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Semin Neurol 2018; 38(01): 032-040
DOI: 10.1055/s-0037-1618600
Review Article
Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Radiomics, Metabolic, and Molecular MRI for Brain Tumors

Philipp Kickingereder
1   Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany
,
Ovidiu Cristian Andronesi
2   Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
› Author Affiliations
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Publication History

Publication Date:
16 March 2018 (online)

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Abstract

Magnetic resonance imaging plays a key role in diagnosis and treatment monitoring of brain tumors. Novel imaging techniques that specifically interrogate aspects of underlying tumor biology and biochemical pathways have great potential in neuro-oncology. This review focuses on the emerging role of 2-hydroxyglutarate-targeted magnetic resonance spectroscopy, as well as radiomics and radiogenomics in establishing diagnosis for isocitrate dehydrogenase mutant gliomas, and for monitoring treatment response and predicting prognosis of this group of brain tumor patients.

Keywords

isocitrate dehydrogenase - 2-hydroxyglutarate - magnetic resonance spectroscopic imaging - radiomics - radiogenomics
 

  • References

  • 1 Ellingson BM, Bendszus M, Boxerman J. , et al; Jumpstarting Brain Tumor Drug Development Coalition Imaging Standardization Steering Committee. Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials. Neuro Oncol 2015; 17 (09) 1188-1198
    PubMedGoogle Scholar
  • 2 Kickingereder P, Götz M, Muschelli J. , et al. Large-scale radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-angiogenic treatment response. Clin Cancer Res 2016; 22 (23) 5765-5771 ; Epub ahead of print
    CrossrefPubMedGoogle Scholar
  • 3 Kickingereder P, Burth S, Wick A. , et al. Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology 2016; 280 (03) 880-889
    CrossrefPubMedGoogle Scholar
  • 4 Itakura H, Achrol AS, Mitchell LA. , et al. Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities. Sci Transl Med 2015; 7 (303) 303ra138
    CrossrefPubMedGoogle Scholar
  • 5 Gevaert O, Mitchell LA, Achrol AS. , et al. Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. Radiology 2014; 273 (01) 168-174
    CrossrefPubMedGoogle Scholar
  • 6 Grossmann P, Narayan V, Chang K. , et al. Quantitative imaging biomarkers for risk stratification of patients with recurrent glioblastoma treated with bevacizumab. Neuro Oncol 2017; 19 (12) 1688-1697
    CrossrefPubMedGoogle Scholar
  • 7 Prasanna P, Patel J, Partovi S, Madabhushi A, Tiwari P. Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings. Eur Radiol 2017; 27 (10) 4188-4197
    CrossrefPubMedGoogle Scholar
  • 8 Diehn M, Nardini C, Wang DS. , et al. Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc Natl Acad Sci U S A 2008; 105 (13) 5213-5218
    CrossrefPubMedGoogle Scholar
  • 9 Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology 2016; 278 (02) 563-577
    CrossrefPubMedGoogle Scholar
  • 10 Parmar C, Grossmann P, Bussink J, Lambin P, Aerts HJ. Machine learning methods for quantitative radiomic biomarkers. Sci Rep 2015; 5: 13087
    CrossrefPubMedGoogle Scholar
  • 11 Lambin P, Rios-Velazquez E, Leijenaar R. , et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012; 48 (04) 441-446
    CrossrefPubMedGoogle Scholar
  • 12 Jafari-Khouzani K, Loebel F, Bogner W. , et al. Volumetric relationship between 2-hydroxyglutarate and FLAIR hyperintensity has potential implications for radiotherapy planning of mutant IDH glioma patients. Neuro Oncol 2016; 18 (11) 1569-1578
    PubMedGoogle Scholar
  • 13 Andronesi OC, Loebel F, Bogner W. , et al. Treatment response assessment in IDH-mutant glioma patients by noninvasive 3D functional spectroscopic mapping of 2-hydroxyglutarate. Clin Cancer Res 2016; 22 (07) 1632-1641
    CrossrefPubMedGoogle Scholar
  • 14 Andronesi OC, Rapalino O, Gerstner E. , et al. Detection of oncogenic IDH1 mutations using magnetic resonance spectroscopy of 2-hydroxyglutarate. J Clin Invest 2013; 123 (09) 3659-3663
    CrossrefPubMedGoogle Scholar
  • 15 Andronesi OC, Kim GS, Gerstner E. , et al. Detection of 2-hydroxyglutarate in IDH-mutated glioma patients by in vivo spectral-editing and 2D correlation magnetic resonance spectroscopy. Sci Transl Med 2012; 4 (116) 116ra4
    PubMedGoogle Scholar
  • 16 Tietze A, Choi C, Mickey B. , et al. Noninvasive assessment of isocitrate dehydrogenase mutation status in cerebral gliomas by magnetic resonance spectroscopy in a clinical setting. J Neurosurg 2017; DOI: 10.3171/2016.10.jns161793.
