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Research progress of quantitative MRI radiomics in multiple sclerosis
CAO Jibin  CUI Lingling  SUN Wenge  FAN Guoguang 

Cite this article as: Cao JB, Cui LL, Sun WG, et al. Research progress of quantitative MRI radiomics in multiple sclerosis[J]. Chin J Magn Reson Imaging, 2021, 12(2): 113-116, 120. DOI:10.12015/issn.1674-8034.2021.02.028.


[Abstract] The incidence of multiple sclerosis (MS) is on the rise globally. MS is believed to be caused by complex gene-environmental factors, but the specific pathogenesis is still unknown. Early detection and treatment is an important means to delay or reduce MS disability rate. With the development of technology and the emergence of new sequences, MRI becomes more important in the diagnosis of multiple sclerosis and the clinical value of monitoring the progress. Advanced MRI techniques are helpful to further explore the pathogenesis of MS. Currently, MS is considered as a brain isomerism process characterized by extensive damage to the central nervous system and not just multiple focal demyelination of the white matter. Quantitative magnetic resonance imaging (qMRI), which is considered a specific marker of axonal dysfunction, can reliably support the hypothesis of generalized disease impairment. In this paper, the application of craniocerebral qMRI in MS in recent years is reviewed.
[Keywords] multiple sclerosis；magnetic resonance imaging；quantitative；radiomics

CAO Jibin   CUI Lingling   SUN Wenge   FAN Guoguang*  

Department of Radiology, the First Hospital of China Medical University, Shenyang 110001, China

Fan GG, E-mail: fanguog@sina.com

Conflicts of interest   None.

Received  2020-06-24
Accepted  2020-08-21
DOI: 10.12015/issn.1674-8034.2021.02.028
Cite this article as: Cao JB, Cui LL, Sun WG, et al. Research progress of quantitative MRI radiomics in multiple sclerosis[J]. Chin J Magn Reson Imaging, 2021, 12(2): 113-116, 120. DOI:10.12015/issn.1674-8034.2021.02.028.

