, Volume 58, Issue 4, pp 339–350 | Cite as

Optimal differentiation of high- and low-grade glioma and metastasis: a meta-analysis of perfusion, diffusion, and spectroscopy metrics

  • Jurgita UsinskieneEmail author
  • Agne Ulyte
  • Atle Bjørnerud
  • Jonas Venius
  • Vasileios K. Katsaros
  • Ryte Rynkeviciene
  • Simona Letautiene
  • Darius Norkus
  • Kestutis Suziedelis
  • Saulius Rocka
  • Andrius Usinskas
  • Eduardas Aleknavicius
Diagnostic Neuroradiology



To perform a meta-analysis of advanced magnetic resonance imaging (MRI) metrics, including relative cerebral blood volume (rCBV), normalized apparent diffusion coefficient (nADC), and spectroscopy ratios choline/creatine (Cho/Cr) and choline/N-acetyl aspartate (Cho/NAA), for the differentiation of high- and low-grade gliomas (HGG, LGG) and metastases (MTS).


For systematic review, 83 articles (dated 2000–2013) were selected from the NCBI database. Twenty-four, twenty-two, and eight articles were included respectively for spectroscopy, rCBV, and nADC meta-analysis. In the meta-analysis, we calculated overall means for rCBV, nADC, Cho/Cr (short TE—from 20 to 35 ms, medium—from 135 to 144 ms), and Cho/NAA for the HGG, LGG, and MTS groups. We used random effects model to obtain weighted averages and select thresholds.


Overall means (with 95 % CI) for rCBV, nADC, Cho/Cr (short and medium echo time, TE), and Cho/NAA were: for HGG 5.47 (4.78–6.15), 1.38 (1.16–1.60), 2.40 (1.67–3.13), 3.27 (2.78–3.77), and 4.71 (3.24–6.19); for LGG 2.00 (1.71–2.28), 1.61 (1.36–1.87), 1.46 (1.20–1.72), 1.71 (1.49–1.93), and 2.36 (1.50–3.23); for MTS 5.06 (3.85–6.27), 1.35 (1.06–1.64), 1.89 (1.72–2.06), 3.14 (1.57–4.72), (Cho/NAA was not available). LGG had significantly lower rCBV, Cho/Cr, and Cho/NAA values than HGG or MTS. No significant differences were found for nADC.


Best differentiation between HGG and LGG is obtained from rCBV, Cho/Cr, and Cho/NAA metrics. MTS could not be reliably distinguished from HGG by the methods investigated.


Brain tumor Magnetic resonance spectroscopy Diffusion magnetic resonance imaging Perfusion magnetic resonance imaging Meta-analysis 


Compliance with ethical standards

We declare that this manuscript is a meta-analysis study based on previous published studies and does not contain our original clinical studies or patient data.

Conflict of interest

We declare that we have no conflict of interest.


  1. 1.
    Covarrubias DJ, Rosen BR, Lev MH (2004) Dynamic magnetic resonance perfusion imaging of brain tumors. Oncologist 9(5):528–537. doi: 10.1634/theoncologist.9-5-528 CrossRefPubMedGoogle Scholar
  2. 2.
    White paper on imaging biomarkers (2010). Insights into imaging. 1(2):42–45. doi: 10.1007/s13244-010-0025-8
  3. 3.
    Danielsen ER (2012) MRS: Ingredients and recipes. Aktinotexnologia. 26–37Google Scholar
  4. 4.
    Ostergaard L, Hochberg FH, Rabinov JD, Sorensen AG, Lev M, Kim L, Weisskoff RM, Gonzalez RG, Gyldensted C, Rosen BR (1999) Early changes measured by magnetic resonance imaging in cerebral blood flow, blood volume, and blood-brain barrier permeability following dexamethasone treatment in patients with brain tumors. J Neurosurg 90(2):300–305. doi: 10.3171/jns.1999.90.2.0300 CrossRefPubMedGoogle Scholar
  5. 5.
    Moher D, Liberati A, Tetzlaff J, Altman DG (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ (Clin Res Ed) 339:b2535. doi: 10.1136/bmj.b2535 CrossRefGoogle Scholar
  6. 6.
    Thomsen H, Steffensen E, Larsson EM (2012) Perfusion MRI (dynamic susceptibility contrast imaging) with different measurement approaches for the evaluation of blood flow and blood volume in human gliomas. Acta Radiol 53(1):95–101. doi: 10.1258/ar.2011.110242 CrossRefPubMedGoogle Scholar
  7. 7.
    Ogura A, Tamura T, Ozaki M, Doi T, Fujimoto K, Miyati T, Ito Y, Maeda F, Tarewaki H, Takahashi M (2015) Apparent diffusion coefficient value is not dependent on magnetic resonance systems and field strength under fixed imaging parameters in brain. J Comput Assist Tomo 39(5):760–765. doi: 10.1097/RCT.0000000000000266 CrossRefGoogle Scholar
  8. 8.
