Abstract
Gliomas are the most common tumor type in the central nervous system (CNS), they account more than 70 % of all brain tumors, and of these, glioblastoma is the most frequent and malignant histologic type. The total incidence of primary CNS tumors is approximately 18.7 per 100,000 person in the United States and 7 per 100,000 worldwide. Diffuse infiltrating gliomas are the second most common primary central nervous system neoplasm [1].
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Bibliography
Vigneswaran K, Neill S, Hadjipanayis CG (2015) Beyond the World Health Organization grading of infiltrating gliomas: advances in the molecular genetics of glioma classification. Ann Transl Med 3(7):95
Ohgaki H (2009) Epidemiology of brain tumors. Methods Mol Biol Cancer Epidemiol 472(14):323–342
Nygaard R et al (1991) Second malignant neoplasms in patients treated for childhood leukemia. A population-based cohort study from Nordic countries. The nordic Society of Pediatric Oncology and Hematology (NOPHO). Acta Paediatr Scand 80:1220–1228
Little MP et al (1998) Risks of brain tumor following treatment for cancer in childhood: modification by genetic factors, radiotherapy and chemotherapy. Int. J Cancer 78:269–275
Walter AW et al (1998) Secondary brain tumors in children treated for acute lymphoblastic leukemia at St Jude Children’s Research Hospital. J Clin Oncol 16:3761–3767
Brustle O et al (1992) Primitive neuroectodermal tumors after prophylactic central nervous system irradiation in children. Association with an activated K-ras gene. Cancer 69:2385–2392
Louis DN, Ohgaki H, Wiestler OD et al (2007) The 2007 WHO classification of tumors of the central nervous system. Acta Neuropathol 114:97–109
Van den Bent MJ (2010) Interobserver variation of the histopathological diagnosis in clinical trials on glioma: a clinician’s perspective. Acta Neuropathol 120:297–304
Pollo B (2011) Neuropathological diagnosis of brain tumours. Neurol Sci 32(Suppl 2):S209–S211
Sun Y, Wei Z, Chen D et al (2014) A glioma classification scheme based on coexpression modules of EGFR and PDGFRA. PNAS 111(9):3539–3543
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
Stupp R, Hegi ME, Mason WP et al (2009) Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. Lancet Oncol 10(5):459–466
Brandes AA, Franceschi E, Ermani M et al (2014) Pattern of care and effectiveness of treatment for glioblastoma patients in the real world: results from a prospective population-based registry. Could survival differ in a high-volume center? Neurooncol Pract 1(4):166–171
Ceccarelli M, Barthel FP, Malta TM et al (2016) Molecular profiling reveals biologically discrete subsets and pathways of progression in diffuse glioma. Cell 164(3):550–563
Louis DN, Perry A, Burger P (2014) International Society of Neuropathology-Haarlem Consensus. Guidelines for nervous system tumor classification and grading. Brain Pathol 24:429–435
The Cancer Genome Atlas Research Network (2015) Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N Engl J Med 372(26):2481–2498
Yan H, Parsons DW, Jin G et al (2009) IDH1 and IDH2 mutations in gliomas. N Engl J Med 360(8):765–773
Cairncross G, Wang M, Shaw E et al (2013) Phase III trial of chemoradiotherapy for anaplastic oligodendroglioma: long-term results of RTOG 9402. J Clin Oncol 31:337–343
Liu XY, Gerges N, Korshunov A et al (2012) Frequent ATRX mutations and loss of expression in adult diffuse astrocytic tumors carrying IDH1/IDH2 and TP53 mutations. Acta Neuropathol 124:615–625
Eckel-Passow JE, Lachance DH, Molinaro AM (2015) Glioma groups based on 1p/19q, IDH, and TERT promoter mutations in tumors. N Engl J Med 372(26):2499–2508
Sahm F, Reuss D, Koelsche C, Capper D, Schittenhelm J, Heim S, Jones DT, Pfister SM, Herold-Mende C, Wick W, Mueller W, Hartmann C, Paulus W, von Deimling A (2014) Farewell to oligoastrocytoma: in situ molecular genetics favor classification as either oligodendroglioma or astrocytoma. Acta Neuropathol 128(4):551–559
Hinrichs BH, Newman S, Appin CL, Dunn W, Cooper L, Pauly R, Kowalski J, Rossi MR, Brat DJ (2016) Farewell to GBM-O: genomic and transcriptomic profiling of glioblastoma with oligodendroglioma component reveals distinct molecular subgroups. Acta Neuropathol Commun 4(1):4
