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The role of imaging in the management of adults with diffuse low grade glioma

A systematic review and evidence-based clinical practice guideline

  • Topic Review & Clinical Guidelines
  • Published:
Journal of Neuro-Oncology Aims and scope Submit manuscript

Abstract

Question

What is the optimal imaging technique to be used in the diagnosis of a suspected low grade glioma, specifically: which anatomic imaging sequences are critical for most accurately identifying or diagnosing a low grade glioma (LGG) and do non-anatomic imaging methods and/or sequences add to the diagnostic specificity of suspected low grade gliomas?

Target population

These recommendations apply to adults with a newly diagnosed lesion with a suspected or histopathologically proven LGG.

Recommendation

Level II

In patients with a suspected brain tumor, the minimum magnetic resonance imaging (MRI) exam should be an anatomic exam with both T2 weighted and pre- and post-gadolinium contrast enhanced T1 weighted imaging.

Critical imaging for the identification and diagnosis of low grade glioma

Level II

In patients with a suspected brain tumor, anatomic imaging sequences should include T1 and T2 weighted and Fluid Attenuation Inversion Recovery (FLAIR) MR sequences and will include T1 weighted imaging after the administration of gadolinium based contrast. Computed tomography (CT) can provide additional information regarding calcification or hemorrhage, which may narrow the differential diagnosis. At a minimum, these anatomic sequences can help identify a lesion as well as its location, and potential for surgical intervention.

Improvement of diagnostic specificity with the addition of non-anatomic (physiologic and advanced imaging) to anatomic imaging

Level II

Class II evidence from multiple studies and a significant number of Class III series support the addition of diffusion and perfusion weighted MR imaging in the assessment of suspected LGGs, for the purposes of discriminating the potential for tumor subtypes and identification of suspicion of higher grade diagnoses.

Level III

Multiple series offer Class III evidence to support the potential for magnetic resonance spectroscopy (MRS) and nuclear medicine methods including positron emission tomography and single-photon emission computed tomography imaging to offer additional diagnostic specificity although these are less well defined and their roles in clinical practice are still being defined.

Question

Which imaging sequences or parameters best predict the biological behavior or prognosis for patients with LGG?

Target population

These recommendations apply to adults with a newly diagnosed lesion with a suspected or histopathologically proven LGG.

Recommendation

Anatomic and advanced imaging methods and prognostic stratification

Level III

Multiple series suggest a role for anatomic and advanced sequences to suggest prognostic stratification among low grade gliomas. Perfusion weighted imaging, particularly when obtained as a part of diagnostic evaluation (as recommended above) can play a role in consideration of prognosis. Other imaging sequences remain investigational in terms of their role in consideration of tumor prognosis as there is insufficient evidence to support more formal recommendations as to their use at this time.

Question

What is the optimal imaging technique to be used in the follow-up of a suspected (or biopsy proven) LGG?

Target population

This recommendation applies to adults with a newly diagnosed low grade glioma.

Recommendations

Level II

In patients with a diagnosis of LGG, anatomic imaging sequences should include T2/FLAIR MR sequences and T1 weighted imaging before and after the administration of gadolinium based contrast. Serial imaging should be performed to identify new areas of contrast enhancement or significant change in tumor size, which may signify transformation to a higher grade.

Level III

Advanced imaging utility may depend on tumor subtype. Multicenter clinical trials with larger cohorts are needed. For astrocytic tumors, baseline and longitudinal elevations in tumor perfusion as assessed by dynamic susceptibility contrast perfusion MRI are associated with shorter time to tumor progression, but can be difficult to standardize in clinical practice. For oligodendrogliomas and mixed gliomas, MRS may be helpful for identification of progression.

