Correlation Between Pre-Operative Diffusion Tensor Imaging Indices and Post-Operative Outcome in Degenerative Cervical Myelopathy: A Systematic Review and Meta-Analysis.

Publication/Presentation Date

7-1-2024

Abstract

STUDY DESIGN: Systematic review.

OBJECTIVES: The correlation between pre-operative diffusion tensor imaging (DTI) metrics and post-operative clinical outcomes in patients with degenerative cervical myelopathy (DCM) has been widely investigated with different studies reporting varied findings. We conducted a systematic review to determine the association between DTI metric and clinical outcomes after surgery.

METHODS: We identified relevant articles that investigated the relationship between pre-operative DTI indices and post-operative outcome in DCM patients by searching PubMed/MEDLINE, Web of Science, Scopus, and EMBASE from inception until October 2023. In addition, quantitative synthesis and meta-analyses were performed.

RESULTS: FA was significantly correlated with postoperative JOA or mJOA across all age and follow up subgroups, changes observed in JOA or mJOA from preoperative to postoperative stages (Δ JOA or Δ mJOA) in subgroups aged 65 and above and in those with a follow-up period of 6 months or more, as well as recovery rate in all studies pooled together and also in the under-65 age bracket. Additionally, a significant correlation was demonstrated between recovery rate and ADC across all age groups. No other significant correlations were discovered between DTI parameters (MD, AD, and ADC) and post-operative outcomes.

CONCLUSION: DTI is a quantitative noninvasive evaluation tool that correlates with severity of DCM. However, the current evidence is still elusive regarding whether DTI metric is a validated tool for predicting the degree of post-operative recovery, which could potentially be useful in patient selection for surgery.

Volume

14

Issue

6

First Page

1800

Last Page

1817

ISSN

2192-5682

Disciplines

Business Administration, Management, and Operations | Health and Medical Administration | Management Sciences and Quantitative Methods

PubMedID

38168663

Department(s)

Administration and Leadership

Document Type

Article

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