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Radiol Oncol. 2014 Apr 25;48(2):127-36. doi: 10.2478/raon-2014-0004. eCollection 2014.

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

Author information

  • 1Magnetic Resonance Unit, Medica Sur Clinic & Foundation, Mexico City, Mexico.
  • 2Department of Neurochemistry, National Institute of Neurology and Neurosurgery, Mexico City, Mexico.
  • 3GE Healthcare, Mexico city, Mexico.
  • 4Radioneurosurgery Unit, National Institute of Neurology and Neurosurgery, Mexico City, Mexico.

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, χ(2) (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.

KEYWORDS:

brain neoplasms; diffusion tensor imaging; discriminant analysis; magnetic resonance imaging; predictive value of tests

PMID:
24991202
[PubMed]
PMCID:
PMC4078031
Free PMC Article
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