European Radiology

, Volume 29, Issue 2, pp 745–758 | Cite as

Imaging prediction of isocitrate dehydrogenase (IDH) mutation in patients with glioma: a systemic review and meta-analysis

  • Chong Hyun Suh
  • Ho Sung KimEmail author
  • Seung Chai Jung
  • Choong Gon Choi
  • Sang Joon Kim



To evaluate the imaging features of isocitrate dehydrogenase (IDH) mutant glioma and to assess the diagnostic performance of magnetic resonance imaging (MRI) for prediction of IDH mutation in patients with glioma.


A systematic search of Ovid-MEDLINE and EMBASE up to 10 October 2017 was conducted to find relevant studies. The search terms combined synonyms for ‘glioma’, ‘IDH mutation’ and ‘MRI’. Studies evaluating the imaging features of IDH mutant glioma and the diagnostic performance of MRI for prediction of IDH mutation in patients with glioma were selected. The pooled summary estimates of sensitivity and specificity and their 95% confidence intervals (CIs) were calculated using a bivariate random-effects model. The results of multiple subgroup analyses are reported.


Twenty-eight original articles in a total of 2,146 patients with glioma were included. IDH mutant glioma showed frontal lobe predominance, less contrast enhancement, well-defined border, high apparent diffusion coefficient (ADC) value and low relative cerebral blood volume (rCBV) value. For the meta-analysis that included 18 original articles, the summary sensitivity was 86% (95% CI, 79%–91%) and the summary specificity was 87% (95% CI, 78–92%). In a subgroup analysis, the summary sensitivity of 2-hydroxyglutarate magnetic resonance spectroscopy (MRS) [96% (95% CI, 91–100%)] was higher than the summary sensitivities of other imaging modalities.


IDH mutant glioma consistently demonstrated less aggressive imaging features than IDH wild-type glioma. Despite the variety of different MRI techniques used, MRI showed the potential to non-invasively predict IDH mutation in patients with glioma. 2-Hydroxyglutarate MRS shows higher pooled sensitivity than other imaging modalities.

Key Points

• IDH mutant glioma showed frontal lobe predominance, less contrast enhancement, well-defined border, high ADC value, and low rCBV value.

• The diagnostic performance of MRI for prediction of IDH mutation in patients with glioma is within a clinically acceptable range, the summary sensitivity was 86% (95% CI, 79–91%) and the summary specificity was 87% (95% CI, 78–92%).

• In a subgroup analysis, the summary sensitivity of 2-hydroxyglutarate MRS [96% (95% CI, 91–100%)] was higher than the summary sensitivities of other imaging modalities.


Glioma Magnetic resonance imaging Diffusion Perfusion Magnetic resonance spectroscopy 



Apparent diffusion coefficient


Amide proton transfer-weighted


Diffusion-weighted imaging


Hierarchical summary receiver operating characteristic


Isocitrate dehydrogenase


Magnetic resonance imaging


Magnetic resonance spectroscopy


Preferred reporting items for systematic reviews and meta-analyses


Perfusion-weighted imaging


Quality assessment of diagnostic accuracy studies-2


Relative cerebral blood volume


World Health Organization



This study was supported by a grant from the National R&D Program for Cancer Control, Ministry of Health and Welfare, Republic of Korea (1720030).

Compliance with ethical standards


The scientific guarantor of this publication is Ho Sung Kim.

Conflict of interest

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.

Statistics and biometry

One of the authors (Chong Hyun Suh) has significant statistical expertise (4 years of experience in a systematic review and meta-analysis).

Ethical approval

Institutional Review Board approval was not required because of the nature of our study, which was a systemic review and meta-analysis.

Informed consent

Written informed consent was not required for this study because of the nature of our study, which was a systemic review and meta-analysis.


• A systemic review and meta-analysis performed at one institution.