    Abramson JH (2011) WINPEPI updated: computer programs for epidemiologists, and their teaching potential. Epidemiol Perspect Innov EP+I 8(1):1. doi: 10.1186/1742-5573-8-1 CrossRefPubMedGoogle Scholar
  9. 9.
    Higgins JP, Thompson SG (2002) Quantifying heterogeneity in a meta-analysis. Stat Med 21(11):1539–1558. doi: 10.1002/sim.1186 CrossRefPubMedGoogle Scholar
  10. 10.
    Katostaras T, Katostara N (2013) Area of the ROC curve when one point is available. St Heal T 190:219–221Google Scholar
  11. 11.
    Gaudino S, Di Lella GM, Russo R, Lo Russo VS, Piludu F, Quaglio FR, Gualano MR, De Waure C, Colosimo C (2012) Magnetic resonance imaging of solitary brain metastases: main findings of nonmorphological sequences. Radiol Med 117(7):1225–1241. doi: 10.1007/s11547-012-0846-2 CrossRefPubMedGoogle Scholar
  12. 12.
    Di Costanzo A, Scarabino T, Trojsi F, Giannatempo GM, Popolizio T, Catapano D, Bonavita S, Maggialetti N, Tosetti M, Salvolini U, d'Angelo VA, Tedeschi G (2006) Multiparametric 3T MR approach to the assessment of cerebral gliomas: tumor extent and malignancy. Neuroradiology 48(9):622–631. doi: 10.1007/s00234-006-0102-3 CrossRefPubMedGoogle Scholar
  13. 13.
    Chiang IC, Kuo YT, Lu CY, Yeung KW, Lin WC, Sheu FO, Liu GC (2004) Distinction between high-grade gliomas and solitary metastases using peritumoral 3-T magnetic resonance spectroscopy, diffusion, and perfusion imagings. Neuroradiology 46(8):619–627. doi: 10.1007/s00234-004-1246-7 CrossRefPubMedGoogle Scholar
  14. 14.
    Yang D, Korogi Y, Sugahara T, Kitajima M, Shigematsu Y, Liang L, Ushio Y, Takahashi M (2002) Cerebral gliomas: prospective comparison of multivoxel 2D chemical-shift imaging proton MR spectroscopy, echoplanar perfusion and diffusion-weighted MRI. Neuroradiology 44(8):656–666. doi: 10.1007/s00234-002-0816-9 CrossRefPubMedGoogle Scholar
  15. 15.
    Zonari P, Baraldi P, Crisi G (2007) Multimodal MRI in the characterization of glial neoplasms: the combined role of single-voxel MR spectroscopy, diffusion imaging and echo-planar perfusion imaging. Neuroradiology 49(10):795–803. doi: 10.1007/s00234-007-0253-x CrossRefPubMedGoogle Scholar
  16. 16.
    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. AJNR Am J Neuroradiol 24(10):1989–1998PubMedGoogle Scholar
  17. 17.
    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(3):324–332. doi: 10.1111/j.0004-8461.2004.01315.x CrossRefPubMedGoogle Scholar
  18. 18.
    Fayed N, Modrego PJ (2005) The contribution of magnetic resonance spectroscopy and echoplanar perfusion-weighted MRI in the initial assessment of brain tumours. J Neuro-Oncol 72(3):261–265. doi: 10.1007/s11060-004-2180-6 CrossRefGoogle Scholar
  19. 19.
    Hourani R, Brant LJ, Rizk T, Weingart JD, Barker PB, Horska A (2008) Can proton MR spectroscopic and perfusion imaging differentiate between neoplastic and nonneoplastic brain lesions in adults? AJNR Am J Neuroradiol 29(2):366–372. doi: 10.3174/ajnr.A0810 CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Fayed N, Davila J, Medrano J, Olmos S (2008) Malignancy assessment of brain tumours with magnetic resonance spectroscopy and dynamic susceptibility contrast MRI. Eur J Radiol 67(3):427–433. doi: 10.1016/j.ejrad.2008.02.039 CrossRefPubMedGoogle Scholar
  21. 21.
    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 Neuro-Oncol 97(1):73–80. doi: 10.1007/s11060-009-9991-4 CrossRefGoogle Scholar
  22. 22.
    Weber MA, Vogt-Schaden M, Bossert O, Giesel FL, Kauczor HU, Essig M (2007) MR perfusion and spectroscopic imaging in WHO grade II astrocytomas. Radiologe 47(9):812–818. doi: 10.1007/s00117-006-1406-3 CrossRefPubMedGoogle Scholar
  23. 23.