1
Kobelt G, Thompson A, Berg J, et al. New insights into the burden and costs of multiple sclerosis in Europe[J]. Mult Scler, 2017, 23(8): 1123-1136. DOI: 10.1177/1352458517694432.
2
Michel L. Environmental factors in the development of multiple sclerosis[J]. Rev Neurol (Paris), 2018, 174(6): 372-377. DOI: 10.1016/j.neurol.2018.03.010.
3
Thompson AJ, Banwell BL, Barkhof F, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria[J]. Lancet Neurol, 2018, 17(2): 162-173. DOI: 10.1016/S1474-4422(17)30470-2.
4
Wattjes MP, Rovira A, Miller D, et al. Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis-establishing disease prognosis and monitoring patients[J]. Nat Rev Neurol, 2015, 11(10): 597-606. DOI: 10.1038/nrneurol.2015.157.
5
Rotstein D, Montalban X. Reaching an evidence-based prognosis for personalized treatment of multiple sclerosis[J]. Nat Rev Neurol, 2019, 15(5): 287-300. DOI: 10.1038/s41582-019-0170-8.
6
Geraldes R, Ciccarelli O, Barkhof F, et al. The current role of MRI in differentiating multiple sclerosis from its imaging mimics[J]. Nat Rev Neurol, 2018, 14(4): 199-213. DOI: 10.1038/nrneurol.2018.14.
7
Neuroimmunlolgy group of neurology branch of Chinese Medical Association, Neuroimmunology branch of Chinese Society of Immunology. Chinese expert consensus on diagnosis and treatment of multiple sclerosis (2014)[J]. Chin J Neurology, 2015, 48(5): 362-367.
8
Neuroimmunology branch of Chinese Society of Immunology, Neuroimmunlolgy group of neurology branch of Chinese Medical Association. Chinese expert consensus on diagnosis and treatment of multiple sclerosis (2018)[J]. Chin J Neuroimmunology & Neurology, 2018, 25(6): 387-394. DOI: 10.3969/j.issn.1006-2963.2018.06.001.
9
Lassmann H. Multiple sclerosis pathology[J]. Cold Spring Harb Perspect Med, 2018, 8(3): 1-5. DOI: 10.1101/cshperspect.a028936.
10
Mahad DH, Trapp BD, Lassmann H. Pathological mechanisms in progressive multiple sclerosis[J]. Lancet Neurol, 2015, 14(2): 183-193. DOI: 10.1016/S1474-4422(14)70256-X.
11
Filippi M, Falini A, Arnold DL, et al. Magnetic resonance techniques for the in vivo assessment of multiple sclerosis pathology: consensus report of the white matter study group[J]. J Magn Reson Imaging, 2005, 21(6): 669-675. DOI: 10.1002/jmri.20336.
12
Miller DH, Thompson AJ, Filippi M. Magnetic resonance studies of abnormalities in the normal appearing white matter and grey matter in multiple sclerosis[J]. J Neurol, 2003, 250(12): 1407-1419. DOI: 10.1007/s00415-003-0243-9.
13
Stankiewicz JM, Glanz BI, Healy BC, et al. Brain MRI lesion load at 1.5 T and 3 T versus clinical status in multiple sclerosis[J]. J Neuroimaging, 2011, 21(2): 50-56. DOI: 10.1111/j.1552-6569.2009.00449.x.
14
Filippi M, Rocca MA, Ciccarelli O, et al. MRI criteria for the diagnosis of multiple sclerosis: MAGNIMS consensus guidelines[J]. Lancet Neurol, 2016, 15(3): 292-303. DOI: 10.1016/S1474-4422(15)00393-2.
15
Frischer JM, Bramow S, Dal-Bianco A, et al. The relation between inflammation and neurodegeneration in multiple sclerosis brains[J]. Brain, 2009, 132(Pt 5): 1175-1189. DOI: 10.1093/brain/awp070.
16
Zivadinov R, Ramasamy DP, Hagemeier J, et al. Evaluation of leptomeningeal contrast enhancement using pre-and postcontrast subtraction 3D-FLAIR imaging in multiple sclerosis[J]. AJNR Am J Neuroradiol, 2018, 39(4): 642-647. DOI: 10.3174/ajnr.A5541.
17
Eichinger P, Kirschke JS, Hoshi MM, et al. Pre-and postcontrast 3D double inversion recovery sequence in multiple sclerosis: A aimple and effective MR imaging protocol[J]. AJNR Am J Neuroradiol, 2017, 38(10): 1941-1945. DOI: 10.3174/ajnr.A5329.
18
