Diffusion basis spectrum imaging predicts long-term clinical outcomes following surgery in cervical spondylotic myelopathy

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. Diffusion basis spectrum imaging (DBSI), an advanced diffusion-weighted MRI technique, provides objective assessments of white...

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Published in:The spine journal 2023-04, Vol.23 (4), p.504-512
Main Authors: Zhang, Justin K., Jayasekera, Dinal, Javeed, Saad, Greenberg, Jacob K., Blum, Jacob, Dibble, Christopher F., Sun, Peng, Song, Sheng-Kwei, Ray, Wilson Z.
Format: Article
Language:English
Subjects:
Biomarkers
Cervical spondylotic myelopathy
Cervical Vertebrae - diagnostic imaging
Cervical Vertebrae - surgery
Diffusion basis spectrum imaging
Diffusion-weighted MRI
Humans
Machine learning
Prospective Studies
Spinal Cord Diseases - diagnostic imaging
Spinal Cord Diseases - surgery
Spondylosis - diagnostic imaging
Spondylosis - surgery
Support vector machine
Treatment Outcome
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Summary: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. Diffusion basis spectrum imaging (DBSI), an advanced diffusion-weighted MRI technique, provides objective assessments of white matter tract integrity that may help prognosticate outcomes in patients undergoing surgery for CSM. To examine the ability of DBSI to predict clinically important CSM outcome measures at 2-years follow-up. Prospective cohort study. Patients undergoing decompressive cervical surgery for CSM. Neurofunctional status was assessed by the mJOA, MDI, and DASH. Quality-of-life was measured by the SF-36 PCS and SF-36 MCS. The NDI evaluated self-reported neck pain, and patient satisfaction was assessed by the NASS satisfaction index. Fifty CSM patients who underwent cervical decompressive surgery were enrolled. Preoperative DBSI metrics assessed white matter tract integrity through fractional anisotropy, fiber fraction, axial diffusivity, and radial diffusivity. To evaluate extra-axonal diffusion, DBSI measures restricted and nonrestricted fractions. Patient-reported outcome measures were evaluated preoperatively and up to 2-years follow-up. Support vector machine classification algorithms were used to predict surgical outcomes at 2-years follow-up. Specifically, three feature sets were built for each of the seven clinical outcome measures (eg, mJOA), including clinical only, DBSI only, and combined feature sets. Twenty-seven mild (mJOA 15–17), 12 moderate (12–14) and 11 severe (0–11) CSM patients were enrolled. Twenty-four (60%) patients underwent anterior decompressive surgery compared with 16 (40%) posterior approaches. The mean (SD) follow-up was 23.2 (5.6, range 6.1–32.8) months. Feature sets built on combined data (ie, clinical+DBSI metrics) performed significantly better for all outcome measures compared with those only including clinical or DBSI data. When predicting improvement in the mJOA, the clinically driven feature set had an accuracy of 61.9 [61.6, 62.5], compared with 78.6 [78.4, 79.2] in the DBSI feature set, and 90.5 [90.2, 90.8] in the combined feature set. When combined with key clinical covariates, preoperative DBSI metrics predicted improvement after surgical decompression for CSM with high accuracy for multiple outcome measures. These results suggest that DBSI may serve as a noninvasive imaging biomarker for CSM valuable in guiding patient selection
ISSN:1529-9430
1878-1632
DOI:10.1016/j.spinee.2022.12.003