    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. AJR Am J Roentgenol 188(1):204–212. doi: 10.2214/ajr.05.1177 CrossRefPubMedGoogle Scholar
  24. 24.
    Guillevin R, Menuel C, Abud L, Costalat R, Capelle L, Hoang-Xuan K, Habas C, Chiras J, Vallee JN (2012) Proton MR spectroscopy in predicting the increase of perfusion MR imaging for WHO grade II gliomas. JMRI-J Magn Reson Im 35(3):543–550. doi: 10.1002/jmri.22862 CrossRefGoogle Scholar
  25. 25.
    Rollin N, Guyotat J, Streichenberger N, Honnorat J, Tran Minh VA, Cotton F (2006) Clinical relevance of diffusion and perfusion magnetic resonance imaging in assessing intra-axial brain tumors. Neuroradiology 48(3):150–159. doi: 10.1007/s00234-005-0030-7 CrossRefPubMedGoogle Scholar
  26. 26.
    Muccio CF, Esposito G, Bartolini A, Cerase A (2008) Cerebral abscesses and necrotic cerebral tumours: differential diagnosis by perfusion-weighted magnetic resonance imaging. Radiol Med 113(5):747–757. doi: 10.1007/s11547-008-0254-9 CrossRefPubMedGoogle Scholar
  27. 27.
    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(2):505–512. doi: 10.1148/radiol.2532081623 CrossRefPubMedGoogle Scholar
  28. 28.
    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-Oncology 13(4):447–455. doi: 10.1093/neuonc/noq197 CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Bai X, Zhang Y, Liu Y, Han T, Liu L (2011) Grading of supratentorial astrocytic tumors by using the difference of ADC value. Neuroradiology 53(7):533–539. doi: 10.1007/s00234-011-0846-2 CrossRefPubMedGoogle Scholar
  30. 30.
    Moon WJ, Choi JW, Roh HG, Lim SD, Koh YC (2012) Imaging parameters of high grade gliomas in relation to the MGMT promoter methylation status: the CT, diffusion tensor imaging, and perfusion MR imaging. Neuroradiology 54(6):555–563. doi: 10.1007/s00234-011-0947-y CrossRefPubMedGoogle Scholar
  31. 31.
    Chan JH, Tsui EY, Chau LF, Chow KY, Chan MS, Yuen MK, Chan TL, Cheng WK, Wong KP (2002) Discrimination of an infected brain tumor from a cerebral abscess by combined MR perfusion and diffusion imaging. Comp Med Imag Grap 26(1):19–23CrossRefGoogle Scholar
  32. 32.
    Calli C, Kitis O, Yunten N, Yurtseven T, Islekel S, Akalin T (2006) Perfusion and diffusion MR imaging in enhancing malignant cerebral tumors. Eur J Radiol 58(3):394–403. doi: 10.1016/j.ejrad.2005.12.032 CrossRefPubMedGoogle Scholar
  33. 33.
    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):76–84CrossRefPubMedGoogle Scholar
  34. 34.
    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(2):462–470. doi: 10.1016/j.ejrad.2010.07.017 CrossRefPubMedGoogle Scholar
  35. 35.
    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(10):953–960. doi: 10.1016/j.crad.2011.05.001 CrossRefPubMedGoogle Scholar
  36. 36.
    Emblem KE, Nedregaard B, Nome T, Due-Tonnessen P, Hald JK, Scheie D, Borota OC, Cvancarova M, Bjornerud A (2008) Glioma grading by using histogram analysis of blood volume heterogeneity from MR-derived cerebral blood volume maps. Radiology 247(3):808–817. doi: 10.1148/radiol.2473070571 CrossRefPubMedGoogle Scholar
  37. 37.
    Fayed N, Morales H, Modrego PJ, Pina MA (2006) Contrast/Noise ratio on conventional MRI and choline/creatine ratio on proton MRI spectroscopy accurately discriminate low-grade from high-grade cerebral gliomas. Acad Radiol 13(6):728–737. doi: 10.1016/j.acra.2006.01.047 CrossRefPubMedGoogle Scholar
  38. 38.
    Zhang K, Li C, Liu Y, Li L, Ma X, Meng X, Feng D (2007) Evaluation of invasiveness of astrocytoma using 1H-magnetic resonance spectroscopy: correlation with expression of matrix metalloproteinase-2. Neuroradiology 49(11):913–919. doi: 10.1007/s00234-007-0271-8 CrossRefPubMedGoogle Scholar
  39. 39. Accessed 21 Sep 2015
  40. 40.
    Garcia-Gomez JM, Luts J, Julia-Sape M, Krooshof P, Tortajada S, Robledo JV, Melssen W, Fuster-Garcia E, Olier I, Postma G, Monleon D, Moreno-Torres A, Pujol J, Candiota AP, Martinez-Bisbal MC, Suykens J, Buydens L, Celda B, Van Huffel S, Arus C, Robles M (2009) Multiproject-multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy. Magn Reson Mater Phy (New York, NY) 22(1):5–18. doi: 10.1007/s10334-008-0146-y CrossRefGoogle Scholar
  41. 41.
