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Glioma Papers
An update of radiogenomics based on MRI features in glioma
WANG Jia  HU Su  HU Chun-hong 

DOI:10.12015/issn.1674-8034.2018.10.002.


[Abstract] Brain gliomas are one of the malignant tumor in central nervous system. Gliomas are of high incidence and recurrence rate with poor prognosis. In recent years, the diagnosis of glioma has been prompt to molecular genetic level because of the increased recognize of tumoral molecular genetics. A new diagnostic guideline is proposed, with the tumoral molecular characterization being taken into account. MRI can be used to reflect the histopathology, cell metabolism and molecular genetic changes of tumor. In this paper, the application of MRI techniques in evaluating the molecular genetics of gliomas is reviewed.
[Keywords] Glioma;Magnetic resonance imaging;Genomics;Molecular genetics

WANG Jia Department of Radiology, the First Affiliated Hospital of Suzhou University, Suzhou 215006, China

HU Su Department of Radiology, the First Affiliated Hospital of Suzhou University, Suzhou 215006, China

HU Chun-hong* Department of Radiology, the First Affiliated Hospital of Suzhou University, Suzhou 215006, China

*Correspondence to: Hu CH, E-mail: hch5305@163.com

Conflicts of interest   None.

Received  2018-07-08
DOI: 10.12015/issn.1674-8034.2018.10.002
DOI:10.12015/issn.1674-8034.2018.10.002.