Absinta M, Vuolo L, Rao A, et al. Gadolinium-based MRI characterization of leptomeningeal inflammation in multiple sclerosis[J]. Neurology, 2015, 85(1): 18-28. DOI: 10.1212/WNL.0000000000001587.
19
Hamcan S, Battal B, Akgun V, et al. The value of qualitative and quantitative assessment of lesion to cerebral cortex signal ratio on double inversion recovery sequence in the differentiation of demyelinating plaques from non-specific T2 hyperintensities[J]. Eur Radiol, 2017, 27(2): 763-771. DOI: 10.1007/s00330-016-4379-2.
20
Futatsuya K, Kakeda S, Yoneda T, et al. Juxtacortical lesions in multiple sclerosis: Assessment of gray matter involvement using phase difference-enhanced imaging (PADRE)[J]. Magn Reson Med Sci, 2016, 15(4): 349-354. DOI: 10.2463/mrms.mp.2015-0099.
21
Tartaglia MC, Arnold DL. The role of MRS and fMRI in multiple sclerosis[J]. Adv Neurol, 2006, 98(1): 185-202.
22
Nordengen K, Heuser C, Rinholm JE, et al. Localisation of N-acetylaspartate in oligodendrocytes/myelin[J]. Brain Struct Funct, 2015, 220(2): 899-917. DOI: 10.1007/s00429-013-0691-7.
23
Nakamura K, Brown RA, Narayanan S, et al. Diurnal fluctuations in brain volume: Statistical analyses of MRI from large populations[J]. Neuroimage, 2015, 118(9): 126-132. DOI: 10.1016/j.neuroimage.2015.05.077.
24
Wattjes MP, Harzheim M, Lutterbey GG, et al. Axonal damage but no increased glial cell activity in the normal-appearing white matter of patients with clinically isolated syndromes suggestive of multiple sclerosis using high-field magnetic resonance spectroscopy[J]. AJNR Am J Neuroradiol, 2007, 28(8): 1517-1522. DOI: 10.3174/ajnr.A0594.
25
Wu CH, Zhang ZG, Xin DY. 1H magentic resonance spectroscopy in the diagnoisis of multiple sclerosis[J]. Chin J Magn Reson Imaging, 2014, 5(1): 19-23. DOI: 10.3969/j.issn.1674-8034.2014.01.005.
26
Srinivasan R, Sailasuta N, Hurd R, et al. Evidence of elevated glutamate in multiple sclerosis using magnetic resonance spectroscopy at 3 T[J]. Brain, 2005, 128(Pt 5): 1016-1025. DOI: 10.1093/brain/awh467.
27
Azevedo CJ, Kornak J, Chu P, et al. In vivo evidence of glutamate toxicity in multiple sclerosis[J]. Ann Neurol, 2014, 76(2): 269-278. DOI: 10.1002/ana.24202.
28
Sun X, Tanaka M, Kondo S, et al. Clinical significance of reduced cerebral metabolism in multiple sclerosis: a combined PET and MRI study[J]. Ann Nucl Med, 1998, 12(2): 89-94. DOI: 10.1007/BF03164835.
29
Rosen BR, Belliveau JW, Vevea JM, et al. Perfusion imaging with NMR contrast agents[J]. Magn Reson Med, 1990, 14(2): 249-265. DOI: 10.1002/mrm.1910140211.
30
Eskildsen SF, Gyldensted L, Nagenthiraja K, et al. Increased cortical capillary transit time heterogeneity in Alzheimer's disease: a DSC-MRI perfusion study[J]. Neurobiol Aging, 2017, 50(2): 107-118. DOI: 10.1016/j.neurobiolaging.2016.11.004.
31
Inglese M, Park SJ, Johnson G, et al. Deep gray matter perfusion in multiple sclerosis: dynamic susceptibility contrast perfusion magnetic resonance imaging at 3 T[J]. Arch Neurol, 2007, 64(2): 196-202. DOI: 10.1001/archneur.64.2.196.
32
Corno S, Giani L, Lagana MM, et al. The brain effect of the migraine attack: an ASL MRI study of the cerebral perfusion during a migraine attack[J]. Neurol Sci, 2018, 39(Suppl 1): 73-74. DOI: 10.1007/s10072-018-3346-x.
33
Telischak NA, Detre JA, Zaharchuk G. Arterial spin labeling MRI: clinical applications in the brain[J]. J Magn Reson Imaging, 2015, 41(5): 1165-1180. DOI: 10.1002/jmri.24751.
34
Ota M, Sato N, Nakata Y, et al. Abnormalities of cerebral blood flow in multiple sclerosis: a pseudocontinuous arterial spin labeling MRI study[J]. Magn Reson Imaging, 2013, 31(6): 990-995. DOI: 10.1016/j.mri.2013.03.016.
35