    Al-Okaili RN, Krejza J, Woo JH, Wolf RL, O'Rourke DM, Judy KD, Poptani H, Melhem ER (2007) Intraaxial brain masses: MR imaging-based diagnostic strategy—initial experience. Radiology 243(2):539–550. doi: 10.1148/radiol.2432060493 CrossRefPubMedGoogle Scholar
  42. 42.
    Guzman-De-Villoria JA, Mateos-Perez JM, Fernandez-Garcia P, Castro E, Desco M (2014) Added value of advanced over conventional magnetic resonance imaging in grading gliomas and other primary brain tumors. Cancer Imaging: Off Publ Int Cancer Imaging Soc 14:35. doi: 10.1186/s40644-014-0035-8 CrossRefGoogle Scholar
  43. 43.
    Hilario A, Sepulveda JM, Perez-Nunez A, Salvador E, Millan JM, Hernandez-Lain A, Rodriguez-Gonzalez V, Lagares A, Ramos A (2014) A prognostic model based on preoperative MRI predicts overall survival in patients with diffuse gliomas. AJNR Am J Neuroradiol 35(6):1096–1102. doi: 10.3174/ajnr.A3837 CrossRefPubMedGoogle Scholar
  44. 44.
    Yan R, Haopeng P, Xiaoyuan F, Jinsong W, Jiawen Z, Chengjun Y, Tianming Q, Ji X, Mao S, Yueyue D, Yong Z, Jianfeng L, Zhenwei Y (2015) Non-Gaussian diffusion MR imaging of glioma: comparisons of multiple diffusion parameters and correlation with histologic grade and MIB-1 (Ki-67 labeling) index. Neuroradiology. doi:10.1007/s00234-015-1606-5Google Scholar
  45. 45.
    Hu YC, Yan LF, Wu L, Du P, Chen BY, Wang L, Wang SM, Han Y, Tian Q, Yu Y, Xu TY, Wang W, Cui GB (2014) Intravoxel incoherent motion diffusion-weighted MR imaging of gliomas: efficacy in preoperative grading. Sc Rep 4:7208. doi: 10.1038/srep07208 CrossRefGoogle Scholar
  46. 46.
    Togao O, Hiwatashi A, Yamashita K, Kikuchi K, Mizoguchi M, Yoshimoto K, Suzuki SO, Iwaki T, Obara M, Van Cauteren M, Honda H (2015) Differentiation of high-grade and low-grade diffuse gliomas by intravoxel incoherent motion MR imaging. Neuro-oncology. doi:10.1093/neuonc/nov147Google Scholar
  47. 47.
    Bisdas S, Koh TS, Roder C, Braun C, Schittenhelm J, Ernemann U, Klose U (2013) Intravoxel incoherent motion diffusion-weighted MR imaging of gliomas: feasibility of the method and initial results. Neuroradiology 55(10):1189–1196. doi: 10.1007/s00234-013-1229-7 CrossRefPubMedGoogle Scholar
  48. 48.
    Federau C, Meuli R, O'Brien K, Maeder P, Hagmann P (2014) Perfusion measurement in brain gliomas with intravoxel incoherent motion MRI. AJNR Am J Neuroradiol 35(2):256–262. doi: 10.3174/ajnr.A3686 CrossRefPubMedGoogle Scholar
  49. 49.
    Kim JH, Chang KH, Na DG, Song IC, Kwon BJ, Han MH, Kim K (2006) 3T 1H-MR spectroscopy in grading of cerebral gliomas: comparison of short and intermediate echo time sequences. AJNR Am J Neuroradiol 27(7):1412–1418PubMedGoogle Scholar
  50. 50.
    Majos C, Julia-Sape M, Alonso J, Serrallonga M, Aguilera C, Acebes JJ, Arus C, Gili J (2004) Brain tumor classification by proton MR spectroscopy: comparison of diagnostic accuracy at short and long TE. AJNR Am J Neuroradiol 25(10):1696–1704PubMedGoogle Scholar
  51. 51.
    Kousi E, Tsougos I, Tsolaki E, Fountas KN, Theodorou K, Fezoulidis I, Kapsalaki E, Kappas C (2012) Spectroscopic evaluation of glioma grading at 3T: the combined role of short and long TE. Sci World J 2012:546171. doi: 10.1100/2012/546171 Google Scholar
  52. 52.
