Literature DB >> 22327898

Prediction of high-grade meningioma by preoperative MRI assessment.

Yosuke Kawahara1, Mitsutoshi Nakada, Yutaka Hayashi, Yutaka Kai, Yasuhiko Hayashi, Naoyuki Uchiyama, Hiroyuki Nakamura, Jun-Ichi Kuratsu, Jun-Ichiro Hamada.   

Abstract

High-grade (World Health Organization grades II and III) meningiomas grow aggressively and recur frequently, resulting in a poor prognosis. Assessment of tumor malignancy before treatment initiation is important. We attempted to determine predictive factors for high-grade meningioma on magnetic resonance (MR) imaging before surgery. We reviewed 65 meningiomas (39 cases, benign; 26 cases, high-grade) and assessed four factors: (1) tumor-brain interface (TBI) on T1-weighted imaging (T1WI), (2) capsular enhancement (CapE), i.e., the layer of the tumor-brain interface on gadolinium-enhanced T1WI (T1Gd), (3) heterogeneity on T1Gd, and (4) tumoral margin on T1Gd. All four factors were useful in distinguishing high-grade from benign meningiomas, according to univariate analysis. On multivariate regression analysis, unclear TBI and heterogeneous enhancement were independent predictive factors for high-grade meningioma. In meningiomas with an unclear TBI and heterogeneous enhancement, the probability of high-grade meningioma was 98%. Our data suggest that this combination of factors obtained from conventional sequences on MR imaging may be useful to predict high-grade meningioma.

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Year:  2012        PMID: 22327898     DOI: 10.1007/s11060-012-0809-4

Source DB:  PubMed          Journal:  J Neurooncol        ISSN: 0167-594X            Impact factor:   4.130


  25 in total

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Journal:  J Neurosurg       Date:  2000-05       Impact factor: 5.115

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  35 in total

1.  The diagnostic value of using combined MR diffusion tensor imaging parameters to differentiate between low- and high-grade meningioma.

Authors:  Kerim Aslan; Hediye Pinar Gunbey; Leman Tomak; Lutfi Incesu
Journal:  Br J Radiol       Date:  2018-05-31       Impact factor: 3.039

2.  68Gallium-DOTATATE PET in meningioma: A reliable predictor of tumor growth rate?

Authors:  Michael Sommerauer; Jan-Karl Burkhardt; Karl Frontzek; Elisabeth Rushing; Alfred Buck; Niklaus Krayenbuehl; Michael Weller; Niklaus Schaefer; Felix P Kuhn
Journal:  Neuro Oncol       Date:  2016-02-09       Impact factor: 12.300

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

4.  Diffusion Profiling via a Histogram Approach Distinguishes Low-grade from High-grade Meningiomas, Can Reflect the Respective Proliferative Potential and Progesterone Receptor Status.

Authors:  Georg Alexander Gihr; Diana Horvath-Rizea; Nikita Garnov; Patricia Kohlhof-Meinecke; Oliver Ganslandt; Hans Henkes; Hans Jonas Meyer; Karl-Titus Hoffmann; Alexey Surov; Stefan Schob
Journal:  Mol Imaging Biol       Date:  2018-08       Impact factor: 3.488

5.  Imaging and diagnostic advances for intracranial meningiomas.

Authors:  Raymond Y Huang; Wenya Linda Bi; Brent Griffith; Timothy J Kaufmann; Christian la Fougère; Nils Ole Schmidt; Jöerg C Tonn; Michael A Vogelbaum; Patrick Y Wen; Kenneth Aldape; Farshad Nassiri; Gelareh Zadeh; Ian F Dunn
Journal:  Neuro Oncol       Date:  2019-01-14       Impact factor: 12.300

Review 6.  Variants of meningiomas: a review of imaging findings and clinical features.

Authors:  Akira Kunimatsu; Natsuko Kunimatsu; Kouhei Kamiya; Masaki Katsura; Harushi Mori; Kuni Ohtomo
Journal:  Jpn J Radiol       Date:  2016-05-02       Impact factor: 2.374

7.  Imaging and extent of surgical resection predict risk of meningioma recurrence better than WHO histopathological grade.

Authors:  William L Hwang; Ariel E Marciscano; Andrzej Niemierko; Daniel W Kim; Anat O Stemmer-Rachamimov; William T Curry; Fred G Barker; Robert L Martuza; Jay S Loeffler; Kevin S Oh; Helen A Shih; Mykol Larvie
Journal:  Neuro Oncol       Date:  2015-11-22       Impact factor: 12.300

8.  Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging.

Authors:  Yae Won Park; Jongmin Oh; Seng Chan You; Kyunghwa Han; Sung Soo Ahn; Yoon Seong Choi; Jong Hee Chang; Se Hoon Kim; Seung-Koo Lee
Journal:  Eur Radiol       Date:  2018-11-15       Impact factor: 5.315

9.  Application of arterial spin labeling perfusion MRI to differentiate benign from malignant intracranial meningiomas.

Authors:  Xin J Qiao; Hyun Grace Kim; Danny J J Wang; Noriko Salamon; Michael Linetsky; Ali Sepahdari; Benjamin M Ellingson; Whitney B Pope
Journal:  Eur J Radiol       Date:  2017-10-07       Impact factor: 3.528

10.  Prediction of pediatric meningioma recurrence by preoperative MRI assessment.

Authors:  Hao Li; Meng Zhao; Shuo Wang; Yong Cao; Jizong Zhao
Journal:  Neurosurg Rev       Date:  2016-04-01       Impact factor: 3.042

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