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References

  1. Prabhu VC, Khaldi A, Barton KP, Melian E, Schneck MJ, Primeau MJ, Lee JM (2010) Management of diffuse low-grade cerebral gliomas. Neurol Clin 28:1037–1059. doi:10.1016/j.ncl.2010.03.022

    Article  PubMed  Google Scholar 

  2. Sanai N, Chang S, Berger MS (2011) Low-grade gliomas in adults. J Neurosurg 115:948–965. doi:10.3171/2011.7.jns101238

    Article  PubMed  Google Scholar 

  3. Pouratian N, Schiff D (2010) Management of low-grade glioma. Curr Neurol Neurosci Rep 10:224–231. doi:10.1007/s11910-010-0105-7

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  4. Gilbert MR, Lang FF (2007) Management of patients with low-grade gliomas. Neurol Clin 25:1073–1088. doi:10.1016/j.ncl.2007.07.007

    Article  PubMed  Google Scholar 

  5. Lote K, Egeland T, Hager B, Skullerud K, Hirschberg H (1998) Prognostic significance of CT contrast enhancement within histological subgroups of intracranial glioma. J Neurooncol 40:161–170

    Article  CAS  PubMed  Google Scholar 

  6. Berger MS, Rostomily RC (1997) Low grade gliomas: functional mapping resection strategies, extent of resection, and outcome. J Neurooncol 34:85–101

    Article  CAS  PubMed  Google Scholar 

  7. Mihara F, Numaguchi Y, Rothman M, Sato S, Fiandaca MS (1995) MR imaging of adult supratentorial astrocytomas: an attempt of semi-automatic grading. Radiat Med 13:5–9

    CAS  PubMed  Google Scholar 

  8. Price SJ (2010) Advances in imaging low-grade gliomas. Adv Tech Stand Neurosurg 35:1–34

    PubMed  Google Scholar 

  9. Law M, Yang S, Wang H, Babb JS, Johnson G, Cha S, Knopp EA, Zagzag D (2003) Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging. Am J Neuroradiol 24:1989–1998

    PubMed  Google Scholar 

  10. Kim MJ, Kim HS, Kim JH, Cho KG, Kim SY (2008) Diagnostic accuracy and interobserver variability of pulsed arterial spin labeling for glioma grading. Acta Radiol 49:450–457. doi:10.1080/02841850701881820

    Article  CAS  PubMed  Google Scholar 

  11. Nguyen TB, Cron GO, Mercier JF, Foottit C, Torres CH, Chakraborty S, Woulfe J, Jansen GH, Caudrelier JM, Sinclair J, Hogan MJ, Thornhill RE, Cameron IG (2012) Diagnostic accuracy of dynamic contrast-enhanced MR imaging using a phase-derived vascular input function in the preoperative grading of gliomas. AJNR Am J Neuroradiol 33:1539–1545. doi:10.3174/ajnr.A3012

    Article  CAS  PubMed  Google Scholar 

  12. Arvinda HR, Kesavadas C, Sarma PS, Thomas B, Radhakrishnan VV, Gupta AK, Kapilamoorthy TR, Nair S (2009) Glioma grading: sensitivity, specificity, positive and negative predictive values of diffusion and perfusion imaging. J Neurooncol 94:87–96. doi:10.1007/s11060-009-9807-6

    Article  CAS  PubMed  Google Scholar 

  13. Warmuth C, Gunther M, Zimmer C (2003) Quantification of blood flow in brain tumors: comparison of arterial spin labeling and dynamic susceptibility-weighted contrast-enhanced MR imaging. Radiology 228:523–532. doi:10.1148/radiol.2282020409

    Article  PubMed  Google Scholar 

  14. Batra A, Tripathi RP, Singh AK (2004) Perfusion magnetic resonance imaging and magnetic resonance spectroscopy of cerebral gliomas showing imperceptible contrast enhancement on conventional magnetic resonance imaging. Australas Radiol 48:324–332. doi:10.1111/j.0004-8461.2004.01315.x

    Article  CAS  PubMed  Google Scholar 

  15. Hakyemez B, Erdogan C, Ercan I, Ergin N, Uysal S, Atahan S (2005) High-grade and low-grade gliomas: differentiation by using perfusion MR imaging. Clin Radiol 60:493–502. doi:10.1016/j.crad.2004.09.009

