfinos J, et al. Perfusion magnetic resonance imaging predicts patient outcome as an adjunct to histopathology: a second reference standard in the surgical and nonsurgical treatment of low-grade gliomas. Neurosurgery. 2006;58(6):1099–107; discussion 1099−107.

    Article  PubMed  Google Scholar 

  28. Castillo M, Kwock L, Mukherji SK. Clinical applications of proton MR spectroscopy. AJNR Am J Neuroradiol. 1996;17(1):1–15.

    PubMed  CAS  Google Scholar 

  29. Cha S, Johnson G, Wadghiri YZ, Jin O, Babb J, Zagzag D, et al. Dynamic, contrast-enhanced perfusion MRI in mouse gliomas: correlation with histopathology. Magn Reson Med. 2003;49(5):848–55.

    Article  PubMed  Google Scholar 

  30. Shin JH, Lee HK, Kwun BD, Kim JS, Kang W, Choi CG, et al. Using relative cerebral blood flow and volume to evaluate the histopathologic grade of cerebral gliomas: preliminary results. AJR Am J Roentgenol. 2002;179(3):783–9.

    Article  PubMed  Google Scholar 

  31. Sugahara T, Korogi Y, Kochi M, Ikushima I, Hirai T, Okuda T, et al. Correlation of MR imaging-determined cerebral blood volume maps with histologic and angiographic determination of vascularity of gliomas. AJR Am J Roentgenol. 1998;171(6):1479–86.

    Article  PubMed  CAS  Google Scholar 

  32. Danielsen ER, Ross B. Magnetic resonance spectroscopy diagnosis of neurological diseases. New York: Marcel Dekker; 1999.

    Google Scholar 

  33. Leclerc X, Huisman TA, Sorensen AG. The potential of proton magnetic resonance spectroscopy ((1)H-MRS) in the diagnosis and management of patients with brain tumors. Curr Opin Oncol. 2002;14(3):292–8.

    Article  PubMed  Google Scholar 

  34. Rees J. Advances in magnetic resonance imaging of brain tumours. Curr Opin Neurol. 2003;16(6):643–50.

    Article  PubMed  Google Scholar 

  35. Nafe R, Herminghaus S, Raab P, Wagner S, Pilatus U, Schneider B, et al. Preoperative proton-MR spectroscopy of gliomas – correlation with quantitative nuclear morphology in surgical specimen. J Neurooncol. 2003;63(3):233–45.

    Article  PubMed  Google Scholar 

  36. Mescher M, Merkle H, Kirsch J, Garwood M, Gruetter R. Simultaneous in vivo spectral editing and water suppression. NMR Biomed. 1998;11(6):266–72.

    Article  PubMed  CAS  Google Scholar 

  37. Choi C, Ganji SK, Deberardinis RJ, Hatanpaa KJ, Rakheja D, Kovacs Z, et al. 2-hydroxyglutarate detection by magnetic resonance spectroscopy in ­IDH-mutated patients with gliomas. Nat Med. 2012;18:624–9.

    Article  PubMed  CAS  Google Scholar 

  38. Guillevin R, Menuel C, Vallee JN, Francoise JP, Capelle L, Habas C, et al. Mathematical modeling of energy metabolism and hemodynamics of WHO grade II gliomas using in vivo MR data. C R Biol. 2011;334(1):31–8.

    Article  PubMed  Google Scholar 

  39. Griguer CE, Oliva CR, Gillespie GY. Glucose metabolism heterogeneity in human and mouse malignant glioma cell lines. J Neurooncol. 2005;74(2):123–33.

    Article  PubMed  CAS  Google Scholar 

  40. Guillevin R, Menuel C, Abud L, Costalat R, Capelle L, Hoang-Xuan K, et al. Proton MR spectroscopy in predicting the increase of perfusion MR imaging for WHO grade II gliomas. J Magn Reson Imaging. 2012;35(3):543–50.

