INTRODUCTION: A major shortcoming in improving care for cervical spondylotic myelopathy (CSM) patients is the lack of robust quantitative imaging tools to guide surgical decision-making.METHODS: A prospective cohort study enrolled fifty CSM patients who underwent cervical decompressive surgery. DBSI metrics assessed white matter tract integrity by fractional anisotropy, axial diffusivity, radial diffusivity, and fiber fraction. To evaluate extra-axonal diffusion, DBSI measures restricted and non-restricted fractions. Neurofunctional status was assessed by the mJOA, MDI, and DASH. Quality of life was measured by the SF-36 PCS and MCS. The NDI was used to measure self-reported neck pain. Patient satisfaction was assessed by the NASS satisfaction index. Support vector machine classification algorithms were used to predict surgical outcomes. Specifically, three models were built for each clinical outcome measure (e.g., mJOA), including clinical, DBSI, and combined models.RESULTS: Twenty-seven mild (mJOA 15-17), 12 moderate (12-14) and 11 severe (0-11) CSM patients were enrolled. Twenty-four (60%) underwent anterior surgery compared to 16 (40%) posterior surgery. The mean (SD) follow-up was 23.2 (5.6, range 6.1-32.8) months. The best performing model was for the prediction of the NASS satisfaction index, with an accuracy [95% CI] of 94.4 [94.3, 94.8]. Conversely, the worst performing model was for the NDI, with an accuracy of 73.8 [73.6, 74.5]. When predicting improvement in the mJOA, the clinically-driven model had an accuracy of 61.9 [61.6, 62.5], compared to 78.6 [78.4, 79.2] in the DBSI model, and 90.5 [90.2, 90.8] in the combined model.CONCLUSIONS: When combined with key clinical covariates, DBSI metrics predicted improvement after surgical decompression with high accuracy. These results suggest that DBSI may serve as a noninvasive imaging biomarker for CSM.
