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Table of Contents    
COMMENTARY
Year : 2019  |  Volume : 67  |  Issue : 6  |  Page : 1446-1447

Developing Standard MRI Quantification for Predicting Meningioma—A Need of Time


Department of Radiodiagnosis, SGPGIMS, Lucknow, Uttar Pradesh, India

Date of Web Publication20-Dec-2019

Correspondence Address:
Dr. Vivek Singh
Department of Radiodiagnosis, SGPGIMS, Lucknow, Uttar Pradesh
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0028-3886.273611

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How to cite this article:
Singh V, Prasad S. Developing Standard MRI Quantification for Predicting Meningioma—A Need of Time. Neurol India 2019;67:1446-7

How to cite this URL:
Singh V, Prasad S. Developing Standard MRI Quantification for Predicting Meningioma—A Need of Time. Neurol India [serial online] 2019 [cited 2020 Jan 26];67:1446-7. Available from: http://www.neurologyindia.com/text.asp?2019/67/6/1446/273611




There have been previous attempts of using MRI to characterize the behavior of meningioma. This prospective study by Karthigeyan et al. is one such study where conventional MRI sequences T1, T2, and fluid-attenuated inversion recovery (FLAIR) were used to assess meningioma consistency, vascularity, and other surgical parameters.[1]

Meningioma is commonly benign encapsulated masses, and being extra-axial in location, if removed surgically have a good outcome. Diagnosing a meningioma on MRI is not difficult but evaluating its detailed architecture by MRI and correlating with intraoperative stiffness of mass is now the current area of interest. Developing MRI parameters that may precisely predict high-grade meningioma is required as they would help in better preoperative planning.

Surgery is the mainstay of treatment and complete excision ensures long-term curative results. Knowing the consistency of the tumor beforehand helps the surgeon in deciding the surgical approach and proper preoperative planning. To some extent, it also helps in predicting the maximum possible amount of excision of the mass and, in turn, determine the long-term prognosis. There have been many factors that predict the overall likelihood of total surgical excision and outcome such as location, size, vascularity, invasion of important vessels, and cranial structures. Results have not been consistent with different MRI parameters as shown by many studies in predicting the consistency and grading of the meningioma.[2],[3]

Typically on MRI, meningioma is hypointense on T1, iso-to-mildly hyperintense on T2-weighted images and shows homogenous avid post-contrast enhancement. On these conventional sequences, one can readily identify tumor location, vascularity, local invasion, interface with brain parenchyma, vasogenic edema, and relation with the adjacent neurovascular structures.

In the mentioned study by Karthigeyan et al., the authors have emphasized the importance of FLAIR imaging in predicting the hardness of the tumor which is an important independent factor besides tumor location and its relation to adjacent neurovascular structures in deciding the possible extent of excision during surgery.[1]

Several authors in the past have attempted to predict the consistency of meningioma based on MR imaging features. No significant relation was found between T1 appearance and consistency of the tumor. However, hypointense appearance on T2-weighted and FLAIR images were suggestive of hard consistency of the mass. T2/FLAIR hypointense appearance is believed to be because of the presence of a large amount of collagen tissue in the fibrous type of meningioma and because of that they also show relatively less or heterogeneous enhancement on post-contrast scans. T2/FLAIR hyperintense tumors are water-rich, so soft in consistency.[2],[3]

Evaluating vascularity and calcification by susceptibility imaging also aids in characterizing meningioma but this sequence is underutilized except for cerebellopontine location and needs more data for quantification.[4],[5] Predicting the grade of meningioma by diffusion-weighted imaging (DWI) has been suggested. In one study by Bano et al.,[3] it was shown that at a high b value of 1000, DWI can differentiate between benign and malignant tumors, but these results were not consistent in other studies.[2]

Few recent studies have shown a positive correlation between increased fractional anisotropy (FA) values and hardness of meningioma on diffusion tensor imaging (DTI), thus providing a kind of objective assessment tool for determining the consistency of the mass.[6] In the future, other emerging advanced MRI techniques such as MR elastography may become useful for such applications. One more advanced MRI imaging tool is texture analysis whose clinical application would help in grading meningioma.

This attempt by the author in using conventional MRI sequences in predicting meningioma behavior is outstanding. As both conventional and advanced MRI sequences are available for predicting tumor consistency, now there is a need for developing standard MRI quantification measures of tumor consistency which would aid in better prediction of meningioma on imaging.



 
  References Top

1.
Karthigeyan M, Dhandapani S, Salunke P, Singh P, Radotra BD, Gupta SK. The predictive value of conventional magnetic resonance imaging sequences on operative findings and histopathology of intracranial meningiomas: A prospective study. Neurol India 2019;67:1439-45.  Back to cited text no. 1
  [Full text]  
2.
Alyamany M, Alshardan MM, Jamea AA, ElBakry N, Soualmi L, Orz Y. Meningioma consistency: Correlation between magnetic resonance imaging characteristics, operative findings, and histopathological features. Asian J Neurosurg 2018;13:324-8.  Back to cited text no. 2
[PUBMED]  [Full text]  
3.
Bano S, Waraich MM, Khan MA, Buzdar SA, Manzur S. Diagnostic value of apparent diffusion coefficient for the accurate assessment and differentiation of intracranial meningiomas. Acta Radiol Short Rep 2013;2:2047981613512484.  Back to cited text no. 3
    
4.
Zhang S, Chiang GC, Knapp JM, Zecca CM, He D, Ramakrishna R, et al. Grading meningiomas utilizing multiparametric MRI with inclusion of susceptibility weighted imaging and quantitative susceptibility mapping. J Neuroradiol 2019. doi: 10.1016/j.neurad. 2019.05.002.  Back to cited text no. 4
    
5.
Mishra A, Thomas B, Kapilamoorthy TR. Susceptibility weighted imaging - A problem-solving tool in differentiation of cerebellopontine angle schwannomas and meningiomas. Neuroradiol J 2017;30:253-8.  Back to cited text no. 5
    
6.
Romani R, Tang WJ, Mao Y, Wang DJ, Tang HL, Zhu FP, et al. Diffusion tensor magnetic resonance imaging for predicting the consistency of intracranial meningiomas. Acta Neurochir 2014;156:1837-45.  Back to cited text no. 6
    




 

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