Solomon DA, Wood MD, Tihan T, Bollen AW, Gupta N, Phillips JJ, Perry A (2015) Diffuse Midline Gliomas with histoneH3-K27M mutation: a series of 47 cases assessing the spectrum of morphologic variation and associated genetic alterations. Brain Pathol. doi:10.1111/bpa.12336
Buczkowicz P, Bartels U, Bouffet E, Becher O, Hawkins C (2014) Histopathologic spectrum of paediatric diffuse intrinsic pontine glioma: diagnostic and therapeutic implications. Acta Neuropathol 128:573–581
Pope WB (2015) Genomics of brain tumor. Neuroimag Clin N Am 25:105–119
Rutman AM, Kuo MD (2009) Radiogenomics creating a link between molecular diagnostics and diagnostic imaging. Eur J Radiol 70(2):232–241
Elbanan MG, Ahmed MA et al (2015) Imaging genomics of glioblastoma. State of the art bridge between genomics and neuroradiology. Neuroimag Clin N Am 25:141–153
Liang Y, Diehn M et al (2005) Gene expression profiling reveals molecularly and clinically distinct subtypes of glioblastoma multiforme. Proc Natl Acad Sci U S A 102(16):5814–5819
Smith AB, Smirniotopoulos JG (2013) Intra-axial neoplasms. Imaging Brain 32:692–727, Saunders Elsevier
Atlas SW, Lavi E, Fisher PG (2002) Intraaxial brain tumors. Magn Res Brain I(14):565–693
Jenkinson MD, Du Plessis DG et al (2007) Advanced MRI in the management of adult gliomas. Br J Neurosurg 21(6):550–561
Kim JJ, Mukherjee S (2013) Static anatomic techniques. Imaging Brain 1:3–22, Saunders Elsevier
RTOG Study Chairs (Coordinanting Group) PHASE III double-blind placebo-controlled trial of conventional concurrent chemoradiation and adjuvant temozolamide plus bevacizumab versus conventional concurrent chemoradiation and adjuvant temozolamide in patients with newly diagnosted glioblastoma. ACRIN: American College of Radiology Imaging Network 2009.aspx. Accessed 14 Jan 2011
Attenberger UI, Runge VM, Morelli JN et al (2010) Evaluation of gadobutrol, a macrocyclic, nonionic gadolinium chelate in a brain glioma model: comparison with gadoterate meglumine and gadopentetate dimeglumine 1.5T, combined with an assessment of field strength dependence, specifically 1.5 versus 3T. J Magn Reson Imaging 31:549–555
Biswas J, Nelson CB, Runge VM et al (2005) Brain tumor enhancement in a magnetic resonance imaging: comparison of signal-noise-ratio (SNR) and contrast-to-noise-ratio (CNR) at 1.5 versus 3 Tesla. Invest Radiol 40:792–797
Wiggins GC, Triantafyllou C, Potthast A et al (2006) 32-channels 3 Tesla receive only phase array head coil with soccer ball element geometry. Magn Reson Med 56:216–223
Wiggins GC, Polimeni JR, Potthast A et al (2009) 96-channel-receive only head coil for 3 Tesla: design optimization and evaluation. Magn Reson Med 62:754–762
Ba-Salamah A, Nobauer-Huhmann IM, Pinker K et al (2003) Effect of contrast dose and field strength in the magnetic resonance detection of brain metastases. Invest Radiol 38:415–422
Schneider G, Kirchin MA, Pirovano G et al (2001) Gadobenate dimeglumine-enhanced magnetic resonance imaging of intracranial metastases: effect of dose on lesion detection and delineation. J Magn Reson Imaging 14:525–539
Engelhorn T, Schwartz MA, Eyupoglu IY et al (2010) Dynamic contrast enhancement of experimental glioma an intra-individual comparative study to assess the optimal time delay. Acad Radiol 17:188–193
Burger PC, Scheithauer BW (1994) Atlas of tumor pathology, 3rd series, fascicle 10: Tumors of the central nervous system. Armed Forces Institute of Pathology, Washington, DC
Kovalikova Z, Hoehn-Berlage MH et al (1987) Age-dependent variation of T1 and T2-relaxation times of adenocarcinoma in mice. Radiology 164:543–548
Hackney DB, Grossman RI et al (1987) Low sensitivity of clinical MR imaging to small changes in the concentration of non-paramagnetic protein. AJNR Am J Neuroradiol 8:1003–1008
Bradley WG, Schmidt PG (1985) Effect of methemoglobin formation on the MR appearance of subarachnoid hemorrhage. Radiology 156:99–103
Gatenby RA, Coia LR et al (1985) Oxygen tension in human tumors: in vivo mapping using CT-guided probes. Radiology 156:211–214
Sze G, Krol G et al (1987) Hemorrhagic neoplasms: MR imaging mimics of occult vascular malformations. AJR Am J Roentgenol 149:1223–1230
