498 Feasibility and Efficacy of Postoperative Diffusion Basis Spectrum Imaging to Evaluate Recovery After Surgery in Cervical Spondylotic Myelopathy

Abstract

INTRODUCTION: Previous studies evaluating diffusion tensor imaging (DTI) in cervical spondylotic myelopathy (CSM) have demonstrated limitations in assessing postoperative outcomes following surgery. Diffusion basis spectrum imaging (DBSI), capable of providing granular assessments of spinal cord microstructure, may help address these shortcomings. METHODS: Twenty-seven CSM patients underwent preoperative diffusion-weighted MRI followed by cervical decompressive surgery and repeat imaging at two-years follow-up. DTI and DBSI metrics were extracted from the C3 spinal cord level. Diffusion-weighted MRI anisotropic tensors evaluated white matter tracts through fractional anisotropy, axial diffusivity, radial diffusivity, and fiber fraction. DBSI isotropic tensors assessed extra-axonal pathology through restricted and non-restricted fraction. Improvement after surgery was defined as a 2-point increase in the mJOA at two-years follow-up. Support vector machine classification algorithms were used to predict surgical outcomes. RESULTS: Fifteen mild (mJOA 15-17), 7 moderate (12-14) and 5 severe (0-11) CSM patients underwent surgical decompression and were followed-up to 23.2 (5.6, range 11.1-32.8) months postoperatively. Among DBSI metrics, isotropic diffusion components changed significantly from baseline to latest follow-up (p < 0.01), whereas anisotropic components remained stable (p > 0.05). When predicting improvement in the mJOA, SVM models built with DTI metrics had an accuracy of 74.1 [95% CI: 73.6, 74.6] compared to 85.2 [84.5, 85.3] in the DBSI model. CONCLUSIONS: Postoperative DBSI metrics predicted long-term outcomes with high accuracy. DBSI may have utility in assessing recovery trajectories for CSM patients following surgery.