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Advances in imaging study on grading and typing of meningiomas
HAN Tao  ZHOU Junlin 

Cite this article as: Han T, Zhou JL. Advances in imaging study on grading and typing of meningiomas[J]. Chin J Magn Reson Imaging,2021, 12(7): 94-97. DOI:10.12015/issn.1674-8034.2021.07.022.


[Abstract] Meningiomas are the second most common intracranial tumors, most of which are benign and have a good prognosis. Compared with grade Ⅰ meningiomas, grade Ⅱ and Ⅲ meningiomas of the World Health Organization (WHO) have the characteristics of malignant growth, exuberant mitosis, rapid growth and uneven growth, and the doubling time of the tumor is short. Patients with grade Ⅱ and Ⅲ meningiomas are often treated by surgery combined with radiotherapy and chemotherapy, which are easy to relapse and metastasize after operation. Different subtypes of meningiomas are treated differently because of their different tissue components. Therefore, the preoperative non-invasive classification and classification of meningiomas, especially the accurate classification of grade Ⅱ and Ⅲ meningiomas were of great significance for the choice of clinical treatment. Traditional CT and MRI are the most commonly used and mature imaging methods for meningiomas, but the hemodynamic information of meningiomas can not be obtained. there are still some deficiencies in qualitative diagnosis, selection of clinical treatment, evaluation of prognosis, evaluation of curative effect and prediction of recurrence of meningiomas. With the application of new techniques such as energy spectrum CT, MRI functional imaging and molecular imaging, the diagnostic accuracy has been improved. In recent years, the rise of artificial intelligence can more effectively and non-invasively predict the classification of meningiomas and further improve the accuracy of preoperative diagnosis. This article reviews the imaging studies on the grading and classification of meningiomas.
[Keywords] meningioma；grading and typing；advances；computed tomography；magnetic resonance imaging

HAN Tao1, 2, 3   ZHOU Junlin1, 3*  

1 Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China

2 Second School of Clinical Medicine, Lanzhou University, Lanzhou 730000, China

3 Key Laboratory of Medical Imaging in Gansu Province, Lanzhou 730030, China

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

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 81772006). Lanzhou University Second Hospital "Cuiying Technology Innovation Plan"Applied Basic Research Project (No. CY2017-MS03).
Received  2021-01-14
Accepted  2021-02-02
DOI: 10.12015/issn.1674-8034.2021.07.022
Cite this article as: Han T, Zhou JL. Advances in imaging study on grading and typing of meningiomas[J]. Chin J Magn Reson Imaging,2021, 12(7): 94-97. DOI:10.12015/issn.1674-8034.2021.07.022.