Smirniotopoulos JG, Smith A et al (2013) Pattern of contrast enhancement. Imaging Brain 5:79–95, Saunders Elsevier
Sage MR (1982) Blood-brain barrier: phenomenon of increasing importance to the imaging clinician. AJR Am J Roentgenol 138:887–898
Takeuki H, Kubota T et al (2004) Ultrastructure of capillary endothelium in pilocytic astrocytomas. Brain Tumor Pathol 21:23–26
Scott JN, Pm B et al (2002) How often are nonenhancing supratentorial gliomas malignant? A population study. Neurology 59:947–949
Diehn M, Nardini C et al (2008) Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc Natl Acad Sci U S A 105(13):5213–5218
Pope WB, Chen JH et al (2008) Relationship between gene-expression and enhancement in glioblastoma multiforme: exploratory DNA microarray analysis. Radiology 249(1):268–277
Carrillo JA, Lai A et al (2012) Relationship between tumor enhancement, edema, IDH1 mutational status, MGMT promoter methylation, and survival in glioblastoma. AJNR Am J Neurorad 33(7):1349–1355
Drabycz S, Roldan G et al (2010) An analysis of image texture, tumor location, and MGMT promoter methylation in glioblastoma using magnetic resonance imaging. Neuroimage 49(2):1398–1405
Ellingson MB, LaViolette PS et al (2011) Spatially quantifying microscopic tumor invasion and proliferation using a Voxel-Wise solution to a glioma growth model and serial diffusion MRI. Magn Reson Med 65(4):1131–1143
Sugahara T, Korogi Y et al (1999) Usefulness of diffusion-weighted MRI with echo-planar technique in the evaluation of cellularity in gliomas. J Magn Reson Imaging 9:53–60
Hayashida Y, Hirai T et al (2006) Diffusion-weighted imaging of metastatic brain tumors: comparison with histologic type and tumor cellularity. AJNR Am J Neurorad 27(7):1419–1425
Kinoshita M, Hashimoto N et al (2008) Fractional anisotropy and tumor cell density of the tumor core show positive correlation in diffusion tensor magnetic resonance imaging of malignant tumors. Neuroimage 43(1):29–35
Kono K, Inoue Y et al (2001) The role of diffusion-weighted imaging in patients with brain tumors. AJNR Am J Neuroradiol 22(6):1081–1088
Maier SE, Sun Y, Mulkern R (2010) Diffusion imaging of brain tumors. NMR Biomed 23(7):849–864
Yamasaki F, Kurisu K et al (2005) Apparent diffusion coefficient of human brain 232 tumors at MR imaging. Radiology 235:985–991
Lee EJ, Ahn KJ et al (2013) Potential role of advanced MRI techniques for the peritumoral region in differentiating glioblastoma multiforme and solitary metastatic lesions. Clin Radiol 68:689–697
Alexiou GA, Tsiouris S et al (2010) Assessment of glioma proliferation using imaging modalities. J Clin Neurosci 17:1233–1238
Kiss R, Dewitte O et al (1997) The combined determination of proliferative activity and cell density in the prognosis of adult patients with supratentorial high-grade astrocytic tumors. Am J Clin Pathol 107:321–331
Muarakami R, Hirai T et al (2009) Grading astrocytic tumor by using apparent diffusion coefficient parameters: superiority of one-versus two parameters pilot method. Radiology 251:838–845
Lee EJ, Lee SK et al (2008) Preoperative grading of presumptive low-grade astrocytomas on MR imaging: diagnostic value of minimum apparent diffusion coefficient. Am J Neuroradiol 29:1872–1877
Yang D, Korogi Y et al (2002) Cerebral gliomas: prospective comparison of multivoxel 2D chemical-shift proton MR spectroscopy, echoplanar perfusion and diffusion-weighted MRI. Neuroradiology 44:656–666
Morita K, Matsuzawa H et al (2005) Diffusion tensor analysis of peritumoral edema using lambda chart analysis indicative of the heterogeneity of the microstructure within edema. J Neurosurg 102(2):336–341
Lying H, Haraldseth O et al (2000) Measurement of cell density and necrotic fraction in human melanoma xenografts by diffusion weighted magnetic resonance imaging. Magn Reson Med 43(6):828–836
Chenevert TL, McKeever PE, Ross BD (1997) Monitoring early response of experimental brain tumors to therapy using diffusion magnetic resonance imaging. Clin Cancer Res 3:1457–1466
Chan YL, Yeug DK et al (2003) Diffusion weighted magnetic resonance imaging in radiation-induced cerebral necrosis. Apparent diffusion coefficient in lesion component. J Comput Assist Tomogr 27(5):674–680