Ma XX, Lv JH, Tang J, et al. Reduced perfusion in normal appearing white matter in relapsing-remitting multiple sclerosis using 3D pseudocontinuous arterial spin labeling[J]. Chin J Magn Reson Imaging, 2017, 8(4): 270-275. DOI: 10.12015/issn.1674-8034.2017.04.007.
36
de la Pena MJ, Pena IC, Garcia PG, et al. Early perfusion changes in multiple sclerosis patients as assessed by MRI using arterial spin labeling[J]. Acta Radiol Open, 2019, 8(12): 2058460119894214. DOI: 10.1177/2058460119894214.
37
Law M, Saindane AM, Ge Y, et al. Microvascular abnormality in relapsing-remitting multiple sclerosis: perfusion MR imaging findings in normal-appearing white matter[J]. Radiology, 2004, 231(3): 645-652. DOI: 10.1148/radiol.2313030996.
38
Bester M, Forkert ND, Stellmann JP, et al. Increased perfusion in normal appearing white matter in high inflammatory multiple sclerosis patients[J]. PLoS One, 2015, 10(3): e0119356. DOI: 10.1371/journal.pone.0119356.
39
Iwasawa T, Matoba H, Ogi A, et al. Diffusion-weighted imaging of the human optic nerve: a new approach to evaluate optic neuritis in multiple sclerosis[J]. Magn Reson Med, 1997, 38(3): 484-491. DOI: 10.1002/mrm.1910380317.
40
Cercignani M, Bozzali M, Iannucci G, et al. Magnetisation transfer ratio and mean diffusivity of normal appearing white and grey matter from patients with multiple sclerosis[J]. J Neurol Neurosurg Psychiatry, 2001, 70(3): 311-317. DOI: 10.1136/jnnp.70.3.311.
41
Roosendaal SD, Geurts JJ, Vrenken H, et al. Regional DTI differences in multiple sclerosis patients[J]. Neuroimage, 2009, 44(4): 1397-1403. DOI: 10.1016/j.neuroimage.2008.10.026.
42
Andersen O, Hildeman A, Longfils M, et al. Diffusion tensor imaging in multiple sclerosis at different final outcomes[J]. Acta Neurol Scand, 2018, 137(2): 165-173. DOI: 10.1111/ane.12797.
43
Kolasa M, Hakulinen U, Brander A, et al. Diffusion tensor imaging and disability progression in multiple sclerosis: A 4-year follow-up study[J]. Brain Behav, 2019, 9(1): e01194. DOI: 10.1002/brb3.1194.
44
Zhang H, Schneider T, Wheeler-Kingshott CA, et al. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain[J]. Neuroimage, 2012, 61(4): 1000-1016. DOI: 10.1016/j.neuroimage.2012.03.072.
45
Schneider T, Brownlee W, Zhang H, et al. Sensitivity of multi-shell NODDI to multiple sclerosis white matter changes: a pilot study[J]. Funct Neurol, 2017, 32(2): 97-101. DOI: 10.11138/fneur/2017.32.2.097.
46
Liu Z, Pardini M, Yaldizli O, et al. Magnetization transfer ratio measures in normal-appearing white matter show periventricular gradient abnormalities in multiple sclerosis[J]. Brain, 2015, 138(Pt 5): 1239-1246. DOI: 10.1093/brain/awv065.
47
Vavasour IM, Huijskens SC, Li DK, et al. Global loss of myelin water over 5 years in multiple sclerosis normal-appearing white matter[J]. Mult Scler, 2018, 24(12): 1557-1568. DOI: 10.1177/1352458517723717.
48
Brown JW, Pardini M, Brownlee WJ, et al. An abnormal periventricular magnetization transfer ratio gradient occurs early in multiple sclerosis[J]. Brain, 2017, 140(2): 387-398. DOI: 10.1093/brain/aww296.
49
Bagnato F, Hametner S, Franco G, et al. Selective inversion recovery quantitative magnetization transfer brain MRI at 7T: Clinical and postmortem validation in multiple sclerosis[J]. J Neuroimaging, 2018, 28(4): 380-388. DOI: 10.1111/jon.12511.
50
Zhang L, Chen T, Tian H, et al. Reproducibility of inhomogeneous magnetization transfer (ihMT): A test-retest, multi-site study[J]. Magn Reson Imaging, 2019, 57(4): 243-249. DOI: 10.1016/j.mri.2018.11.010.
51
Habib CA, Liu M, Bawany N, et al. Assessing abnormal iron content in the deep gray matter of patients with multiple sclerosis versus healthy controls[J]. AJNR Am J Neuroradiol, 2012, 33(2): 252-258. DOI: 10.3174/ajnr.A2773.
52
