European Radiology

, Volume 26, Issue 8, pp 2670–2684 | Cite as

The diagnostic performance of magnetic resonance spectroscopy in differentiating high-from low-grade gliomas: A systematic review and meta-analysis

  • Qun Wang
  • Hui Zhang
  • JiaShu Zhang
  • Chen Wu
  • WeiJie Zhu
  • FangYe Li
  • XiaoLei ChenEmail author
  • BaiNan XuEmail author
Magnetic Resonance



Magnetic resonance spectroscopy (MRS) is a powerful tool for preoperative grading of gliomas. We performed a meta-analysis to evaluate the diagnostic performance of MRS in differentiating high-grade gliomas (HGGs) from low-grade gliomas (LGGs).


PubMed and Embase databases were systematically searched for relevant studies of glioma grading assessed by MRS through 27 March 2015. Based on the data from eligible studies, pooled sensitivity, specificity, diagnostic odds ratio and areas under summary receiver operating characteristic curve (SROC) of different metabolite ratios were obtained.


Thirty articles comprising a total sample size of 1228 patients were included in our meta-analysis. Quantitative synthesis of studies showed that the pooled sensitivity/specificity of Cho/Cr, Cho/NAA and NAA/Cr ratios was 0.75/0.60, 0.80/0.76 and 0.71/0.70, respectively. The area under the curve (AUC) of the SROC was 0.83, 0.87 and 0.78, respectively.


MRS demonstrated moderate diagnostic performance in distinguishing HGGs from LGGs using tumoural metabolite ratios including Cho/Cr, Cho/NAA and NAA/Cr. Although there was no significant difference in AUC between Cho/Cr and Cho/NAA groups, Cho/NAA ratio showed higher sensitivity and specificity than Cho/Cr ratio and NAA/Cr ratio. We suggest that MRS should combine other advanced imaging techniques to improve diagnostic accuracy in differentiating HGGs from LGGs.

Key points

MRS has moderate diagnostic performance in distinguishing HGGs from LGGs.

There is no significant difference in AUC between Cho/Cr and Cho/NAA ratios.

Cho/NAA ratio is superior to NAA/Cr ratio.

Cho/NAA ratio shows higher sensitivity and specificity than Cho/Cr and NAA/Cr ratios.

MRS should combine other advanced imaging techniques to improve diagnostic accuracy.


Magnetic resonance spectroscopy Glioma Differentiation Systematic review Meta-analysis 



Area under the curve




Confidence intervals




Diagnostic odds ratio


Diffusion tensor imaging


Diffusion-weighted imaging


False negative


False positive


High-grade gliomas


Inconsistency index




Low-grade gliomas


Lipids and lactate


Positive likelihood ratio


Negative likelihood ratio


Long echo time




Magnetic resonance imaging


Magnetic resonance spectroscopy


Multi-voxel spectroscopy




Normalized choline


Normalized creatine




Positron-emission tomography


Quality Assessment Tool for Diagnostic Accuracy Studies version 2






Single photon mission computed tomography


Summary receiver-operating characteristic curve


Short echo time


Single-voxel spectroscopy


True negative


True positive



The scientific guarantor of this publication is Hui Zhang, PHD. The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article. The authors state that this work has not received any funding. One of the authors (Hui Zhang) has significant statistical expertise. Neither institutional review board approval nor written informed consent were required, because of the nature of our study, which was a systemic review and meta-analysis. Methodology: Meta-analysis, performed at one institution.