    CrossrefPubMedGoogle Scholar
  • 17 de la Fuente MI, Young RJ, Rubel J. , et al. Integration of 2-hydroxyglutarate-proton magnetic resonance spectroscopy into clinical practice for disease monitoring in isocitrate dehydrogenase-mutant glioma. Neuro Oncol 2016; 18 (02) 283-290
    CrossrefPubMedGoogle Scholar
  • 18 Choi C, Raisanen JM, Ganji SK. , et al. 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 2016; 34 (33) 4030-4039
    CrossrefPubMedGoogle Scholar
  • 19 Choi C, Ganji SK, DeBerardinis RJ. , et al. 2-hydroxyglutarate detection by magnetic resonance spectroscopy in IDH-mutated patients with gliomas. Nat Med 2012; 18 (04) 624-629
    CrossrefPubMedGoogle Scholar
  • 20 Sala E, Mema E, Himoto Y. , et al. Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. Clin Radiol 2017; 72 (01) 3-10
    CrossrefPubMedGoogle Scholar
  • 21 Saha A, Yu X, Sahoo D. , et al. Effect of MRI scanner parameters on breast cancer radiomics. Expert Syst Appl 2017; 87: 384-391
    CrossrefPubMedGoogle Scholar
  • 22 Zhao B, Tan Y, Tsai WY. , et al. Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci Rep 2016; 6: 23428
    CrossrefPubMedGoogle Scholar
  • 23 Zacharaki EI, Wang S, Chawla S. , et al. Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med 2009; 62 (06) 1609-1618
    CrossrefPubMedGoogle Scholar
  • 24 Shinohara RT, Sweeney EM, Goldsmith J. , et al; Australian Imaging Biomarkers Lifestyle Flagship Study of Ageing; Alzheimer's Disease Neuroimaging Initiative. Statistical normalization techniques for magnetic resonance imaging. Neuroimage Clin 2014; 6: 9-19
    CrossrefPubMedGoogle Scholar
  • 25 Velazquez ER, Parmar C, Jermoumi M. , et al. Volumetric CT-based segmentation of NSCLC using 3D-Slicer. Sci Rep 2013; 3: 3529
    CrossrefPubMedGoogle Scholar
  • 26 Parmar C, Rios Velazquez E, Leijenaar R. , et al. Robust Radiomics feature quantification using semiautomatic volumetric segmentation. PLoS One 2014; 9 (07) e102107
    CrossrefPubMedGoogle Scholar
  • 27 Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern 1973; SMC-3 (06) 610-621
    CrossrefPubMedGoogle Scholar
  • 28 Aerts HJ, Velazquez ER, Leijenaar RT. , et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014; 5: 4006
    CrossrefPubMedGoogle Scholar
  • 29 Hu P, Wang J, Zhong H. , et al. Reproducibility with repeat CT in radiomics study for rectal cancer. Oncotarget 2016; 7 (44) 71440-71446
    PubMedGoogle Scholar
  • 30 Shiri I, Abdollahi H, Shaysteh S, Mahdavi SR. Test-retest reproducibility and robustness analysis of recurrent glioblastoma MRI radiomics texture features. Iran J Radiol 2017; 14 (03) e48035
    PubMedGoogle Scholar
  • 31 Hira ZM, Gillies DF. A review of feature selection and feature extraction methods applied on microarray data. Adv Bioinforma 2015; 2015: 198363
    PubMedGoogle Scholar
  • 32 Taylor JM, Ankerst DP, Andridge RR. Validation of biomarker-based risk prediction models. Clin Cancer Res 2008; 14 (19) 5977-5983
    CrossrefPubMedGoogle Scholar
  • 33 Limkin EJ, Sun R, Dercle L. , et al. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol 2017; 28 (06) 1191-1206
