Literature DB >> 29174756

Differentiating meningioma grade by imaging features on magnetic resonance imaging.

Andrew T Hale1, Li Wang2, Megan K Strother3, Lola B Chambless4.   

Abstract

Atypical meningioma has an aggressive clinical course. Distinguishing atypical from benign meningioma preoperatively could affect surgical planning and improve treatment outcomes. In this study, we examined whether pre-operative magnetic resonance imaging (MRI) features could distinguish between benign and atypical meningioma. Imaging factors analyzed included peritumoral edema, the presence of a draining vein, tumor necrosis, tumor location and tumor volume. Using univariate analysis, the most striking predictor of grade was tumor volume (p < .001). When adjusting for the degree of peritumoral edema, volume remained a positive predictor of higher histological grade meningioma (p = .042) and was the strongest single predictor of higher-grade meningioma in this study. Additional imaging features associated with increased risk for atypical pathology in univariate analysis included the presence of tumor necrosis (p = .012), peritumoral edema (p = .022) and location along the falx and convexity (p = .026). Despite statistically significant associations using univariate analysis, in multivariate analysis, we found that only presence of peritumoral edema was predictive of a higher-grade meningioma. Further multivariate analyses suggests that edema, draining vein and necrosis are all positive predictors of tumor volume (p < .0001). Overall, these data suggest that radiographic features including presence of tumor necrosis, and tumor location along the falx or convexity may be predictive of higher-grade meningioma when considered alone. However, most strikingly, our data point to tumor volume as the most robust pre-operative indicator of higher-grade meningioma.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Meningioma; Pre-operative imaging features of meningioma; Skull-base

Mesh:

Year:  2017        PMID: 29174756     DOI: 10.1016/j.jocn.2017.11.013

Source DB:  PubMed          Journal:  J Clin Neurosci        ISSN: 0967-5868            Impact factor:   1.961


  16 in total

1.  Grading meningiomas utilizing multiparametric MRI with inclusion of susceptibility weighted imaging and quantitative susceptibility mapping.

Authors:  Shun Zhang; Gloria Chia-Yi Chiang; Jacquelyn Marion Knapp; Christina M Zecca; Diana He; Rohan Ramakrishna; Rajiv S Magge; David J Pisapia; Howard Alan Fine; Apostolos John Tsiouris; Yize Zhao; Linda A Heier; Yi Wang; Ilhami Kovanlikaya
Journal:  J Neuroradiol       Date:  2019-05-25       Impact factor: 3.447

2.  WHO grade of intracranial meningiomas differs with respect to patient's age, location, tumor size and peritumoral edema.

Authors:  Anne Ressel; Susanne Fichte; Michael Brodhun; Steffen K Rosahl; Ruediger Gerlach
Journal:  J Neurooncol       Date:  2019-10-01       Impact factor: 4.130

3.  Differentiating microcystic meningioma from atypical meningioma using diffusion-weighted imaging.

Authors:  Ke Xiaoai; Zhou Qing; Han Lei; Zhou Junlin
Journal:  Neuroradiology       Date:  2020-01-29       Impact factor: 2.804

Review 4.  Natural history of intraventricular meningiomas: systematic review.

Authors:  Benedito Jamilson Araújo Pereira; Antônio Nogueira de Almeida; Wellingson Silva Paiva; Paulo Henrique Pires de Aguiar; Manoel Jacobsen Teixeira; Suely Kazue Nagahashi Marie
Journal:  Neurosurg Rev       Date:  2018-08-15       Impact factor: 3.042

5.  Peritumoral edema correlates with mutational burden in meningiomas.

Authors:  Corey M Gill; Joshua Loewenstern; John W Rutland; Hanane Arib; Margaret Pain; Melissa Umphlett; Yayoi Kinoshita; Russell B McBride; Joshua Bederson; Michael Donovan; Robert Sebra; Mary Fowkes; Raj K Shrivastava
Journal:  Neuroradiology       Date:  2020-08-12       Impact factor: 2.804

6.  Are the clinical manifestations of CT scan and location associated with World Health Organization histopathological grades of meningioma?: A retrospective study.

Authors:  Razieh Behzadmehr; Rezvaneh Behzadmehr
Journal:  Ann Med Surg (Lond)       Date:  2021-04-30

7.  Characterization of Progesterone Receptor Expression in Intracranial Meningiomas of Patients Treated in a High-Complexity Hospital in Bogota, Colombia.

Authors:  Raul Ramirez Grueso; Linda Barcenas; Jaime A Arias; Carlos Colegial; Claudia L Avendaño; Jose Chaves; Jorge Galvis; Santiago Moreno
Journal:  Cureus       Date:  2020-12-29

8.  Tumor recurrence in parasagittal and falcine atypical meningiomas invading the superior sagittal sinus.

Authors:  Andrei Ionuţ Cucu; Mihaela Dana Turliuc; Claudia Florida Costea; Cristina Gena Dascălu; Gabriela Florenţa Dumitrescu; Anca Sava; Şerban Turliuc; Dragoş Viorel Scripcariu; Ion Poeată
Journal:  Rom J Morphol Embryol       Date:  2020 Apr-Jun       Impact factor: 1.033

9.  MRI predictors for brain invasion in meningiomas.

Authors:  Thomas Ong; Aditya Bharatha; Reema Alsufayan; Sunit Das; Amy Wei Lin
Journal:  Neuroradiol J       Date:  2020-09-14

10.  Histogram Profiling of Postcontrast T1-Weighted MRI Gives Valuable Insights into Tumor Biology and Enables Prediction of Growth Kinetics and Prognosis in Meningiomas.

Authors:  Georg Alexander Gihr; Diana Horvath-Rizea; Patricia Kohlhof-Meinecke; Oliver Ganslandt; Hans Henkes; Cindy Richter; Karl-Titus Hoffmann; Alexey Surov; Stefan Schob
Journal:  Transl Oncol       Date:  2018-06-18       Impact factor: 4.243

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