  1. 1.
    Inoue T, Ogasawara K, Beppu T, Ogawa A, Kabasawa H (2005) Diffusion tensor imaging for preoperative evaluation of tumor grade in gliomas. Clin Neurol Neurosurg 107:174–180CrossRefPubMedGoogle Scholar
  2. 2.
    Lu H, Pollack E, Young R et al (2008) Predicting grade of cerebral glioma using vascular-space occupancy MR imaging. AJNR Am J Neuroradiol 29:373–378CrossRefPubMedGoogle Scholar
  3. 3.
    Chung C, Metser U, Menard C (2015) Advances in magnetic resonance imaging and positron emission tomography imaging for grading and molecular characterization of glioma. Semin Radiat Oncol 25:164–171CrossRefPubMedGoogle Scholar
  4. 4.
    Bulik M, Jancalek R, Vanicek J, Skoch A, Mechl M (2013) Potential of MR spectroscopy for assessment of glioma grading. Clin Neurol Neurosurg 115:146–153CrossRefPubMedGoogle Scholar
  5. 5.
    Herminghaus S, Dierks T, Pilatus U et al (2003) Determination of histopathological tumor grade in neuroepithelial brain tumors by using spectral pattern analysis of in vivo spectroscopic data. J Neurosurg 98:74–81CrossRefPubMedGoogle Scholar
  6. 6.
    Dhermain FG, Hau P, Lanfermann H, Jacobs AH, van den Bent MJ (2010) Advanced MRI and PET imaging for assessment of treatment response in patients with gliomas. Lancet Neurol 9:906–920CrossRefPubMedGoogle Scholar
  7. 7.
    Zhang H, Ma L, Wang Q, Zheng X, Wu C, Xu BN (2014) Role of magnetic resonance spectroscopy for the differentiation of recurrent glioma from radiation necrosis: a systematic review and meta-analysis. Eur J Radiol 83:2181–2189CrossRefPubMedGoogle Scholar
  8. 8.
    Hollingworth W, Medina LS, Lenkinski RE et al (2006) A systematic literature review of magnetic resonance spectroscopy for the characterization of brain tumors. AJNR Am J Neuroradiol 27:1404–1411PubMedGoogle Scholar
  9. 9.
    Whiting PF, Rutjes AW, Westwood ME et al (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155:529–536CrossRefPubMedGoogle Scholar
  10. 10.
    Deville WL, Buntinx F, Bouter LM et al (2002) Conducting systematic reviews of diagnostic studies: didactic guidelines. BMC Med Res Methodol 2:9CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Zamora J, Abraira V, Muriel A, Khan K, Coomarasamy A (2006) Meta-DiSc: a software for meta-analysis of test accuracy data. BMC Med Res Methodol 6:31CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Higgins JP, Thompson SG, Deeks JJ, Altman DG (2003) Measuring inconsistency in meta-analyses. BMJ 327:557–560CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Leeflang MM, Deeks JJ, Gatsonis C, Bossuyt PM (2008) Systematic reviews of diagnostic test accuracy. Ann Intern Med 149:889–897CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Altman DG, Bland JM (2003) Interaction revisited: the difference between two estimates. BMJ 326:219CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Deeks JJ, Macaskill P, Irwig L (2005) The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed. J Clin Epidemiol 58:882–893CrossRefPubMedGoogle Scholar
  16. 16.
    Fudaba H, Shimomura T, Abe T et al (2014) Comparison of multiple parameters obtained on 3T pulsed arterial spin-labeling, diffusion tensor imaging, and MRS and the Ki-67 labeling index in evaluating glioma grading. AJNR Am J Neuroradiol 35:2091–2098CrossRefPubMedGoogle Scholar
  17. 17.
    Dunet V, Maeder P, Nicod-Lalonde M et al (2014) Combination of MRI and dynamic FET PET for initial glioma grading. Nuklearmedizin 53:155–161CrossRefPubMedGoogle Scholar
  18. 18.
    Caulo M, Panara V, Tortora D et al (2014) Data-driven grading of brain gliomas: a multiparametric MR imaging study. Radiology 272:494–503CrossRefPubMedGoogle Scholar
  19. 19.
    Darweesh AMN, Badawy ME, Hamesa M, Saber N (2014) Magnetic resonance spectroscopy and diffusion imaging in the evaluation of neoplastic brain lesions. Egypt J Radiol Nucl Med 45:485–493CrossRefGoogle Scholar
