Advanced MRI prediction of meningioma histopathological classification: a literature review and case presentations

Sri Andreani Utomo; Abdul Hafid Bajamal; Yuyun Yueniwati PW; Irwan Barlian Immadoel Haq; Dyah Fauziah; Eunike Serfina Fajarini

doi:10.15562/bmj.v11i1.3100

Advanced MRI prediction of meningioma histopathological classification: a literature review and case presentations

Sri Andreani Utomo ,

Abdul Hafid Bajamal ,

Yuyun Yueniwati PW ,

Irwan Barlian Immadoel Haq ,

Dyah Fauziah ,

Eunike Serfina Fajarini ,

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DOI: https://doi.org/10.15562/bmj.v11i1.3100 |
Published: 2022-02-05

Abstract

Meningioma is not uncommon case; however, the differentiation of high-grade from low-grade meningioma is important. The rate of recurrence of grade I meningioma is 7-20%, but in grade II meningioma is 30-40% and in grade III 50-80%. Non-invasive MRI techniques that can differentiate high-grade from low-grade meningiomas before surgery are useful for surgical planning and subsequent treatment. We present a review article and some case studies of low-grade (WHO grade I) and high-grade (WHO grade II and grade III) meningioma with conventional MRI and continue with advanced MRI; we performed diffusion weighted imaging (DWI) with apparent diffusion coefficient (ADC) value, dynamic susceptibility contrast (DSC), dynamic contrast-enhanced (DCE) magnetic resonance (MR) perfusion and 3D ASL. From these three cases show that advanced magnetic resonance imaging with ADC value, DSC, DCE, and 3D arterial spin-labelling (ASL) is an essential sequence to differentiate high-grade from low-grade meningioma.

References

Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol. 2016;131(6):803–20.
Pavlisa G, Rados M, Pazanin L, Padovan RS, Ozretic D, Pavlisa G. Characteristics of typical and atypical meningiomas on ADC maps concerning schwannomas. Clin Imaging. 2008;32(1):22–7.
Chidambaram S, Pannullo SC, Roytman M, Pisapia DJ, Liechty B, Magge RS, et al. Dynamic contrast-enhanced magnetic resonance imaging perfusion characteristics in meningiomas treated with resection and adjuvant radiosurgery. Neurosurg Focus. 2019;46(6): E10.
Id ST, Liu X, He L, Liu B, Qin B, Feng C. Application of a 3D pseudo-continuous arterial spin-labeled perfusion MRI scan combined with a post-labeling delay value in the diagnosis of neonatal hypoxic-ischemic encephalopathy. 2019;1–14.
Huang RY, Bi WL, Griffith B, Kaufmann TJ, La Fougère C, Schmidt NO, et al. Imaging and diagnostic advances for intracranial meningiomas. Neuro Oncol. 2019;21:I44–61.
Tourdias T, Rodrigo S, Oppenheim C, Naggara O, Varlet P, Amoussa S, et al. Pulsed arterial spin labeling applications in brain tumors: Practical review. J Neuroradiol. 2008;35(2):79–89.
Dangouloff-Ros V, Deroulers C, Foissac F, Badoual M, Shotar E, Grévent D, et al. Arterial spin labeling to predict brain tumor grading in children: Correlations between histopathologic vascular density and perfusion MR imaging. Radiology. 2016;281(2):553–66.
Qiao XJ, Kim HG, Wang DJJ, Salamon N, Linetsky M, Sepahdari A, et al. Application of arterial spin labeling perfusion MRI to differentiate benign from malignant intracranial meningiomas. Eur J Radiol. 2017 Dec 1;97:31–6.
Kimura H, Takeuchi H, Koshimoto Y, Arishima H, Uematsu H, Kawamura Y, et al. Perfusion Imaging of Meningioma by Using Continuous Arterial Spin-Labeling: Comparison with Dynamic Susceptibility-weighted Contrast-Enhanced MR Images and Histopathologic Features [Internet]. [cited 2020 Oct 6]. Available from: www.ajnr.org
Qiao XJ, Kim HG, Wang DJJ, Salamon N, Linetsky M, Sepahdari A, et al. Application of arterial spin labeling perfusion MRI to differentiate benign from malignant intracranial meningiomas. Eur J Radiol. 2017;97(March):31–6.
Shiroishi MS, Habibi M, Rajderkar D, Yurko C, Go JL, Lerner A, et al. Perfusion and permeability MR imaging of gliomas. Technol Cancer Res Treat. 2011;10(1):59–71.
Keil VC, Pintea B, Gielen GH, Hittatiya K, Datsi A, Simon M, et al. Meningioma assessment: Kinetic parameters in dynamic contrast-enhanced MRI appear independent from microvascular anatomy and VEGF expression. J Neuroradiol. 2018;45(4):242–8.
van Rijswijk CSP, Hogendoorn PCW, Taminiau AHM, Bloem JL. Synovial sarcoma: dynamic contrast-enhanced MR imaging features. Skeletal Radiol [Internet]. 2001;30(1):25–30. Available from: https://doi.org/10.1007/s002560000295
Todua F, Chedia S. Differentiation between benign and malignant meningiomas using diffusion and perfusion MR imaging. Georgian Med News. 2012;(206):16–22.
Joseph DRA. Accuracy of Perfusion MRI in Differentiating Benign From Atypical and Malignant Meningiomas. J Med Sci Clin Res. 2017;05(04):20454–60.

