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Research advances in MRI diffusion imaging on grading and typing of meningiomas
LUO Jian  WANG Yinhua  TAN Yan 

Cite this article as: Luo J, Wang YH, Tan Y. Research advances in MRI diffusion imaging on grading and typing of meningiomas[J]. Chin J Magn Reson Imaging, 2022, 13(1): 140-142, 150. DOI:10.12015/issn.1674-8034.2022.01.032.


[Abstract] Meningioma originated from arachnoid cap cells and is the most common non-neuroepithelial tumor in the brain. In 2016, the WHO Classification of Tumors of the Central Nervous System divided meningiomas into 15 subtypes of grade Ⅰ, Ⅱ and Ⅲ. In 2021, the WHO Classification of Tumors of the Central Nervous System replaced previous grades Ⅰ, Ⅱ and Ⅲ with grades 1, 2 and 3, emphasizing that the criteria for defining atypical or anaplastic (grades 2 and 3) meningiomas should apply to any potential subtype and that some molecular markers are also associated with the grading and typing of meningiomas. Different grades of meningiomas have different treatment and prognosis, MRI diffusion imaging, such as diffusion weighted imaging, intravoxel incoherent motion weighted imaging, diffusion tensor imaging and diffusion kurtosis imaging, can be used to grade meningiomas and distinguish subtypes. The radiomics method combined with MRI diffusion imaging has showed good performance also, which is beneficial for patients and clinicians to select treatment methods and evaluate prognosis. Based on previous studies at home and abroad, this paper will summarize the research advances in MRI diffusion imaging on grading and typing of meningiomas.
[Keywords] meningioma；grade；subtype；diffusion magnetic resonance imaging；radiomics

LUO Jian1   WANG Yinhua2   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

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

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 82071893, 81701681); Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province (No. 20200003); Youth Innovation Fund of First Hospital of Shanxi Medical University (No. YC1426).
Received  2021-08-10
Accepted  2021-12-21
DOI: 10.12015/issn.1674-8034.2022.01.032
Cite this article as: Luo J, Wang YH, Tan Y. Research advances in MRI diffusion imaging on grading and typing of meningiomas[J]. Chin J Magn Reson Imaging, 2022, 13(1): 140-142, 150. DOI:10.12015/issn.1674-8034.2022.01.032.