Mardor Y, Roth Y et al (2004) Pre-treatment prediction of brain tumors response to radiation therapy using high b-value diffusion-weighted MRI. Neoplasia 6(2):136–142
Basser PJ, Mattiello J, LeBihan D (1994) Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 103(3):247–254
Lin CP, Tseng WY et al (2001) Validation of diffusion tensor magnetic resonance axonal fiber imaging with registered manganese-enhanced optic tracts. Neuroimage 14(5):1035–1047
Beaulieu C (2002) The basis of anisotropic water diffusion in the nervous system-a technical review. NMR Biomed 15(7–8):435–455
Mori S, Barker PB (1999) Diffusion magnetic resonance imaging: its principle and applications. Anat Rec 257(3):102–109
Mori S, van Zijl PC (2002) Fiber tracking: principles and strategies – a technical review. NMR Biomed 15(7–8):468–480
Pajevic S, Pierpaoli C (1999) Color schemes to represent the orientation of the anisotropic tissue from diffusion tensor data: application to white matter fiber tract mapping in the human brain. Magn Reson Med 42(3):526–540
Witwer BP, Moftakhar R et al (2002) Diffusion-tensor imaging of white matter tracts in patients with cerebral neoplasm. J Neurosurg 97(3):568–575
Bello L, Gambini A, Castellano A et al (2008) Motor and language DTI fiber tracking combined with intraoperative sub cortical mapping for surgical removal of gliomas. Neuroimage 39(1):369–382
Lu S, Ahn D et al (2003) Peritumoral diffusion tensor imaging of high grade gliomas and metastatic brain tumors. AJNR Am J Neuroradiol 58(6):937–994
Smits M, Vernooij MW et al (2007) Incorporating functional MR imaging into diffusion tensor tractography in the preoperative assessment of the corticospinal tract in patients with brain tumors. AJNR Am J Neuroradiol 28(7):1354–1361
Wieshmann UC, Symms MR et al (2000) Diffusion tensor imaging demonstrates deviation of fibres in normal appearing white matter adjacent to a brain tumour. J Neurol Neurosurg Psychiatry 68(4):501–503
Yamada K, Kizu O, Mori S et al (2003) Brain fiber tracking with clinically feasible diffusion-tensor MR imaging: initial experience. Radiology 227(1):295–301
Field AS, Alexander AL et al (2004) Diffusion tension eigenvector directional color imaging patterns in the evaluation of cerebral white matter tracts altered by tumor. J Magn Reson Imaging 20(4):555–562
Golby AJ, Kindlmann G et al (2011) Interactive diffusion tensor tractography visualization for neurosurgical planning. Neurosurgery 68(2):496–505
Gupta A, Shah A et al (2010) Imaging of brain tumors: functional magnetic resonance imaging and diffusion tensor imaging. Neuroimaging Clin N Am 20:379–400
Byrnes TJ, Barrick TR et al (2011) Diffusion tensor imaging discriminates between glioblastoma and cerebral metastases in vivo. NMR Biomed 24:54–60
Sinha S, Bastin ME et al (2002) Diffusion tensor imaging of high-grade cerebral gliomas. AJNR Am J Neuroradiol 23:520–527
Beppu T, Inoue T et al (2003) Measurement of fractional anisotropy using diffusion tensor MRI in supratentorial astrocytic tumors. J Neurooncol 6:109–116
White ML, Zhang Y et al (2011) Diffusion tensor MR imaging of cerebral gliomas: evaluation fractional anisotropy characteristics. AJNR Am J Neuroradiol 32:374–381
Alexiou GA, Zikou A et al (2014) Correlation of diffusion tensor, dynamic susceptibility contrast MRI and 99mTc-Tetrofosmin brain SPECT with tumour grade and Ki-67 immunohistochemistry in glioma. Clin Neurol Neurosurg 116:41–45
Goebell E, Paustenbach S et al (2006) Low-grade and anaplastic gliomas: differences in architecture evaluated with diffusion-tensor MR imaging. Radiology 239(1):217–222
Castellano A, Bello L et al (2012) Role of diffusion tensor magnetic resonance tractography in predicting the extent of resection in glioma surgery. Neuro Oncol 14(2):192–202
Server A, Graff BA et al (2014) Analysis of diffusion tensor imaging metrics for gliomas grading at 3 T. Eur J Radiol 83:156–165
Yuan W, Holland SK et al (2008) Characterization of abnormal diffusion properties of supratentorial brain tumors: a preliminary diffusion tensor imaging study. J Neurosurg Pediatr 1(4):263–269
Budde MD, Xie M et al (2009) Axial diffusivity is the primary correlate of axonal injury in the experimental autoimmune encephalomyelitis spinal cord: a quantitative pixelwise analysis. J Neurosci 29(9):2805–2813
