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Diffusion tensor imaging can predict surgical outcomes of patients with cervical compression myelopathy

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PurposeThe aim of this study was to assess the potential role of diffusion tensor imaging (DTI) as a predictor of surgical outcomes in patients with cervical compressive myelopathy (CCM). Surgical… Click to show full abstract

PurposeThe aim of this study was to assess the potential role of diffusion tensor imaging (DTI) as a predictor of surgical outcomes in patients with cervical compressive myelopathy (CCM). Surgical decompression is often recommended for symptomatic CCM. It is important to know the prognosis of surgical outcomes and to recommend appropriate timing for surgery.MethodsWe enrolled 26 patients with CCM who underwent surgery. The Japanese Orthopaedic Association (JOA) score for cervical myelopathy was evaluated before and 6 months after surgery. Surgical outcomes were regarded as good if there was a change in JOA score of three points or more, or the recovery rate of JOA score was 50% or more. The patients were examined using a 3.0 T magnetic resonance system before surgery. Measured diffusion parameters were fractional anisotropy (FA) and mean diffusivity (MD). The correlations between DTI parameters and surgical outcomes were analyzed.ResultsBoth change and recovery rate of JOA score moderately correlated with FA. Furthermore, the area under the receiver–operator characteristic curve based on FA for prognostic precision of surgical outcomes indicates that FA is a good predictive factor. The cut-off values of FA for predicting good surgical outcomes evaluated by change and recovery rate of JOA score were 0.65 and 0.57, respectively. Neither change nor recovery rate of JOA score correlated with MD.ConclusionsFA in spinal cord DTI can moderately predict surgical outcomes. DTI can serve as a supplementary tool for decision-making to guide surgical intervention in patients with CCM.

Keywords: tensor imaging; diffusion tensor; joa score; surgical outcomes

Journal Title: European Spine Journal
Year Published: 2017

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