    Article  CAS  PubMed  Google Scholar 

  16. Fan GG, Deng QL, Wu ZH, Guo QY (2006) Usefulness of diffusion/perfusion-weighted MRI in patients with non-enhancing supratentorial brain gliomas: a valuable tool to predict tumour grading? Br J Radiol 79:652–658. doi:10.1259/bjr/25349497

    Article  CAS  PubMed  Google Scholar 

  17. Pauliah M, Saxena V, Haris M, Husain N, Rathore RK, Gupta RK (2007) Improved T(1)-weighted dynamic contrast-enhanced MRI to probe microvascularity and heterogeneity of human glioma. Magn Reson Imaging 25:1292–1299. doi:10.1016/j.mri.2007.03.027

    Article  PubMed  Google Scholar 

  18. Morita N, Wang S, Chawla S, Poptani H, Melhem ER (2010) Dynamic susceptibility contrast perfusion weighted imaging in grading of nonenhancing astrocytomas. J Magn Reson Imaging 32:803–808. doi:10.1002/jmri.22324

    Article  PubMed  Google Scholar 

  19. Liu X, Tian W, Kolar B, Yeaney GA, Qiu X, Johnson MD, Ekholm S (2011) MR diffusion tensor and perfusion-weighted imaging in preoperative grading of supratentorial nonenhancing gliomas. Neuro Oncol 13:447–455. doi:10.1093/neuonc/noq197

    Article  PubMed Central  PubMed  Google Scholar 

  20. Law M, Oh S, Babb JS, Wang E, Inglese M, Zagzag D, Knopp EA, Johnson G (2006) Low-grade gliomas: dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging—prediction of patient clinical response. Radiology 238:658–667. doi:10.1148/radiol.2382042180

    Article  PubMed  Google Scholar 

  21. Ding B, Ling HW, Chen KM, Jiang H, Zhu YB (2006) Comparison of cerebral blood volume and permeability in preoperative grading of intracranial glioma using CT perfusion imaging. Neuroradiology 48:773–781. doi:10.1007/s00234-006-0120-1

    Article  PubMed  Google Scholar 

  22. Maia AC Jr, Malheiros SM, da Rocha AJ, da Silva CJ, Gabbai AA, Ferraz FA, Stavale JN (2005) MR cerebral blood volume maps correlated with vascular endothelial growth factor expression and tumor grade in nonenhancing gliomas. Am J Neuroradiol 26:777–783

    PubMed  Google Scholar 

  23. Lev MH, Ozsunar Y, Henson JW, Rasheed AA, Barest GD, Harsh GRT, Fitzek MM, Chiocca EA, Rabinov JD, Csavoy AN, Rosen BR, Hochberg FH, Schaefer PW, Gonzalez RG (2004) 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]. Am J Neuroradiol 25:214–221

    PubMed  Google Scholar 

  24. Spampinato MV, Smith JK, Kwock L, Ewend M, Grimme JD, Camacho DL, Castillo M (2007) Cerebral blood volume measurements and proton MR spectroscopy in grading of oligodendroglial tumors. Am J Roentgenol 188:204–212. doi:10.2214/ajr.05.1177

    Article  Google Scholar 

  25. Emblem KE, Scheie D, Due-Tonnessen P, Nedregaard B, Nome T, Hald JK, Beiske K, Meling TR, Bjornerud A (2008) Histogram analysis of MR imaging-derived cerebral blood volume maps: combined glioma grading and identification of low-grade oligodendroglial subtypes. Am J Neuroradiol 29:1664–1670. doi:10.3174/ajnr.A1182

