多模态磁共振成像在脑胶质瘤分级诊断及预后评估中的研究进展

王伟; 沈龙山; 程雯; 陈刘成; 王震寰

doi:10.12122/j.issn.1674-4500.2022.05.31

多模态磁共振成像在脑胶质瘤分级诊断及预后评估中的研究进展

doi: 10.12122/j.issn.1674-4500.2022.05.31

王伟^1,,
沈龙山^{1, 2, 3, ,},
程雯⁴,
陈刘成^{2, 3, 5},
王震寰^{2, 6}

1.
蚌埠医学院第二附属医院放射科，安徽蚌埠 233040
2.
数字医学与智慧健康安徽普通高校重点实验室(蚌埠医学院)，蚌埠医学院临床应用解剖研究所，安徽蚌埠 233004
3.
蚌埠医学院医学影像学院，安徽蚌埠 233030
4.
蚌埠学院文学与教育学院，安徽蚌埠 233000
5.
蚌埠医学院第一附属医院放射科，安徽蚌埠 233004
6.
蚌埠医学院人体解剖教研室，安徽蚌埠 233030

基金项目:

蚌埠医学院转化医学重点项目 BYTM2019045

详细信息

作者简介:
王伟，在读硕士研究生，住院医师，E-mail: 1325673570@qq.com

通讯作者:
沈龙山，硕士，主任医师，E-mail: trede@163.com

计量
- 文章访问数: 258
- HTML全文浏览量: 144
- PDF下载量: 30
- 被引次数: 0
出版历程
- 收稿日期: 2022-06-18
- 刊出日期: 2022-09-20

Research progress of multimodal magnetic resonance imaging in grading diagnosis and prognosis evaluation of glioma

WANG Wei^1
,,
SHEN Longshan^{1, 2, 3
, ,},
CHENG Wen⁴,
CHEN Liucheng^{2, 3, 5},
WANG Zhenhuan^{2, 6}

1.
Department of Radiology, the Second Affiliated Hospital of Bengbu Medical College, Bengbu 233040, China
2.
Key Laboratory of Digital Medi-cine and Intelligent Health in Anhui Universities (Bengbu Medical College), Institute of Clinical Applied Anatomy, Bengbu Medical College, Bengbu 233004, China
3.
School of Medical Imaging, Bengbu Medical College, Bengbu 233030, China
4.
School of Literature and Education, Bengbu University, Bengbu 233000, China
5.
Department of Radiology, the First Affiliated Hospital of Bengbu Medical College, Bengbu 233004, China
6.
Department of Human Anatomy, Bengbu Medical College, Bengbu 233030, China

摘要

摘要: 脑胶质瘤是中枢神经系统中常见的恶性肿瘤，其具有弥漫性浸润生长的特点，在治疗的过程中具有较大的难度，肿瘤恶性程度高，容易复发、预后较差。多模态磁共振成像技术多用于肿瘤术前的初步诊断，在胶质瘤分级判断以及预测预后方面具有重要的价值。本文就扩散加权磁共振成像、弥散张量磁共振成像、动态磁敏感对比灌注加权成像、动态对比增强磁共振成像、动脉自旋标记成像和磁共振波谱成像等几种多模态磁共振成像技术在脑胶质瘤术前分级诊断及预后评估方面的研究进展进行综述。
- 多模态磁共振成像 /
- 扩散加权成像 /
- 磁共振波谱成像 /
- 脑胶质瘤 /
- 分级诊断 /
- 预后
Abstract: Glioma is a common malignant tumor in the central nervous system. It has the characteristics of diffuse infiltration and growth and it is difficult to treat it. The tumor has a high degree of malignancy, easy recurrence and poor prognosis. Multimodal magnetic resonance imaging is often used in the preliminary diagnosis of tumors before surgery. It has an important value in grading and predicting the prognosis of gliomas. This article reviews the research progress of multimodal magnetic resonance imaging including diffusion weighted magnetic resonance imaging, diffusion tensor magnetic resonance imaging, dynamic magnetic sensitive contrast perfusion weighted imaging, dynamic contrast- enhanced magnetic resonance imaging, arterial spin labeling imaging and magnetic resonance spectroscopy imaging in preoperative grading diagnosis and prognosis evaluation of glioma.
- multimodal magnetic resonance imaging /
- diffusion weighted imaging /
- magnetic resonance spectroscopy imaging /
- glioma /
- graded diagnosis /
- prognosis

