Advertisement

MRI predictive score of pial vascularization of supratentorial intracranial meningioma

  • Guillaume FriconnetEmail author
  • Victor Hugo Espíndola Ala
  • Kevin Janot
  • Waleed Brinjikji
  • Clément Bogey
  • Leslie Lemnos
  • Henri Salle
  • Suzana Saleme
  • Charbel Mounayer
  • Aymeric Rouchaud
Neuro

Abstract

Objective

Meningiomas are highly vascularized tumors which may recruit pial blood supply. Pial supply complicates tumor treatment in numerous ways. The objective of this study was to establish a reliable MRI-based diagnostic score to predict the existence of pial blood supply in supratentorial intracranial meningiomas and then correlate the score with clinical and surgical outcomes and histopathological findings.

Methods

We performed a retrospective analysis of supratentorial histologically proven meningiomas in our institution from 2010 to 2018. A score was built based on MRI criteria and correlated with digital subtraction angiography (DSA) pial vascularization assessment. The score was then validated on a second independent population recruited with the same modalities.

Results

Logistic regression identified four parameters related to pial blood supply which were used to build the score: skull base location, tumor size > 45 mm, peritumoral flow voids, and incomplete cerebrospinal fluid rim. The overall diagnostic performance in predicting pial blood supply was as follows: sensitivity 97.8%, specificity 76.9%, predictive positive value 88.2%, negative predictive value 95.2%, and accuracy 90.3%. Inter-reader agreement and Cohen’s kappa were good, respectively, of 90.7% and 0.69. A high score was associated with aggressive meningioma (World Health Organization II–III) (p = 0.04) and with greater importance of pial supply relative to dural supply.

Conclusions

We have identified a reliable way to use MRI to predict the existence of pial blood supply in supratentorial intracranial meningiomas. A higher score also predicted higher grade meningioma.

Key Points

• Accurate and reproducible MRI score composed of four items to predict the existence of pial blood supply in supratentorial meningioma.

• High score is associated with high-grade meningioma (WHO II–III) but also with greater importance of pial supply relative to dural supply.

Keywords

Magnetic resonance imaging Meningioma Retrospective studies Supratentorial neoplasms 

Abbreviations

DSA

Digital subtraction angiography

ECA

External carotid artery

PTBE

Peritumoral brain edema

WHO

World Health Organization

Notes

Funding

The authors state that this work has not received any funding.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Aymeric Rouchaud.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study/observational

• multicenter study

Supplementary material

330_2019_6197_MOESM1_ESM.docx (23 kb)
ESM 1 (DOCX 23 kb)