    Ranjith G, Parvathy R, Vikas V, Chandrasekharan K, Nair S (2015) Machine learning methods for the classification of gliomas: initial results using features extracted from MR spectroscopy. Neuroradiol J 28(2):106–111. doi: 10.1177/1971400915576637 CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Fellows GA, Wright AJ, Sibtain NA, Rich P, Opstad KS, McIntyre DJ, Bell BA, Griffiths JR, Howe FA (2010) Combined use of neuroradiology and 1H-MR spectroscopy may provide an intervention limiting diagnosis of glioblastoma multiforme. JMRI-J Magn Reson Im 32(5):1038–1044. doi: 10.1002/jmri.22350 CrossRefGoogle Scholar
  54. 54.
    Tsolaki E, Svolos P, Kousi E, Kapsalaki E, Fountas K, Theodorou K, Tsougos I (2013) Automated differentiation of glioblastomas from intracranial metastases using 3T MR spectroscopic and perfusion data. Int J Comput Assist Radiol Surg 8(5):751–761. doi: 10.1007/s11548-012-0808-0 CrossRefPubMedGoogle Scholar
  55. 55.
    Svolos P, Tsolaki E, Kapsalaki E, Theodorou K, Fountas K, Fezoulidis I, Tsougos I (2013) Investigating brain tumor differentiation with diffusion and perfusion metrics at 3T MRI using pattern recognition techniques. Magn Reson Imaging 31(9):1567–1577. doi: 10.1016/j.mri.2013.06.010 CrossRefPubMedGoogle Scholar
  56. 56.
    Emblem KE, Zoellner FG, Tennoe B, Nedregaard B, Nome T, Due-Tonnessen P, Hald JK, Scheie D, Bjornerud A (2008) Predictive modeling in glioma grading from MR perfusion images using support vector machines. Magn Reson Med 60(4):945–952. doi: 10.1002/mrm.21736 CrossRefPubMedGoogle Scholar
  57. 57.
    Vellido A, Romero E, Julia-Sape M, Majos C, Moreno-Torres A, Pujol J, Arus C (2012) Robust discrimination of glioblastomas from metastatic brain tumors on the basis of single-voxel (1)H MRS. NMR Biomed 25(6):819–828. doi: 10.1002/nbm.1797 CrossRefPubMedGoogle Scholar
  58. 58.
    Moller-Hartmann W, Herminghaus S, Krings T, Marquardt G, Lanfermann H, Pilatus U, Zanella FE (2002) Clinical application of proton magnetic resonance spectroscopy in the diagnosis of intracranial mass lesions. Neuroradiology 44(5):371–381. doi: 10.1007/s00234-001-0760-0 CrossRefPubMedGoogle Scholar
  59. 59.
    Chaudhry IH, O'Donovan DG, Brenchley PE, Reid H, Roberts IS (2001) Vascular endothelial growth factor expression correlates with tumour grade and vascularity in gliomas. Histopathology 39(4):409–415CrossRefPubMedGoogle Scholar
  60. 60.
    Sie M, de Bont ES, Scherpen FJ, Hoving EW, den Dunnen WF (2010) Tumour vasculature and angiogenic profile of paediatric pilocytic astrocytoma; is it much different from glioblastoma? Neuropath Appl Neuro 36(7):636–647. doi: 10.1111/j.1365-2990.2010.01113.x CrossRefGoogle Scholar
  61. 61.
    Cha S, Tihan T, Crawford F, Fischbein NJ, Chang S, Bollen A, Nelson SJ, Prados M, Berger MS, Dillon WP (2005) Differentiation of low-grade oligodendrogliomas from low-grade astrocytomas by using quantitative blood-volume measurements derived from dynamic susceptibility contrast-enhanced MR imaging. AJNR Am J Neuroradiol 26(2):266–273PubMedGoogle Scholar
  62. 62.
    Kapoor GS, Gocke TA, Chawla S, Whitmore RG, Nabavizadeh A, Krejza J, Lopinto J, Plaum J, Maloney-Wilensky E, Poptani H, Melhem ER, Judy KD, O'Rourke DM (2009) Magnetic resonance perfusion-weighted imaging defines angiogenic subtypes of oligodendroglioma according to 1p19q and EGFR status. J Neuro-Oncol 92(3):373–386. doi: 10.1007/s11060-009-9880-x CrossRefGoogle Scholar
  63. 63.
    Whitmore RG, Krejza J, Kapoor GS, Huse J, Woo JH, Bloom S, Lopinto J, Wolf RL, Judy K, Rosenfeld MR, Biegel JA, Melhem ER, O'Rourke DM (2007) Prediction of oligodendroglial tumor subtype and grade using perfusion weighted magnetic resonance imaging. J Neurosurg 107(3):600–609. doi: 10.3171/jns-07/09/0600 CrossRefPubMedGoogle Scholar
  64. 64.
    Lev MH, Ozsunar Y, Henson JW, Rasheed AA, Barest GD, Harsh GR, 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]. AJNR Am J Neuroradiol 25(2):214–221PubMedGoogle Scholar
  65. 65.