1
Buerki RA, Horbinski CM, Kruser T, et al. An overview of meningiomas[J]. Future Oncol, 2018, 14(21): 2161-2177. DOI: 10.2217/fon-2018-0006.
2
Zhang J, Yao K, Liu P, et al. A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: a multicentre study[J]. EbioMedicine, 2020, 58: 102933. DOI: 10.1016/j.ebiom.2020.102933.
3
Buttrick S, Shah AH, Komotar RJ, et al. Management of atypical and anaplastic meningiomas[J]. Neurosurg Clin N Am, 2016, 27(2): 239-247. DOI: 10.1016/j.nec.2015.11.003.
4
Fountain DM, Young AMH, Santarius T. Malignant meningiomas[J]. Handb Clin Neurol, 2020, 170: 245-250. DOI: 10.1016/B978-0-12-822198-3.00044-6.
5
Ma T, Cao LX, Li HJ, et al. Different energy spectrum ct findings between anterior mediastinal lymphoma and thymic carcinoma[J]. Zhongguo Yi Xue Ke Xue Yuan Xue Bao, 2020, 42(4): 431-435. DOI: 10.3881/j.issn.1000-503X.11708.
6
Yue SH. Study on the Application of CT Energy Spectrum combined with MR Spectroscopy in the grading and typing of meningiomas[D]. Lanzhou University, 2015.
7
Sotoudeh H. Spinal canal meningioma mimicking posterior fossa ischemia on CT perfusion: A CT perfusion pitfall[J]. Radiol Case Rep, 2018, 13(5): 1069-1071. DOI: 10.1016/j.radcr.2018.04.020.
8
Zhang JQ. Evaluation of CT perfusion imaging in the diagnosis of different subtypes of meningioma[J]. Modern medical imaging, 2017, 26(3): 648-649.
9
Zhang YS, Li W. Application of CT perfusion imaging in diagnosis and classification of meningioma[J]. Chin J CT MRI, 2016, 14(1): 27-29, 36.
10
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.
11
Yao X. Application of DTI and PWI in pathological classification and surgical treatment of meningioma[D]. Southwest Medical University, 2017: 1-57.
12
Siempis T, Tsakiris C, Alexiou GA, et al. Diagnostic performance of diffusion and perfusion MRI in differentiating high from low-grade meningiomas: a systematic review and meta-analysis[J]. Clin Neurol Neurosurg, 2020, 190: 105643. DOI: 10.1016/j.clineuro.2019.105643.
13
Wang Q, The value of 3D ASL perfusion imaging and MRI contrast enhanced scan in preoperative diagnosis of brain tumors[J]. Chin J Imaging Integr Tradition Chin West Med, 2018, 16(6): 558-560, 563.
14
Siempis T, Tsakiris C, Alexiou GA, et al. Diagnostic performance of diffusion and perfusion MRI in differentiating high from low-grade meningiomas: a systematic review and meta-analysis[J]. Clin Neurol Neurosurg, 2020, 190: 105643. DOI: 10.1016/j.clineuro.2019.105643.
15
Shi R, Jiang T, Si L, et al. Correlations of magnetic resonance, perfusion-weighed imaging parameters and microvessel density in meningioma[J]. J BUON, 2016, 21(3): 709-713.
16
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.
17
Qiao XJ, Kim HG, Wang DJ, et al. Application of arterial spin labeling perfusion MRI to differentiate benign from malignant intracranial meningiomas[J]. Eur J Radiol, 2017, 97: 31-36. DOI: 10.1016/j.ejrad.2017.10.005.
18
Rohilla S, Garg HK, Singh I, et al. rCBV- and ADC-based grading of meningiomas with glimpse into emerging molecular diagnostics[J]. Basic Clin Neurosci, 2018, 9(6): 417-428. DOI: 10.32598/bcn.9.6.417.
19
Todua F, Chedia S. Differentiation between benign and malignant meningiomas using diffusion and perfusion MR imaging[J]. Georgian Med News, 2012, (206): 16-22.
20
Surov A, Gottschling S, Mawrin C, et al. Diffusion-weighted imaging in meningioma: prediction of tumor grade and association with histopathological parameters[J]. Transl Oncol, 2015, 8(6): 517-523. DOI: 10.1016/j.tranon.2015.11.012.
21
Surov A, Ginat DT, Sanverdi E, et al. Use of diffusion weighted imaging in differentiating between maligant and benign meningiomas. A multicenter analysis[J]. World Neurosurg, 2016, 88: 598-602. DOI: 10.1016/j.wneu.2015.10.049.
22
Lin L, Chen X, Jiang R, et al. Differentiation between vestibular schwannomas and meningiomas with atypical appearance using diffusion kurtosis imaging and three-dimensional arterial spin labeling imaging[J]. Eur J Radiol, 2018, 109: 13-18. DOI: 10.1016/j.ejrad.2018.10.009.
23
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.
