Literature DB >> 32924772

MRI predictors for brain invasion in meningiomas.

Thomas Ong1,2,3, Aditya Bharatha1,2,4, Reema Alsufayan1,2,5, Sunit Das4, Amy Wei Lin1,2.   

Abstract

BACKGROUND AND
PURPOSE: In the 2016 revision of the World Health Organization classification of central nervous system tumours, brain invasion was added as an independent histological criterion for the diagnosis of a World Health Organization grade II atypical meningioma. The aim of this study was to assess whether magnetic resonance imaging characteristics can predict brain invasion for meningiomas.
MATERIALS AND METHODS: We conducted a retrospective review of all meningiomas resected at our institution between 2005 and 2016 which had preoperative magnetic resonance imaging and included brain tissue within the pathology specimen. One hundred meningiomas were included in the study, 60 of which had histopathological brain invasion, 40 of which did not. Magnetic resonance imaging characteristics of tumours were evaluated for potential predictors of brain invasion. Tumour location, size, perilesional oedema, contour, cerebrospinal fluid cleft, peritumoral cyst, dural venous sinus invasion, bone invasion, hyperostosis and the presence of enlarged pial arteries and veins were evaluated. Data were analysed using conventional chi-square, Fisher's exact test and logistic regression.
RESULTS: The volume of peritumoral oedema was significantly higher in the brain-invasive meningioma group compared to the non-brain-invasive group. The presence of a complete cleft was a rare finding that was only found in non-brain-invasive meningiomas. The presence of enlarged pial feeding arteries was a rare finding that was only found in brain-invasive meningiomas.
CONCLUSIONS: An increased volume of perilesional oedema is associated with the likelihood of brain invasion for meningiomas.

Entities:  

Keywords:  MRI; Meningioma; brain invasion

Mesh:

Year:  2020        PMID: 32924772      PMCID: PMC7868592          DOI: 10.1177/1971400920953417

Source DB:  PubMed          Journal:  Neuroradiol J        ISSN: 1971-4009


  30 in total

Review 1.  Pathogenesis of peri-tumoral edema in intracranial meningiomas.

Authors:  Moncef Berhouma; Timothee Jacquesson; Emmanuel Jouanneau; François Cotton
Journal:  Neurosurg Rev       Date:  2017-08-24       Impact factor: 3.042

2.  Hyperostosis associated with meningioma of the cranial base: secondary changes or tumor invasion.

Authors:  D R Pieper; O Al-Mefty; Y Hanada; D Buechner
Journal:  Neurosurgery       Date:  1999-04       Impact factor: 4.654

3.  Predicting the probability of meningioma recurrence based on the quantity of peritumoral brain edema on computerized tomography scanning.

Authors:  R E Mantle; B Lach; M R Delgado; S Baeesa; G Bélanger
Journal:  J Neurosurg       Date:  1999-09       Impact factor: 5.115

4.  Anatomic location is a risk factor for atypical and malignant meningiomas.

Authors:  Ari J Kane; Michael E Sughrue; Martin J Rutkowski; Gopal Shangari; Shanna Fang; Michael W McDermott; Mitchel S Berger; Andrew T Parsa
Journal:  Cancer       Date:  2010-11-08       Impact factor: 6.860

5.  Prediction of high-grade meningioma by preoperative MRI assessment.

Authors:  Yosuke Kawahara; Mitsutoshi Nakada; Yutaka Hayashi; Yutaka Kai; Yasuhiko Hayashi; Naoyuki Uchiyama; Hiroyuki Nakamura; Jun-Ichi Kuratsu; Jun-Ichiro Hamada
Journal:  J Neurooncol       Date:  2012-02-12       Impact factor: 4.130

6.  Diffusion-weighted MR imaging: diagnosing atypical or malignant meningiomas and detecting tumor dedifferentiation.

Authors:  V A Nagar; J R Ye; W H Ng; Y H Chan; F Hui; C K Lee; C C T Lim
Journal:  AJNR Am J Neuroradiol       Date:  2008-03-20       Impact factor: 3.825

7.  Differentiation between classic and atypical meningiomas with use of diffusion tensor imaging.

Authors:  C-H Toh; M Castillo; A M-C Wong; K-C Wei; H-F Wong; S-H Ng; Y-L Wan
Journal:  AJNR Am J Neuroradiol       Date:  2008-06-26       Impact factor: 3.825

8.  Brain invasion assessability in meningiomas is related to meningioma size and grade, and can be improved by extensive sampling of the surgically removed meningioma specimen.

Authors:  Joze Pizem; Tomaz Velnar; Borut Prestor; Jernej Mlakar; Mara Popovic
Journal:  Clin Neuropathol       Date:  2014 Sep-Oct       Impact factor: 1.368

Review 9.  2016 Updates to the WHO Brain Tumor Classification System: What the Radiologist Needs to Know.

Authors:  Derek R Johnson; Julie B Guerin; Caterina Giannini; Jonathan M Morris; Lawrence J Eckel; Timothy J Kaufmann
Journal:  Radiographics       Date:  2017-10-13       Impact factor: 5.333

10.  Imaging characteristics and surgical treatment of invasive meningioma.

Authors:  Weina Hou; Yongqian Ma; Hongshun Xing; Yuehui Yin
Journal:  Oncol Lett       Date:  2017-03-09       Impact factor: 2.967

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  3 in total

1.  Nomogram based on MRI can preoperatively predict brain invasion in meningioma.

Authors:  Jing Zhang; Yuntai Cao; Guojin Zhang; Zhiyong Zhao; Jianqing Sun; Wenyi Li; Jialiang Ren; Tao Han; Junlin Zhou; Kuntao Chen
Journal:  Neurosurg Rev       Date:  2022-09-30       Impact factor: 2.800

2.  A Clinical Semantic and Radiomics Nomogram for Predicting Brain Invasion in WHO Grade II Meningioma Based on Tumor and Tumor-to-Brain Interface Features.

Authors:  Ning Li; Yan Mo; Chencui Huang; Kai Han; Mengna He; Xiaolan Wang; Jiaqi Wen; Siyu Yang; Haoting Wu; Fei Dong; Fenglei Sun; Yiming Li; Yizhou Yu; Minming Zhang; Xiaojun Guan; Xiaojun Xu
Journal:  Front Oncol       Date:  2021-10-22       Impact factor: 6.244

Review 3.  Updated Systematic Review on the Role of Brain Invasion in Intracranial Meningiomas: What, When, Why?

Authors:  Lara Brunasso; Lapo Bonosi; Roberta Costanzo; Felice Buscemi; Giuseppe Roberto Giammalva; Gianluca Ferini; Vito Valenti; Anna Viola; Giuseppe Emmanuele Umana; Rosa Maria Gerardi; Carmelo Lucio Sturiale; Alessio Albanese; Domenico Gerardo Iacopino; Rosario Maugeri
Journal:  Cancers (Basel)       Date:  2022-08-27       Impact factor: 6.575

  3 in total

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