[1] Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol. 2016;131(6):803–20.

[2] Pavlisa G, Rados M, Pazanin L, Padovan RS, Ozretic D, Pavlisa G. Characteristics of typical and atypical meningiomas on ADC maps concerning schwannomas. Clin Imaging. 2008;32(1):22–7.

[3] Chidambaram S, Pannullo SC, Roytman M, Pisapia DJ, Liechty B, Magge RS, et al. Dynamic contrast-enhanced magnetic resonance imaging perfusion characteristics in meningiomas treated with resection and adjuvant radiosurgery. Neurosurg Focus. 2019;46(6): E10.

[4] Id ST, Liu X, He L, Liu B, Qin B, Feng C. Application of a 3D pseudo-continuous arterial spin-labeled perfusion MRI scan combined with a post-labeling delay value in the diagnosis of neonatal hypoxic-ischemic encephalopathy. 2019;1–14.

[5] Huang RY, Bi WL, Griffith B, Kaufmann TJ, La Fougère C, Schmidt NO, et al. Imaging and diagnostic advances for intracranial meningiomas. Neuro Oncol. 2019;21:I44–61.

[6] Tourdias T, Rodrigo S, Oppenheim C, Naggara O, Varlet P, Amoussa S, et al. Pulsed arterial spin labeling applications in brain tumors: Practical review. J Neuroradiol. 2008;35(2):79–89.

[7] Dangouloff-Ros V, Deroulers C, Foissac F, Badoual M, Shotar E, Grévent D, et al. Arterial spin labeling to predict brain tumor grading in children: Correlations between histopathologic vascular density and perfusion MR imaging. Radiology. 2016;281(2):553–66.

[8] Qiao XJ, Kim HG, Wang DJJ, Salamon N, Linetsky M, Sepahdari A, et al. Application of arterial spin labeling perfusion MRI to differentiate benign from malignant intracranial meningiomas. Eur J Radiol. 2017 Dec 1;97:31–6.

[9] Kimura H, Takeuchi H, Koshimoto Y, Arishima H, Uematsu H, Kawamura Y, et al. Perfusion Imaging of Meningioma by Using Continuous Arterial Spin-Labeling: Comparison with Dynamic Susceptibility-weighted Contrast-Enhanced MR Images and Histopathologic Features [Internet]. [cited 2020 Oct 6]. Available from: www.ajnr.org

[10] Qiao XJ, Kim HG, Wang DJJ, Salamon N, Linetsky M, Sepahdari A, et al. Application of arterial spin labeling perfusion MRI to differentiate benign from malignant intracranial meningiomas. Eur J Radiol. 2017;97(March):31–6.

[11] Shiroishi MS, Habibi M, Rajderkar D, Yurko C, Go JL, Lerner A, et al. Perfusion and permeability MR imaging of gliomas. Technol Cancer Res Treat. 2011;10(1):59–71.

[12] Keil VC, Pintea B, Gielen GH, Hittatiya K, Datsi A, Simon M, et al. Meningioma assessment: Kinetic parameters in dynamic contrast-enhanced MRI appear independent from microvascular anatomy and VEGF expression. J Neuroradiol. 2018;45(4):242–8.

[13] van Rijswijk CSP, Hogendoorn PCW, Taminiau AHM, Bloem JL. Synovial sarcoma: dynamic contrast-enhanced MR imaging features. Skeletal Radiol [Internet]. 2001;30(1):25–30. Available from: https://doi.org/10.1007/s002560000295

[14] Todua F, Chedia S. Differentiation between benign and malignant meningiomas using diffusion and perfusion MR imaging. Georgian Med News. 2012;(206):16–22.

[15] Joseph DRA. Accuracy of Perfusion MRI in Differentiating Benign From Atypical and Malignant Meningiomas. J Med Sci Clin Res. 2017;05(04):20454–60.

Advanced MRI prediction of meningioma histopathological classification: a literature review and case presentations

Abstract

References

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