Case Control Study
Copyright ©The Author(s) 2015. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Radiol. Nov 28, 2015; 7(11): 405-414
Published online Nov 28, 2015. doi: 10.4329/wjr.v7.i11.405
Partial correlation analyses of global diffusion tensor imaging-derived metrics in glioblastoma multiforme: Pilot study
David Cortez-Conradis, Camilo Rios, Sergio Moreno-Jimenez, Ernesto Roldan-Valadez
David Cortez-Conradis, Magnetic Resonance Unit, Medica Sur Clinic and Foundation, Mexico City CP 14050, Mexico
Camilo Rios, Department of Neurochemistry, National Institute of Neurology and Neurosurgery, Mexico City CP 14269, Mexico
Sergio Moreno-Jimenez, Radioneurosurgery Unit, National Institute of Neurology and Neurosurgery, Mexico City CP 14269, Mexico
Ernesto Roldan-Valadez, Faculty of Health Sciences, Panamerican University, Mexico City CP 03920, Mexico
Ernesto Roldan-Valadez, Magnetic Resonance Unit, Medica Sur Clinic and Foundation, Puente de Piedra # 150, Col, Toriello Guerra, Deleg, Tlalpan, Mexico City CP 14050, Mexico
Author contributions: Roldan-Valadez E drafted the manuscript; all authors participated in the literature search, summary and interpretation; all authors edited and approved the final manuscript.
Supported by The Medica Sur Clinic and Foundation (in part); David Cortez-Conradis was research fellow at the MRI Unit of Medica Sur Clinic and Foundation from 2012 to 2014. Ernesto Roldan-Valadez was Coordinator of Research at the MRI Unit of Medica Sur Clinic and Foundation from 2010 to April 2015.
Institutional review board statement: The protocol for this study was previously accepted by the institutional review board of Medica Sur Clinic and Foundation (Project #2011-EXT-05).
Informed consent statement: Because this was a retrospective study using exclusively, quantitative parameter of MRI postprocessed images, the approved protocol included a waiver of informed consent statement.
Conflict-of-interest statement: The authors have no conflicts of interests to declare.
Data sharing statement: The dataset of this study is available from the corresponding author at Dryad repository.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Correspondence to: Ernesto Roldan-Valadez, MD, MSc, PhD, Magnetic Resonance Unit, Medica Sur Clinic and Foundation, Puente de Piedra # 150, Col, Toriello Guerra, Deleg, Tlalpan, Mexico City CP 14050, Mexico. ernest.roldan@usa.net
Telephone: +52-55-54247230 Fax: +52-55-54244429
Received: April 20, 2015
Peer-review started: April 21, 2015
First decision: June 9, 2015
Revised: August 31, 2015
Accepted: October 12, 2015
Article in press: October 13, 2015
Published online: November 28, 2015
Abstract

AIM: To determine existing correlates among diffusion tensor imaging (DTI)-derived metrics in healthy brains and brains with glioblastoma multiforme (GBM).

METHODS: Case-control study using DTI data from brain magnetic resonance imaging of 34 controls (mean, 41.47; SD, ± 21.94 years; range, 21-80 years) and 27 patients with GBM (mean, SD; 48.41 ± 15.18 years; range, 18-78 years). Image postprocessing using FSL software calculated eleven tensor metrics: fractional (FA) and relative anisotropy; pure isotropic (p) and anisotropic diffusions (q), total magnitude of diffusion (L); linear (Cl), planar (Cp) and spherical tensors (Cs); mean (MD), axial (AD) and radial diffusivities (RD). Partial correlation analyses (controlling the effect of age and gender) and multivariate Mancova were performed.

RESULTS: There was a normal distribution for all metrics. Comparing healthy brains vs brains with GBM, there were significant very strong bivariate correlations only depicted in GBM: [FA↔Cl (+)], [FA↔q (+)], [p↔AD (+)], [AD↔MD (+)], and [MD↔RD (+)]. Among 56 pairs of bivariate correlations, only seven were significantly different. The diagnosis variable depicted a main effect [F-value (11, 23) = 11.842, P≤ 0.001], with partial eta squared = 0.850, meaning a large effect size; age showed a similar result. The age also had a significant influence as a covariate [F (11, 23) = 10.523, P < 0.001], with a large effect size (partial eta squared = 0.834).

CONCLUSION: DTI-derived metrics depict significant differences between healthy brains and brains with GBM, with specific magnitudes and correlations. This study provides reference data and makes a contribution to decrease the underlying empiricism in the use of DTI parameters in brain imaging.

Keywords: Brain neoplasms, Diffusion tensor imaging, Magnetic resonance imaging, Software tools, Statistics as topic

Core tip: Diffusion tensor imaging (DTI)-derived metrics depict specific magnitudes and correlations; and significant differences between healthy brains and brains with glioblastoma multiforme (GBM). For example, only 5 bivariate correlations in GBM depicted significant very strong association: [FA↔Cl (+)], [FA↔q (+)], [p↔AD (+)], [D↔MD (+)], and [MD↔RD (+)]. Among 56 pairs of correlations, only seven were significantly different. Diagnosis showed a main effect [F-value (11, 23) = 11.842, P≤ 0.001], with a large effect size (partial eta squared = 0.850); a similar result was observed for age. This study makes a contribution to decrease the empiricism in the use of DTI parameters in brain imaging.