    CrossrefPubMedGoogle Scholar
  • 34 Bickelhaupt S, Paech D, Kickingereder P. , et al. Prediction of malignancy by a radiomic signature from contrast agent-free diffusion MRI in suspicious breast lesions found on screening mammography. J Magn Reson Imaging 2017; 46 (02) 604-616
    CrossrefPubMedGoogle Scholar
  • 35 Aerts HJ, Grossmann P, Tan Y. , et al. Defining a radiomic response phenotype: a pilot study using targeted therapy in NSCLC. Sci Rep 2016; 6: 33860
    CrossrefPubMedGoogle Scholar
  • 36 Ahmed A, Gibbs P, Pickles M, Turnbull L. Texture analysis in assessment and prediction of chemotherapy response in breast cancer. J Magn Reson Imaging 2013; 38 (01) 89-101
    CrossrefPubMedGoogle Scholar
  • 37 Nie K, Shi L, Chen Q. , et al. Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI. Clin Cancer Res 2016; 22 (21) 5256-5264
    CrossrefPubMedGoogle Scholar
  • 38 Coroller TP, Grossmann P, Hou Y. , et al. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol 2015; 114 (03) 345-350
    CrossrefPubMedGoogle Scholar
  • 39 Huang YQ, Liang CH, He L. , et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol 2016; 34 (18) 2157-2164
    CrossrefPubMedGoogle Scholar
  • 40 Liang C, Huang Y, He L. , et al. The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage I-II and stage III-IV colorectal cancer. Oncotarget 2016; 7 (21) 31401-31412
    PubMedGoogle Scholar
  • 41 Parmar C, Leijenaar RT, Grossmann P. , et al. Radiomic feature clusters and prognostic signatures specific for lung and head & neck cancer. Sci Rep 2015; 5: 11044
    CrossrefPubMedGoogle Scholar
  • 42 Leijenaar RT, Carvalho S, Hoebers FJ. , et al. External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma. Acta Oncol 2015; 54 (09) 1423-1429
    CrossrefPubMedGoogle Scholar
  • 43 Yang D, Rao G, Martinez J, Veeraraghavan A, Rao A. Evaluation of tumor-derived MRI-texture features for discrimination of molecular subtypes and prediction of 12-month survival status in glioblastoma. Med Phys 2015; 42 (11) 6725-6735
    CrossrefPubMedGoogle Scholar
  • 44 Cui Y, Tha KK, Terasaka S. , et al. Prognostic imaging biomarkers in glioblastoma: development and independent validation on the basis of multiregion and quantitative analysis of MR images. Radiology 2016; 278 (02) 546-553
    CrossrefPubMedGoogle Scholar
  • 45 Chinot OL, Wick W, Mason W. , et al. Bevacizumab plus radiotherapy-temozolomide for newly diagnosed glioblastoma. N Engl J Med 2014; 370 (08) 709-722
    CrossrefPubMedGoogle Scholar
  • 46 Gilbert MR, Dignam JJ, Armstrong TS. , et al. A randomized trial of bevacizumab for newly diagnosed glioblastoma. N Engl J Med 2014; 370 (08) 699-708
    CrossrefPubMedGoogle Scholar
  • 47 Wick W. , et al. Phase III trial exploring the combination of bevacizumab and lomustine in patients with first recurrence of a glioblastoma: the EORTC 26101 trial, in 2015 SNO Annual Meeting . 2015: San Antonio
    PubMedGoogle Scholar
  • 48 Lu-Emerson C, Duda DG, Emblem KE. , et al. Lessons from anti-vascular endothelial growth factor and anti-vascular endothelial growth factor receptor trials in patients with glioblastoma. J Clin Oncol 2015; 33 (10) 1197-1213
