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Progress in magnetic resonance imaging for predicting molecular phenotype of glioma
KE Xiaoai  ZHOU Junlin 

Cite this article as: Ke XA, Zhou JL. Progress in magnetic resonance imaging for predicting molecular phenotype of glioma. Chin J Magn Reson Imaging, 2019, 10(8): 611-614. DOI:10.12015/issn.1674-8034.2019.08.011.


[Abstract] Gliomas are the most common primary tumors in the brain. As infiltrating tumors, they are characterized by high recurrence and disability rate and high mortality. Molecular markers can help clinical diagnosis of gliomas, formulate personalized treatment plans and predict the prognosis of tumors. Magnetic resonance imaging is the preferred method for preoperative diagnosis and prognosis evaluation of central nervous system tumors. Functional magnetic resonance imaging is helpful to reflect the microbiological changes of tumors noninvasively from cell level, molecular level and gene mutation status. The application of functional magnetic resonance imaging in molecular phenotype of glioma is reviewed in this paper.
[Keywords] glioma；magnetic resonance imaging；functional magnetic resonance imaging；molecular phenotype

KE Xiaoai Radiology Imaging Center, Second Hospital of Lanzhou University; Second Clinical Medical College of Lanzhou University; Gansu Key Laboratory of Medical Imaging, Lanzhou 730030, China

ZHOU Junlin* Radiology Imaging Center, Second Hospital of Lanzhou University; Second Clinical Medical College of Lanzhou University; Gansu Key Laboratory of Medical Imaging, Lanzhou 730030, China

*Corresponding to: Zhou JL, E-mail: lzuzjl601@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS  This work was part of National Natural Science Foundation of China No.81772006
Received  2019-03-19
Accepted  2019-04-30
DOI: 10.12015/issn.1674-8034.2019.08.011
Cite this article as: Ke XA, Zhou JL. Progress in magnetic resonance imaging for predicting molecular phenotype of glioma. Chin J Magn Reson Imaging, 2019, 10(8): 611-614. DOI:10.12015/issn.1674-8034.2019.08.011.