Supplementary material

330_2018_5608_MOESM1_ESM.docx (5.5 mb)
ESM 1 (DOCX 5602 kb)


  1. 1.
    Louis DN, Perry A, Reifenberger G et al (2016) The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol 131:803–820CrossRefGoogle Scholar
  2. 2.
    Hartmann C, Meyer J, Balss J et al (2009) Type and frequency of IDH1 and IDH2 mutations are related to astrocytic and oligodendroglial differentiation and age: a study of 1,010 diffuse gliomas. Acta Neuropathol 118:469–474CrossRefGoogle Scholar
  3. 3.
    Kickingereder P, Sahm F, Radbruch A et al (2015) IDH mutation status is associated with a distinct hypoxia/angiogenesis transcriptome signature which is non-invasively predictable with rCBV imaging in human glioma. Sci Rep 5:16238CrossRefGoogle Scholar
  4. 4.
    Brat DJ, Verhaak RG, Aldape KD et al (2015) Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas. N Engl J Med 372:2481–2498CrossRefGoogle Scholar
  5. 5.
    Eckel-Passow JE, Lachance DH, Molinaro AM et al (2015) Glioma groups based on 1p/19q, IDH, and TERT promoter mutations in tumors. N Engl J Med 372:2499–2508CrossRefGoogle Scholar
  6. 6.
    Miller JJ, Shih HA, Andronesi OC, Cahill DP (2017) Isocitrate dehydrogenase-mutant glioma: evolving clinical and therapeutic implications. Cancer 123:4535–4546CrossRefGoogle Scholar
  7. 7.
    Zhou H, Vallieres M, Bai HX et al (2017) MRI features predict survival and molecular markers in diffuse lower-grade gliomas. Neuro Oncol 19:862–870CrossRefGoogle Scholar
  8. 8.
    Zhang B, Chang K, Ramkissoon S et al (2017) Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas. Neuro Oncol 19:109–117CrossRefGoogle Scholar
  9. 9.
    Yu J, Shi Z, Lian Y et al (2017) Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma. Eur Radiol 27:3509–3522CrossRefGoogle Scholar
  10. 10.
    Xing Z, Yang X, She D, Lin Y, Zhang Y, Cao D (2017) Noninvasive assessment of IDH mutational status in World Health Organization grade II and III astrocytomas using DWI and DSC-PWI combined with conventional MR imaging. AJNR Am J Neuroradiol 38:1134–1144CrossRefGoogle Scholar
  11. 11.
    Tietze A, Choi C, Mickey B et al (2018) Noninvasive assessment of isocitrate dehydrogenase mutation status in cerebral gliomas by magnetic resonance spectroscopy in a clinical setting. J Neurosurg 128:391–398CrossRefGoogle Scholar
  12. 12.
    Tan W, Xiong J, Huang W, Wu J, Zhan S, Geng D (2017) Noninvasively detecting Isocitrate dehydrogenase 1 gene status in astrocytoma by dynamic susceptibility contrast MRI. J Magn Reson Imaging 45:492–499CrossRefGoogle Scholar
  13. 13.
    Stadlbauer A, Zimmermann M, Kitzwogerer M et al (2017) MR Imaging-derived oxygen metabolism and neovascularization characterization for grading and IDH gene mutation detection of gliomas. Radiology 283:799–809CrossRefGoogle Scholar
  14. 14.
    Price SJ, Allinson K, Liu H et al (2017) Less invasive phenotype found in isocitrate dehydrogenase-mutated glioblastomas than in isocitrate dehydrogenase wild-type glioblastomas: a diffusion-tensor imaging study. Radiology 283:215–221CrossRefGoogle Scholar
  15. 15.
    Patel SH, Poisson LM, Brat DJ et al (2017) T2-FLAIR mismatch, an imaging biomarker for IDH and 1p/19q status in lower-grade gliomas: a TCGA/TCIA project. Clin Cancer Res 23:6078–6085CrossRefGoogle Scholar
  16. 16.
    Nakae S, Murayama K, Sasaki H et al (2017) Prediction of genetic subgroups in adult supra tentorial gliomas by pre- and intraoperative parameters. J Neurooncol 131:403–412CrossRefGoogle Scholar
  17. 17.
    Leu K, Ott GA, Lai A et al (2017) Perfusion and diffusion MRI signatures in histologic and genetic subtypes of WHO grade II–III diffuse gliomas. J Neurooncol 134:177–188CrossRefGoogle Scholar
  18. 18.
