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MR diffusion imaging: research advances in prognosis prediction of gliomas
LIU Zeliang  WANG Xiaochun  ZHANG Hui  TAN Yan 

Cite this article as: Liu ZL, Wang XC, Zhang H, et al. MR diffusion imaging: research advances in prognosis prediction of gliomas[J]. Chin J Magn Reson Imaging, 2021, 12(1): 77-80. DOI:10.12015/issn.1674-8034.2021.01.017.


[Abstract] Glioma is the most common malignant tumor in central nervous system. The prognosis of glioma varies greatly with different pathological grades and genotypes. MRI diffusion imaging reflects the changes of tissue structure by detecting the micro movement of water molecules, which has important clinical significance for the prognosis prediction of glioma. Diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), stretched exponential model diffusion-weighted imaging (SEM DWI), ultra-high b-value DWI imaging and neurite orientation dispersion and density imaging (NODDI) can quantitatively detect the diffusion information of water molecules in tissues, reflect the heterogeneity of tumor and cell proliferation, which provides new ideas for accurately predicting the prognosis of glioma. This paper reviews the research progress of MRI diffusion imaging in predicting the prognosis of glioma.
[Keywords] magnetic resonance imaging；diffusion imaging；glioma；prognosis

LIU Zeliang1   WANG Xiaochun2   ZHANG Hui2   TAN Yan2*  

1 College of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China

2 Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan 030001, China

*Corresponding author: Tan Y, E-mail: tanyan123456@sina.com

Conflicts of interest   None.

ACKNOWLEDGMENTS  This article is supported by the National Natural Science Found of China No.82071893 the National Natural Science Found of China No.81701681 the National Natural Science Found of China No.81771824 and the National Natural Science Found of China No81971593
Received  2020-09-03
Accepted  2020-11-28
DOI: 10.12015/issn.1674-8034.2021.01.017
Cite this article as: Liu ZL, Wang XC, Zhang H, et al. MR diffusion imaging: research advances in prognosis prediction of gliomas[J]. Chin J Magn Reson Imaging, 2021, 12(1): 77-80. DOI:10.12015/issn.1674-8034.2021.01.017.