HTML全文

参考文献(45)

[1]	Kang XW, Xi YB, Liu TT, et al. Grading of Glioma: combined diagnostic value of amide proton transfer weighted, arterial spin labeling and diffusion weighted magnetic resonance imaging[J]. BMC Med Imaging, 2020, 20(1): 50. doi: 10.1186/s12880-020-00450-x
[2]	Shukla G, Alexander GS, Bakas S, et al. Advanced magnetic resonance imaging in glioblastoma: a review[J]. Chin Clin Oncol, 2017, 6(4): 40. doi: 10.21037/cco.2017.06.28
[3]	Cui Y, Zeng W, Jiang HH, et al. Higher Cho/NAA ratio in postoperative peritumoral edema zone is associated with earlier recurrence of glioblastoma[J]. Front Neurol, 2020, 11: 592155. doi: 10.3389/fneur.2020.592155
[4]	Förster A, Brehmer S, Seiz-Rosenhagen M, et al. Heterogeneity of glioblastoma with gliomatosis cerebri growth pattern on diffusion and perfusion MRI[J]. J Neurooncol, 2019, 142(1): 103-9. doi: 10.1007/s11060-018-03068-w
[5]	Zhang L, Min ZQ, Tang M, et al. The utility of diffusion MRI with quantitative ADC measurements for differentiating high- grade from low-grade cerebral gliomas: evidence from a meta- analysis[J]. J Neurol Sci, 2017, 373: 9-15. doi: 10.1016/j.jns.2016.12.008
[6]	Eyüboğlu İ, Çakir İM, Aslan S, et al. Diagnostic efficacy of apparent diffusion coefficient measurements in differentiation of malignant intra-axial brain tumors[J]. Turk J Med Sci, 2021, 51(1): 256-67. doi: 10.3906/sag-2006-1
[7]	Cao HM, Xiao X, Hua J, et al. The added value of inflow-based vascular-space-occupancy and diffusion-weighted imaging in preoperative grading of gliomas[J]. Neurodegener Dis, 2020, 20(4): 123-30. doi: 10.1159/000512545
[8]	Zeng Q, Ling C, Shi F, et al. Glioma infiltration sign on high b-value diffusion-weighted imaging in gliomas and its prognostic value[J]. J Magn Reson Imaging, 2018: 2018Mar1.
[9]	Al-Agha M, Abushab K, Quffa K, et al. Efficiency of high and standard b value diffusion- weighted magnetic resonance imaging in grading of gliomas[J]. J Oncol, 2020, 2020: 6942406.
[10]	Kim BS, Kim ST, Kim JH, et al. Apparent diffusion coefficient as a predictive biomarker for survival in patients with treatment-naive glioblastoma using quantitative multiparametric magnetic resonance profiling[J]. World Neurosurg, 2019, 122: e812-20. doi: 10.1016/j.wneu.2018.10.151
[11]	Lerner A, Mogensen MA, Kim PE, et al. Clinical applications of diffusion tensor imaging[J]. World Neurosurg, 2014, 82(1/2): 96-109.
[12]	Assaf Y, Pasternak O. Diffusion tensor imaging (DTI)-based white matter mapping in brain research: a review[J]. J Mol Neurosci, 2008, 34(1): 51-61. doi: 10.1007/s12031-007-0029-0
[13]	Duy Hung N, Minh Duc N, Van Anh NT, et al. Diagnostic performance of diffusion tensor imaging for preo-perative glioma grading[J]. Clin Ter, 2021, 172(4): 315-21.
[14]	Huber T, Bette S, Wiestler B, et al. Fractional anisotropy correlates with overall survival in glioblastoma[J]. World Neurosurg, 2016, 95: 525-34. e1. doi: 10.1016/j.wneu.2016.08.055
[15]	Li C, Wang S, Yan JL, et al. Intratumoral heterogeneity of glioblastoma infiltration revealed by joint histogram analysis of diffusion tensor imaging[J]. Neurosurgery, 2019, 85(4): 524-34. doi: 10.1093/neuros/nyy388
