Diffusion Basis Spectrum Imaging Identifies Clinically Relevant Disease Phenotypes of Cervical Spondylotic Myelopathy. Issue 3 (28th April 2023)

• Record Type: Journal Article
• Authors: Zhang, Justin K.; Javeed, Saad; Greenberg, Jacob K.; Dibble, Christopher F.; Song, Sheng-Kwei; Ray, Wilson Z.
• Is Part Of: Clinical spine surgery. Volume 36:Issue 3(2023)
• Page Start: 134
• Page End: 142
• Publication Date: 2023-04-28
• Subjects: diffusion-weighted MRI -- diffusion basis spectrum imaging -- cervical spondylotic myelopathy -- k-means clustering -- machine learning; Spinal cord -- Diseases -- Periodicals; Spinal cord -- Surgery -- Periodicals; 617.56059
• DOI: 10.1097/BSD.0000000000001451
• Languages: English
• ISSNs: 2380-0186

Abstract

Abstract : Study Design: Prospective cohort study. Objective: Apply a machine learning clustering algorithm to baseline imaging data to identify clinically relevant cervical spondylotic myelopathy (CSM) patient phenotypes. Summary of Background Data: A major shortcoming in improving care for CSM patients is the lack of robust quantitative imaging tools to guide surgical decision-making. Advanced diffusion-weighted magnetic resonance imaging (MRI) techniques, such as diffusion basis spectrum imaging (DBSI), may help address this limitation by providing detailed evaluations of white matter injury in CSM. Methods: Fifty CSM patients underwent comprehensive clinical assessments and diffusion-weighted MRI, followed by DBSI modeling. DBSI metrics included fractional anisotropy, axial and radial diffusivity, fiber fraction, extra-axonal fraction, restricted fraction, and nonrestricted fraction. Neurofunctional status was assessed by the modified Japanese Orthopedic Association, myelopathic disability index, and disabilities of the arm, shoulder, and hand. Quality-of-life was measured by the 36-Item Short Form Survey physical component summary and mental component summary. The neck disability index was used to measure self-reported neck pain. K- means clustering was applied to baseline DBSI measures to identify 3 clinically relevant CSM disease phenotypes. Baseline demographic, clinical, radiographic, and patient-reported outcome measures were compared among clusters using one-way analysis of variance (ANOVA). Results: Twenty-three (55%) mild, 9 (21%) moderate, and 10 (24%) severe myelopathy patients were enrolled. Eight patients were excluded due to MRI data of insufficient quality. Of the remaining 42 patients, 3 groups were generated by k-means clustering. When compared with clusters 1 and 2, cluster 3 performed significantly worse on the modified Japanese Orthopedic Association and all patient-reported outcome measures ( P <0.001), except the 36-Item Short Form Survey mental component summary ( P >0.05). Cluster 3 also possessed the highest proportion of non-Caucasian patients (43%, P =0.04), the worst hand dynamometer measurements ( P <0.05), and significantly higher intra-axonal axial diffusivity and extra-axonal fraction values ( P <0.001). Conclusions: Using baseline imaging data, we delineated a clinically meaningful CSM disease phenotype, characterized by worse neurofunctional status, quality-of-life, and pain, and more severe imaging markers of vasogenic edema. Level of Evidence: II.