    Article  PubMed  Google Scholar 

  41. Cha S. Update on brain tumor imaging: from anatomy to physiology. AJNR Am J Neuroradiol. 2006;27(3):475–87.

    PubMed  CAS  Google Scholar 

  42. Law M, Yang S, Babb JS, Knopp EA, Golfinos JG, Zagzag D, et al. Comparison of cerebral blood volume and vascular permeability from dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade. AJNR Am J Neuroradiol. 2004;25(5):746–55.

    PubMed  Google Scholar 

  43. Law M, Yang S, Wang H, Babb JS, Johnson G, Cha S, et al. Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging. AJNR Am J Neuroradiol. 2003;24(10):1989–98.

    PubMed  Google Scholar 

  44. Lev MH, Rosen BR. Clinical applications of intracranial perfusion MR imaging. Neuroimaging Clin N Am. 1999;9(2):309–31.

    PubMed  CAS  Google Scholar 

  45. Aronen HJ, Perkio J. Dynamic susceptibility contrast MRI of gliomas. Neuroimaging Clin N Am. 2002;12(4):501–23.

    Article  PubMed  Google Scholar 

  46. Law M, Oh S, Babb JS, Wang E, Inglese M, Zagzag D, et al. Low-grade gliomas: dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging – prediction of patient clinical response. Radiology. 2006;238(2):658–67.

    Article  PubMed  Google Scholar 

  47. Law M, Young RJ, Babb JS, Peccerelli N, Chheang S, Gruber ML, et al. Gliomas: predicting time to progression or survival with cerebral blood volume measurements at dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology. 2008;247(2):490–8.

    Article  PubMed  Google Scholar 

  48. Caseiras GB, Chheang S, Babb J, Rees JH, Pecerrelli N, Tozer DJ, et al. Relative cerebral blood volume measurements of low-grade gliomas predict patient outcome in a multi-institution setting. Eur J Radiol. 2010;73(2):215–20.

    Article  PubMed  Google Scholar 

  49. Callot V, Galanaud D, Figarella-Branger D, Lefur Y, Metellus P, Nicoli F, et al. Correlations between MR and endothelial hyperplasia in low-grade gliomas. J Magn Reson Imaging. 2007;26(1):52–60.

    Article  PubMed  Google Scholar 

  50. Danchaivijitr N, Waldman AD, Tozer DJ, Benton CE, Brasil Caseiras G, Tofts PS, et al. Low-grade gliomas: do changes in rCBV measurements at longitudinal perfusion-weighted MR imaging predict malignant transformation? Radiology. 2008;247(1):170–8.

    Article  PubMed  Google Scholar 

  51. Xu M, See SJ, Ng WH, Arul E, Back MF, Yeo TT, et al. Comparison of magnetic resonance spectroscopy and perfusion-weighted imaging in presurgical grading of oligodendroglial tumors. Neurosurgery. 2005;56(5):919–26; discussion 919−26.

    PubMed  Google Scholar 

  52. Yang D, Korogi Y, Sugahara T, Kitajima M, Shigematsu Y, Liang L, et al. Cerebral gliomas: prospective comparison of multivoxel 2D chemical-shift imaging proton MR spectroscopy, echoplanar perfusion and diffusion-weighted MRI. Neuroradiology. 2002;44(8):656–66.

    Article  PubMed  CAS  Google Scholar 

  53. Cao Y, Shen Z, Chenevert TL, Ewing JR. Estimate of vascular permeability and cerebral blood volume using Gd-DTPA contrast enhancement and dynamic T2*-weighted MRI. J Magn Reson Imaging. 2006;24(2):288–96.

    Article  PubMed  Google Scholar 

  54. Dhermain F, Saliou G, Parker F, Page P, Hoang-Xuan K, Lacroix C, et al. Microvascular leakage and contrast enhancement as prognostic factors for recurrence in unfavorable low-grade gliomas. J Neurooncol. 2010;97(1):81–8.

    Article  PubMed  CAS  Google Scholar 

  55. Aronen HJ, Pardo FS, Kennedy DN, Belliveau JW, Packard SD, Hsu DW, et al. High microvascular blood volume is associated with high glucose uptake and tumor angiogenesis in human gliomas. Clin Cancer Res. 2000;6(6):2189–200.