    Sunwoo L, Choi SH, Yoo RE, Kang KM, Yun TJ, Kim TM, Lee SH, Park CK, Kim JH, Park SW, Sohn CH, Won JK, Park SH, Kim IH (2015) Paradoxical perfusion metrics of high-grade gliomas with an oligodendroglioma component: quantitative analysis of dynamic susceptibility contrast perfusion MR imaging. Neuroradiology 57(11):1111–1120. doi: 10.1007/s00234-015-1569-6 CrossRefPubMedGoogle Scholar
  66. 66.
    Xu M, See SJ, Ng WH, Arul E, Back MF, Yeo TT, Lim CC (2005) Comparison of magnetic resonance spectroscopy and perfusion-weighted imaging in presurgical grading of oligodendroglial tumors. Neurosurgery 56(5):919–926, discussion 919–926PubMedGoogle Scholar
  67. 67.
    Chawla S, Krejza J, Vossough A, Zhang Y, Kapoor GS, Wang S, O'Rourke DM, Melhem ER, Poptani H (2013) Differentiation between oligodendroglioma genotypes using dynamic susceptibility contrast perfusion-weighted imaging and proton MR spectroscopy. AJNR Am J Neuroradiol 34(8):1542–1549. doi: 10.3174/ajnr.A3384 CrossRefPubMedGoogle Scholar
  68. 68.
    Kanno H, Nishihara H, Narita T, Yamaguchi S, Kobayashi H, Tanino M, Kimura T, Terasaka S, Tanaka S (2012) Prognostic implication of histological oligodendroglial tumor component: clinicopathological analysis of 111 cases of malignant gliomas. PLoS One 7(7):e41669. doi: 10.1371/journal.pone.0041669 CrossRefPubMedPubMedCentralGoogle Scholar
  69. 69.
    Laxton RC, Popov S, Doey L, Jury A, Bhangoo R, Gullan R, Chandler C, Brazil L, Sadler G, Beaney R, Sibtain N, King A, Bodi I, Jones C, Ashkan K, Al-Sarraj S (2013) Primary glioblastoma with oligodendroglial differentiation has better clinical outcome but no difference in common biological markers compared with other types of glioblastoma. Neuro-Oncology 15(12):1635–1643. doi: 10.1093/neuonc/not125 CrossRefPubMedPubMedCentralGoogle Scholar
  70. 70.
    Wang Y, Li S, Chen L, You G, Bao Z, Yan W, Shi Z, Chen Y, Yao K, Zhang W, Kang C, Jiang T (2012) Glioblastoma with an oligodendroglioma component: distinct clinical behavior, genetic alterations, and outcome. Neuro-Oncology 14(4):518–525. doi: 10.1093/neuonc/nor232 CrossRefPubMedPubMedCentralGoogle Scholar
  71. 71.
    Senturk S, Oguz KK, Cila A (2009) Dynamic contrast-enhanced susceptibility-weighted perfusion imaging of intracranial tumors: a study using a 3T MR scanner. Diagn Interv Radiol 15(1):3–12PubMedGoogle Scholar
  72. 72.
    Hakyemez B, Erdogan C, Gokalp G, Dusak A, Parlak M (2010) Solitary metastases and high-grade gliomas: radiological differentiation by morphometric analysis and perfusion-weighted MRI. Clin Radiol 65(1):15–20. doi: 10.1016/j.crad.2009.09.005 CrossRefPubMedGoogle Scholar
  73. 73.
    Lehmann P, Saliou G, de Marco G, Monet P, Souraya SE, Bruniau A, Vallee JN, Ducreux D (2012) Cerebral peritumoral oedema study: does a single dynamic MR sequence assessing perfusion and permeability can help to differentiate glioblastoma from metastasis? Eur J Radiol 81(3):522–527. doi: 10.1016/j.ejrad.2011.01.076 CrossRefPubMedGoogle Scholar
  74. 74.
    Fan G, Sun B, Wu Z, Guo Q, Guo Y (2004) In vivo single-voxel proton MR spectroscopy in the differentiation of high-grade gliomas and solitary metastases. Clin Radiol 59(1):77–85CrossRefPubMedGoogle Scholar
  75. 75.
    Durst CR, Raghavan P, Shaffrey ME, Schiff D, Lopes MB, Sheehan JP, Tustison NJ, Patrie JT, Xin W, Elias WJ, Liu KC, Helm GA, Cupino A, Wintermark M (2014) Multimodal MR imaging model to predict tumor infiltration in patients with gliomas. Neuroradiology 56(2):107–115. doi: 10.1007/s00234-013-1308-9 CrossRefPubMedGoogle Scholar
  76. 76.