  20. 20.
    Yoon JH, Kim JH, Kang WJ et al (2014) Grading of cerebral glioma with multiparametric MR imaging and 18F-FDG-PET: concordance and accuracy. Eur Radiol 24:380–389CrossRefPubMedGoogle Scholar
  21. 21.
    Metwally LIA, El-Din SE, Abdelaziz O, Hamdy IM, Elsamman AK, Abdelalim AM (2014) Predicting grade of cerebral gliomas using Myo-inositol/Creatine ratio. Egypt J Radiol Nucl Med 45:211–217CrossRefGoogle Scholar
  22. 22.
    Sahin N, Melhem ER, Wang S et al (2013) Advanced MR imaging techniques in the evaluation of nonenhancing gliomas: perfusion-weighted imaging compared with proton magnetic resonance spectroscopy and tumor grade. Neuroradiol J 26:531–541CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Roy B, Gupta RK, Maudsley AA et al (2013) Utility of multiparametric 3-T MRI for glioma characterization. Neuroradiology 55:603–613CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Rao PJ, Jyoti R, Mews PJ, Desmond P, Khurana VG (2013) Preoperative magnetic resonance spectroscopy improves diagnostic accuracy in a series of neurosurgical dilemmas. Br J Neurosurg 27:646–653CrossRefPubMedGoogle Scholar
  25. 25.
    Chawalparit O, Sangruchi T, Witthiwej T et al (2013) Diagnostic performance of advanced MRI in differentiating high-grade from low-grade gliomas in a setting of routine service. J Med Assoc Thai 96:1365–1373PubMedGoogle Scholar
  26. 26.
    Aprile I, Torni C, Fiaschini P, Muti M (2012) High-Grade Cerebral Glioma Characterization: Usefulness of MR Spectroscopy and Perfusion Imaging Associated Evaluation. Neuroradiol J 25:57–66CrossRefPubMedGoogle Scholar
  27. 27.
    Shokry A (2012) MRS of brain tumors: diagrammatic representations and diagnostic approach. Egypt J Radiol Nucl Med 43:603–612CrossRefGoogle Scholar
  28. 28.
    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 40:76–84CrossRefPubMedGoogle Scholar
  29. 29.
    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 66:953–960CrossRefPubMedGoogle Scholar
  30. 30.
    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 29:25–31CrossRefPubMedGoogle Scholar
  31. 31.
    Widhalm G, Krssak M, Minchev G et al (2011) Value of 1H-magnetic resonance spectroscopy chemical shift imaging for detection of anaplastic foci in diffusely infiltrating gliomas with non-significant contrast-enhancement. J Neurol Neurosurg Psychiatry 82:512–520CrossRefPubMedGoogle Scholar
  32. 32.
    Chernov MF, Ono Y, Muragaki Y et al (2008) Differentiation of high-grade and low-grade gliomas using pattern analysis of long-echo single-voxel proton magnetic resonance spectroscopy ((1)H-MRS). Neuroradiol J 21:338–349CrossRefPubMedGoogle Scholar
  33. 33.
    Di CA, Scarabino T, Trojsi F et al (2008) Proton MR spectroscopy of cerebral gliomas at 3 T: spatial heterogeneity, and tumour grade and extent. Eur Radiol 18:1727–1735CrossRefGoogle Scholar
  34. 34.
    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 49:795–803CrossRefPubMedGoogle Scholar
  35. 35.
    Zhang K, Li C, Liu Y et al (2007) Evaluation of invasiveness of astrocytoma using 1H-magnetic resonance spectroscopy: correlation with expression of matrix metalloproteinase-2. Neuroradiology 49:913–919CrossRefPubMedGoogle Scholar
  36. 36.
    Kim JH, Chang KH, Na DG et al (2006) 3T 1H-MR spectroscopy in grading of cerebral gliomas: comparison of short and intermediate echo time sequences. AJNR Am J Neuroradiol 27:1412–1418PubMedGoogle Scholar
  37. 37.
    Stadlbauer A, Gruber S, Nimsky C et al (2006) Preoperative grading of gliomas by using metabolite quantification with high-spatial-resolution proton MR spectroscopic imaging. Radiology 238:958–969CrossRefPubMedGoogle Scholar
  38. 38.
    Jeun SS, Kim MC, Kim BS et al (2005) Assessment of malignancy in gliomas by 3T 1H MR spectroscopy. Clin Imaging 29:10–15CrossRefPubMedGoogle Scholar
