Journal of Neuro-Oncology

, Volume 142, Issue 2, pp 299–307 | Cite as

Machine learning reveals multimodal MRI patterns predictive of isocitrate dehydrogenase and 1p/19q status in diffuse low- and high-grade gliomas

  • Hao Zhou
  • Ken Chang
  • Harrison X. BaiEmail author
  • Bo Xiao
  • Chang Su
  • Wenya Linda Bi
  • Paul J. Zhang
  • Joeky T. Senders
  • Martin Vallières
  • Vasileios K. Kavouridis
  • Alessandro Boaro
  • Omar Arnaout
  • Li YangEmail author
  • Raymond Y. HuangEmail author
Clinical Study



Isocitrate dehydrogenase (IDH) and 1p19q codeletion status are importantin providing prognostic information as well as prediction of treatment response in gliomas. Accurate determination of the IDH mutation status and 1p19q co-deletion prior to surgery may complement invasive tissue sampling and guide treatment decisions.


Preoperative MRIs of 538 glioma patients from three institutions were used as a training cohort. Histogram, shape, and texture features were extracted from preoperative MRIs of T1 contrast enhanced and T2-FLAIR sequences. The extracted features were then integrated with age using a random forest algorithm to generate a model predictive of IDH mutation status and 1p19q codeletion. The model was then validated using MRIs from glioma patients in the Cancer Imaging Archive.


Our model predictive of IDH achieved an area under the receiver operating characteristic curve (AUC) of 0.921 in the training cohort and 0.919 in the validation cohort. Age offered the highest predictive value, followed by shape features. Based on the top 15 features, the AUC was 0.917 and 0.916 for the training and validation cohort, respectively. The overall accuracy for 3 group prediction (IDH-wild type, IDH-mutant and 1p19q co-deletion, IDH-mutant and 1p19q non-codeletion) was 78.2% (155 correctly predicted out of 198).


Using machine-learning algorithms, high accuracy was achieved in the prediction of IDH genotype in gliomas and moderate accuracy in a three-group prediction including IDH genotype and 1p19q codeletion.


Glioma Isocitrate dehydrogenase (IDH) 1p19q codeletion Machine learning Random forest MRI 



This work was supported by the Natural Science Foundation of China (81301988 to L.Y.), ShenghuaYuying Project of Central South University to L.Y. and the Natural Science Foundation of Hunan Province for Young Scientists, China (Grant No: 2018JJ3709 to Li Yang). This project was supported by a training grant from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health under Award Number 5T32EB1680 to K. Chang. This project was supported by the RSNA research fellow Grant to H.X.B (RF1802), SIR Foundation resident research grant to H.X.B, and Research Fund for International Young Scientist by the National Natural Science Foundation of China (818580410556 to H.X.B.). This research was carried out in whole or in part at the Athinoula A. Martinos Center for Biomedical Imaging at the Massachusetts General Hospital, using resources provided by the Center for Functional Neuroimaging Technologies, P41EB015896, a P41 Biotechnology Resource Grant supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB), National Institutes of Health.


This work was supported by the Natural Science Foundation of China (81301988 to L.Y.), ShenghuaYuying Project of Central South University to L.Y. Project supported by the Natural Science Foundation of Hunan Province for Young Scientists, China (Grant No: 2018JJ3709 to Li Yang). This project was supported by a training grant from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health under Award Number 5T32EB1680 to K. Chang. This project was supported by the RSNA research fellow grant to H.X.B (RF1802), SIR Foundation resident research grant to H.X.B, and Research Fund for International Young Scientist by the National Natural Science Foundation of China (818580410556 to H.X.B.).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

For this type of study, formal consent is not required.

Supplementary material

11060_2019_3096_MOESM1_ESM.pdf (494 kb)
Supplementary material 1 (PDF 493 KB)