Klawiter EC, Schmidt RE et al (2011) Radial diffusivity predicts demyelination in ex vivo multiple sclerosis spinal cords. Neuroimage 55(4):1454–1460
Inano R, Oishi N et al (2014) Voxel-based clustered imaging by multiparameter diffusion tensor images for glioma grading. Neuroimage Clin 5:396–407
Law M, Babb S et al (2006) Low-grade gliomas: dynamic susceptibility-weighted contrast enhanced perfusion MR imaging – prediction of patient clinical response. Radiology 238:658–667
Tuch DS (2004) Q-ball imaging. Magn Reson Med 52(6):1358–1372
Tan WL, Huang WY et al (2014) Can diffusion tensor imaging noninvasively detect IDH1 gene mutations in astrogliomas? A retrospective study of 112 cases. AJNR Am J Neuroradiol 35:920–927
Bisdas S et al (2013) Intravoxel incoherent motion diffusion-weighted MR imaging of gliomas; feasibility of the method and initial results. Neuroradiology 55:1189–1196
Hu Y-C, L-F Y et al (2014) Intravoxel incoherent motion diffusion-weighted MR imaging of gliomas: efficacy in preoperative grading. Sci Rep 4:7208
Higano S et al (2006) Malignant astrocytic tumors: clinical importance of apparent diffusion coefficient in prediction of grade and prognosis. Radiology 241:839–846
Wen PY, Kesari S (2008) Malignant gliomas in adults. N Engl J Med 359:492–507
Iima M et al (2014) Characterization of glioma microcirculation and tissue features using intravoxel incoherent motion magnetic resonance imaging in a rat brain model. Invest Radiol 49:485–490
Kang Y et al (2011) Gliomas: histogram analysis of apparent diffusion coefficient maps with standard- or high-b-value diffusion-weighted MR imaging–correlation with tumor grade. Radiology 261:882–890
Plate KH, Breier G, Weich HA, Risau W (1992) Vascular endothelial growth factor is a potential tumour angiogenesis factor in human gliomas in vivo. Nature 359:845–848
Luciani A et al (2008) Liver cirrhosis: intravoxel incoherent motion MR imaging–pilot study. Radiology 249:891–899
Thompson G, Mills SJ, Coope DJ, O’Connor JP, Jackson A (2011) Imaging biomarkers of angiogenesis and the microvascular environment in cerebral tumours. Br J Radiol 84 Spec No 2:S127–S144
Shin JH et al (2002) Using relative cerebral blood flow and volume to evaluate the histopathologic grade of cerebral gliomas: preliminary results. AJR Am J Roentgenol 179:783–789
Hu LS et al (2012) Correlations between perfusion MR imaging cerebral blood volume, microvessel quantification, and clinical outcome using stereotactic analysis in recurrent high-grade glioma. AJNR Am J Neuroradiol 33:69–76
Essig M, Shiroishi MS et al (2013) Perfusion MRI: the five most frequently asked technical questions. AJR Am J Roentgenol 200(1):24–34
Brix G, Semmler W et al (1991) Pharmacokinetic parameters in CNS Gd-DTPA enhanced MR imaging. J Comput Assist Tomogr 15:621–628
Kim HS, Kim SY (2007) A prospective study on the added value of pulsed arterial spin labeling and apparent diffusion coefficients in the grading of gliomas. AJNR Am J Neuroradiol 28:1693–1699
Kwong KK, Chesler DA et al (1995) MR perfusion studies with T1-weighted echo planar imaging. Magn Reson Med 34:878–887
Petersen ET, Zimine I et al (2006) Non-invasive measurement of perfusion: a critical review of arterial spin labelling techniques. Br J Radiol 79:688–701
Golay X, Hendrikse J, Lim TC (2004) Perfusion imaging using arterial spin labelling. Top Magn Reson Imaging 15:10–27
Wu WC, Jiang SC, Lien SH (2011) Pseudocontinuous arterial spin labelling perfusion magnetic resonance imaging: a normative study of reproducibility in the human brain. Neuroimage 56:1244–1250
Jenkinson MD, Smith TS, Joyce KA et al (2006) Cerebral Blood volume, genotype and chemosensitivity in oligodendroglial tumours. Neuroradiology 48:703–713
Jarnum H, Stefferson EG et al (2010) Perfusion MRI of brain tumours: a comparative study of pseudocontinuous arterial spin labeling and dynamic susceptibility contrast imaging. Neuroradiology 52:307–317
Vaupel P, Mayer A (2007) Hypoxia in cancer: significance and impact on clinical outcome. Cancer Metastasis Rev 26:225–239
Bar EE (2011) Glioblastoma, cancer stem cells and hypoxia. Brain Pathol 21:119–129
Folkman J (1971) Tumor angiogenesis: therapeutic implications. N Engl J Med 285:1182–1186
