摘要
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 ageand 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.
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 ageand 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.
基金
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