  1. 1.
    Ostrom QT et al (2014) The epidemiology of glioma in adults: a “state of the science” review. Neuro Oncol 16(7):896–913CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Bi WL, Beroukhim R (2014) Beating the odds: extreme long-term survival with glioblastoma. Neuro Oncol 16(9):1159–1160CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Recht LD, Lew R, Smith TW (1992) Suspected low-grade glioma: is deferring treatment safe? Ann Neurol 31(4):431–436CrossRefPubMedGoogle Scholar
  4. 4.
    Parsons DW et al (2008) An integrated genomic analysis of human glioblastoma multiforme. Science 321(5897):1807–1812CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Eckel-Passow JE et al (2015) Glioma Groups based on 1p/19q, IDH, and TERT promoter mutations in tumors. N Engl J Med 372(26):2499–2508CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Hartmann C et al (2010) Patients with IDH1 wild type anaplastic astrocytomas exhibit worse prognosis than IDH1-mutated glioblastomas, and IDH1 mutation status accounts for the unfavorable prognostic effect of higher age: implications for classification of gliomas. Acta Neuropathol 120(6):707–718CrossRefPubMedGoogle Scholar
  7. 7.
    Houillier C et al (2010) IDH1 or IDH2 mutations predict longer survival and response to temozolomide in low-grade gliomas. Neurology 75(17):1560–1566CrossRefPubMedGoogle Scholar
  8. 8.
    Louis DN et al (2016) The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131(6):803–820CrossRefPubMedGoogle Scholar
  9. 9.
    Weller M et al (2013) Molecular neuro-oncology in clinical practice: a new horizon. Lancet Oncol 14(9):e370–e379CrossRefPubMedGoogle Scholar
  10. 10.
    SongTao Q et al (2012) IDH mutations predict longer survival and response to temozolomide in secondary glioblastoma. Cancer Sci 103(2):269–273CrossRefPubMedGoogle Scholar
  11. 11.
    Molenaar RJ et al (2015) Radioprotection of IDH1-mutated cancer cells by the IDH1-mutant inhibitor AGI-5198. Cancer Res 75(22):4790–4802CrossRefPubMedGoogle Scholar
  12. 12.
    Mohrenz IV et al (2013) Isocitrate dehydrogenase 1 mutant R132H sensitizes glioma cells to BCNU-induced oxidative stress and cell death. Apoptosis 18(11):1416–1425CrossRefPubMedGoogle Scholar
  13. 13.
    Lowery MA et al (2017) Phase I study of AG-120, an IDH1 mutant enzyme inhibitor: results from the cholangiocarcinoma dose escalation and expansion cohorts. J Clin Oncol 35(15_suppl):4015–4015CrossRefGoogle Scholar
  14. 14.
    Mellinghoff IK et al. (2016) ACTR-46. AG120, a first-in-class mutant IDH1 inhibitor in patients with recurrent or progressive IDH1 mutant glioma: results from the phase 1 glioma expansion cohorts. Oxford University Press, OxfordGoogle Scholar
  15. 15.
    Erdem-Eraslan L et al (2013) Intrinsic molecular subtypes of glioma are prognostic and predict benefit from adjuvant procarbazine, lomustine, and vincristine chemotherapy in combination with other prognostic factors in anaplastic oligodendroglial brain tumors: a report from EORTC study 26951. J Clin Oncol 31(3):328CrossRefPubMedGoogle Scholar
  16. 16.
    Cairncross G et al (2013) Phase III trial of chemoradiotherapy for anaplastic oligodendroglioma: long-term results of RTOG 9402. J Clin Oncol 31(3):337CrossRefPubMedGoogle Scholar
  17. 17.
    Tang L et al (2018) Reduced expression of DNA repair genes and chemosensitivity in 1p19q codeleted lower-grade gliomas. J Neuro-Oncol. CrossRefGoogle Scholar
  18. 18.
    Brat DJ et al (2015) Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N Engl J Med 372(26):2481–2498CrossRefPubMedGoogle Scholar
  19. 19.
    Kickingereder P 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 5:16238CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Biller 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(1):66–73CrossRefPubMedGoogle Scholar
  21. 21.
    Pope WB et al (2012) Non-invasive detection of 2-hydroxyglutarate and other metabolites in IDH1 mutant glioma patients using magnetic resonance spectroscopy. J Neurooncol 107(1):197–205CrossRefPubMedGoogle Scholar
  22. 22.
    Andronesi OC et al (2012) Detection of 2-hydroxyglutarate in IDH-mutated glioma patients by in vivo spectral-editing and 2D correlation magnetic resonance spectroscopy. Sci Transl Med 4(116):116ra4CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Choi C et al (2012) 2-hydroxyglutarate detection by magnetic resonance spectroscopy in IDH-mutated patients with gliomas. Nat Med 18(4):624–629CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Stadlbauer A et al (2017) MR imaging-derived oxygen metabolism and neovascularization characterization for grading and IDH gene mutation detection of gliomas. Radiology 283(3):799–809CrossRefPubMedGoogle Scholar
  25. 25.
    Iwadate Y et al (2016) Molecular imaging of 1p/19q deletion in oligodendroglial tumours with 11C-methionine positron emission tomography. J Neurol Neurosurg Psychiatry 87:1016–1021CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Fellah S et al (2013) Multimodal MR imaging (diffusion, perfusion, and spectroscopy): is it possible to distinguish oligodendroglial tumor grade and 1p/19q codeletion in the pretherapeutic diagnosis? Am J Neuroradiol 34(7):1326–1333CrossRefPubMedGoogle Scholar