[16]	Aliotta E, Batchala PP, Schiff D, et al. Increased intratumoral infiltration in IDH wild-type lower- grade gliomas observed with diffusion tensor imaging[J]. J Neurooncol, 2019, 145(2): 257-63. doi: 10.1007/s11060-019-03291-z
[17]	Shan W, Wang XL. Clinical application value of 3.0T MR diffusion tensor imaging in grade diagnosis of gliomas[J]. Oncol Lett, 2017, 14 (2): 2009-14. doi: 10.3892/ol.2017.6378
[18]	Price SJ, Allinson K, Liu HX, et al. Less invasive phenotype found in isocitrate dehydrogenase- mutated glioblastomas than in isocitrate dehydrogenase wild-type glioblastomas: a diffusion-tensor imaging study[J]. Radiology, 2017, 283(1): 215-21. doi: 10.1148/radiol.2016152679
[19]	Chen S, Wang L, Wang L, et al. Consistency analysis between 3D arterial spin labeling and dynamic susceptibility contrast perfusion magnetic resonance imaging in perfusion imaging of brain tumor[J]. Nan Fang Yi Ke Da Xue Xue Bao, 2021, 41(8): 1283-6.
[20]	Shoaib Y, Nayil K, Makhdoomi R, et al. Role of diffusion and perfusion magnetic resonance imaging in predicting the histopathological grade of gliomas-A prospective study[J]. Asian J Neurosurg, 2019, 14(1): 47-51. doi: 10.4103/ajns.AJNS_191_16
[21]	Ma H, Wang ZZ, Xu K, et al. Three-dimensional arterial spin label-ing imaging and dynamic susceptibility contrast perfusion-weighted imaging value in diagnosing glioma grade prior to surgery[J]. Exp Ther Med, 2017, 13(6): 2691-8. doi: 10.3892/etm.2017.4370
[22]	梅邹, 毕俊英. 三种MRI灌注技术术前评估脑胶质瘤分级的价值对比[J]. 磁共振成像, 2022, 13(2): 83-6, 95. https://www.cnki.com.cn/Article/CJFDTOTAL-CGZC202202017.htm
[23]	Quan GM, Zhang KX, Liu YW, et al. Role of dynamic susceptibility contrast perfusion MRI in glioma progression evaluation[J]. J Oncol, 2021, 2021: 1696387.
[24]	Soliman RK, Gamal SA, Essa AHA, et al. Preoperative grading of glioma using dynamic susceptibility contrast MRI: relative cerebral blood volume analysis of intra-tumoural and peri-tumoural tissue[J]. Clin Neurol Neurosurg, 2018, 167: 86-92. doi: 10.1016/j.clineuro.2018.01.003
[25]	Brendle C, Klose U, Hempel JM, et al. Association of dynamic susceptibility magnetic resonance imaging at initial tumor diagnosis with the prognosis of different molecular glioma subtypes[J]. Neurol Sci, 2020, 41(12): 3625-32. doi: 10.1007/s10072-020-04474-7
[26]	Qiu J, Tao ZC, Deng KX, et al. Diagnostic accuracy of dynamic contrast-enhanced magnetic resonance imaging for distinguishing pseudoprogression from glioma recurrence: a meta-analysis[J]. Chin Med J(Engl), 2021, 134(21): 2535-43.
[27]	Jin Y, Peng H, Peng J. Brain glioma localization diagnosis based on magneticresonanceimaging[J]. WorldNeurosurg, 2021, 149: 325-32.
[28]	何金龙, 高阳, 李波, 等. DCE-MRI联合MTI在脑胶质瘤分级及浸润中的应用价值[J]. 临床放射学杂志, 2021, 40(4): 651-5. doi: 10.13437/j.cnki.jcr.2021.04.007
[29]	Ulyte A, Katsaros VK, Liouta E, et al. Prognostic value of preoperative dynamic contrast- enhanced MRI perfusion parameters for high-grade glioma patients[J]. Neuroradiology, 2016, 58(12): 1197-208. doi: 10.1007/s00234-016-1741-7
[30]	黄琦, 喻强, 刘江, 等. 3.0T磁共振动态对比增强检查对脑胶质瘤分级诊断及其预后评估的价值[J]. 安徽医药, 2021, 25(9): 1855-8, 1911. doi: 10.3969/j.issn.1009-6469.2021.09.037
