Discrimination between glioblastoma multiforme and solitary metastasis using morphological features derived from thep:qtensor decomposition of diffusion tensor imaging

2014 ◽  
Vol 27 (9) ◽  
pp. 1103-1111 ◽  
Author(s):  
Guang Yang ◽  
Timothy L. Jones ◽  
Thomas R. Barrick ◽  
Franklyn A. Howe
Keyword(s):  
Diffusion Tensor Imaging ◽  
Glioblastoma Multiforme ◽  
Diffusion Tensor ◽  
Morphological Features ◽  
Solitary Metastasis

scholarly journals Repeatability of quantitative parameters derived from diffusion tensor imaging in patients with glioblastoma multiforme

2009 ◽  
Vol 29 (5) ◽  
pp. 1199-1205 ◽  
Author(s):  
Michael J. Paldino ◽  
Daniel Barboriak ◽  
Annick Desjardins ◽  
Henry S. Friedman ◽  
James J. Vredenburgh
Keyword(s):  
Diffusion Tensor Imaging ◽  
Glioblastoma Multiforme ◽  
Diffusion Tensor ◽  
Quantitative Parameters

scholarly journals Global diffusion tensor imaging derived metrics differentiate glioblastoma multiforme vs. normal brains by using discriminant analysis: introduction of a novel whole-brain approach

2014 ◽  
Vol 48 (2) ◽  
pp. 127-136 ◽  
Author(s):  
Ernesto Roldan-Valadez ◽  
Camilo Rios ◽  
David Cortez-Conradis ◽  
Rafael Favila ◽  
Sergio Moreno-Jimenez
Keyword(s):  
Diffusion Tensor Imaging ◽  
Discriminant Analysis ◽  
Glioblastoma Multiforme ◽  
Predictive Model ◽  
Predictive Accuracy ◽  
Diffusion Tensor ◽  
Mean Diffusivity ◽  
Continuous Variables ◽  
Whole Brain ◽  
Axial Diffusivity

Abstract Background. Histological behavior of glioblastoma multiforme suggests it would benefit more from a global rather than regional evaluation. A global (whole-brain) calculation of diffusion tensor imaging (DTI) derived tensor metrics offers a valid method to detect the integrity of white matter structures without missing infiltrated brain areas not seen in conventional sequences. In this study we calculated a predictive model of brain infiltration in patients with glioblastoma using global tensor metrics. Methods. Retrospective, case and control study; 11 global DTI-derived tensor metrics were calculated in 27 patients with glioblastoma multiforme and 34 controls: mean diffusivity, fractional anisotropy, pure isotropic diffusion, pure anisotropic diffusion, the total magnitude of the diffusion tensor, linear tensor, planar tensor, spherical tensor, relative anisotropy, axial diffusivity and radial diffusivity. The multivariate discriminant analysis of these variables (including age) with a diagnostic test evaluation was performed. Results. The simultaneous analysis of 732 measures from 12 continuous variables in 61 subjects revealed one discriminant model that significantly differentiated normal brains and brains with glioblastoma: Wilks’ λ = 0.324, χ2 (3) = 38.907, p < .001. The overall predictive accuracy was 92.7%. Conclusions. We present a phase II study introducing a novel global approach using DTI-derived biomarkers of brain impairment. The final predictive model selected only three metrics: axial diffusivity, spherical tensor and linear tensor. These metrics might be clinically applied for diagnosis, follow-up, and the study of other neurological diseases.

Diffusion tensor imaging for target volume definition in glioblastoma multiforme

2014 ◽  
Vol 190 (10) ◽  
pp. 939-943 ◽  
Author(s):  
Jatta Berberat ◽  
Jane McNamara ◽  
Luca Remonda ◽  
Stephan Bodis ◽  
Susanne Rogers
Keyword(s):  
Diffusion Tensor Imaging ◽  
Glioblastoma Multiforme ◽  
Diffusion Tensor ◽  
Target Volume ◽  
Target Volume Definition

scholarly journals Diffusion Tensor Imaging in Patients with Glioblastoma Multiforme Using the Supertoroidal Model

PLoS ONE ◽  
2016 ◽  
Vol 11 (1) ◽  
pp. e0146693 ◽  
Author(s):  
Choukri Mekkaoui ◽  
Philippe Metellus ◽  
William J. Kostis ◽  
Roberto Martuzzi ◽  
Fabricio R. Pereira ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Glioblastoma Multiforme ◽  
Diffusion Tensor
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