  27. 27.
    Jansen NL et al (2012) Prediction of oligodendroglial histology and LOH 1p/19q using dynamic [18F] FET-PET imaging in intracranial WHO grade II and III gliomas. Neuro-oncology 14(12):1473–1480CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Clark K et al (2013) The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26(6):1045–1057CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
  30. 30.
    Chang K et al (2016) Multimodal imaging patterns predict survival in recurrent glioblastoma patients treated with bevacizumab. Neuro Oncol 18(12):1680–1687CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Fedorov A et al (2012) 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 30(9):1323–1341CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Iglesias JE et al (2011) Robust brain extraction across datasets and comparison with publicly available methods. IEEE Trans Med Imaging 30(9):1617–1634CrossRefPubMedGoogle Scholar
  33. 33.
    Breiman L (2001) Random forests. Mach Learn 45(1):5–32CrossRefGoogle Scholar
  34. 34.
    James G et al. (2013) An introduction to statistical learning, vol 112. Springer, New YorkCrossRefGoogle Scholar
  35. 35.
    Hanley JA, McNeil BJ (1983) A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 148(3):839–843CrossRefGoogle Scholar
  36. 36.
    Eoli M et al (2006) Reclassification of oligoastrocytomas by loss of heterozygosity studies. Int J Cancer 119(1):84–90CrossRefPubMedGoogle Scholar
  37. 37.
    Megyesi JF et al (2004) Imaging correlates of molecular signatures in oligodendrogliomas. Clin Cancer Res 10(13):4303–4306CrossRefPubMedGoogle Scholar
  38. 38.
    Jenkinson MD et al (2006) Histological growth patterns and genotype in oligodendroglial tumours: correlation with MRI features. Brain 129(Pt 7):1884–1891CrossRefPubMedGoogle Scholar
  39. 39.
    Brown R et al (2008) The use of magnetic resonance imaging to noninvasively detect genetic signatures in oligodendroglioma. Clin Cancer Res 14(8):2357–2362CrossRefPubMedGoogle Scholar
  40. 40.
    Liu C et al (2012) Towards MIB-1 and p53 detection in glioma magnetic resonance image: a novel computational image analysis method. Phys Med Biol 57(24):8393–8404CrossRefPubMedGoogle Scholar
  41. 41.
    Metellus P et al (2010) Absence of IDH mutation identifies a novel radiologic and molecular subtype of WHO grade II gliomas with dismal prognosis. Acta Neuropathol 120(6):719–729CrossRefPubMedGoogle Scholar
  42. 42.
    Qi S et al (2014) Isocitrate dehydrogenase mutation is associated with tumor location and magnetic resonance imaging characteristics in astrocytic neoplasms. Oncol Lett 7(6):1895–1902CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Park Y et al (2018) Prediction of IDH1-mutation and 1p/19q-codeletion status using preoperative MR imaging phenotypes in lower grade gliomas. Am J Neuroradiol 39(1):37–42CrossRefPubMedGoogle Scholar
  44. 44.
    Zhang B et al (2017) Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas. Neuro Oncol 19(1):109–117CrossRefPubMedGoogle Scholar
  45. 45.
    Yu J et al (2017) Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma. Eur Radiol 27(8):3509–3522CrossRefPubMedGoogle Scholar
  46. 46.
    Zhou H et al (2017) MRI features predict survival and molecular markers in diffuse lower-grade gliomas. Neuro Oncol 19(6):862–870CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Akkus Z et al (2017) Predicting deletion of chromosomal arms 1p/19q in low-grade gliomas from MR images using machine intelligence. J Digit Imaging 30(4):469–476CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Parmar C et al (2015) Machine learning methods for quantitative radiomic biomarkers. Sci Rep 5:13087CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Fernández-Delgado M et al (2014) Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res 15(1):3133–3181Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of NeurologyXiangya Hospital of Central South UniversityChangshaChina
  2. 2.Department of RadiologyBrigham and Women’s HospitalBostonUSA
  3. 3.Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General HospitalHarvard Medical SchoolBostonUSA
  4. 4.Department of NeurologyThe Second Xiangya Hospital of Central South UniversityChangshaChina
  5. 5.Department of RadiologyHospital of the University of PennsylvaniaPhiladelphiaUSA
  6. 6.Department of PathologyHospital of the University of PennsylvaniaPhiladelphiaUSA
  7. 7.Yale School of MedicineNew HavenUSA
  8. 8.Medical Physics UnitMcGill UniversityMontréalCanada
  9. 9.Center for Skull Base and Pituitary Surgery, Department of Neurosurgery, Brigham and Women’s HospitalHarvard Medical SchoolBostonUSA
  10. 10.Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women’s HospitalHarvard Medical SchoolBostonUSA

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