    CrossrefPubMedGoogle Scholar
  • 49 Mayer TM. Can we predict bevacizumab responders in patients with glioblastoma?. J Clin Oncol 2015; 33 (25) 2721-2722
    CrossrefPubMedGoogle Scholar
  • 50 Friedman HS, Prados MD, Wen PY. , et al. Bevacizumab alone and in combination with irinotecan in recurrent glioblastoma. J Clin Oncol 2009; 27 (28) 4733-4740
    CrossrefPubMedGoogle Scholar
  • 51 Chin L. , et al; Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 2008; 455 (7216): 1061-1068
    CrossrefPubMedGoogle Scholar
  • 52 Verhaak RG, Hoadley KA, Purdom E. , et al; Cancer Genome Atlas Research Network. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 2010; 17 (01) 98-110
    CrossrefPubMedGoogle Scholar
  • 53 Parsons DW, Jones S, Zhang X. , et al. An integrated genomic analysis of human glioblastoma multiforme. Science 2008; 321 (5897): 1807-1812
    CrossrefPubMedGoogle Scholar
  • 54 Patel AP, Tirosh I, Trombetta JJ. , et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 2014; 344 (6190): 1396-1401
    CrossrefPubMedGoogle Scholar
  • 55 Eckel-Passow JE, Lachance DH, Molinaro AM. , et al. Glioma groups based on 1p/19q, IDH, and TERT promoter mutations in tumors. N Engl J Med 2015; 372 (26) 2499-2508
    CrossrefPubMedGoogle Scholar
  • 56 Louis DN, Perry A, Reifenberger G. , et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol 2016; 131 (06) 803-820
    CrossrefPubMedGoogle Scholar
  • 57 Ostrom QT, Gittleman H, Fulop J. , et al. CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2008-2012. Neuro Oncol 2015; 17 (Suppl. 04) iv1-iv62
    CrossrefPubMedGoogle Scholar
  • 58 Ahluwalia MS, Chang SM. Medical therapy of gliomas. J Neurooncol 2014; 119 (03) 503-512
    CrossrefPubMedGoogle Scholar
  • 59 Yen KE, Bittinger MA, Su SM, Fantin VR. Cancer-associated IDH mutations: biomarker and therapeutic opportunities. Oncogene 2010; 29 (49) 6409-6417
    CrossrefPubMedGoogle Scholar
  • 60 Waitkus MS, Diplas BH, Yan H. Isocitrate dehydrogenase mutations in gliomas. Neuro Oncol 2016; 18 (01) 16-26
    CrossrefPubMedGoogle Scholar
  • 61 Yan H, Parsons DW, Jin G. , et al. IDH1 and IDH2 mutations in gliomas. N Engl J Med 2009; 360 (08) 765-773
    CrossrefPubMedGoogle Scholar
  • 62 Dang L, White DW, Gross S. , et al. Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature 2009; 462 (7274): 739-744
    CrossrefPubMedGoogle Scholar
  • 63 Mardis ER, Ding L, Dooling DJ. , et al. Recurring mutations found by sequencing an acute myeloid leukemia genome. N Engl J Med 2009; 361 (11) 1058-1066
    CrossrefPubMedGoogle Scholar
  • 64 Amary MF, Bacsi K, Maggiani F. , et al. IDH1 and IDH2 mutations are frequent events in central chondrosarcoma and central and periosteal chondromas but not in other mesenchymal tumours. J Pathol 2011; 224 (03) 334-343
    CrossrefPubMedGoogle Scholar
  • 65 Borger DR, Tanabe KK, Fan KC. , et al. Frequent mutation of isocitrate dehydrogenase (IDH)1 and IDH2 in cholangiocarcinoma identified through broad-based tumor genotyping. Oncologist 2012; 17 (01) 72-79
    CrossrefPubMedGoogle Scholar