Sparacia G, Agnello F, Gambino A, et al. Multiple sclerosis: High prevalence of the 'central vein' sign in white matter lesions on susceptibility-weighted images[J]. Neuroradiol J, 2018, 31(4): 356-361. DOI: 10.1177/1971400918763577.
53
Schweser F, Raffaini AL, Hagemeier J, et al. Mapping of thalamic magnetic susceptibility in multiple sclerosis indicates decreasing iron with disease duration: A proposed mechanistic relationship between inflammation and oligodendrocyte vitality[J]. Neuroimage, 2018, 167(2): 438-452. DOI: 10.1016/j.neuroimage.2017.10.063.
54
Chen W, Gauthier SA, Gupta A, et al. Quantitative susceptibility mapping of multiple sclerosis lesions at various ages[J]. Radiology, 2014, 271(1): 183-192. DOI: 10.1148/radiol.13130353.
55
Langkammer C, Liu T, Khalil M, et al. Quantitative susceptibility mapping in multiple sclerosis[J]. Radiology, 2013, 267(2): 551-559. DOI: 10.1148/radiol.12120707.
56
Hametner S, Wimmer I, Haider L, et al. Iron and neurodegeneration in the multiple sclerosis brain[J]. Ann Neurol, 2013, 74(6): 848-861. DOI: 10.1002/ana.23974.
57
Wisnieff C, Ramanan S, Olesik J, et al. Quantitative susceptibility mapping (QSM) of white matter multiple sclerosis lesions: Interpreting positive susceptibility and the presence of iron[J]. Magn Reson Med, 2015, 74(2): 564-570. DOI: 10.1002/mrm.25420.
58
Hagiwara A, Hori M, Cohen-Adad J, et al. Linearity, bias, intrascanner repeatability, and interscanner reproducibility of quantitative multidynamic multiecho sequence for rapid simultaneous relaxometry at 3 T: A validation study with a standardized phantom and healthy controls[J]. Invest Radiol, 2019, 54(1): 39-47. DOI: 10.1097/RLI. 0000000000000510.
59
Warntjes M, Engstrom M, Tisell A, et al. Modeling the presence of myelin and edema in the brain based on multi-parametric quantitative MRI[J]. Front Neurol, 2016, 7(1): 1-15. DOI: 10.3389/fneur.2016.00016.
60
Krauss W, Gunnarsson M, Nilsson M, et al. Conventional and synthetic MRI in multiple sclerosis: a comparative study[J]. Eur Radiol, 2018, 28(4): 1692-1700. DOI: 10.1007/s00330-017-5100-9.
61
Louapre C, Bodini B, Lubetzki C, et al. Imaging markers of multiple sclerosis prognosis[J]. Curr Opin Neurol, 2017, 30(3): 231-236. DOI: 10.1097/WCO.0000000000000456.
62
Rovira A, de Stefano N. MRI monitoring of spinal cord changes in patients with multiple sclerosis[J]. Curr Opin Neurol, 2016, 29(4): 445-452. DOI: 10.1097/WCO.0000000000000343.
63
Hasan KM, Walimuni IS, Abid H, et al. Human brain atlas-based multimodal MRI analysis of volumetry, diffusimetry, relaxometry and lesion distribution in multiple sclerosis patients and healthy adult controls: implications for understanding the pathogenesis of multiple sclerosis and consolidation of quantitative MRI results in MS[J]. J Neurol Sci, 2012, 313(1-2): 99-109. DOI: 10.1016/j.jns.2011.09.015.
64
Steenwijk MD, Vrenken H, Jonkman LE, et al. High-resolution T1-relaxation time mapping displays subtle, clinically relevant, gray matter damage in long-standing multiple sclerosis[J]. Mult Scler, 2016, 22(10): 1279-1288. DOI: 10.1177/1352458515615953.
65
Warntjes JBM, Persson A, Berge J, et al. Myelin detection using rapid quantitative MR imaging correlated to macroscopically registered luxol fast blue-stained brain specimens[J]. AJNR Am J Neuroradiol, 2017, 38(6): 1096-1102. DOI: 10.3174/ajnr.A5168.
66
Saccenti L, Hagiwara A, Andica C, et al. Myelin measurement using quantitative magnetic resonance imaging: A correlation study comparing various imaging techniques in patients with multiple sclerosis[J]. Cells, 2020, 9(2): 393-406. DOI: 10.3390/cells9020393.

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