    Tietze A, Mouridsen K, Mikkelsen IK (2015) The impact of reliable prebolus T 1 measurements or a fixed T 1 value in the assessment of glioma patients with dynamic contrast enhancing MRI. Neuroradiology 57(6):561–572. doi: 10.1007/s00234-015-1502-z CrossRefPubMedGoogle Scholar
  77. 77.
    Awasthi R, Rathore RK, Soni P, Sahoo P, Awasthi A, Husain N, Behari S, Singh RK, Pandey CM, Gupta RK (2012) Discriminant analysis to classify glioma grading using dynamic contrast-enhanced MRI and immunohistochemical markers. Neuroradiology 54(3):205–213. doi: 10.1007/s00234-011-0874-y CrossRefPubMedGoogle Scholar
  78. 78.
    Arevalo-Perez J, Peck KK, Young RJ, Holodny AI, Karimi S, Lyo JK (2015) Dynamic contrast-enhanced perfusion MRI and diffusion-weighted imaging in grading of gliomas. J Neuroimaging 25(5):792–798. doi: 10.1111/jon.12239 CrossRefPubMedGoogle Scholar
  79. 79.
    Zhang N, Zhang L, Qiu B, Meng L, Wang X, Hou BL (2012) Correlation of volume transfer coefficient Ktrans with histopathologic grades of gliomas. JMRI-J Magn Reson Im 36(2):355–363. doi: 10.1002/jmri.23675 CrossRefGoogle Scholar
  80. 80.
    Li X, Zhu Y, Kang H, Zhang Y, Liang H, Wang S, Zhang W (2015) Glioma grading by microvascular permeability parameters derived from dynamic contrast-enhanced MRI and intratumoral susceptibility signal on susceptibility weighted imaging. Cancer Imaging 15:4. doi: 10.1186/s40644-015-0039-z CrossRefPubMedPubMedCentralGoogle Scholar
  81. 81.
    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 (2015) Preoperative prognostic value of dynamic contrast-enhanced MRI-derived contrast transfer coefficient and plasma volume in patients with cerebral gliomas. AJNR Am J Neuroradiol 36(1):63–69. doi: 10.3174/ajnr.A4006 CrossRefPubMedGoogle Scholar
  82. 82.
    Piludu F, Marzi S, Pace A, Villani V, Fabi A, Carapella CM, Terrenato I, Antenucci A, Vidiri A (2015) Early biomarkers from dynamic contrast-enhanced magnetic resonance imaging to predict the response to antiangiogenic therapy in high-grade gliomas. Neuroradiology 57(12):1269–1280. doi: 10.1007/s00234-015-1582-9 CrossRefPubMedGoogle Scholar
  83. 83.
    Falk A, Fahlstrom M, Rostrup E, Berntsson S, Zetterling M, Morell A, Larsson HB, Smits A, Larsson EM (2014) Discrimination between glioma grades II and III in suspected low-grade gliomas using dynamic contrast-enhanced and dynamic susceptibility contrast perfusion MR imaging: a histogram analysis approach. Neuroradiology 56(12):1031–1038. doi: 10.1007/s00234-014-1426-z CrossRefPubMedGoogle Scholar
  84. 84.
    Borenstein M, Hedges LV, Higgins JPT, Rothstein HR (2009) Introduction to meta-analysis. John Wiley & Sons, Ltd., ChichesterCrossRefGoogle Scholar
  85. 85.
    Toh CH, Wei KC, Chang CN, Ng SH, Wong HF (2013) Differentiation of primary central nervous system lymphomas and glioblastomas: comparisons of diagnostic performance of dynamic susceptibility contrast-enhanced perfusion MR imaging without and with contrast-leakage correction. AJNR Am J Neuroradiol 34(6):1145–1149. doi: 10.3174/ajnr.A3383 CrossRefPubMedGoogle Scholar
  86. 86.
    Law M, Young R, Babb J, Pollack E, Johnson G (2007) Histogram analysis versus region of interest analysis of dynamic susceptibility contrast perfusion MR imaging data in the grading of cerebral gliomas. AJNR Am J Neuroradiol 28(4):761–766PubMedGoogle Scholar
  87. 87.
    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(4):493–502. doi: 10.1016/j.crad.2004.09.009 CrossRefPubMedGoogle Scholar
  88. 88.
    Shin JH, Lee HK, Kwun BD, Kim JS, Kang W, Choi CG, Suh DC (2002) Using relative cerebral blood flow and volume to evaluate the histopathologic grade of cerebral gliomas: preliminary results. AJR Am J Roentgenol 179(3):783–789. doi: 10.2214/ajr.179.3.1790783 CrossRefPubMedGoogle Scholar
  89. 89.
    Lee SJ, Kim JH, Kim YM, Lee GK, Lee EJ, Park IS, Jung JM, Kang KH, Shin T (2001) Perfusion MR imaging in gliomas: comparison with histologic tumor grade. Korean J Radiol 2(1):1–7CrossRefPubMedPubMedCentralGoogle Scholar
  90. 90.