  • 66 Reitman ZJ, Jin G, Karoly ED. , et al. Profiling the effects of isocitrate dehydrogenase 1 and 2 mutations on the cellular metabolome. Proc Natl Acad Sci U S A 2011; 108 (08) 3270-3275
    CrossrefPubMedGoogle Scholar
  • 67 Bleeker FE, Atai NA, Lamba S. , et al. The prognostic IDH1( R132 ) mutation is associated with reduced NADP+-dependent IDH activity in glioblastoma. Acta Neuropathol 2010; 119 (04) 487-494
    CrossrefPubMedGoogle Scholar
  • 68 Tateishi K, Wakimoto H, Iafrate AJ. , et al. Extreme vulnerability of IDH1 mutant cancers to NAD+ depletion. Cancer Cell 2015; 28 (06) 773-784
    CrossrefPubMedGoogle Scholar
  • 69 Maxwell PH, Wiesener MS, Chang GW. , et al. The tumour suppressor protein VHL targets hypoxia-inducible factors for oxygen-dependent proteolysis. Nature 1999; 399 (6733): 271-275
    CrossrefPubMedGoogle Scholar
  • 70 Koivunen P, Lee S, Duncan CG. , et al. Transformation by the (R)-enantiomer of 2-hydroxyglutarate linked to EGLN activation. Nature 2012; 483 (7390): 484-488
    CrossrefPubMedGoogle Scholar
  • 71 Kickingereder P, Sahm F, Radbruch A. , et al. 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 2015; 5: 16238
    CrossrefPubMedGoogle Scholar
  • 72 Xu W, Yang H, Liu Y. , et al. Oncometabolite 2-hydroxyglutarate is a competitive inhibitor of α-ketoglutarate-dependent dioxygenases. Cancer Cell 2011; 19 (01) 17-30
    CrossrefPubMedGoogle Scholar
  • 73 Noushmehr H, Weisenberger DJ, Diefes K. , et al; Cancer Genome Atlas Research Network. Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. Cancer Cell 2010; 17 (05) 510-522
    CrossrefPubMedGoogle Scholar
  • 74 Turcan S, Rohle D, Goenka A. , et al. IDH1 mutation is sufficient to establish the glioma hypermethylator phenotype. Nature 2012; 483 (7390): 479-483
    CrossrefPubMedGoogle Scholar
  • 75 Chowdhury R, Yeoh KK, Tian YM. , et al. The oncometabolite 2-hydroxyglutarate inhibits histone lysine demethylases. EMBO Rep 2011; 12 (05) 463-469
    CrossrefPubMedGoogle Scholar
  • 76 Lu C, Ward PS, Kapoor GS. , et al. IDH mutation impairs histone demethylation and results in a block to cell differentiation. Nature 2012; 483 (7390): 474-478
    CrossrefPubMedGoogle Scholar
  • 77 Kraus JA, Koopmann J, Kaskel P. , et al. Shared allelic losses on chromosomes 1p and 19q suggest a common origin of oligodendroglioma and oligoastrocytoma. J Neuropathol Exp Neurol 1995; 54 (01) 91-95
    CrossrefPubMedGoogle Scholar
  • 78 von Deimling A, Louis DN, von Ammon K, Petersen I, Wiestler OD, Seizinger BR. Evidence for a tumor suppressor gene on chromosome 19q associated with human astrocytomas, oligodendrogliomas, and mixed gliomas. Cancer Res 1992; 52 (15) 4277-4279
    PubMedGoogle Scholar
  • 79 Heaphy CM, de Wilde RF, Jiao Y. , et al. Altered telomeres in tumors with ATRX and DAXX mutations. Science 2011; 333 (6041): 425
    CrossrefPubMedGoogle Scholar
  • 80 Jiao Y, Killela PJ, Reitman ZJ. , et al. Frequent ATRX, CIC, FUBP1 and IDH1 mutations refine the classification of malignant gliomas. Oncotarget 2012; 3 (07) 709-722
    PubMedGoogle Scholar