References

  1. 1.
    Shah A, Choudhri O, Jung H, Li G (2015) Preoperative endovascular embolization of meningiomas: update on therapeutic options. Neurosurg Focus 38:E7.  https://doi.org/10.3171/2014.12.FOCUS14728 CrossRefGoogle Scholar
  2. 2.
    Dubel GJ, Ahn SH, Soares GM (2013) Contemporary endovascular embolotherapy for meningioma. Semin Intervent Radiol 30:263–277.  https://doi.org/10.1055/s-0033-1353479 CrossRefGoogle Scholar
  3. 3.
    Aihara M, Naito I, Shimizu T et al (2015) Preoperative embolization of intracranial meningiomas using n-butyl cyanoacrylate. Neuroradiology 57:713–719.  https://doi.org/10.1007/s00234-015-1521-9 CrossRefGoogle Scholar
  4. 4.
    Takeguchi T, Miki H, Shimizu T et al (2003) Prediction of tumor-brain adhesion in intracranial meningiomas by MR imaging and DSA. Magn Reson Med Sci 2:171–179CrossRefGoogle Scholar
  5. 5.
    Alvernia JE, Sindou MP (2004) Preoperative neuroimaging findings as a predictor of the surgical plane of cleavage: prospective study of 100 consecutive cases of intracranial meningioma. J Neurosurg 100:422–430.  https://doi.org/10.3171/jns.2004.100.3.0422 CrossRefGoogle Scholar
  6. 6.
    Ildan F, Erman T, Göçer AI et al (2007) Predicting the probability of meningioma recurrence in the preoperative and early postoperative period: a multivariate analysis in the midterm follow-up. Skull Base 17:157–171.  https://doi.org/10.1055/s-2007-970554 CrossRefGoogle Scholar
  7. 7.
    Nowak A, Dziedzic T, Krych P et al (2015) Benign versus atypical meningiomas: risk factors predicting recurrence. Neurol Neurochir Pol 49:1–10.  https://doi.org/10.1016/j.pjnns.2014.11.003 CrossRefGoogle Scholar
  8. 8.
    Pistolesi S, Fontanini G, Camacci T et al (2002) Meningioma-associated brain oedema: the role of angiogenic factors and pial blood supply. J Neurooncol 60:159–164CrossRefGoogle Scholar
  9. 9.
    Nakasu S, Fukami T, Jito J, Matsuda M (2005) Microscopic anatomy of the brain-meningioma interface. Brain Tumor Pathol 22:53–57.  https://doi.org/10.1007/s10014-005-0187-0 CrossRefGoogle Scholar
  10. 10.
    Bitzer M, Wöckel L, Luft AR et al (1997) The importance of pial blood supply to the development of peritumoral brain edema in meningiomas. J Neurosurg 87:368–373.  https://doi.org/10.3171/jns.1997.87.3.0368 CrossRefGoogle Scholar
  11. 11.
    Sindou MP, Alaywan M (1998) Most intracranial meningiomas are not cleavable tumors: anatomic-surgical evidence and angiographic predictibility. Neurosurgery 42:476–480CrossRefGoogle Scholar
  12. 12.
    Sato M, Nakai Y, Tsurushima H, Shiigai M, Masumoto T, Matsumura A (2013) Risk factors of ischemic lesions related to cerebral angiography and neuro-interventional procedures. Neurol Med Chir (Tokyo) 53:381–387Google Scholar
  13. 13.
    Enokizono M, Morikawa M, Matsuo T et al (2014) The rim pattern of meningioma on 3D FLAIR imaging: correlation with tumor-brain adhesion and histological grading. Magn Reson Med Sci 13:251–260.  https://doi.org/10.2463/mrms.2013-0132 CrossRefGoogle Scholar
  14. 14.
    Tsutsumi S, Izumi H, Yasumoto Y, Ito M (2013) Convexity en plaque meningioma manifesting as subcutaneous mass: case report. Neurol Med Chir (Tokyo) 53:727–729.  https://doi.org/10.2176/nmc.cr2012-0324 CrossRefGoogle Scholar
  15. 15.
    Lagman C, Ong V, Nguyen T et al (2018) The Meningioma Vascularity Index: a volumetric analysis of flow voids to predict intraoperative blood loss in nonembolized meningiomas. J Neurosurg 1–6.  https://doi.org/10.3171/2018.1.JNS172724
  16. 16.
    El Sanharawi M, Naudet F (2013) Understanding logistic regression. J Fr Ophtalmol 36:710–715.  https://doi.org/10.1016/j.jfo.2013.05.008 CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Guillaume Friconnet
    • 1
    Email author
  • Victor Hugo Espíndola Ala
    • 1
  • Kevin Janot
    • 2
  • Waleed Brinjikji
    • 3
  • Clément Bogey
    • 1
  • Leslie Lemnos
    • 4
  • Henri Salle
    • 4
  • Suzana Saleme
    • 1
  • Charbel Mounayer
    • 1
    • 5
  • Aymeric Rouchaud
    • 1
    • 5
  1. 1.Department of RadiologyCentre Hospitalier et Universitaire DupuytrenLimogesFrance
  2. 2.Department of RadiologyFondation Adolphe-de-RothschildParisFrance
  3. 3.Department of RadiologyMayo ClinicRochesterUSA
  4. 4.Department of NeurosurgeryCentre Hospitalier et Universitaire DupuytrenLimogesFrance
  5. 5.CNRS, XLIM, UMR 7252LimogesFrance

Personalised recommendations