1
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(6): 803-820. DOI: 10.1007/s00401-016-1545-1
2
Schittenhelm J. Recent advances in subtyping tumors of the central nervous system using molecular data. Expert Rev Mol Diagn, 2017, 17(1): 83-94. DOI: 10.1080/14737159.2017.1266259
3
Deangelis LM. Brain tumors. N Engl J Med, 2001, 344(2): 114-123. DOI: 10.1056/nejm200101113440207
4
Tan Y, Mu W, Wang X C, et al. Whole-tumor radiomics analysis of DKI and DTI may improve the prediction of genotypes for astrocytomas: a preliminary study. Eur J Radiol, 2020, 124: 108785. DOI: 10.1016/j.ejrad.2019.108785
5
Li C, Wang S, Yan JL, et al. Intratumoral heterogeneity of glioblastoma infiltration revealed by joint histogram analysis of diffusion tensor imaging. Neurosurgery, 2019, 85(4): 524-534. DOI: 10.1093/neuros/nyy388
6
Zhang J, Chen X, Chen D, et al. Grading and proliferation assessment of diffuse astrocytic tumors with monoexponential, biexponential, and stretched-exponential diffusion-weighted imaging and diffusion kurtosis imaging. Eur J Radiol, 2018, 109: 188-195. DOI: 10.1016/j.ejrad.2018.11.003
7
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, 2019, 141(1): 195-203. DOI: 10.1007/s11060-018-03025-7
8
Basser PJ, Pierpaoli C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. 1996. J Magn Reson, 2011, 213(2): 560-570. DOI: 10.1016/j.jmr.2011.09.022
9
Grover VP, Tognarelli JM, Crossey MM, et al. Magnetic resonance imaging: principles and techniques: lessons for clinicians. J Clin Exp Hepatol, 2015, 5(3): 246-255. DOI: 10.1016/j.jceh.2015.08.001
10
Huber T, Bette S, Wiestler B, et al. Fractional anisotropy correlates with overall survival in glioblastoma. World Neurosurg, 2016, 95: 525-534. DOI: 10.1016/j.wneu.2016.08.055
11
Flores-Alvarez E, Durand-Muñoz C, Cortes-Hernandez F, et al. Clinical significance of fractional anisotropy measured in peritumoral edema as a biomarker of overall survival in glioblastoma: evidence using correspondence analysis. Neurol India, 2019, 67(4): 1074-1081. DOI: 10.4103/0028-3886.266284
12
Mohan S, Wang S, Coban G, et al. Detection of occult neoplastic infiltration in the corpus callosum and prediction of overall survival in patients with glioblastoma using diffusion tensor imaging. Eur J Radiol, 2019, 112: 106-111. DOI: 10.1016/j.ejrad.2019.01.015
13
Jamjoom AA, Rodriguez D, Rajeb AT, et al. Magnetic resonance diffusion metrics indexing high focal cellularity and sharp transition at the tumour boundary predict poor outcome in glioblastoma multiforme. Clin Radiol, 2015, 70(12): 1400-1407. DOI: 10.1016/j.crad.2015.08.006
14
Beppu T, Inoue T, Shibata Y, et al. Measurement of fractional anisotropy using diffusion tensor MRI in supratentorial astrocytic tumors. J Neurooncol, 2003, 63(2): 109-116. DOI: 10.1023/a:1023977520909
15
Goebell E, Paustenbach S, Vaeterlein O, et al. Low-grade and anaplastic gliomas: differences in architecture evaluated with diffusion- tensor MR imaging. Radiology, 2006, 239(1): 217-222. DOI: 10.1148/radiol.2383050059
16
Brancato V, Nuzzo S, Tramontano L, et al. Predicting Survival in Glioblastoma Patients Using Diffusion MR Imaging Metrics-A Systematic Review. Cancers, 2020, 12(10): 2858. DOI: 10.3390/cancers12102858
17
Jensen JH, Helpern JA, Ramani A, et al. Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med, 2005, 53(6): 1432- 1440. DOI: 10.1002/mrm.20508
18
Glenn GR, Helpern JA, Tabesh A, et al. Quantitative assessment of diffusional kurtosis anisotropy. NMR Biomed, 2015, 28(4): 448-459. DOI: 10.1002/nbm.3271
19
Zhang J, Jiang J, Zhao L, et al. Survival prediction of high-grade glioma patients with diffusion kurtosis imaging. Am J Transl Res, 2019, 11(6): 3680-3688.
20
Hempel JM, Brendle C, Bender B, et al. Diffusion kurtosis imaging histogram parameter metrics predicting survival in integrated molecular subtypes of diffuse glioma: an observational cohort study. Eur J Radiol, 2019, 112: 144-152. DOI: 10.1016/j.ejrad.2019.01.014
21
Wang X, Li F, Wang D, et al. Diffusion kurtosis imaging combined with molecular markers as a comprehensive approach to predict overall survival in patients with gliomas. Eur J Radiol, 2020, 128: 108985. DOI: 10.1016/j.ejrad.2020.108985
22
Wang X, Gao W, Li F, et al. Diffusion kurtosis imaging as an imaging biomarker for predicting prognosis of the patients with high-grade gliomas. Magn Reson Imaging, 2019, 63: 131-136. DOI: 10.1016/j.mri.2019.08.001
23
Iima M, Le Bihan D. Clinical intravoxel incoherent motion and diffusion mr imaging: past, present, and future. Radiology, 2016, 278(1): 13-32. DOI: 10.1148/radiol.2015150244
24
Le Bihan D, Breton E, Lallemand D, et al. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology, 1988, 168(2): 497-505. DOI: 10.1148/radiology.168.2.3393671
25