    Hakyemez B, Erdogan C, Bolca N, Yildirim N, Gokalp G, Parlak M (2006) Evaluation of different cerebral mass lesions by perfusion-weighted MR imaging. JMRI-J Magn Reson Im 24(4):817–824. doi: 10.1002/jmri.20707 CrossRefGoogle Scholar
  91. 91.
    Sadeghi N, Salmon I, Tang BN, Denolin V, Levivier M, Wikler D, Rorive S, Baleriaux D, Metens T, Goldman S (2006) Correlation between dynamic susceptibility contrast perfusion MRI and methionine metabolism in brain gliomas: preliminary results. JMRI-J Magn Reson Im 24(5):989–994. doi: 10.1002/jmri.20757 CrossRefGoogle Scholar
  92. 92.
    Preul C, Kuhn B, Lang EW, Mehdorn HM, Heller M, Link J (2003) Differentiation of cerebral tumors using multi-section echo planar MR perfusion imaging. Eur J Radiol 48(3):244–251CrossRefPubMedGoogle Scholar
  93. 93.
    Spampinato MV, Wooten C, Dorlon M, Besenski N, Rumboldt Z (2006) Comparison of first-pass and second-bolus dynamic susceptibility perfusion MRI in brain tumors. Neuroradiology 48(12):867–874. doi: 10.1007/s00234-006-0134-8 CrossRefPubMedGoogle Scholar
  94. 94.
    Law M, Brodsky JE, Babb J, Rosenblum M, Miller DC, Zagzag D, Gruber ML, Johnson G (2007) High cerebral blood volume in human gliomas predicts deletion of chromosome 1p: preliminary results of molecular studies in gliomas with elevated perfusion. JMRI-J Magn Reson Im 25(6):1113–1119. doi: 10.1002/jmri.20920 CrossRefGoogle Scholar
  95. 95.
    Kremer S, Grand S, Remy C, Esteve F, Lefournier V, Pasquier B, Hoffmann D, Benabid AL, Le Bas JF (2002) Cerebral blood volume mapping by MR imaging in the initial evaluation of brain tumors. J Neuroradiol 29(2):105–113PubMedGoogle Scholar
  96. 96.
    Boxerman JL, Schmainda KM, Weisskoff RM (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):859–867PubMedGoogle Scholar
  97. 97.
    Singh A, Haris M, Rathore D, Purwar A, Sarma M, Bayu G, Husain N, Rathore RK, Gupta RK (2007) Quantification of physiological and hemodynamic indices using T(1) dynamic contrast-enhanced MRI in intracranial mass lesions. JMRI-J Magn Reson Im 26(4):871–880. doi: 10.1002/jmri.21080 CrossRefGoogle Scholar
  98. 98.
    Haris M, Gupta RK, Singh A, Husain N, Husain M, Pandey CM, Srivastava C, Behari S, Rathore RK (2008) Differentiation of infective from neoplastic brain lesions by dynamic contrast-enhanced MRI. Neuroradiology 50(6):531–540. doi: 10.1007/s00234-008-0378-6 CrossRefPubMedGoogle Scholar
  99. 99.
    Haris M, Husain N, Singh A, Husain M, Srivastava S, Srivastava C, Behari S, Rathore RK, Saksena S, Gupta RK (2008) Dynamic contrast-enhanced derived cerebral blood volume correlates better with leak correction than with no correction for vascular endothelial growth factor, microvascular density, and grading of astrocytoma. J Comput Assist Tomo 32(6):955–965. doi: 10.1097/RCT.0b013e31816200d1 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Jurgita Usinskiene
    • 1
    Email author
  • Agne Ulyte
    • 2
  • Atle Bjørnerud
    • 3
    • 4
  • Jonas Venius
    • 1
  • Vasileios K. Katsaros
    • 5
  • Ryte Rynkeviciene
    • 1
  • Simona Letautiene
    • 1
    • 2
  • Darius Norkus
    • 1
  • Kestutis Suziedelis
    • 1
  • Saulius Rocka
    • 2
    • 6
  • Andrius Usinskas
    • 7
  • Eduardas Aleknavicius
    • 1
    • 2
  1. 1.National Cancer Institute, Radiology CenterVilniusLithuania
  2. 2.Faculty of MedicineVilnius UniversityVilniusLithuania
  3. 3.Department of PhysicsOslo University HospitalOsloNorway
  4. 4.The Intervention CentreOslo University HospitalOsloNorway
  5. 5.General Anti-Cancer and Oncological Hospital “St. Savvas”AthensGreece
  6. 6.Neuroangiosurgery CenterFaculty of Medicine Vilnius UniversityVilniusLithuania
  7. 7.Department of Electronic SystemsVilnius Gedimino Technical UniversityVilniusLithuania

Personalised recommendations