  • 81 Tran AN, Lai A, Li S. , et al. Increased sensitivity to radiochemotherapy in IDH1 mutant glioblastoma as demonstrated by serial quantitative MR volumetry. Neuro-oncol 2014; 16 (03) 414-420
    PubMedGoogle Scholar
  • 82 Beiko J, Suki D, Hess KR. , et al. IDH1 mutant malignant astrocytomas are more amenable to surgical resection and have a survival benefit associated with maximal surgical resection. Neuro Oncol 2014; 16 (01) 81-91
    CrossrefPubMedGoogle Scholar
  • 83 Watanabe T, Nobusawa S, Kleihues P, Ohgaki H. IDH1 mutations are early events in the development of astrocytomas and oligodendrogliomas. Am J Pathol 2009; 174 (04) 1149-1153
    CrossrefPubMedGoogle Scholar
  • 84 Kunz M, Thon N, Eigenbrod S. , et al. Hot spots in dynamic (18)FET-PET delineate malignant tumor parts within suspected WHO grade II gliomas. Neuro Oncol 2011; 13 (03) 307-316
    CrossrefPubMedGoogle Scholar
  • 85 Ward PS, Cross JR, Lu C. , et al. Identification of additional IDH mutations associated with oncometabolite R(-)-2-hydroxyglutarate production. Oncogene 2012; 31 (19) 2491-2498
    CrossrefPubMedGoogle Scholar
  • 86 Ward PS, Patel J, Wise DR. , et al. The common feature of leukemia-associated IDH1 and IDH2 mutations is a neomorphic enzyme activity converting alpha-ketoglutarate to 2-hydroxyglutarate. Cancer Cell 2010; 17 (03) 225-234
    CrossrefPubMedGoogle Scholar
  • 87 Capper D, Zentgraf H, Balss J, Hartmann C, von Deimling A. Monoclonal antibody specific for IDH1 R132H mutation. Acta Neuropathol 2009; 118 (05) 599-601
    CrossrefPubMedGoogle Scholar
  • 88 Fathi AT, Sadrzadeh H, Borger DR. , et al. Prospective serial evaluation of 2-hydroxyglutarate, during treatment of newly diagnosed acute myeloid leukemia, to assess disease activity and therapeutic response. Blood 2012; 120 (23) 4649-4652
    CrossrefPubMedGoogle Scholar
  • 89 Nakamizo S, Sasayama T, Shinohara M. , et al. GC/MS-based metabolomic analysis of cerebrospinal fluid (CSF) from glioma patients. J Neurooncol 2013; 113 (01) 65-74
    CrossrefPubMedGoogle Scholar
  • 90 Lombardi G, Corona G, Bellu L. , et al. Diagnostic value of plasma and urinary 2-hydroxyglutarate to identify patients with isocitrate dehydrogenase-mutated glioma. Oncologist 2015; 20 (05) 562-567
    CrossrefPubMedGoogle Scholar
  • 91 Fathi AT, Nahed BV, Wander SA. , et al. Elevation of urinary 2-hydroxyglutarate in IDH-mutant glioma. Oncologist 2016; 21 (02) 214-219
    CrossrefPubMedGoogle Scholar
  • 92 Carrillo JA, Lai A, Nghiemphu PL. , et al. Relationship between tumor enhancement, edema, IDH1 mutational status, MGMT promoter methylation, and survival in glioblastoma. AJNR Am J Neuroradiol 2012; 33 (07) 1349-1355
    CrossrefPubMedGoogle Scholar
  • 93 Elkhaled A, Jalbert LE, Phillips JJ. , et al. Magnetic resonance of 2-hydroxyglutarate in IDH1-mutated low-grade gliomas. Sci Transl Med 2012; 4 (116) 116ra5
    PubMedGoogle Scholar
  • 94 Santagata S, Eberlin LS, Norton I. , et al. Intraoperative mass spectrometry mapping of an onco-metabolite to guide brain tumor surgery. Proc Natl Acad Sci U S A 2014; 111 (30) 11121-11126
    CrossrefPubMedGoogle Scholar