    Lasocki A, Tsui A, Gaillard F, Tacey M, Drummond K, Stuckey S (2017) Reliability of noncontrast-enhancing tumor as a biomarker of IDH1 mutation status in glioblastoma. J Clin Neurosci 39:170–175CrossRefGoogle Scholar
  19. 19.
    Jiang S, Zou T, Eberhart CG et al (2017) Predicting IDH mutation status in grade II gliomas using amide proton transfer-weighted (APTw) MRI. Magn Reson Med 78:1100–1109CrossRefGoogle Scholar
  20. 20.
    Hsieh KL, Chen CY, Lo CM (2017) Radiomic model for predicting mutations in the isocitrate dehydrogenase gene in glioblastomas. J Neurooncol 8:45888–45897Google Scholar
  21. 21.
    Hempel JM, Schittenhelm J, Brendle C et al (2017) Histogram analysis of diffusion kurtosis imaging estimates for in vivo assessment of 2016 WHO glioma grades: a cross-sectional observational study. Eur J Radiol 95:202–211CrossRefGoogle Scholar
  22. 22.
    Grabner G, Kiesel B, Wohrer A et al (2017) Local image variance of 7 Tesla SWI is a new technique for preoperative characterization of diffusely infiltrating gliomas: correlation with tumour grade and IDH1 mutational status. Eur Radiol 27:1556–1567CrossRefGoogle Scholar
  23. 23.
    Delfanti RL, Piccioni DE, Handwerker J et al (2017) Imaging correlates for the 2016 update on WHO classification of grade II/III gliomas: implications for IDH, 1p/19q and ATRX status. J Neurooncol 135:601–609CrossRefGoogle Scholar
  24. 24.
    Yamashita K, Hiwatashi A, Togao O et al (2016) MR imaging-based analysis of glioblastoma multiforme: estimation of IDH1 mutation status. AJNR Am J Neuroradiol 37:58–65CrossRefGoogle Scholar
  25. 25.
    Xiong J, Tan W, Wen J et al (2016) Combination of diffusion tensor imaging and conventional MRI correlates with isocitrate dehydrogenase 1/2 mutations but not 1p/19q genotyping in oligodendroglial tumours. Eur Radiol 26:1705–1715CrossRefGoogle Scholar
  26. 26.
    Wang K, Wang Y, Fan X et al (2016) Radiological features combined with IDH1 status for predicting the survival outcome of glioblastoma patients. Neuro Oncol 18:589–597CrossRefGoogle Scholar
  27. 27.
    Choi C, Raisanen JM, Ganji SK et al (2016) Prospective longitudinal analysis of 2-hydroxyglutarate magnetic resonance spectroscopy identifies broad clinical utility for the management of patients with IDH-mutant glioma. J Clin Oncol 34:4030–4039CrossRefGoogle Scholar
  28. 28.
    Biller A, Badde S, Nagel A et al (2016) Improved brain tumor classification by sodium mr imaging: prediction of IDH mutation status and tumor progression. AJNR Am J Neuroradiol 37:66–73CrossRefGoogle Scholar
  29. 29.
    Wasserman JK, Nicholas G, Yaworski R et al (2015) Radiological and pathological features associated with IDH1-R132H mutation status and early mortality in newly diagnosed anaplastic astrocytic tumours. PLoS One 10:e0123890CrossRefGoogle Scholar
  30. 30.
    Sonoda Y, Shibahara I, Kawaguchi T et al (2015) Association between molecular alterations and tumor location and MRI characteristics in anaplastic gliomas. Brain Tumor Pathol 32:99–104CrossRefGoogle Scholar
  31. 31.
    Lee S, Choi SH, Ryoo I et al (2015) Evaluation of the microenvironmental heterogeneity in high-grade gliomas with IDH1/2 gene mutation using histogram analysis of diffusion-weighted imaging and dynamic-susceptibility contrast perfusion imaging. J Neurooncol 121:141–150CrossRefGoogle Scholar
  32. 32.
    Reyes-Botero G, Dehais C, Idbaih A et al (2014) Contrast enhancement in 1p/19q-codeleted anaplastic oligodendrogliomas is associated with 9p loss, genomic instability, and angiogenic gene expression. Neuro Oncol 16:662–670CrossRefGoogle Scholar
  33. 33.
    Qi S, Yu L, Li H et al (2014) Isocitrate dehydrogenase mutation is associated with tumor location and magnetic resonance imaging characteristics in astrocytic neoplasms. Oncol Lett 7:1895–1902CrossRefGoogle Scholar