[1]
Louis DN, Perry A, Reifenberger G, et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary[J]. Acta Neuropathol, 2016, 131(6): 803-820. DOI: 10.1007/s00401-016-1545-1.
[2]
Ostrom QT, Cioffi G, Gittleman H, et al. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2012-2016[J]. Neuro Oncol, 2019, 21(Suppl 5): v1-v100. DOI: 10.1093/neuonc/noz150.
[3]
Louis DN, Perry A, Wesseling P, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary[J]. Neuro Oncol. 2021, 23(8): 1231-1251. DOI: 10.1093/neuonc/noab106.
[4]
Goldbrunner R, Stavrinou P, Jenkinson MD, et al. EANO guideline on the diagnosis and management of meningiomas[J]. Neuro Oncol., 2021, 23(11): 1821-1834. DOI: 10.1093/neuonc/noab150.
[5]
Rydzewski NR, Lesniak MS, Chandler JP, et al. Gross total resection and adjuvant radiotherapy most significant predictors of improved survival in patients with atypical meningioma[J]. Cancer, 2018, 124(4): 734-742. DOI: 10.1002/cncr.31088.
[6]
Sohu DM, Sohail S, Shaikh R. Diagnostic accuracy of diffusion weighted MRI in differentiating benign and malignant meningiomas[J]. Pak J Med Sci, 2019, 35(3): 726-730. DOI: 10.12669/pjms.35.3.1011.
[7]
Abdel-Kerim A, Shehata M, Sabaa B EI, et al. Differentiation between benign and atypical cranial Meningiomas. Can ADC measurement help? MRI findings with hystopathologial correlation[J]. The Egyptian Journal of Radiology and Nuclear Medicine, 2018, 49(1): 172-175. DOI: 10.1016/j.ejrnm.2017.10.004.
[8]
Atalay B, Ediz SS, Ozbay NO. Apparent Diffusion Coefficient in Predicting the Preoperative Grade of Meningiomas[J]. J Coll Physicians Surg Pak, 2020, 30(11): 1126-1132. DOI: 10.29271/jcpsp.2020.11.1126.
[9]
Wang HM, Xie Q, Liao Q, et al. Application of apparent diffusion coefficient of MRI diffusion-weighted imaging in the differential diagnosis of low- and high-grade meningiomas[J]. Chin J Anat Clin, 2020, 25(04): 344-349. DOI: 10.3760/cma.j.cn101202-20200304-00063.
[10]
Bozdağ M, Er A, Ekmekçi S. Association of apparent diffusion coefficient with Ki-67 proliferation index, progesterone-receptor status and various histopathological parameters, and its utility in predicting the high grade in meningiomas[J]. Acta Radiol, 2021, 62(3): 401-413. DOI: 10.1177/0284185120922142.
[11]
Haghighi Borujeini M., Farsizaban M., Yazdi SR. et al. Grading of meningioma tumors based on analyzing tumor volumetric histograms obtained from conventional MRI and apparent diffusion coefficient images[J]. Egypt J Radiol Nucl Med, 2021, 52: 167. DOI: 10.1186/s43055-021-00545-7.
[12]
Lu YP, Kawai S, Wen JB, et al. Evaluation parameters between intra-voxel incoherent motion and diffusion-weighted imaging in grading and differentiating histological subtypes of meningioma: A prospective pilot study[J]. J Neurol Sci, 2017, 372: 60-69. DOI: 10.1016/j.jns.2016.11.037.
[13]
Yu F, Shan Y, Zhao ZL, et al. Application of MR DWI in differential diagnosis of low- and high- grade meningioma[J]. J Med Imaging, 2019, 29(8): 1257-1262.
[14]
Qin X, Wang XL, Guo N, et al. Value of MR Diffusion-Weighted Imaging Combined with Enhanced Scan in Preoperative Diagnosis of Angiomatous Meningioma[J]. J Clin Radiol, 2020, 39(4): 782-786. DOI: 10.13437/j.cnki.jcr.2020.04.034.
[15]
Baal JD, Chen WC, Solomon DA, et al. Preoperative MR Imaging to Differentiate Chordoid Meningiomas from Other Meningioma Histologic Subtypes[J]. AJNR Am J Neuroradiol, 2019, 40(3): 433-439. DOI: 10.3174/ajnr.A5996.
[16]
Terada Y, Toda H, Okumura R, et al. Reticular Appearance on Gadolinium-enhanced T1- and Diffusion-weighted MRI, and Low Apparent Diffusion Coefficient Values in Microcystic Meningioma Cysts[J]. Clin Neuroradiol, 2018, 28(1): 109-115. DOI: 10.1007/s00062-016-0527-y.
[17]
Ke XA, Zhou Q, Han L, et al. Differentiating microcystic meningioma from atypical meningioma using diffusion-weighted imaging[J]. Neuroradiology, 2020, 62(5): 601-607. DOI: 10.1007/s00234-020-02374-3.