  39. 39.
    Magalhaes A, Godfrey W, Shen Y, Hu J, Smith W (2005) Proton magnetic resonance spectroscopy of brain tumors correlated with pathology. Acad Radiol 12:51–57CrossRefPubMedGoogle Scholar
  40. 40.
    Chen CY, Lirng JF, Chan WP, Fang CL (2004) Proton magnetic resonance spectroscopy-guided biopsy for cerebral glial tumors. J Formos Med Assoc 103:448–458PubMedGoogle Scholar
  41. 41.
    Fountas KN, Kapsalaki EZ, Vogel RL, Fezoulidis I, Robinson JS, Gotsis ED (2004) Noninvasive histologic grading of solid astrocytomas using proton magnetic resonance spectroscopy. Stereotact Funct Neurosurg 82:90–97CrossRefPubMedGoogle Scholar
  42. 42.
    Law M, Yang S, Wang H 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 24:1989–1998PubMedGoogle Scholar
  43. 43.
    Kumar A, Kaushik S, Tripathi RP, Kaur P, Khushu S (2003) Role of in vivo proton MR spectroscopy in the evaluation of adult brain lesions: our preliminary experience. Neurol India 51:474–478PubMedGoogle Scholar
  44. 44.
    Yang D, Korogi Y, Sugahara T et al (2002) Cerebral gliomas: prospective comparison of multivoxel 2D chemical-shift imaging proton MR spectroscopy, echoplanar perfusion and diffusion-weighted MRI. Neuroradiology 44:656–666CrossRefPubMedGoogle Scholar
  45. 45.
    Furuya S, Naruse S, Ide M et al (1997) Evaluation of metabolic heterogeneity in brain tumors using 1H-chemical shift imaging method. NMR Biomed 10:25–30CrossRefPubMedGoogle Scholar
  46. 46.
    Wang W, Hu Y, Lu P et al (2014) Evaluation of the diagnostic performance of magnetic resonance spectroscopy in brain tumors: a systematic review and meta-analysis. PLoS One 9, e112577CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Glas AS, Lijmer JG, Prins MH, Bonsel GJ, Bossuyt PM (2003) The diagnostic odds ratio: a single indicator of test performance. J Clin Epidemiol 56:1129–1135CrossRefPubMedGoogle Scholar
  48. 48.
    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 80:462–470CrossRefPubMedGoogle Scholar
  49. 49.
    Chen J, Huang SL, Li T, Chen XL (2006) In vivo research in astrocytoma cell proliferation with 1H-magnetic resonance spectroscopy: correlation with histopathology and immunohistochemistry. Neuroradiology 48:312–318CrossRefPubMedGoogle Scholar
  50. 50.
    Moller-Hartmann W, Herminghaus S, Krings T et al (2002) Clinical application of proton magnetic resonance spectroscopy in the diagnosis of intracranial mass lesions. Neuroradiology 44:371–381CrossRefPubMedGoogle Scholar
  51. 51.
    Bertholdo D, Watcharakorn A, Castillo M (2013) Brain proton magnetic resonance spectroscopy: introduction and overview. Neuroimaging Clin N Am 23:359–380CrossRefPubMedGoogle Scholar
  52. 52.
    Howe FA, Barton SJ, Cudlip SA et al (2003) Metabolic profiles of human brain tumors using quantitative in vivo 1H magnetic resonance spectroscopy. Magn Reson Med 49:223–232CrossRefPubMedGoogle Scholar
  53. 53.
    Pamir MN, Ozduman K, Yildiz E, Sav A, Dincer A (2013) Intraoperative magnetic resonance spectroscopy for identification of residual tumor during low-grade glioma surgery: clinical article. J Neurosurg 118:1191–1198CrossRefPubMedGoogle Scholar
  54. 54.
    Bradac O, Vrana J, Jiru F et al (2014) Recognition of anaplastic foci within low-grade gliomas using MR spectroscopy. Br J Neurosurg 28:631–636CrossRefPubMedGoogle Scholar
  55. 55.
    Hattingen E, Raab P, Franz K et al (2008) Prognostic value of choline and creatine in WHO grade II gliomas. Neuroradiology 50:759–767CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2015

Authors and Affiliations

  1. 1.Department of NeurosurgeryChinese PLA General HospitalBeijingChina
  2. 2.Department of NeurosurgeryAir Force General Hospital of the Chinese PLABeijingChina
  3. 3.Department of Neurosurgery, General HospitalJi’nan Military Area CommandJi’nanChina

Personalised recommendations