Provenzale JM, Wang GR et al (2002) Comparison of permeability in high-grade and low-grade brain tumors using dynamic susceptibility contrast MR imaging. AJR Am J Roentgenol 178(3):711–716
Maia ACM Jr, Malheiros SMF et al (2005) MR cerebral Blood volume maps correlated with vascular endothelial growth factor expression and tumor grade in nonenhancing gliomas. AJNR Am J Neuroradiol 26(4):777–783
Aronen HJ, Perkio J (2002) Dynamic susceptibility contrast MRI of gliomas. Neuroimaging Clin N Am 12:501–523
Law M, Yang S et al (2003) 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 24(10):1989–1998
Shin JH, Lee HK et al (2002) Using relative cerebral blood flow and volume to evaluate the histopathologic grade of cerebral gliomas: preliminary results. Am J Roentgenol 179(3):783–789
Wong JC, Provenzale JM, Petrella JR (2000) Perfusion MR imaging of rain neoplasms. Am J Roentgenol 174(4):1147–1157
Lacerda S, Law M (2009) Magnetic resonance perfusion and permeability imaging in brain tumors. Neuroimag Clin N Am 19:527–557
Law M, Young R et al (2006) Comparing perfusion metrics obtained from a single compartment versus pharmacokinetic modeling methods using dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade. AJNR Am J Neuroradiol 27(9):1975–1982
Aronen HJ, Gazit IE et al (1994) Cerebral blood volume maps of gliomas: comparison with tumor grade and histologic findings. Radiology 191(1):41–51
Lev MH, Rosen BR (1999) Clinical applications of intracranial perfusion MR imaging. Neuroimaging Clin N Am 9(2):309–331
Law M, Yang S 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(5):746–755
Lupo JM, Cha S et al (2005) Dynamic susceptibility-weighted perfusion imaging of high-grade gliomas: characterization of spatial heterogeneity. AJNR Am J Neuroradiol 26(6):1446–1454
Hacklander T, Hofer M et al (1995) Possibilities of the use of MR tomography-based cerebral blood volume maps in the diagnosis of brain tumors. Rofo 163:484–489
Lam WW, Chan KW et al (2001) Pre-operative grading of intracranial glioma. Acta Radiol 42:548–554
Boxerman JL, Schmainda KM et al (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(4):859–867
Boxerman JL, Hamberg LM et al (1995) MR contrast due to intravascular magnetic susceptibility perturbations. Magn Reson Med 34:555–566
Donahue KM, HG K et al (2000) Utility of simultaneously acquired gradient-echo and spin-echo cerebral blood volume and morphology maps in brain tumor patients. Magn Reson Med 43:845–853
Schmainda KM, Rand SD et al (2004) Characterization of a first-pass gradient-echo spin-echo method to predict brain tumor grade and angiogenesis. AJNR Am J Neuroradiol 25:1524–1532
Kerbel RS (2008) Tumor angiogenesis. N Engl J Med 358(19):2039–2049
Danchaivijitr N, Waldman AD et al (2008) Low-grade gliomas: do changes in rCBV measurements at longitudinal perfusion-weighted MR imaging predict malignant transformation? Radiology 247(1):170–178
Bobek Billewicz B, Stasik-Pres G et al (2014) Anaplastic transformation of low-grade glioma (WHO II) on magnetic resonance imaging. Folia Neuropathol 52(2):128–140
Henson JW, Ulmer S et al (2008) Brain tumor imaging in clinical trials. AJNR Am J Neuroradiol 29:419–424
Liu X, Tian W et al (2011) MR diffusion tensor and perfusion weighted imaging in preoperative grading of supratentorial nonenhancing gliomas. Neuro Oncol 13:447–455
Quant EC, Wen PY (2011) Response assessment in neuro-oncology. Curr Oncol Rep 13:50–56
Law M, Young RJ et al (2008) Gliomas: predicting time to progression or survival with cerebral blood volume measurement at dynamic susceptibility-weighted contrast-enhancement perfusion MR imaging. Radiology 247:490–498
Batchelor TT, Sorensen AG et al (2007) AZD2171, a pan-VEGF receptor tyrosine kinase inhibitor normalizes tumor vasculature and alleviates edema in glioblastoma patients. Cancer Cell 11(1):83–95
Jain R, Poisson L et al (2013) Genomic mapping and survival prediction in glioblastoma: molecular subclassification strengthened by hemodynamic imaging biomarkers. Radiology 267(1):212–220
Naeini KM, Pope WB et al (2013) Identifying the mesenchymal molecular subtype of glioblastoma using quantitative volumetric analysis of anatomic magnetic resonance images. Neuro Oncol 15(5):626–634