[18]
Han T, Zhang J, Liu XW, et al. Differential diagnosis of atypical meningiomas and anaplastic meningiomas by MRI basic signs and DWI parameters[J]. Chin J Magn Reson Imaging, 2021, 12(4): 12-16. DOI: 10.12015/issn.1674-8034.2021.04.003.
[19]
Le Bihan D, Breton E, Lallemand D, et al. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging[J]. Radiology, 1988, 168(2): 497-505. DOI: 10.1148/radiology.168.2.3393671.
[20]
Lin L, Xue Y, Duan Q, et al. Grading meningiomas using mono-exponential, bi-exponential and stretched exponential model-based diffusion-weighted MR imaging[J]. Clin Radiol, 2019, 74(8): 651.e15-651.e23. DOI: 10.1016/j.crad.2019.04.007.
[21]
Sacco S, Ballati F, Gaetani C, et al. Multi-parametric qualitative and quantitative MRI assessment as predictor of histological grading in previously treated meningiomas[J]. Neuroradiology, 2020, 62(11): 1441-1449. DOI: 10.1007/s00234-020-02476-y.
[22]
Zampini MA, Buizza G, Paganelli C, et al. Perfusion and diffusion in meningioma tumors: a preliminary multiparametric analysis with Dynamic Susceptibility Contrast and IntraVoxel Incoherent Motion MRI[J]. Magn Reson Imaging, 2020, 67: 69-78. DOI: 10.1016/j.mri.2019.12.003.
[23]
Bohara M, Nakajo M, Kamimura K, et al. Histological Grade of Meningioma: Prediction by Intravoxel Incoherent Motion Histogram Parameters[J]. Acad Radiol, 2020, 27(3): 342-353. DOI: 10.1016/j.acra.2019.04.012.
[24]
Zikou A, Alexiou GA, Goussia A, et al. The role of diffusion tensor imaging and dynamic susceptibility perfusion MRI in the evaluation of meningioma grade and subtype[J]. Clin Neurol Neurosurg, 2016, 146: 109-115. DOI: 10.1016/j.clineuro.2016.05.005.
[25]
Aslan K, Gunbey HP, Tomak L, et al. The diagnostic value of using combined MR diffusion tensor imaging parameters to differentiate between low- and high-grade meningioma[J]. Br J Radiol, 2018, 91(1088): 20180088. DOI: 10.1259/bjr.20180088.
[26]
Lin L, Bhawana R, Xue Y, et al. Comparative Analysis of Diffusional Kurtosis Imaging, Diffusion Tensor Imaging, and Diffusion-Weighted Imaging in Grading and Assessing Cellular Proliferation of Meningiomas[J]. AJNR Am J Neuroradiol, 2018, 39(6): 1032-1038. DOI: 10.3174/ajnr.A5662.
[27]
Xing F, Tu N, Koh TS, et al. MR diffusion kurtosis imaging predicts malignant potential and the histological type of meningioma[J]. Eur J Radiol, 2017, 95: 286-292. DOI: 10.1016/j.ejrad.2017.08.019.
[28]
Chen X, Lin L, Wu J, et al. Histogram analysis in predicting the grade and histological subtype of meningiomas based on diffusion kurtosis imaging[J]. Acta Radiol, 2020, 61(9): 1228-1239. DOI: 10.1177/0284185119898656.
[29]
Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48(4): 441-446. DOI: 10.1016/j.ejca.2011.11.036.
[30]
Yu XY, Geng CJ, Feng YB, et al. Clinical value of MRI texture analysis for preoperative grading of meningiomas based on conventional MRI images[J]. Chin J Radiol, 2018, 52(5): 356-362. DOI: 10.19732/j.cnki.2096-6210.2019.05.011.
[31]
Laukamp KR, Shakirin G, Baeßler B, et al. Accuracy of Radiomics-Based Feature Analysis on Multiparametric Magnetic Resonance Images for Noninvasive Meningioma Grading[J]. World Neurosurg, 2019, 132: e366-e390. DOI: 10.1016/j.wneu.2019.08.14.
[32]
Hu J, Zhao Y, Li M, et al. Machine learning-based radiomics analysis in predicting the meningioma grade using multiparametric MRI[J]. Eur J Radiol, 2020, 131: 109251. DOI: 10.1016/j.ejrad.2020.109251.
[33]
Park YW, Oh J, You SC, et al. Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging[J]. Eur Radiol, 2019, 29(8): 4068-4076. DOI: 10.1007/s00330-018-5830-3.
[34]
Hamerla G, Meyer HJ, Schob S, et al. Comparison of machine learning classifiers for differentiation of grade 1 from higher gradings in meningioma: A multicenter radiomics study[J]. Magn Reson Imaging, 2019, 63: 244-249. DOI: 10.1016/j.mri.2019.08.011.

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