    Article  CAS  PubMed  Google Scholar 

  26. Narang J, Jain R, Scarpace L, Saksena S, Schultz LR, Rock JP, Rosenblum M, Patel SC, Mikkelsen T (2011) Tumor vascular leakiness and blood volume estimates in oligodendrogliomas using perfusion CT: an analysis of perfusion parameters helping further characterize genetic subtypes as well as differentiate from astroglial tumors. J Neurooncol 102:287–293. doi:10.1007/s11060-010-0317-3

    Article  PubMed  Google Scholar 

  27. Chaskis C, Stadnik T, Michotte A, Van Rompaey K, D’Haens J (2006) Prognostic value of perfusion-weighted imaging in brain glioma: a prospective study. Acta neurochirurgica 148: 277-285; discussion 285 doi:10.1007/s00701-005-0718-9

  28. Alvarez-Linera J, Benito-Leon J, Escribano J, Rey G (2008) Predicting the histopathological grade of cerebral gliomas using high b value MR DW imaging at 3-tesla. J Neuroimaging 18:276–281. doi:10.1111/j.1552-6569.2008.00263.x

    Article  PubMed  Google Scholar 

  29. Server A, Kulle B, Gadmar OB, Josefsen R, Kumar T, Nakstad PH (2011) Measurements of diagnostic examination performance using quantitative apparent diffusion coefficient and proton MR spectroscopic imaging in the preoperative evaluation of tumor grade in cerebral gliomas. Eur J Radiol 80:462–470. doi:10.1016/j.ejrad.2010.07.017

    Article  PubMed  Google Scholar 

  30. Tozer DJ, Jager HR, Danchaivijitr N, Benton CE, Tofts PS, Rees JH, Waldman AD (2007) Apparent diffusion coefficient histograms may predict low-grade glioma subtype. NMR Biomed 20:49–57. doi:10.1002/nbm.1091

    Article  PubMed  Google Scholar 

  31. Khayal IS, McKnight TR, McGue C, Vandenberg S, Lamborn KR, Chang SM, Cha S, Nelson SJ (2009) Apparent diffusion coefficient and fractional anisotropy of newly diagnosed grade II gliomas. NMR Biomed 22:449–455. doi:10.1002/nbm.1357

    Article  PubMed Central  PubMed  Google Scholar 

  32. Diffusion Weighted Sequence (2015). http://www.mr-tip.com/serv1.php?type=db1&dbs=Diffusion%20Weighted%20Sequence. Accessed March 24 2015

  33. Diffusion (2015). http://www.mr-tip.com/serv1.php?type=db1&dbs=Diffusion. Accessed March 24 2015

  34. Lee EJ, Lee SK, Agid R, Bae JM, Keller A, Terbrugge K (2008) Preoperative grading of presumptive low-grade astrocytomas on MR imaging: diagnostic value of minimum apparent diffusion coefficient. Am J Neuroradiol 29:1872–1877. doi:10.3174/ajnr.A1254

    Article  CAS  PubMed  Google Scholar 

  35. Jakab A, Molnar P, Emri M, Berenyi E (2011) Glioma grade assessment by using histogram analysis of diffusion tensor imaging-derived maps. Neuroradiology 53:483–491. doi:10.1007/s00234-010-0769-3

    Article  PubMed  Google Scholar 

  36. Jolapara M, Patro SN, Kesavadas C, Saini J, Thomas B, Gupta AK, Bodhey N, Radhakrishnan VV (2011) Can diffusion tensor metrics help in preoperative grading of diffusely infiltrating astrocytomas? A retrospective study of 36 cases. Neuroradiology 53:63–68. doi:10.1007/s00234-010-0761-y

    Article  PubMed  Google Scholar 

  37. White ML, Zhang Y, Yu F, Jaffar Kazmi SA (2011) Diffusion tensor MR imaging of cerebral gliomas: evaluating fractional anisotropy characteristics. Am J Neuroradiol 32:374–381. doi:10.3174/ajnr.A2267

    Article  CAS  PubMed  Google Scholar 

  38. Bulakbasi N, Kocaoglu M, Ors F, Tayfun C, Ucoz T (2003) Combination of single-voxel proton MR spectroscopy and apparent diffusion coefficient calculation in the evaluation of common brain tumors. AJNR 24:225–233