  34. 34.
    Carrillo JA, Lai A, Nghiemphu PL et al (2012) Relationship between tumor enhancement, edema, IDH1 mutational status, MGMT promoter methylation, and survival in glioblastoma. AJNR Am J Neuroradiol 33:1349–1355CrossRefGoogle Scholar
  35. 35.
    Choi C, Ganji SK, DeBerardinis RJ et al (2012) 2-hydroxyglutarate detection by magnetic resonance spectroscopy in IDH-mutated patients with gliomas. Nat Med 18:624–629CrossRefGoogle Scholar
  36. 36.
    Li Z, Wang Y, Yu J, Guo Y, Cao W (2017) Deep Learning based Radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma. Sci Rep 7:5467CrossRefGoogle Scholar
  37. 37.
    Dang L, Yen K, Attar EC (2016) IDH mutations in cancer and progress toward development of targeted therapeutics. Ann Oncol 27:599–608CrossRefGoogle Scholar
  38. 38.
    Liberati A, Altman DG, Tetzlaff J et al (2009) The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. Ann Intern Med 151:W65–W94CrossRefGoogle Scholar
  39. 39.
    Louis DN, Ohgaki H, Wiestler OD et al (2007) The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol 114:97–109CrossRefGoogle Scholar
  40. 40.
    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 155:529–536CrossRefGoogle Scholar
  41. 41.
    Higgins J, Green S Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0. The Cochrane Collaboration. http:// identifying_and_measuring_heterogeneity. htm. Updated March 2011. Accessed 2 October 2017
  42. 42.
    Deville WL, Buntinx F, Bouter LM et al (2002) Conducting systematic reviews of diagnostic studies: didactic guidelines. BMC Med Res Methodol 2:9CrossRefGoogle Scholar
  43. 43.
    Suh CH, Park SH (2016) Successful publication of systematic review and meta-analysis of studies evaluating diagnostic test accuracy. Korean J Radiol 17:5–6CrossRefGoogle Scholar
  44. 44.
    Kim KW, Lee J, Choi SH, Huh J, Park SH (2015) Systematic review and meta-analysis of studies evaluating diagnostic test accuracy: a practical review for clinical researchers—Part I. General guidance and tips. Korean J Radiol 16:1175–1187CrossRefGoogle Scholar
  45. 45.
    Lee J, Kim KW, Choi SH, Huh J, Park SH (2015) Systematic review and meta-analysis of studies evaluating diagnostic test accuracy: a practical review for clinical researchers—Part II. Statistical methods of meta-analysis. Korean J Radiol 16:1188–1196CrossRefGoogle Scholar
  46. 46.
    Reitsma JB, Glas AS, Rutjes AW, Scholten RJ, Bossuyt PM, Zwinderman AH (2005) Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol 58:982–990CrossRefGoogle Scholar
  47. 47.
    Rutter CM, Gatsonis CA (2001) A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations. Stat Med 20:2865–2884CrossRefGoogle Scholar
  48. 48.
    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 58:882–893CrossRefGoogle Scholar
  49. 49.
    de la Fuente MI, Young RJ, Rubel J et al (2016) Integration of 2-hydroxyglutarate-proton magnetic resonance spectroscopy into clinical practice for disease monitoring in isocitrate dehydrogenase-mutant glioma. Neuro Oncol 18:283–290CrossRefGoogle Scholar
  50. 50.
    Deeks JJ, Bossuyt PM, Gatsonis C (eds) 2013 Cochrane handbook for systematic reviews of diagnostic test accuracy version 1.0.0. The Cochrane Collaboration. Accessed 9 Oct 2017
  51. 51.
    Trikalinos TA, Balion CM, Coleman CI et al (2012) Chapter 8: meta-analysis of test performance when there is a “gold standard”. J Gen Intern Med 27(Suppl 1):S56–S66CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of Radiology and Research Institute of RadiologyUniversity of Ulsan College of Medicine, Asan Medical CenterSongpa-GuRepublic of Korea

Personalised recommendations