Literature DB >> 18095119

Scoring radiologic characteristics to predict proliferative potential in meningiomas.

Tetsuo Hashiba1, Naoya Hashimoto, Motohiko Maruno, Shuichi Izumoto, Tsuyoshi Suzuki, Naoki Kagawa, Toshiki Yoshimine.   

Abstract

We investigated the feasibility of using radiologic characteristics to predict the proliferative potential in meningiomas. Our statistical analysis revealed that the presence of peritumoral edema, an ambiguous brain-tumor border, and irregular tumor shape were significantly correlated with a higher MIB-1 staining index (SI) value. We developed the following scoring system for specific features in each tumor: peritumoral edema (tumor with edema = 1, tumor without edema = 0); brain-tumor border (tumor with any ambiguous border = 1, tumor circumscribed by a distinct rim = 0); and tumor shape (tumor with irregular shape = 1, tumor with smooth shape = 0). Using Spearman's correlation coefficient analysis, we found a significant correlation (P < 0.005) between total score calculated for each patient and SI value. Our findings suggest that the proliferative potential of meningiomas can be predicted using a less invasive preoperative examination focusing on the presence of peritumoral edema, ambiguous brain-tumor border, and irregular tumor shape.

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Year:  2006        PMID: 18095119     DOI: 10.1007/s10014-006-0199-4

Source DB:  PubMed          Journal:  Brain Tumor Pathol        ISSN: 1433-7398            Impact factor:   3.298


  17 in total

1.  An unusual growth of an intraventricular meningioma: a case report.

Authors:  Pierpaolo Lunardi; Carlo Conti; Rodolfo Corinaldesi; Giovanni Ghetti
Journal:  Neurol Sci       Date:  2011-01-14       Impact factor: 3.307

2.  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

3.  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

4.  Principal component analysis of texture features for grading of meningioma: not effective from the peritumoral area but effective from the tumor area.

Authors:  Teiji Tominaga; Kei Takase; Naoko Mori; Shunji Mugikura; Toshiki Endo; Hidenori Endo; Yo Oguma; Li Li; Akira Ito; Mika Watanabe; Masayuki Kanamori
Journal:  Neuroradiology       Date:  2022-08-31       Impact factor: 2.995

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

6.  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

7.  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

8.  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

9.  Use of preoperative magnetic resonance imaging T1 and T2 sequences to determine intraoperative meningioma consistency.

Authors:  Jason M Hoover; Jonathan M Morris; Fredric B Meyer
Journal:  Surg Neurol Int       Date:  2011-10-12

10.  Preoperative radiologic classification of convexity meningioma to predict the survival and aggressive meningioma behavior.

Authors:  Yi Liu; Silky Chotai; Ming Chen; Shi Jin; Song-tao Qi; Jun Pan
Journal:  PLoS One       Date:  2015-03-18       Impact factor: 3.240

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