    PubMed  Google Scholar 

  39. Senft C, Hattingen E, Pilatus U, Franz K, Schanzer A, Lanfermann H, Seifert V, Gasser T (2009) Diagnostic value of proton magnetic resonance spectroscopy in the noninvasive grading of solid gliomas: comparison of maximum and mean choline values. Neurosurgery 65: 908-913; discussion 913 doi:10.1227/01.neu.0000356982.82378.ba

  40. Zeng Q, Liu H, Zhang K, Li C, Zhou G (2011) Noninvasive evaluation of cerebral glioma grade by using multivoxel 3D proton MR spectroscopy. Magn Reson Imaging 29:25–31. doi:10.1016/j.mri.2010.07.017

    Article  PubMed  Google Scholar 

  41. Zou QG, Xu HB, Liu F, Guo W, Kong XC, Wu Y (2011) In the assessment of supratentorial glioma grade: the combined role of multivoxel proton MR spectroscopy and diffusion tensor imaging. Clin Radiol 66:953–960. doi:10.1016/j.crad.2011.05.001

    Article  PubMed  Google Scholar 

  42. Liu ZL, Zhou Q, Zeng QS, Li CF, Zhang K (2012) Noninvasive evaluation of cerebral glioma grade by using diffusion-weighted imaging-guided single-voxel proton magnetic resonance spectroscopy. J Int Med Res 40(1):76–84

    Article  PubMed  Google Scholar 

  43. Delbeke D, Meyerowitz C, Lapidus RL, Maciunas RJ, Jennings MT, Moots PL, Kessler RM (1995) Optimal cutoff levels of F-18 fluorodeoxyglucose uptake in the differentiation of low-grade from high-grade brain tumors with PET. Radiology 195:47–52

    Article  CAS  PubMed  Google Scholar 

  44. Stockhammer F, Thomale UW, Plotkin M, Hartmann C, Von Deimling A (2007) Association between fluorine-18-labeled fluorodeoxyglucose uptake and 1p and 19q loss of heterozygosity in World Health Organization Grade II gliomas. J Neurosurg 106:633-637 doi:10.3171/jns.2007.106.4.633

    Article  CAS  PubMed  Google Scholar 

  45. Singhal T, Narayanan TK, Jacobs MP, Bal C, Mantil JC (2012) 11C-methionine PET for grading and prognostication in gliomas: a comparison study with 18F-FDG PET and contrast enhancement on MRI. J Nucl Med 53:1709–1715. doi:10.2967/jnumed.111.102533

    Article  PubMed  Google Scholar 

  46. Roessler K, Nasel C, Czech T, Matula C, Lassmann H, Koos WT (1996) Histological heterogeneity of neuroradiologically suspected adult low grade gliomas detected by Xenon enhanced computerized tomography (CT). Acta Neurochir (Wien) 138:1341–1347

    Article  CAS  Google Scholar 

  47. Kunz M, Thon N, Eigenbrod S, Hartmann C, Egensperger R, Herms J, Geisler J, la Fougere C, Lutz J, Linn J, Kreth S, von Deimling A, Tonn JC, Kretzschmar HA, Popperl G, Kreth FW (2011) Hot spots in dynamic (18)FET-PET delineate malignant tumor parts within suspected WHO grade II gliomas. Neuro Oncol 13:307–316. doi:10.1093/neuonc/noq196

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  48. Calcagni ML, Galli G, Giordano A, Taralli S, Anile C, Niesen A, Baum RP (2011) Dynamic O-(2-[18F]fluoroethyl)-l-tyrosine (F-18 FET) PET for glioma grading: assessment of individual probability of malignancy. Clin Nucl Med 36:841–847. doi:10.1097/RLU.0b013e3182291b40