    PubMed  CAS  Google Scholar 

  56. Wu WC, Chen CY, Chung HW, Juan CJ, Hsueh CJ, Gao HW. Discrepant MR spectroscopic and perfusion imaging results in a case of malignant transformation of cerebral glioma. AJNR Am J Neuroradiol. 2002;23(10):1775–8.

    PubMed  Google Scholar 

  57. De Palma A, Thisse J. Discrete choice models. Ann Econ Stat. 1989;9:152–90.

    Google Scholar 

  58. Foucart T. Collinearity and linear regression analysis. Math Sci Hum. 2006;44e année, 173(1):5–25.

    Google Scholar 

  59. Wolter K, Fuller W. Estimation of the quadratic errors-in-variables model. Math Phys Sci. 1982;69(1):175–82.

    Google Scholar 

  60. Lev MH, Ozsunar Y, Henson JW, Rasheed AA, Barest GD, Harsh GR, et al. Glial tumor grading and outcome prediction using dynamic spin-echo MR susceptibility mapping compared with conventional contrast-enhanced MR: confounding effect of elevated rCBV of oligodendrogliomas [corrected]. AJNR Am J Neuroradiol. 2004;25(2):214–21.

    PubMed  Google Scholar 

  61. Ricard D, Kaloshi G, Amiel-Benouaich A, Lejeune J, Marie Y, Mandonnet E, et al. Dynamic history of low-grade gliomas before and after temozolomide treatment. Ann Neurol. 2007;61(5):484–90.

    Article  PubMed  CAS  Google Scholar 

  62. Swanson KR, Bridge C, Murray JD, Alvord Jr EC. Virtual and real brain tumors: using mathematical modeling to quantify glioma growth and invasion. J Neurol Sci. 2003;216(1):1–10.

    Article  PubMed  Google Scholar 

  63. Hoang-Xuan K, Capelle L, Kujas M, Taillibert S, Duffau H, Lejeune J, et al. Temozolomide as initial treatment for adults with low-grade oligodendrogliomas or oligoastrocytomas and correlation with chromosome 1p deletions. J Clin Oncol. 2004;22(15):3133–8.

    Article  PubMed  CAS  Google Scholar 

  64. Gill SS, Thomas DG, Van Bruggen N, Gadian DG, Peden CJ, Bell JD, et al. Proton MR spectroscopy of intracranial tumours: in vivo and in vitro studies. J Comput Assist Tomogr. 1990;14(4):497–504.

    Article  PubMed  CAS  Google Scholar 

  65. Murphy PS, Viviers L, Abson C, Rowland IJ, Brada M, Leach MO, et al. Monitoring temozolomide treatment of low-grade glioma with proton magnetic resonance spectroscopy. Br J Cancer. 2004;90(4):781–6.

    Article  PubMed  CAS  Google Scholar 

  66. Julia-Sape M, Acosta D, Majos C, Moreno-Torres A, Wesseling P, Acebes JJ, et al. Comparison between neuroimaging classifications and histopathological diagnoses using an international multicenter brain tumor magnetic resonance imaging database. J Neurosurg. 2006;105(1):6–14.

    Article  PubMed  Google Scholar 

  67. Guillevin R, Menuel C, Taillibert S, Capelle L, Costalat R, Abud L, et al. Predicting the outcome of grade II glioma treated with temozolomide using proton magnetic resonance spectroscopy. Br J Cancer. 2011;104(12):1854–61.

    Article  PubMed  CAS  Google Scholar 

  68. Miller BL, Chang L, Booth R, Ernst T, Cornford M, Nikas D, et al. In vivo 1H MRS choline: correlation with in vitro chemistry/histology. Life Sci. 1996;58(22):1929–35.

    Article  PubMed  CAS  Google Scholar 

  69. Hlaihel C, Guilloton L, Guyotat J, Streichenberger N, Honnorat J, Cotton F. Predictive value of multimodality MRI using conventional, perfusion, and spectroscopy MR in anaplastic transformation of low-grade oligodendrogliomas. J Neurooncol. 2010;97(1):73–80.