Salibi N, Brown MA (1998) Clinical MR spectroscopy: first principles, vol 1, pp 1–4
Cecil Kim M (2013) Proton magnetic resonance spectroscopy. Technique for the neuroradiologist. Neuroimag Clin N Am 23:381–392
Bulik M, Jancalek R et al (2013) Potential of MR spectroscopy for assessment of glioma grading. Clin Neurol Neurosurg 115:146–153
Bertholdo D, Watcharakorn A, Castillo M (2013) Brain proton magnetic resonance spectroscopy. introduction and overview. Neuroimag Clin N Am 23:359–380
Fayed N, Olmos S et al (2006) Physical basis of magnetic resonance spectroscopy and its application to central nervous system diseases. Am J Appl Sci 3:1836–1845
Nagae-Poetscher LM, Bonekamp D et al (2004) Asymmetry and gender effect in functionally lateralized cortical regions: a proton MRS imaging study. J Magn Reson Imaging 19(1):27–33
Hetherington HP, Mason GF et al (1994) Evaluation of cerebral gray and white matter metabolite difference by spectroscopic imaging at 4.1T. Magn Reson Med 32:565–571
Kreis R, Ernst T et al (1993) Absolute quantitation of water and metabolites in the human brain. II. Metabolite concentrations. J Magn Reson B 102:9–19
Moller-Hartmann W, Hermighaus S et al (2002) Clinical application of proton magnetic resonance spectroscopy in the diagnosis of intracranial mass lesions. Neuroradiology 44:371–381
Warren KE, Frank JA et al (2000) Proton magnetic resonance spectroscopic imaging in children with recurrent primary brain tumors. J Clin Oncol 18:1020–1026
Young GS (2007) Advanced MRI of adult brain tumors. Neurol Clin 25:947–973
Hernandez-Alcoceba R, Saninger L et al (1997) Choline kinase inhibitors as a novel approach for antiproliferative drug design. Oncogene 15:2289–2301
Scholzen T, Gerdes J (2000) The Ki-67 protein: from the known and the unknown. J Cell Physiol 182:311–322
Barbarella G, Ricci R et al (1998) In vivo single voxel 1HMRS of glial brain tumor: correlation with tissue histology and in vitro MRS. Int J Oncol 12:461–468
Shimizu H, Kumabe T et al (2000) Correlation between choline level measured by proton MR spectroscopy and Ki-67 labeling index in gliomas. AJNR Am J Neuroradiol 21:659–665
Kreis R, Ernst T, Ross BD (1993) Development of the human brain: in vivo quantification of metabolite and water content with proton magnetic resonance spectroscopy. Magn Reson Med 30:424–437
Castillo M, Smith JK et al (2000) Correlation of myo-inositol levels and grading of cerebral astrocytomas. AJNR Am J Neuroradiol 21:1645–1649
Brandao LA, Castillo M (2013) Adult brain tumors. Clinical applications of magnetic resonance spectroscopy. Neuroimaging Clin N Am 23:527–555
Esmaeili M, Vettukattil R, Bathen TF (2013) 2-Hydroxyglutarate as a magnetic resonance biomarker for glioma subtyping. Trans Oncol 6(2):92–98
Andronesi OC, Kim GS et al (2012) Detection of 2-Hydroxyglutarate in iDH-mutated glioma patients by in vivo spectral-editing and 2D correlation magnetic resonance spectroscopy. Sci Transl Med 4(116):116ra4
Lazovic J, Soto H et al (2012) Detection of 2-Hydroxyglutaric acid in vivo by proton magnetic resonance spectroscopy in U87 glioma cells overexpressing isocitrate dehydrogenase-1 mutation. Neuro Oncol 14(12):1465–1472
Hwang JH, Egnaczyk GF et al (1998) Proton MR spectroscopic characteristic of pediatric pilocytic astrocytomas. AJNR Am J Neuroradiol 19:535–540
Sutton LN, Wang Z et al (1992) Proton magnetic resonance spectroscopy of pediatric brain tumors. Neurosurgery 31:195–202
Davies NP, Wilson M et al (2008) Identification and characterisation of childhood cerebellar tumours by in vivo proton MRS. NMR Biomed 21:908–918
Harris LN, Davies NP et al (2008) Magnetic resonance spectroscopy in the assessment of pilocytic astrocytomas. Eur J Cancer 44:2640–2647
Furnari FB, Fenton T et al (2007) Malignant astrocytic glioma: genetics, biology, and paths to treatment. Genes Dev 21:2683–2710
Londono A, Castillo M et al (2003) Unusual MR spectro-myo-inositol and glycine levels. AJNR Am J Neuroradiol 24:942–945
Saraf-Lavi E, Bowen BC et al (2003) Proton MR spectroscopy of gliomatosis cerebri: case report of elevated myoinositol with normal choline levels. AJNR Am J Neuroradiol 24:946–951
McKnight TR, Lamborn KR et al (2007) Correlation of magnetic resonance spectroscopic and growth characteristics within Grades II and III gliomas. J Neurosurg 106:660–666