[31]	Soldozy S, Galindo J, Snyder H, et al. Clinical utility of arterial spin labeling imaging in disorders of the nervous system[J]. Neurosurg Focus, 2019, 47(6): E5. doi: 10.3171/2019.9.FOCUS19567
[32]	Cebeci H, Aydin O, Ozturk-Isik E, et al. Assesment of perfusion in glial tumors with arterial spin labeling; comparison with dynamic susceptibility contrast method[J]. Eur J Radiol, 2014, 83(10): 1914-9. doi: 10.1016/j.ejrad.2014.07.002
[33]	Batalov AI, Zakharova NE, Pronin IN, et al. 3D pCASL-perfusion in preoperative assessment of brain gliomas in large cohort of patients[J]. Sci Rep, 2022, 12: 2121. doi: 10.1038/s41598-022-05992-4
[34]	Qu Y, Zhou LS, Jiang J, et al. Combination of three-dimensional arterial spin labeling and stretched-exponential model in grading of gliomas[J]. Medicine, 2019, 98(25): e16012. doi: 10.1097/MD.0000000000016012
[35]	张格, 陈旺生, 陈峰, 等. 磁共振多模态影像在脑胶质瘤诊断及分级中的应用效果分析[J]. 中国CT和MRI杂志, 2020, 18(2): 44-7. doi: 10.3969/j.issn.1672-5131.2020.02.014
[36]	Flies CM, Snijders TJ, Van Seeters T, et al. Perfusion imaging with arterial spin labeling (ASL)-MRI predicts malignant progression in low-grade (WHO grade Ⅱ) gliomas[J]. Neuroradiology, 2021, 63 (12): 2023-33. doi: 10.1007/s00234-021-02737-4
[37]	Manning P, Daghighi S, Rajaratnam MK, et al. Differentiation of progressive disease from pseudoprogression using 3D PCASL and DSC perfusion MRI in patients with glioblastoma[J]. J Neurooncol, 2020, 147(3): 681-90. doi: 10.1007/s11060-020-03475-y
[38]	Yao R, Cheng AL, Liu ML, et al. The diagnostic value of apparent diffusion coefficient and proton magnetic resonance spectroscopy in the grading of pediatric gliomas[J]. J Comput Assist Tomogr, 2021, 45(2): 269-76. doi: 10.1097/RCT.0000000000001130
[39]	Tong T, Yang Z, Chen JW, et al. Dynamic 1H- MRS assessment of brain tumors: a novel approach for differential diagnosis of glioma[J]. Oncotarget, 2015, 6(31): 32257-65. doi: 10.18632/oncotarget.4899
[40]	Shakir TM, Liang FL, Guo CG, et al. 1H-MR spectroscopy in grad-ing of cerebral glioma: a new view point, MRS image quality assess-ment[J]. Acta Radiol Open, 2022, 11(2): 20584601221077068.
[41]	刘海, 万涛, 许华, 等. 弥散张量成像及氢质子磁共振波谱影像表现对颅脑高级别胶质瘤瘤周浸润的预测[J]. 分子影像学杂志, 2021, 44(5): 849-52. doi: 10.12122/j.issn.1674-4500.2021.05.23
[42]	Lin Q, Tang LL, Lin ZQ. Application value of 3.0-T multivoxel ¹H-MR spectroscopy in the peritumoral tissue of brain astrocytic tumors[J]. Neurol India, 2020, 68(2): 389. doi: 10.4103/0028-3886.280633
[43]	Tolia M, Verganelakis D, Tsoukalas N, et al. Prognostic value of MRS metabolites in postoperative irradiated high grade gliomas[J]. Biomed Res Int, 2015, 2015: 341042.
[44]	Roldan-Valadez E, Rios C, Motola-Kuba D, et al. Choline-to-N-ace-tyl aspartate and lipids-lactate-to-creatine ratios together with age as-semble a significant Cox's proportional-hazards regression model for prediction of survival in high-grade gliomas[J]. Br J Radiol, 2016, 89 (1067): 20150502. doi: 10.1259/bjr.20150502
[45]	Carrete LR, Young JS, Cha S. Advanced imaging techniques for newly diagnosed and recurrent gliomas[J]. Front Neurosci, 2022, 16: 787755.