    Article  PubMed  Google Scholar 

  70. Lamari F, La Schiazza R, Guillevin R, Hainque B, Foglietti MJ, Beaudeux JL, et al. Biochemical exploration of energetic metabolism and oxidative stress in low grade gliomas: central and peripheral tumor tissue analysis. Ann Biol Clin (Paris). 2008;66(2):143–50.

    CAS  Google Scholar 

  71. Byrne TN. Response of low-grade oligodendroglial tumors to temozolomide. J Neurooncol. 2004;70(3):279–80.

    Article  PubMed  Google Scholar 

  72. Chinot O. Chemotherapy for the treatment of oligodendroglial tumors. Semin Oncol. 2001;28(4 Suppl 13):13–8.

    Article  PubMed  CAS  Google Scholar 

  73. Kumar AJ, Leeds NE, Fuller GN, Van Tassel P, Maor MH, Sawaya RE, et al. Malignant gliomas: MR imaging spectrum of radiation therapy- and chemotherapy-induced necrosis of the brain after treatment. Radiology. 2000;217(2):377–84.

    PubMed  CAS  Google Scholar 

  74. Asao C, Korogi Y, Kitajima M, Hirai T, Baba Y, Makino K, et al. Diffusion-weighted imaging of radiation-induced brain injury for differentiation from tumor recurrence. AJNR Am J Neuroradiol. 2005;26(6):1455–60.

    PubMed  Google Scholar 

  75. Hein PA, Eskey CJ, Dunn JF, Hug EB. Diffusion-weighted imaging in the follow-up of treated high-grade gliomas: tumor recurrence versus radiation injury. AJNR Am J Neuroradiol. 2004;25(2):201–9.

    PubMed  Google Scholar 

  76. Lam WW, Poon WS, Metreweli C. Diffusion MR imaging in glioma: does it have any role in the pre-operation determination of grading of glioma? Clin Radiol. 2002;57(3):219–25.

    Article  PubMed  CAS  Google Scholar 

  77. Zeng QS, Li CF, Liu H, Zhen JH, Feng DC. Distinction between recurrent glioma and radiation injury using magnetic resonance spectroscopy in combination with diffusion-weighted imaging. Int J Radiat Oncol Biol Phys. 2007;68(1):151–8.

    Article  PubMed  Google Scholar 

  78. Sugahara T, Korogi Y, Tomiguchi S, Shigematsu Y, Ikushima I, Kira T, et al. Posttherapeutic intraaxial brain tumor: the value of perfusion-sensitive contrast-enhanced MR imaging for differentiating tumor recurrence from nonneoplastic contrast-enhancing tissue. AJNR Am J Neuroradiol. 2000;21(5):901–9.

    PubMed  CAS  Google Scholar 

  79. Matsusue E, Fink JR, Rockhill JK, Ogawa T, Maravilla KR. Distinction between glioma progression and post-radiation change by combined physiologic MR imaging. Neuroradiology. 2010;52(4):297–306.

    Article  PubMed  Google Scholar 

  80. Plotkin M, Eisenacher J, Bruhn H, Wurm R, Michel R, Stockhammer F, et al. 123I-IMT SPECT and 1H MR-spectroscopy at 3.0T in the differential diagnosis of recurrent or residual gliomas: a comparative study. J Neurooncol. 2004;70(1):49–58.

    Article  PubMed  Google Scholar 

  81. Zhou J, Tryggestad E, Wen Z, Lal B, Zhou T, Grossmann R, et al. Differentiation between glioma and radiation necrosis using molecular magnetic resonance imaging of endogenous proteins and peptides. Nat Med. 2011;17(1):130–4.

    Article  PubMed  CAS  Google Scholar 

  82. Aubert A, Pellerin L, Magistretti PJ, Costalat R. A coherent neurobiological framework for functional neuroimaging provided by a model integrating compartmentalized energy metabolism. Proc Natl Acad Sci USA. 2007;104(10):4188–93.