    Article  PubMed  Google Scholar 

  49. Shibamoto Y, Kitakabu Y, Takahashi M, Yamashita J, Oda Y, Kikuchi H, Abe M (1993) Supratentorial low-grade astrocytoma. Correlation of computed tomography findings with effect of radiation therapy and prognostic variables. Cancer 72:190–195

    Article  CAS  PubMed  Google Scholar 

  50. Schuurman PR, Troost D, Verbeeten B Jr, Bosch DA (1997) 5-year survival and clinical prognostic factors in progressive supratentorial diffuse “low-grade” astrocytoma: a retrospective analysis of 46 cases. Acta Neurochir (Wien) 139:2–7

    Article  CAS  Google Scholar 

  51. Bauman G, Lote K, Larson D, Stalpers L, Leighton C, Fisher B, Wara W, MacDonald D, Stitt L, Cairncross JG (1999) Pretreatment factors predict overall survival for patients with low-grade glioma: a recursive partitioning analysis. Int J Radiat Oncol Biol Phys 45:923–929

    Article  CAS  PubMed  Google Scholar 

  52. Hattingen E, Raab P, Franz K, Lanfermann H, Setzer M, Gerlach R, Zanella FE, Pilatus U (2008) Prognostic value of choline and creatine in WHO grade II gliomas. Neuroradiology 50:759–767. doi:10.1007/s00234-008-0409-3

    Article  PubMed  Google Scholar 

  53. Dhermain F, Saliou G, Parker F, Page P, Hoang-Xuan K, Lacroix C, Tournay E, Bourhis J, Ducreux D (2010) Microvascular leakage and contrast enhancement as prognostic factors for recurrence in unfavorable low-grade gliomas. J Neurooncol 97:81–88. doi:10.1007/s11060-009-9992-3

    Article  CAS  PubMed  Google Scholar 

  54. Caseiras GB, Chheang S, Babb J, Rees JH, Pecerrelli N, Tozer DJ, Benton C, Zagzag D, Johnson G, Waldman AD, Jager HR, Law M (2010) Relative cerebral blood volume measurements of low-grade gliomas predict patient outcome in a multi-institution setting. Eur J Radiol 73:215–220. doi:10.1016/j.ejrad.2008.11.005

    Article  PubMed  Google Scholar 

  55. Ribom D, Smits A (2005) Baseline 11C-methionine PET reflects the natural course of grade 2 oligodendrogliomas. Neurol Res 27:516–521. doi:10.1179/016164105x39833

    PubMed  Google Scholar 

  56. Smits A, Westerberg E, Ribom D (2008) Adding 11C-methionine PET to the EORTC prognostic factors in grade 2 gliomas. Eur J Nucl Med Mol Imaging 35:65–71. doi:10.1007/s00259-007-0531-1

    Article  CAS  PubMed  Google Scholar 

  57. Floeth FW, Pauleit D, Sabel M, Stoffels G, Reifenberger G, Riemenschneider MJ, Jansen P, Coenen HH, Steiger HJ, Langen KJ (2007) Prognostic value of O-(2-18F-fluoroethyl)-l-tyrosine PET and MRI in low-grade glioma. J Nucl Med 48:519–527

    Article  CAS  PubMed  Google Scholar 

  58. Afra D, Osztie E (1997) Histologically confirmed changes on CT of reoperated low-grade astrocytomas. Neuroradiology 39:804–810

    Article  CAS  PubMed  Google Scholar 

  59. Bauman G, Pahapill P, Macdonald D, Fisher B, Leighton C, Cairncross G (1999) Low grade glioma: a measuring radiographic response to radiotherapy. Can J Neurol Sci 26:18–22

    CAS  PubMed  Google Scholar 

  60. Mandonnet E, Delattre JY, Tanguy ML, Swanson KR, Carpentier AF, Duffau H, Cornu P, Van Effenterre R, Alvord EC Jr, Capelle L (2003) Continuous growth of mean tumor diameter in a subset of grade II gliomas. Ann Neurol 53:524–528. doi:10.1002/ana.10528