[1]
Grant R, Kolb L, Moliterno J. Molecular and genetic pathways in gliomas: the future of personalized therapeutics. CNS Oncol, 2014, 3(2): 123-136.
[2]
Lu S, Ahn D, Johnson G, et al. Peritumoral diffusion tensor imaging of high-grade gliomas and metastatic brain tumors. AJNR Am J Neuroradiol, 2003, 24(5): 937-941.
[3]
可赞，王良，邓明，等. MR扩散张量成像定量参数FA和ADC值在前列腺移行带结节良恶性分级中的应用价值．磁共振成像, 2016, 7(5): 332-336.
[4]
刘媛媛，安迪，高娟，等．磁共振弥散张量成像在中枢神经系统疾病中的临床应用进展.重庆医科大学学报, 2015, 40(12): 1499-1502.
[5]
Figini M, Riva M, Graham M, et al. Prediction of isocitrate dehydrogenase genotype in brain gliomas with MRI: single-shell versus multishell diffusion models. Radiology, 2018, 289(3): 788-796.
[6]
Xiong J, Tan W, Wen J, et al. 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, 2016, 26(6): 1705-1715.
[7]
Price SJ, Boonzaier N, Lupson V, et al. O7.03IDH-1 mutated glioblastomas have a less invasive phenotype than IDH-1 wild type glioblastomas: a diffusion tensor imaging study. Neuro Oncol, 2014, 16(Suppl 6): 16-17.
[8]
Fieremans E, Jensen JH, Helpern JA. White matter characterization with diffusional kurtosis imaging. Neuroimage, 2011, 58(1): 177-188.
[9]
曾丁巳，肖新兰.扩散峰度成像(DKI）在中枢神经系统的应用．临床放射学杂志, 2011, 30(9): 1400-1402.
[10]
Zhao J, Wang YL, Li XB, et al. Comparative analysis of the diffusion kurtosis imaging and diffusion tensor imaging in grading gliomas, predicting tumour cell proliferation and IDH-1 gene mutation status. J Neurooncol, 2018, 141(1): 195-203.
[11]
武文杰，张辉，王效春，等．扩散峰度成像在WHO Ⅱ级脑胶质瘤IDH基因分型的预测研究．磁共振成像, 2018, 9(10): 32-36.
[12]
Hempel JM, Bisdas S, Schittenhelm J, et al. In vivo molecular profiling of human glioma using diffusion kurtosis imaging. J Neurooncol, 2017, 131(1): 93-101.
[13]
Wang XC, Lei Y, Wang L, et al. Diffusion kurtosis imaging reflects glial fibrillary acidic protein (GFAP), topo Ⅱ α, and O6-Methylguanine-DNA Methyltransferase (MGMT) expression in astrocytomas. Med Sci Monit, 2018, 24: 8822-8830.
[14]
Galanaud D, Nicoli F, Figarella-Branger D, et al. MR spectroscopy of brain tumors. J Radiol, 2006, 87(6Pt 2): 822.
[15]
Reitman ZJ, Yan H. Isocitrate dehydrogenase 1 and 2 mutations in cancer: alterations at a crossroads of cellular metabolism. Cancerspectrum Knowledge Env, 2010, 102(13): 932-941.
[16]
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(4): 624-629.
[17]
Branzoli F, Di SA, Capelle L, et al. Highly specific determination of IDH status using edited in vivo magnetic resonance spectroscopy. Neuro Oncol, 2018, 20(7): 907-916.
[18]
Bisdas S, Chadzynski GL, Braun C, et al. MR spectroscopy for in vivo assessment of the oncometabolite 2-hydroxyglutarate and its effects on cellular metabolism in human brain gliomas at 9.4 T. J Magn Reson Imaging, 2016, 44(4): 823-833.
[19]
Cha S, Knopp EA, Johnson G, et al. Intracranial mass lesions: dynamic contrast-enhanced susceptibility-weighted echo-planar perfusion MR imaging. Radiology, 2002, 223(1): 11-29.
[20]
Tan W, Xiong J, Huang W, et al. Noninvasively detecting Isocitrate dehydrogenase 1 gene status in astrocytoma by dynamic susceptibility contrast MRI. J Magn Reson Imaging, 2017, 45(2): 492.
[21]
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.
[22]
Xing Z, Yang X, She D, et al. 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(6): 1138-1144.
[23]
Kapoor GS, Gocke TA, Chawla S, et al. Magnetic resonance perfusion-weighted imaging defines angiogenic subtypes of oligodendroglioma according to 1p19q and EGFR status. J Neurooncol, 2009, 92(3): 373-386.
[24]
Jain KK, Sahoo P, Tyagi R, et al. Prospective glioma grading using single-dose dynamic contrast-enhanced perfusion MRI. Clin Radiol, 2015, 70(10): 1128-1135.
[25]
Gutman DA, Cooper LA, Hwang SN, et al. MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. Radiology, 2013, 267(2): 560-569.
[26]
Tykocinski ES, Grant RA, Kapoor GS, et al. Use of magnetic perfusion-weighted imaging to determine epidermal growth factor receptor variant Ⅲ expression in glioblastoma. Neuro Oncol, 2012, 14(5): 613.
[27]
Arevalo-Perez J, Thomas AA, Kaley T, et al. T1-Weighted dynamic contrast-enhanced MRI as a noninvasive biomarker of epidermal growth factor receptor vⅢ status. Am J Neuroradiol, 2015, 36(12): 2256.
[28]
Ivanidze J, Lum M, Pisapia D, et al. MRI features associated with TERT promoter mutation status in glioblastoma. J Neuroimaging, 2019, 29(3): 357-363.
[29]
Sung SA, Na-Young S, Jong HC, 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.
[30]
Brendle C, Hempel JM, Schittenhelm J, et al. Glioma grading and determination of IDH mutation status and ATRX loss by DCE and ASL perfusion. Clin Neuroradiol, 2017, 28(3): 421-428.

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