  • 95 Tan WL, Huang WY, Yin B, Xiong J, Wu JS, Geng DY. Can diffusion tensor imaging noninvasively detect IDH1 gene mutations in astrogliomas? A retrospective study of 112 cases. AJNR Am J Neuroradiol 2014; 35 (05) 920-927
    CrossrefPubMedGoogle Scholar
  • 96 Xing Z, Yang X, She D, Lin Y, Zhang Y, Cao D. 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 2017; 38 (06) 1138-1144
    CrossrefPubMedGoogle Scholar
  • 97 Lee S, Choi SH, Ryoo I. , et al. 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 2015; 121 (01) 141-150
    CrossrefPubMedGoogle Scholar
  • 98 Metellus P, Colin C, Taieb D. , et al. IDH mutation status impact on in vivo hypoxia biomarkers expression: new insights from a clinical, nuclear imaging and immunohistochemical study in 33 glioma patients. J Neurooncol 2011; 105 (03) 591-600
    CrossrefPubMedGoogle Scholar
  • 99 Verger A, Metellus P, Sala Q. , et al. IDH mutation is paradoxically associated with higher 18F-FDOPA PET uptake in diffuse grade II and grade III gliomas. Eur J Nucl Med Mol Imaging 2017; 44 (08) 1306-1311
    CrossrefPubMedGoogle Scholar
  • 100 Pope WB, Prins RM, Albert Thomas M. , et al. Non-invasive detection of 2-hydroxyglutarate and other metabolites in IDH1 mutant glioma patients using magnetic resonance spectroscopy. J Neurooncol 2012; 107 (01) 197-205
    CrossrefPubMedGoogle Scholar
  • 101 An Z, Ganji SK, Tiwari V. , et al. Detection of 2-hydroxyglutarate in brain tumors by triple-refocusing MR spectroscopy at 3T in vivo. Magn Reson Med 2017; 78 (01) 40-48
    CrossrefPubMedGoogle Scholar
  • 102 Ganji SK, An Z, Tiwari V. , et al. In vivo detection of 2-hydroxyglutarate in brain tumors by optimized point-resolved spectroscopy (PRESS) at 7T. Magn Reson Med 2017; 77 (03) 936-944
    CrossrefPubMedGoogle Scholar
  • 103 Emir UE, Larkin SJ, de Pennington N. , et al. Noninvasive quantification of 2-hydroxyglutarate in human gliomas with IDH1 and IDH2 mutations. Cancer Res 2016; 76 (01) 43-49
    PubMedGoogle Scholar
  • 104 Chaumeil MM, Larson PE, Yoshihara HA. , et al. Non-invasive in vivo assessment of IDH1 mutational status in glioma. Nat Commun 2013; 4: 2429
    CrossrefPubMedGoogle Scholar
  • 105 Esmaeili M, Hamans BC, Navis AC. , et al. IDH1 R132H mutation generates a distinct phospholipid metabolite profile in glioma. Cancer Res 2014; 74 (17) 4898-4907
    CrossrefPubMedGoogle Scholar
  • 106 Rohle D, Popovici-Muller J, Palaskas N. , et al. An inhibitor of mutant IDH1 delays growth and promotes differentiation of glioma cells. Science 2013; 340 (6132): 626-630
    CrossrefPubMedGoogle Scholar
  • 107 Schumacher T, Bunse L, Pusch S. , et al. A vaccine targeting mutant IDH1 induces antitumour immunity. Nature 2014; 512 (7514): 324-327
    CrossrefPubMedGoogle Scholar
  • 108 Seltzer MJ, Bennett BD, Joshi AD. , et al. Inhibition of glutaminase preferentially slows growth of glioma cells with mutant IDH1. Cancer Res 2010; 70 (22) 8981-8987
    CrossrefPubMedGoogle Scholar
  • 109 Upadhyay VA, Brunner AM, Fathi AT. Isocitrate dehydrogenase (IDH) inhibition as treatment of myeloid malignancies: progress and future directions. Pharmacol Ther 2017; 177 (17) 123-128
    CrossrefPubMedGoogle Scholar