    Article  PubMed  Google Scholar 

  61. Pallud J, Mandonnet E, Duffau H, Kujas M, Guillevin R, Galanaud D, Taillandier L, Capelle L (2006) Prognostic value of initial magnetic resonance imaging growth rates for World Health Organization grade II gliomas. Ann Neurol 60:380–383. doi:10.1002/ana.20946

    Article  PubMed  Google Scholar 

  62. Ricard D, Kaloshi G, Amiel-Benouaich A, Lejeune J, Marie Y, Mandonnet E, Kujas M, Mokhtari K, Taillibert S, Laigle-Donadey F, Carpentier AF, Omuro A, Capelle L, Duffau H, Cornu P, Guillevin R, Sanson M, Hoang-Xuan K, Delattre JY (2007) Dynamic history of low-grade gliomas before and after temozolomide treatment. Ann Neurol 61:484–490. doi:10.1002/ana.21125

    Article  CAS  PubMed  Google Scholar 

  63. Mandonnet E, Pallud J, Fontaine D, Taillandier L, Bauchet L, Peruzzi P, Guyotat J, Bernier V, Baron MH, Duffau H, Capelle L (2010) Inter- and intrapatients comparison of WHO grade II glioma kinetics before and after surgical resection. Neurosurg Rev 33:91–96. doi:10.1007/s10143-009-0229-x

    Article  PubMed  Google Scholar 

  64. Brasil Caseiras G, Ciccarelli O, Altmann DR, Benton CE, Tozer DJ, Tofts PS, Yousry TA, Rees J, Waldman AD, Jager HR (2009) Low-grade gliomas: six-month tumor growth predicts patient outcome better than admission tumor volume, relative cerebral blood volume, and apparent diffusion coefficient. Radiology 253:505–512. doi:10.1148/radiol.2532081623

    Article  PubMed  Google Scholar 

  65. Rees J, Watt H, Jager HR, Benton C, Tozer D, Tofts P, Waldman A (2009) Volumes and growth rates of untreated adult low-grade gliomas indicate risk of early malignant transformation. Eur J Radiol 72:54–64. doi:10.1016/j.ejrad.2008.06.013

    Article  PubMed  Google Scholar 

  66. van den Bent MJ, Wefel JS, Schiff D, Taphoorn MJ, Jaeckle K, Junck L, Armstrong T, Choucair A, Waldman AD, Gorlia T, Chamberlain M, Baumert BG, Vogelbaum MA, Macdonald DR, Reardon DA, Wen PY, Chang SM, Jacobs AH (2011) Response assessment in neuro-oncology (a report of the RANO group): assessment of outcome in trials of diffuse low-grade gliomas. Lancet Oncol 12:583–593. doi:10.1016/s1470-2045(11)70057-2

    Article  PubMed  Google Scholar 

  67. Voglein J, Tuttenberg J, Weimer M, Gerigk L, Kauczor HU, Essig M, Weber MA (2011) Treatment monitoring in gliomas: comparison of dynamic susceptibility-weighted contrast-enhanced and spectroscopic MRI techniques for identifying treatment failure. Invest Radiol 46:390–400. doi:10.1097/RLI.0b013e31820e1511

    Article  PubMed  Google Scholar 

  68. Danchaivijitr N, Waldman AD, Tozer DJ, Benton CE, Brasil Caseiras G, Tofts PS, Rees JH, Jager HR (2008) Low-grade gliomas: do changes in rCBV measurements at longitudinal perfusion-weighted MR imaging predict malignant transformation? Radiology 247:170–178. doi:10.1148/radiol.2471062089

    Article  PubMed  Google Scholar 

  69. Hlaihel C, Guilloton L, Guyotat J, Streichenberger N, Honnorat J, Cotton F (2010) Predictive value of multimodality MRI using conventional, perfusion, and spectroscopy MR in anaplastic transformation of low-grade oligodendrogliomas. J Neurooncol 97:73–80. doi:10.1007/s11060-009-9991-4