    Article  PubMed  CAS  Google Scholar 

  83. Kuhr WG, Korf J. Extracellular lactic acid as an indicator of brain metabolism: continuous on-line measurement in conscious, freely moving rats with intrastriatal dialysis. J Cereb Blood Flow Metab. 1988;8(1):130–7.

    Article  PubMed  CAS  Google Scholar 

  84. Duffau H. New concepts in surgery of WHO grade II gliomas: functional brain mapping, connectionism and plasticity – a review. J Neurooncol. 2006;79(1):77–115.

    Article  PubMed  Google Scholar 

  85. Durmaz R, Vural M, Isildi E, Cosan E, Ozkara E, Bal C, et al. Efficacy of prognostic factors on survival in patients with low grade glioma. Turk Neurosurg. 2008;18(4):336–44.

    PubMed  Google Scholar 

  86. Mandonnet E, Delattre JY, Tanguy ML, Swanson KR, Carpentier AF, Duffau H, et al. Continuous growth of mean tumor diameter in a subset of grade II gliomas. Ann Neurol. 2003;53(4):524–8.

    Article  PubMed  Google Scholar 

  87. Pellerin L. Brain energetics (thought needs food). Curr Opin Clin Nutr Metab Care. 2008;11(6):701–5.

    Article  PubMed  Google Scholar 

  88. Aubert A, Costalat R. Interaction between astrocytes and neurons studied using a mathematical model of compartmentalized energy metabolism. J Cereb Blood Flow Metab. 2005;25(11):1476–90.

    Article  PubMed  CAS  Google Scholar 

  89. Aubert A, Costalat R, Magistretti PJ, Pellerin L. Brain lactate kinetics: Modeling evidence for neuronal lactate uptake upon activation. Proc Natl Acad Sci USA. 2005;102(45):16448–53.

    Article  PubMed  CAS  Google Scholar 

  90. Hubesch B, Sappey-Marinier D, Roth K, Meyerhoff DJ, Matson GB, Weiner MW. P-31 MR spectroscopy of normal human brain and brain tumors. Radiology. 1990;174(2):401–9.

    PubMed  CAS  Google Scholar 

  91. Mathupala SP, Colen CB, Parajuli P, Sloan AE. Lactate and malignant tumors: a therapeutic target at the end stage of glycolysis. J Bioenerg Biomembr. 2007;39(1):73–7.

    Article  PubMed  CAS  Google Scholar 

  92. Costalat R, Francoise JP, Menuel C, Lahutte M, Vallee JN, De Marco G, et al. Mathematical modeling of metabolism and hemodynamics of lactate for analysis of low grade gliomas. Acta Biotheor. 2012;60(1-2):99–107. doi:10.1007/s10441-012-9157-1. Epub 2012 Mar 11.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Radiology Department and MPIM Laboratory, Teaching Hospital and University of Poitiers, 2, rue de La Milétrie, BP 577, Poitiers, 86021, France

    Rémy Guillevin MD, PhD (Head)

  2. Functional Laboratory Imaging, Neuroradiology Department, Teaching Hospital, University of Poitiers, rue de la Milétrie, 86021, Poitiers, France

    Rémy Guillevin MD, PhD (Head)

Authors
  1. Rémy Guillevin MD, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rémy Guillevin MD, PhD .

Editor information

Editors and Affiliations

  1. , INSERM U1051, Institute for Neurosciences of Montpelli, av. Augustin Fliche 80, Montpellier, 34295, France

    Hugues Duffau

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag London

About this chapter

Cite this chapter

Guillevin, R. (2013). Metabolic-Oncological MR Imaging of Diffuse Low-Grade Glioma: A Dynamic Approach. In: Duffau, H. (eds) Diffuse Low-Grade Gliomas in Adults. Springer, London. https://doi.org/10.1007/978-1-4471-2213-5_15

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-2213-5_15

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-2212-8

  • Online ISBN: 978-1-4471-2213-5

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation