Literature DB >> 18379322

Diffusion-tensor imaging for glioma grading at 3-T magnetic resonance imaging: analysis of fractional anisotropy and mean diffusivity.

Ho Yun Lee1, Dong Gyu Na, In-Chan Song, Dong Hoon Lee, Hyung Suk Seo, Ji-hoon Kim, Kee-Hyun Chang.   

Abstract

PURPOSE: To retrospectively determine whether fractional anisotropy (FA) or mean diffusivity (MD) value at 3-T diffusion-tensor imaging is different between low- and high-grade gliomas and may be useful for glioma grading.
METHODS: Review board approval was obtained, and informed consent was waived. Diffusion-tensor imaging was performed in 27 patients with surgically proved gliomas (19 high-grade and 8 low-grade gliomas). Fractional anisotropy and MD values were measured in 3 regions; peritumoral edema, and enhancing and nonenhancing tumor regions. We compared mean FA and MD values of nonenhancing tumor regions between low- and high-grade gliomas and compared the FA and MD values among the 3 mentioned regions in high-grade gliomas. The relationship between FA and MD values of tumors was also investigated. Statistical analysis was performed using the Student t test and Pearson correlation coefficients.
RESULTS: In the nonenhancing regions of tumors, FA ratios were not significantly different between low- and high-grade gliomas (0.472 and 0.701, P = 0.075), but MD ratios were significantly lower in high-grade gliomas (1.899 and 1.23, P < 0.001). In high-grade gliomas, enhancing tumors showed a tendency toward a lower FA ratio than nonenhancing tumors (P = 0.034), but FA values or ratios of peritumoral edema were not significantly different from those of enhancing or nonenhancing tumor. No strong relationship was found between FA and MD values.
CONCLUSIONS: Fractional anisotropy values of low- and high-grade gliomas were not significantly different. However, MD values of nonenhancing low-grade gliomas were significantly higher than those of nonenhancing high-grade gliomas, which will be useful for the grading of nonenhancing infiltrative gliomas.

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Year:  2008        PMID: 18379322     DOI: 10.1097/RCT.0b013e318076b44d

Source DB:  PubMed          Journal:  J Comput Assist Tomogr        ISSN: 0363-8715            Impact factor:   1.826


  20 in total

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2.  Potential role of fractional anisotropy derived from diffusion tensor imaging in differentiating high-grade gliomas from low-grade gliomas: a meta-analysis.

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Journal:  Int J Clin Exp Med       Date:  2014-10-15

3.  Multimodal MR imaging model to predict tumor infiltration in patients with gliomas.

Authors:  Christopher R Durst; Prashant Raghavan; Mark E Shaffrey; David Schiff; M Beatriz Lopes; Jason P Sheehan; Nicholas J Tustison; James T Patrie; Wenjun Xin; W Jeff Elias; Kenneth C Liu; Greg A Helm; A Cupino; Max Wintermark
Journal:  Neuroradiology       Date:  2013-12-15       Impact factor: 2.804

4.  Glioma grade assessment by using histogram analysis of diffusion tensor imaging-derived maps.

Authors:  András Jakab; Péter Molnár; Miklós Emri; Ervin Berényi
Journal:  Neuroradiology       Date:  2010-09-21       Impact factor: 2.804

5.  Diffusion tensor MR imaging of cerebral gliomas: evaluating fractional anisotropy characteristics.

Authors:  M L White; Y Zhang; F Yu; S A Jaffar Kazmi
Journal:  AJNR Am J Neuroradiol       Date:  2010-10-14       Impact factor: 3.825

6.  Prediction of Lower Grade Insular Glioma Molecular Pathology Using Diffusion Tensor Imaging Metric-Based Histogram Parameters.

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7.  Assessment of diffusion tensor imaging metrics in differentiating low-grade from high-grade gliomas.

Authors:  Lamiaa El-Serougy; Ahmed Abdel Khalek Abdel Razek; Amani Ezzat; Hany Eldawoody; Ahmad El-Morsy
Journal:  Neuroradiol J       Date:  2016-08-25

8.  Grading of Gliomas by Using Monoexponential, Biexponential, and Stretched Exponential Diffusion-weighted MR Imaging and Diffusion Kurtosis MR Imaging.

Authors:  Yan Bai; Yusong Lin; Jie Tian; Dapeng Shi; Jingliang Cheng; E Mark Haacke; Xiaohua Hong; Bo Ma; Jinyuan Zhou; Meiyun Wang
Journal:  Radiology       Date:  2015-07-31       Impact factor: 11.105

9.  Meta-analysis of diffusion metrics for the prediction of tumor grade in gliomas.

Authors:  V Z Miloushev; D S Chow; C G Filippi
Journal:  AJNR Am J Neuroradiol       Date:  2014-09-04       Impact factor: 3.825

10.  Diagnostic Value of Combined Intravoxel Incoherent Motion Diffusion-Weighted Magnetic Resonance Imaging with Diffusion Tensor Imaging in Predicting Parametrial Infiltration in Cervical Cancer.

Authors:  Ting-Ting Lin; Xin-Xiang Li; Wei-Fu Lv; Jiang-Ning Dong; Chao Wei; Ting-Ting Wang; Chuan-Bin Wang; Ping Zhang
Journal:  Contrast Media Mol Imaging       Date:  2021-05-11       Impact factor: 3.161

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