24
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.
25
Marchetti M, Conti A, Beltramo G, et al. Multisession radiosurgery for perioptic meningiomas: medium-to-long term results from a cyberknife cooperative study[J]. J Neurooncol, 2019, 143(3): 597-604. DOI: 10.1007/s11060-019-03196-x.
26
Lin MC, Li CZ, Hsieh CC, et al. Preoperative grading of intracranial meningioma by magnetic resonance spectroscopy (1H-MRS) [J]. PLoS One, 2018, 13(11): e0207612. DOI: 10.1371/journal.pone.0207612.
27
Zhang T, Yu JM, Wang YQ, et al. WHO grade I meningioma subtypes: MRI features and pathological analysis[J]. Life Sci, 2018, 213: 50-56. DOI: 10.1016/j.lfs.2018.08.061.
28
Ohba S, Murayama K, Nishiyama Y, et al. Clinical and radiographic features for differentiating solitary fibrous tumor/hemangiopericytoma from meningioma[J]. World Neurosurg, 2019, 130: e383-e392. DOI: 10.1016/j.wneu.2019.06.094.
29
Jaskólski DJ, Fortuniak J, Stefańczyk L, et al. Differential diagnosis of intracranial meningiomas based on magnetic resonance spectroscopy[J]. Neurol Neurochir Pol, 2013, 47(3): 247-255. DOI: 10.5114/ninp.2013.32998.
30
Chernov MF, Kasuya H, Nakaya K, et al. ¹H-MRS of intracranial meningiomas: what it can add to known clinical and MRI predictors of the histopathological and biological characteristics of the tumor[J]. Clin Neurol Neurosurg, 2011, 113(3): 202-212. DOI: 10.1016/j.clineuro.2010.11.008.
31
Galldiks N, Albert NL, Sommerauer M, et al. PET imaging in patients with meningioma-report of the RANO/PET Group[J]. Neuro Oncol, 2017, 19(12): 1576-1587. DOI: 10.1093/neuonc/nox112.
32
Okuchi S, Okada T, Yamamoto A, et al. Grading meningioma: a comparative study of thallium-SPECT and FDG-PET[J]. Medicine (Baltimore), 2015, 94(6): e549. DOI: 10.1097/MD.0000000000000549.
33
Mitamura K, Yamamoto Y, Norikane T, et al. Correlation of 18F-FDG and 11C-methionine uptake on PET/CT with Ki-67 immunohistochemistry in newly diagnosed intracranial meningiomas[J]. Ann Nucl Med, 2018, 32(9): 627-633. DOI: 10.1007/s12149-018-1284-6.
34
Slot KM, Verbaan D, Buis DR, et al. Prediction of meningioma WHO grade using PET findings: a systematic review and Meta-analysis[J]. J Neuroimaging, 2021, 31(1): 6-19. DOI: 10.1111/jon.12795.
35
Mitamura K, Yamamoto Y, Norikane T, et al. Correlation of (18)F-FDG and (11)C-methionine uptake on PET/CT with Ki67 immunohistochemistry in newly diagnosed intracranial meningiomas[J]. Ann Nucl Med2018, 32: 627-33.
36
Yang LY, Wen G. Application of deep learning in medical imaging[J]. J Molecular Imaging, 2020, 43(2): 183-187.
37
Zhu Y, Man C, Gong L, et al. A deep learning radiomics model for preoperative grading in meningioma[J]. Eur J Radiol, 2019, 116: 128-134. DOI: 10.1016/j.ejrad.2019.04.022.
38
Niu L, Zhou XM, Duan CF, et al. Differentiation researches on the meningioma subtypes by radiomics from contrast-enhanced magnetic resonance imaging: a preliminary study[J]. World Neurosurg, 2019, 126: e646-e652. DOI: 10.1016/j.wneu.2019.02.109.
39
Chu HR, Lin XQ, He J, et al. Value of MRI radiomics based on enhanced T1WI images in prediction of meningiomas grade[J]. Acad Radiol, 2021, 28(5): 687-693. DOI: 10.1016/j.acra.2020.03.034.
40
Coroller TP, Bi WL, Huynh E, et al. Radiographic prediction of meningioma grade by semantic and radiomic features[J]. PLoS One, 2017, 12(11): 12(11): e0187908. DOI: 10.1371/journal.pone.0187908.
41
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.
42
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.2019.08.148.
43
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.
44
Chen CY, Guo XY, Wang J, et al. The diagnostic value of radiomics-based machine learning in predicting the grade of meningiomas using conventional magnetic resonance imaging: a preliminary study[J]. Front Oncol, 2019, 9: 1338. DOI: 10.3389/fonc.2019.01338.
45
Hale AT, Stonko DP, Wang L, et al. Machine learning analyses can differentiate meningioma grade by features on magnetic resonance imaging[J]. Neurosurg Focus, 2018, 45(5): E4. DOI: 10.3171/2018.8.FOCUS18191.

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