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PLoS One. 2015 Oct 2;10(10):e0139451. doi: 10.1371/journal.pone.0139451. eCollection 2015.

Prediction of Neurological Impairment in Cervical Spondylotic Myelopathy using a Combination of Diffusion MRI and Proton MR Spectroscopy.

Author information

1
Department of Radiological Sciences, David Geffen School of Medicine, University of California-Los Angeles, United States of America; Department of Biomedical Physics, David Geffen School of Medicine, University of California-Los Angeles, United States of America; Department of Bioengineering, Henri Samueli School of Engineering and Applied Sciences, University of California-Los Angeles, United States of America; Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California-Los Angeles, United States of America.
2
Department of Radiological Sciences, David Geffen School of Medicine, University of California-Los Angeles, United States of America.
3
Department of Radiological Sciences, David Geffen School of Medicine, University of California-Los Angeles, United States of America; Department of Biomedical Physics, David Geffen School of Medicine, University of California-Los Angeles, United States of America.
4
Department of Neurosurgery and Orthopaedics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States of America.

Abstract

PURPOSE:

In the present study we investigated a combination of diffusion tensor imaging (DTI) and magnetic resonance spectroscopic (MRS) biomarkers in order to predict neurological impairment in patients with cervical spondylosis.

METHODS:

Twenty-seven patients with cervical spondylosis were evaluated. DTI and single voxel MRS were performed in the cervical cord. N-acetylaspartate (NAA) and choline (Cho) metabolite concentration ratios with respect to creatine were quantified, as well as the ratio of choline to NAA. The modified mJOA scale was used as a measure of neurologic deficit. Linear regression was performed between DTI and MRS parameters and mJOA scores. Significant predictors from linear regression were used in a multiple linear regression model in order to improve prediction of mJOA. Parameters that did not add value to model performance were removed, then an optimized multiparametric model was established to predict mJOA.

RESULTS:

Significant correlations were observed between the Torg-Pavlov ratio and FA (R2 = 0.2021, P = 0.019); DTI fiber tract density and FA, MD, Cho/NAA (R2 = 0.3412, P = 0.0014; R2 = 0.2112, P = 0.016; and R2 = 0.2352, P = 0.010 respectively); along with FA and Cho/NAA (R2 = 0.1695, P = 0.033). DTI fiber tract density, MD and FA at the site of compression, along with Cho/NAA at C2, were significantly correlated with mJOA score (R2 = 0.05939, P < 0.0001; R2 = 0.4739, P < 0.0001; R2 = 0.7034, P < 0.0001; R2 = 0.4649, P < 0.0001). A combination biomarker consisting of DTI fiber tract density, MD, and Cho/NAA showed the best prediction of mJOA (R2 = 0.8274, P<0.0001), with post-hoc tests suggesting fiber tract density, MD, and Cho/NAA were all significant contributors to predicting mJOA (P = 0.00053, P = 0.00085, and P = 0.0019, respectively).

CONCLUSION:

A linear combination of DTI and MRS measurements within the cervical spinal cord may be useful for accurately predicting neurological deficits in patients with cervical spondylosis. Additional studies may be necessary to validate these observations.

PMID:
26431174
PMCID:
PMC4592013
DOI:
10.1371/journal.pone.0139451
[Indexed for MEDLINE]
Free PMC Article

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