Burger PC, Vogel FS et al (1985) Glioblastoma multiforme and anaplastic astrocytoma. Pathologic criteria and prognostic implications. Cancer 56:1106–1111
Giese A, Bjerkvig R et al (2003) Cost of migration: invasion of malignant gliomas and implications for treatment. J Clin Oncol 21:1624–1636
Howe FA, Barton SJ et al (2003) Metabolic profiles of human brain tumors using quantitative in vivo 1H magnetic resonance spectroscopy. Magn Reson Med 49:223–232
Remy C, Fouilhe N et al (1997) Evidence that mobile lipids detected in rat brain glioma by 1 H nuclear magnetic resonance correspond to lipid droplets. Cancer Res 57:407–414
Raza SM, Lang FF et al (2002) Necrosis and glioblastoma: a friend or a foe? A review and a hypothesis. Neurosurgery 51:2–12 , discussion 12–13
Brat DJ, Van Meir EG (2004) Vaso-occlusive and prothrombotic mechanism associated with tumor hypoxia, necrosis, and accelerated growth in glioblastoma. Laboratory investigation. J Tech Methods Pathol 84:397–405
Olivier L, Olivier C et al (2009) Hypoxia and the malignant glioma microenvironment: regulation and implications for therapy. Curr Mol Pharmacol 2:263–284
Kleihues P (2000) Pathology and genetics of tumours of the nervous system. In: Kleihues P, WK C (eds) World Health Organization classification of tumors. IARC Press, Lyon
Kallenberg K, Bock HC et al (2009) Untreated glioblastoma multiforme: increased myo-inositol and glutamine levels in the contralateral cerebral hemisphere at proton MR spectroscopy. Radiology 253:805–812
Gonzalez-Bonet LG (2008) Stereotactic biopsy versus spectroscopy in cases of gliomas with a high degree of malignancy. A review of the literature. Rev Neurol 47:310–314
Roy B, Gupta KR et al (2013) Utility of multiparametric 3-T MRI for glioma characterization. Neuroradiology 55:603–613
Van Cauter S, De Keyzer F et al (2014) Integrating diffusion kurtosis imaging, dynamic susceptibility-weighted contrast-enhanced MRI, and short echo time chemical shift imaging for grading gliomas. Neurooncology 16(7):1010–1021
Macdonald DR, Cascino T et al (1990) Response criteria for phase II studies of supretentorial malignant glioma. J Clin Oncol 8:1277–1280
Hygino da Cruz LC Jr, Rodriguez I et al (2011) Pseudoprogression and pseudoresponse: imaging challenges in the assessment of posttreatment glioma. AJNR Am J Neuroradiol 32(11):1978–1985
Wen PY, Macdonald DR et al (2010) Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol 28:1963–1972
Cha J, Kim ST et al (2004) Differentiation of tumor progression from pseudoprogression in patients with posttreatment glioblastoma using multiparametric histogram analysis. AJNR Am J Neuroradiol 35(7):1309–1317
Kong DS, Kim ST et al (2011) Diagnostic dilemma of pseudoprogression in the treatment of newly diagnosed glioblastoma the role of assessing relative cerebral blood flow volume and oxygen-6-methylguanine-DNA methyltransferase promoter methylation status. AJNR Am J Neuroradiol 32:382–387
Tsien C, Galban CJ et al (2010) Parametric response map as an imaging biomarker to distinguish progression from pseudoprogression in high-grade glioma. J Clin Oncol 28(13):2293–2299
Norden AD, Young GS et al (2008) Bevacizumab for recurrent malignant gliomas: efficacy, toxicity and patterns of recurrence. Neurology 70:779–787
Gupta A, Young RJ, Karimi S (2011) Isolated diffusion restriction precedes the development of enhancing tumor in a subset of patients with glioblastoma. AJNR Am J Neuroradiol 32:1301–1306
Mong S, Elligson BM et al (2012) Persistent diffusion-restricted lesions in bevacizumab-treated malignant gliomas are associated with improved survival compared with matched controls. AJNR Am J Neuroradiol 33:1763–1770
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Bacci, A., Marucci, G., Budai, C., Sacchetti, F., Agati, R. (2017). 3.0T Imaging of Brain Gliomas. In: Scarabino, T., Pollice, S., Popolizio, T. (eds) High Field Brain MRI. Springer, Cham. https://doi.org/10.1007/978-3-319-44174-0_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-44174-0_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-44173-3
Online ISBN: 978-3-319-44174-0
eBook Packages: MedicineMedicine (R0)