[1]
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 4): 1-62.
[2]
王凯,张姝,施露,等. 2016年世界卫生组织中枢神经系统肿瘤分类概述.磁共振成像, 2016, 7(12): 881-896.
[3]
Komori T, Muragaki Y, Chernov MF. Pathology and genetics of gliomas. Prog Neurol Surg, 2018, 31(1): 1-37.
[4]
Jain R, Poisson LM, Gutman D, et al. Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor. Radiology, 2014, 272(2): 484-493.
[5]
Hu LS, Ning S, Eschbacher JM, et al. Radiogenomics to characterize regional genetic heterogeneity in glioblastoma. Neuro Oncol, 2017, 19(1): 128-137.
[6]
Parsons DW, Jones S, Zhang X, et al. An integrated genomic analysis of human glioblastoma multiforme. Science, 2008, 321(5897): 1807-1812.
[7]
Sabha N, Knobbe CB, Maganti M, et al. Analysis of IDH mutation, 1p/19q deletion, and PTEN loss delineates prognosis in clinical low-grade diffuse gliomas. Neuro Oncol, 2014, 16(7): 914-923.
[8]
Qi S, Yu L, Li H, et al. Isocitrate dehydrogenase mutation is associated with tumor location and magnetic resonance imaging characteristics in astrocytic neoplasms. Oncol Lett, 2014, 7(6): 1895-1902.
[9]
Viswanath P, Chaumeil MM, Ronen SM. Molecular imaging of metabolic reprograming in mutant IDH cells. Front Oncol, 2016, 6(3): 60.
[10]
Esmaeili M, Vettukattil R, Bathen TF. 2-hydroxyglutarate as a magnetic resonance biomarker for glioma subtyping. Transl Oncol, 2013, 6(2): 92-98.
[11]
Choi C, Ganji S, Hulsey K, et al. A comparative study of short- and long-TE 1H MRS at 3 T for in vivo detection of 2-hydroxyglutarate in brain tumors. NMR Biomed, 2013, 26(10): 1242-1250.
[12]
Kickingereder P, Sahm F, Radbruch A, et al. IDH mutation status is associated with a distinct hypoxia/angiogenesis transcriptome signature which is noninvasively predictable with rCBV imaging in human glioma. Sci Rep, 2015, 5(1): 16238.
[13]
Eichinger P, Alberts E, Delbridge C, et al. Diffusion tensor image features predict IDH genotype in newly diagnosed WHO grade II/III gliomas. Sci Rep, 2017, 7(1): 13396.
[14]
Zhang B, Chang K, Ramkissoon S, et al. Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas. Neuro Oncol, 2017, 19(1): 109-117.
[15]
Yamashita K, Hiwatashi A, Togao O, et al. MR imaging-based analysis of glioblastoma multiforme: estimation of IDH1 mutation status. AJNR Am J Neuroradiol, 2016, 37(1): 58-65.
[16]
Frenel JS, Leux C, Loussouarn D, et al. Combining two biomarkers, IDH1/2 mutations and 1p/19q codeletion, to stratify anaplastic oligodendroglioma in three groups: a single-center experience. J Neurooncol, 2013, 114(1): 85-91.
[17]
Frenel JS, Leux C, Loussouarn D, et al. Genetically defined oligodendroglioma is characterized by indistinct tumor borders at MRI. J Neurooncol, 2013, 114(1): 85-91.
[18]
Zlatescu MC, Tehraniyazdi A, Sasaki H, et al. Tumor location and growth pattern correlate with genetic signature in oligodendroglial neoplasms. Cancer Res, 2001, 61(18): 6713-6715.
[19]
Jansen RW, van Amstel P, Martens RM, et al. Non-invasive tumor genotyping using radiogenomic biomarkers, a systematic review and oncology-wide pathway analysis. Oncotarget, 2018, 9(28): 20134-20155
[20]
Jenkinson MD, du Plessis DG, Smith TS, et al. Histological growth patterns and genotype in oligodendroglial tumours: correlation with MRI features. Brain, 2006, 129(Pt7): 1884-1891.
[21]
Jenkinson MD, Smith TS, Joyce KA, et al. Cerebral blood volume, genotype and chemosensitivity in oligodendroglial tumors. Neuroradiology, 2006, 48(10): 703-713.
[22]
Jenkinson MD, Smith TS, Brodbelt AR, et al. Apparent diffusion coefficients in oligodendroglial tumors characterized by genotype. J Magn Reson Imaging, 2007, 26(6): 1405-1412.
[23]
Fellah S, Caudal D, De Paula AM, et al. Multimodal MR imaging (diffusion, perfusion, and spectroscopy): is it possible to distinguish oligodendroglial tumor grade and 1p/19q codeletion in the pretherapeutic diagnosis?. AJNR Am J Neuroradiol, 2013, 34(7): 1326-1333.
[24]
Zhou H, Vallières M, Bai HX, et al. MRI features predict survival and molecular markers in diffuse lower-grade gliomas. Neuro Oncol, 2017, 19(6): 862-870.
[25]
Rui W, Ren Y, Wang Y, et al. MR textural analysis on T2 FLAIR images for the prediction of true oligodendroglioma by the 2016 WHO genetic classification. J Magn Reson Imaging, 2018, 48(1): 74-83.
[26]
Wang K, Wang Y, Fan X, et al. Regional specificity of 1p/19q co-deletion combined with radiological features for predicting the survival outcomes of anaplastic oligodendroglial tumor patients. J Neurooncol, 2018, 136(3): 523-531.
[27]
Cairncross G, Wang M, Shaw E, et al. Phase III trial of chemoradiotherapy for anaplastic oligodendroglioma: long-term results of RTOG 9402. J Clin Oncol, 2013, 31(3): 337-343.
[28]
Liu J, Zhang C, Feng Z. Tumor suppressor p53 and its gain-of-function mutants in cancer. Acta Biochim Biophys Sin (Shanghai), 2014, 46(3): 170-179.
[29]
Fu XR, Sun ZC, Chang Y. Expression and clinical significance of P53, O6-methylguanine-dna methyltransferase and epidermal growth factor receptor in glioma. J Biol Regul Homeost Agents, 2015, 29(4): 853-858.
[30]
Wang YY, Zhang T, Li SW, et al. Mapping p53 mutations in low-grade glioma: a voxel-based neuroimaging analysis. AJNR Am J Neuroradiol, 2015, 36(1): 70-76.
[31]
Zhang T, Wang Y, Fan X, et al. Anatomical localization of p53 mutated tumors: A radiographic study of human glioblastomas. J Neurol Sci, 2014, 346(1-2): 94-98.
[32]
周东海,何宁,黄进,等.幕上星形细胞瘤瘤周水肿MRI表现与P53蛋白的对照研究.中国临床医学影像杂志, 2007, 18(3): 167-170.
[33]
Li Y, Qian Z, Xu K, et al. MRI features predict p53 status in lower-grade gliomas via a machine-learning approach. Neuroimage Clin, 2017, 17(10): 306-311.
[34]
Yuan Q, Matsumoto K, Nakabeppu Y, et al. A comparative immunohistochemistry of O6-methylguanine-DNA methyltransferase and p53 in diffusely infiltrating astrocytomas. Neuropathology, 2003, 23(3): 203-209.
[35]
Esteller M, Garcia-Foncillas J, Andion E, et al. Inactivation of the DNA-repair gene MGMT and the clinical response of gliomas to alkylating agents. N Engl J Med, 2000, 343(19): 1350-1354.
[36]
Korfiatis P, Kline TL, Coufalova L, et al. MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas. Med Phys, 2016, 43(6): 2835-2844.
[37]
Ellingson BM, Lai A, Harris RJ, et al. Probabilistic radiographic atlas of glioblastoma phenotypes. AJNR Am J Neuroradiol, 2013, 34(3): 533-540.
[38]
Romano A, Calabria LF, Tavanti F, et al. Apparent diffusion coefficient obtained by magnetic resonance imaging as a prognostic marker in glioblastomas: correlation with MGMT promoter methylation status. Eur Radiol, 2013, 23(2): 513-520.
[39]
Ahn SS, Shin N, Chang JH, et al. Prediction of methylguanine methyltransferase promoter methylation in glioblastoma using dynamic contrast-enhanced magnetic resonance and diffusion tensor imaging. J Neurosurg, 2014, 121(2): 367-373.
[40]
Han Y, Yan LF, Wang XB, et al. Structural and advanced imaging in predicting MGMT promoter methylation of primary glioblastoma: a region of interest based analysis. BMC Cancer, 2018, 18(1): 215.
[41]
Kanas VG, Zacharaki EI, Thomas GA, et al. Learning MRI-based classification models for MGMT methylation status prediction in glioblastoma. Comput Methods Programs Biomed, 2017, 140(3): 249-257.
[42]
Han L, Kamdar MR. MRI to MGMT: predicting methylation status in glioblastoma patients using convolutional recurrent neural networks. Pac Symp Biocomput, 2018, 23(1): 331-342.
[43]
Brendle C, Hempel J, Schittenhelm J, et al. Glioma grading and determination of IDH mutation status and ATRX loss by DCE and ASL perfusion. Clin Neuroradiol, 2017, 27(2): 1-8.
[44]
Gupta A, Young RJ, Shah AD, et al. Pretreatment dynamic susceptibility contrast MRI perfusion in glioblastoma: prediction of EGFR gene amplification. Clin Neuroradiol, 2015, 25(2): 143-150.
[45]
Li Y, Liu X, Xu K, et al. MRI features can predict EGFR expression in lower grade gliomas: A voxel-based radiomic analysis. Eur Radiol, 2018, 28(1): 356-362.

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