Koh DM, Collins DJ, Orton MR. Intravoxel incoherent motion in body diffusion-weighted MRI: reality and challenges. AJR Am J Roentgenol, 2011, 196(6): 1351-1361. DOI: 10.2214/ajr.10.5515
26
Zhu L, Wu J, Zhang H, et al. The value of intravoxel incoherent motion imaging in predicting the survival of patients with astrocytoma. Acta Radiol, 2020. [ DOI: ]. DOI: 10.1177/0284185120926907
27
Puig J, Sánchez-González J, Blasco G, et al. Intravoxel incoherent motion metrics as potential biomarkers for survival in glioblastoma. PloS One, 2016, 11(7): e0158887. DOI: 10.1371/journal.pone.0158887
28
Federau C, Cerny M, Roux M, et al. IVIM perfusion fraction is prognostic for survival in brain glioma. Clin Neuroradiol, 2017, 27(4): 485-492. DOI: 10.1007/s00062-016-0510-7
29
Iima M, Reynaud O, Tsurugizawa T, et al. Characterization of glioma microcirculation and tissue features using intravoxel incoherent motion magnetic resonance imaging in a rat brain model. Invest Radiol, 2014, 49(7): 485-490. DOI: 10.1097/rli.0000000000000040
30
Le Bihan D, Johansen-Berg H. Diffusion MRI at 25: exploring brain tissue structure and function. NeuroImage, 2012, 61(2): 324-341. DOI: 10.1016/j.neuroimage.2011.11.006
31
Bennett KM, Schmainda KM, Bennett RT, et al. Characterization of continuously distributed cortical water diffusion rates with a stretched-exponential model. Magn Reson Med, 2003, 50(4): 727-734. DOI: 10.1002/mrm.10581
32
Bai Y, Lin Y, Tian J, et al. Grading of gliomas by using monoexponential, biexponential, and stretched exponential diffusion- weighted MR imaging and diffusion kurtosis MR imaging. Radiology, 2016, 278(2): 496-504. DOI: 10.1148/radiol.2015142173
33
Yan R, Haopeng P, Xiaoyuan F, et al. Non-Gaussian diffusion MR imaging of glioma: comparisons of multiple diffusion parameters and correlation with histologic grade and MIB-1 (Ki-67 labeling) index. Neuroradiology, 2016, 58(2): 121-132. DOI: 10.1007/s00234-015-1606-5
34
Kusunoki M, Kikuchi K, Togao O, et al. Differentiation of high-grade from low-grade diffuse gliomas using diffusion-weighted imaging: a comparative study of mono-, bi-, and stretched-exponential diffusion models. Neuroradiology, 2020, 62(7): 815-823. DOI: 10.1007/s00234-020-02456-2
35
Chen X, Jiang J, Shen N, et al. Stretched-exponential model diffusion-weighted imaging as a potential imaging marker in preoperative grading and assessment of proliferative activity of gliomas. Am J Transl Res, 2018, 10(8): 2659-2668.
36
Kayaselçuk F, Zorludemir S, Gümürdühü D, et al. PCNA and Ki-67 in central nervous system tumors: correlation with the histological type and grade. J Neurooncol, 2002, 57(2): 115-121. DOI: 10.1023/a:1015739130208
37
Chaloob MK, Ali HH, Qasim BJ, et al. Immunohistochemical expression of Ki-67, PCNA and CD34 in astrocytomas: a clinicopathological study. Oman Med J, 2012, 27(5): 368-374. DOI: 10.5001/omj.2012.93
38
Suo XL, Gong QY. Application and research progress of DWI technology in central nervous system. Radiol Practice, 2018, 33(2): 210-214. DOI: 10.13609/j.cnki.1000-0313.2018.02.021
39
Li JH, Li QJ, Yu B, et al. Molecular imaging of aquaporin by DWI-MRI with multiple b values: mechanism and method. J Chin Clin Med Imaging, 2014, 25(3): 186-189.
40
Li QJ, Li JH, Zhao ZS, et al. Assessment of aquaporins function of early-stage liver fibrosis using multi-b diffusion weighted magnetic resonance imaging. J Chin Clin Med Imaging, 2014, 25(10): 719-723.
41
Zhang G, Ma W, Dong H, et al. Based on histogram analysis: ADC (aqp) derived from ultra-high b-value DWI could be a non-invasive specific biomarker for rectal cancer prognosis. Sci Rep, 2020, 10(1): 10158. DOI: 10.1038/s41598-020-67263-4
42
Tan Y, Zhang H, Wang XC, et al. The value of multi ultra high-b-value DWI in grading cerebral astrocytomas and its association with aquaporin-4. Br J Radiol, 2018, 91(1086): 20170696. DOI: 10.1259/bjr.20170696
43
Papadopoulos MC, Verkman AS. Aquaporin water channels in the nervous system. Nat Rev Neurosci, 2013, 14(4): 265-77. DOI: 10.1038/nrn3468
44
Assaf Y, Basser PJ. Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. NeuroImage, 2005, 27(1): 48-58. DOI: 10.1016/j.neuroimage.2005.03.042
45
Zhang H, Schneider T, Wheeler-Kingshott CA, et al. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage, 2012, 61(4): 1000-1016. DOI: 10.1016/j.neuroimage.2012.03.072
46
Zhao J, Li JB, Wang JY, et al. Quantitative analysis of neurite orientation dispersion and density imaging in grading gliomas and detecting IDH-1 gene mutation status. Neuroimage Clin, 2018, 19: 174-181. DOI: 10.1016/j.nicl.2018.04.011
47
Li SH, Jiang RF, Zhang J, et al. Application of neurite orientation dispersion and density imaging in assessing glioma grades and cellular proliferation. World Neurosurg, 2019, 131: e247-e54. DOI: 10.1016/j.wneu.2019.07.121

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