    Article  PubMed  Google Scholar 

  70. Reijneveld JC, van der Grond J, Ramos LM, Bromberg JE, Taphoorn MJ (2005) Proton MRS imaging in the follow-up of patients with suspected low-grade gliomas. Neuroradiology 47:887–891. doi:10.1007/s00234-005-1435-z

    Article  CAS  PubMed  Google Scholar 

  71. Arbizu J, Tejada S, Marti-Climent JM, Diez-Valle R, Prieto E, Quincoces G, Vigil C, Idoate MA, Zubieta JL, Penuelas I, Richter JA (2012) Quantitative volumetric analysis of gliomas with sequential MRI and (1)(1)C-methionine PET assessment: patterns of integration in therapy planning. Eur J Nucl Med Mol Imaging 39:771–781. doi:10.1007/s00259-011-2049-9

    Article  PubMed  Google Scholar 

  72. Imani F, Boada FE, Lieberman FS, Davis DK, Deeb EL, Mountz JM (2012) Comparison of proton magnetic resonance spectroscopy with fluorine-18 2-fluoro-deoxyglucose positron emission tomography for assessment of brain tumor progression. J Neuroimaging 22:184–190. doi:10.1111/j.1552-6569.2010.00561.x

    Article  PubMed Central  PubMed  Google Scholar 

  73. Santra A, Kumar R, Sharma P, Bal C, Julka PK, Malhotra A (2011) F-18 FDG PET-CT for predicting survival in patients with recurrent glioma: a prospective study. Neuroradiology 53:1017–1024. doi:10.1007/s00234-011-0898-3

    Article  PubMed  Google Scholar 

  74. Soffietti R, Baumert BG, Bello L, von Deimling A, Duffau H, Frenay M, Grisold W, Grant R, Graus F, Hoang-Xuan K, Klein M, Melin B, Rees J, Siegal T, Smits A, Stupp R, Wick W (2010) Guidelines on management of low-grade gliomas: report of an EFNS-EANO Task Force. Eur J Neurol 17:1124-1133 doi:10.1111/j.1468-1331.2010.03151.x

    Article  CAS  PubMed  Google Scholar 

  75. Shaw EG , Berkey B, Coons SW, Bullard D, Brachman D, Buckner JC, Stelzer KJ, Barger GR, Brown PD, Gilbert MR, Mehta M (2008) Recurrence following neurosurgeon-determined gross-total resection of adult supratentorial low-grade glioma: results of a prospective clinical trial. J Neurosurg 109(5):835–841

    Article  PubMed  Google Scholar 

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Acknowledgments

We acknowledge the significant contributions of Laura Mitchell, Senior Manager of Guidelines for the CNS, and also the AANS/CNS Joint Guidelines Committee (JGC) for their review, comments and suggestions, as well as Anne Woznica, Medical Research Librarian. We also acknowledge the following individual JGC members for their contributions throughout the review process: Kevin Cockroft, MD, Sepideh Amin-Hanjani, MD, Kimon Bekelis, MD, Isabelle Germano, MD, Daniel Hoh, MD, Steven Hwang, MD, Cheerag Dipakkumar Upadhyaya, MD, Christopher Winfree, MD, and Brad Zacharia, MD.

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Correspondence to Sarah Jost Fouke.

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Dr. Kalkanis is a consultant for Arbor and Varian. Dr. Olson is a consultant for the American Cancer Society; has received research funding from the National Cancer Institute, Genentech, and Millenium; and has received investigational drug provision from Merck.

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This research did not involve human or animal subjects and thus no informed consent was required.

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Fouke, S.J., Benzinger, T., Gibson, D. et al. The role of imaging in the management of adults with diffuse low grade glioma. J Neurooncol 125, 457–479 (2015). https://doi.org/10.1007/s11060-015-1908-9

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  • DOI: https://doi.org/10.1007/s11060-015-1908-9

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