Literature DB >> 24457139

Analysis of diffusion tensor imaging metrics for gliomas grading at 3 T.

Andrés Server1, Bjørn A Graff2, Roger Josefsen3, Tone E D Orheim4, Till Schellhorn5, Wibeke Nordhøy6, Per H Nakstad7.   

Abstract

OBJECTIVES: To assess the diagnostic accuracy of axial diffusivity (AD), radial diffusivity (RD), apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values derived from DTI for grading of glial tumors, and to estimate the correlation between DTI parameters and tumor grades.
METHODS: Seventy-eight patients with glial tumors underwent DTI. AD, RD, ADC and FA values of tumor, peritumoral edema and contralateral normal-appearing white matter (NAWM) and AD, RD, ADC and FA ratios: lowest average AD, RD, ADC and FA values in tumor or peritumoral edema to AD, RD, ADC and FA of NAWM were calculated. DTI parameters and tumor grades were analyzed statistically and with Pearson correlation. Receiver operating characteristic (ROC) curve analysis was also performed.
RESULTS: The differences in ADC, AD and RD tumor values, and ADC and RD tumor ratios were statistically significant between grades II and III, grades II and IV, and between grades II and III-IV. The AD tumor ratio differed significantly among all tumor grades. Tumor ADC, AD, RD and glial tumor grades were strongly correlated. In the ROC curve analysis, the area under the curve (AUC) of the parameter tumor ADC was the largest for distinguishing grade II from grades III to IV (98.5%), grade II from grade IV (98.9%) and grade II from grade III (97.0%).
CONCLUSION: ADC, RD and AD are useful DTI parameters for differentiation between low- and high-grade gliomas with a diagnostic accuracy of more than 90%. Our study revealed a good inverse correlation between ADC, RD, AD and WHO grades II-IV astrocytic tumors.
Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Apparent diffusion coefficient; Axial diffusivity; Brain tumor; Diffusion tensor imaging; Fractional anisotropy; Radial diffusivity

Mesh:

Year:  2014        PMID: 24457139     DOI: 10.1016/j.ejrad.2013.12.023

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  15 in total

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

Authors:  Ruofei Liang; Xiang Wang; Mao Li; Yuan Yang; Jiewen Luo; Qing Mao; Yanhui Liu
Journal:  Int J Clin Exp Med       Date:  2014-10-15

2.  Assessment of tissue heterogeneity using diffusion tensor and diffusion kurtosis imaging for grading gliomas.

Authors:  Rajikha Raja; Neelam Sinha; Jitender Saini; Anita Mahadevan; Kvl Narasinga Rao; Aarthi Swaminathan
Journal:  Neuroradiology       Date:  2016-10-29       Impact factor: 2.804

3.  Brain MR diffusion tensor imaging in Kennedy's disease.

Authors:  Francesco Garaci; Nicola Toschi; Simona Lanzafame; Girolama A Marfia; Simone Marziali; Alessandro Meschini; Francesca Di Giuliano; Giovanni Simonetti; Maria Guerrisi; Roberto Massa; Roberto Floris
Journal:  Neuroradiol J       Date:  2015-05-11

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

Authors:  Zhenxing Huang; Changyu Lu; Gen Li; Zhenye Li; Shengjun Sun; Yazhuo Zhang; Zonggang Hou; Jian Xie
Journal:  Front Oncol       Date:  2021-03-10       Impact factor: 6.244

Review 5.  Diffusion imaging could aid to differentiate between glioma progression and treatment-related abnormalities: a meta-analysis.

Authors:  Rik van den Elshout; Tom W J Scheenen; Chantal M L Driessen; Robert J Smeenk; Frederick J A Meijer; Dylan Henssen
Journal:  Insights Imaging       Date:  2022-10-04

6.  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

7.  Deep Convolutional Radiomic Features on Diffusion Tensor Images for Classification of Glioma Grades.

Authors:  Zhiwei Zhang; Jingjing Xiao; Shandong Wu; Fajin Lv; Junwei Gong; Lin Jiang; Renqiang Yu; Tianyou Luo
Journal:  J Digit Imaging       Date:  2020-08       Impact factor: 4.056

8.  Comparison among conventional and advanced MRI, 18F-FDG PET/CT, phenotype and genotype in glioblastoma.

Authors:  Maria Consuelo Valentini; Marta Mellai; Laura Annovazzi; Antonio Melcarne; Tetyana Denysenko; Paola Cassoni; Cristina Casalone; Cristiana Maurella; Silvia Grifoni; Piercarlo Fania; Angelina Cistaro; Davide Schiffer
Journal:  Oncotarget       Date:  2017-10-04

Review 9.  Clinical PET/MRI in neurooncology: opportunities and challenges from a single-institution perspective.

Authors:  Lisbeth Marner; Otto M Henriksen; Michael Lundemann; Vibeke Andrée Larsen; Ian Law
Journal:  Clin Transl Imaging       Date:  2016-11-18

10.  A Sensitive and Automatic White Matter Fiber Tracts Model for Longitudinal Analysis of Diffusion Tensor Images in Multiple Sclerosis.

Authors:  Claudio Stamile; Gabriel Kocevar; François Cotton; Françoise Durand-Dubief; Salem Hannoun; Carole Frindel; Charles R G Guttmann; David Rousseau; Dominique Sappey-Marinier
Journal:  PLoS One       Date:  2016-05-25       Impact factor: 3.240

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