Literature DB >> 25824207

Prediction of hard meningiomas: quantitative evaluation based on the magnetic resonance signal intensity.

Keita Watanabe1, Shingo Kakeda2, Junkoh Yamamoto3, Satoru Ide2, Norihiro Ohnari2, Shigeru Nishizawa3, Yukunori Korogi2.   

Abstract

BACKGROUND: From a surgical perspective, presurgical prediction of meningioma consistency is beneficial.
PURPOSE: To quantitatively analyze the correlation between the magnetic resonance (MR) signal intensity (SI) or apparent diffusion coefficient (ADC) and meningioma consistency and to determine which MR sequence could help predicting hard meningiomas.
MATERIAL AND METHODS: This study included 43 patients with meningiomas who underwent preoperative MR imaging (MRI), including T1-weighted (T1W) imaging, T2-weighted (T2W) imaging, fluid-attenuated inversion recovery (FLAIR), diffusion-weighted imaging (DWI), contrast-enhanced (CE)-T1W imaging, and CE-fast imaging employing steady-state acquisition (FIESTA). A neurosurgeon evaluated the tumor consistency using a visual analog scale (VAS) with the anchors "soft" (score = 0) and "hard" (score = 10). The SI ratio (tumor to cerebral cortex SI) and ADC value were compared with the tumor consistency. The sensitivity, specificity, and accuracy for predicting hard meningiomas (VAS score ≥8; 9 of 43 patients) were calculated using cutoff values for the SI ratio that were obtained in a receiver operating characteristic curve analysis.
RESULTS: A significant negative correlation was observed between the tumor consistency and the SI ratio on T2W imaging, FLAIR, and CE-FIESTA (P < 0.05) but not on T1W imaging, CE-T1W imaging, and the ADC value. The sensitivity, specificity, and accuracy for predicting hard meningiomas were 89%, 79%, and 81% with T2W imaging; 89%, 76%, and 79% with FLAIR; and 100%, 74%, and 79% with CE-FIESTA, respectively.
CONCLUSION: Our results suggest that a quantitative assessment using conventional T2W imaging or FLAIR may be a simple and useful method for predicting hard meningiomas. © The Foundation Acta Radiologica 2015.

Entities:  

Keywords:  Meningioma; apparent diffusion coefficient (ADC); consistency; magnetic resonance imaging (MRI); signal intensity

Mesh:

Substances:

Year:  2015        PMID: 25824207     DOI: 10.1177/0284185115578323

Source DB:  PubMed          Journal:  Acta Radiol        ISSN: 0284-1851            Impact factor:   1.990


  7 in total

Review 1.  Predicting Meningioma Consistency on Preoperative Neuroimaging Studies.

Authors:  Mark S Shiroishi; Steven Y Cen; Benita Tamrazi; Francesco D'Amore; Alexander Lerner; Kevin S King; Paul E Kim; Meng Law; Darryl H Hwang; Orest B Boyko; Chia-Shang J Liu
Journal:  Neurosurg Clin N Am       Date:  2016-02-18       Impact factor: 2.509

Review 2.  Can MRI predict meningioma consistency?: a correlation with tumor pathology and systematic review.

Authors:  Amy Yao; Margaret Pain; Priti Balchandani; Raj K Shrivastava
Journal:  Neurosurg Rev       Date:  2016-11-21       Impact factor: 3.042

3.  Improving contrast enhancement in magnetic resonance imaging using 5-aminolevulinic acid-induced protoporphyrin IX for high-grade gliomas.

Authors:  Junkoh Yamamoto; Shingo Kakeda; Tetsuya Yoneda; Shun-Ichiro Ogura; Shohei Shimajiri; Tohru Tanaka; Yukunori Korogi; Shigeru Nishizawa
Journal:  Oncol Lett       Date:  2016-12-27       Impact factor: 2.967

Review 4.  Utility of preoperative meningioma consistency measurement with magnetic resonance elastography (MRE): a review.

Authors:  Alexander G Chartrain; Mehmet Kurt; Amy Yao; Rui Feng; Kambiz Nael; J Mocco; Joshua B Bederson; Priti Balchandani; Raj K Shrivastava
Journal:  Neurosurg Rev       Date:  2017-05-31       Impact factor: 3.042

5.  Features of tumor texture influence surgery and outcome in intracranial meningioma.

Authors:  Thomas Sauvigny; Franz L Ricklefs; Lena Hoffmann; Raphael Schwarz; Manfred Westphal; Nils Ole Schmidt
Journal:  Neurooncol Adv       Date:  2020-09-10

6.  Whole-brain 3D MR fingerprinting brain imaging: clinical validation and feasibility to patients with meningioma.

Authors:  Thomaz R Mostardeiro; Ananya Panda; Robert J Witte; Norbert G Campeau; Kiaran P McGee; Yi Sui; Aiming Lu
Journal:  MAGMA       Date:  2021-05-04       Impact factor: 2.310

7.  Preoperative Prediction of Meningioma Consistency via Machine Learning-Based Radiomics.

Authors:  Yixuan Zhai; Dixiang Song; Fengdong Yang; Yiming Wang; Xin Jia; Shuxin Wei; Wenbin Mao; Yake Xue; Xinting Wei
Journal:  Front Oncol       Date:  2021-05-26       Impact factor: 6.244

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.