Literature DB >> 22914703

Assessment of cervical spondylotic myelopathy using diffusion tensor magnetic resonance imaging parameter at 3.0 tesla.

Takehiro Uda1, Toshihiro Takami, Naohiro Tsuyuguchi, Shinichi Sakamoto, Toru Yamagata, Hidetoshi Ikeda, Takashi Nagata, Kenji Ohata.   

Abstract

STUDY
DESIGN: Cross-sectional study.
OBJECTIVE: To assess spinal cord condition in patients with cervical spondylosis (CS), using diffusion tensor imaging parameter. SUMMARY OF BACKGROUND DATA: Although myelopathy is a common symptom after CS, clinically objective assessment for determination of surgical intervention is not straightforward.
METHODS: Twenty-six patients with CS and 30 normal control subjects were enrolled. Diffusion tensor imaging was obtained using a single-shot fast spin-echo-based sequence at 3.0 T. Mean diffusivity (MD) and fractional anisotropy (FA) were measured in the axial plane at 6 spinal levels. To evaluate MD and FA in patients with CS considering the normal variation at each spinal level and between spinal levels, MD and FA at the most compressed spinal level were transformed to normalized values with a z score. Presence of myelopathy was predicted with the MD and FA z scores. Diagnostic validity of MD and FA was compared with receiver operating characteristic analysis. More effective parameter and the optimal cutoff value for prediction were determined.
RESULTS: In normal subjects, MD and FA were significantly different between spinal levels. In patients with myelopathy, an MD increase or an FA decrease was demonstrated in most cases. Although both an MD increase and an FA decrease had diagnostic validity for myelopathy, receiver operating characteristic analysis demonstrated a higher sensitivity and specificity for prediction of an MD increase than an FA decrease (areas under the curve for MD and FA were 0.903 and 0.760, respectively). An MD z score of 1.40 was considered to be the best diagnostic cutoff value with 100% sensitivity and 75% specificity.
CONCLUSION: Myelopathy can be predicted with high accuracy with diffusion tensor imaging parameter, with the MD z score at the most compressed spinal level. LEVEL OF EVIDENCE: 3.

Entities:  

Mesh:

Year:  2013        PMID: 22914703     DOI: 10.1097/BRS.0b013e31826f25a3

Source DB:  PubMed          Journal:  Spine (Phila Pa 1976)        ISSN: 0362-2436            Impact factor:   3.468


  25 in total

1.  Application of diffusion tensor imaging for the diagnosis of segmental level of dysfunction in cervical spondylotic myelopathy.

Authors:  Y Suetomi; T Kanchiku; S Nishijima; Y Imajo; H Suzuki; Y Yoshida; N Nishida; T Taguchi
Journal:  Spinal Cord       Date:  2015-10-27       Impact factor: 2.772

2.  Quantitative assessment of column-specific degeneration in cervical spondylotic myelopathy based on diffusion tensor tractography.

Authors:  Jiao-Long Cui; Xiang Li; Tin-Yan Chan; Kin-Cheung Mak; Keith Dip-Kei Luk; Yong Hu
Journal:  Eur Spine J       Date:  2014-08-24       Impact factor: 3.134

3.  Diffusion tensor imaging predicts functional impairment in mild-to-moderate cervical spondylotic myelopathy.

Authors:  Benjamin M Ellingson; Noriko Salamon; John W Grinstead; Langston T Holly
Journal:  Spine J       Date:  2014-02-20       Impact factor: 4.166

4.  The functional relevance of diffusion tensor imaging in comparison to conventional MRI in patients with cervical compressive myelopathy.

Authors:  Young-Mi Yang; Woo-Kyoung Yoo; Je Hyun Yoo; Yoon Hae Kwak; Jae-Keun Oh; Ji-Sun Song; Seok Woo Kim
Journal:  Skeletal Radiol       Date:  2017-07-17       Impact factor: 2.199

5.  Clinically Feasible Microstructural MRI to Quantify Cervical Spinal Cord Tissue Injury Using DTI, MT, and T2*-Weighted Imaging: Assessment of Normative Data and Reliability.

Authors:  A R Martin; B De Leener; J Cohen-Adad; D W Cadotte; S Kalsi-Ryan; S F Lange; L Tetreault; A Nouri; A Crawley; D J Mikulis; H Ginsberg; M G Fehlings
Journal:  AJNR Am J Neuroradiol       Date:  2017-04-20       Impact factor: 3.825

6.  A Novel MRI Biomarker of Spinal Cord White Matter Injury: T2*-Weighted White Matter to Gray Matter Signal Intensity Ratio.

Authors:  A R Martin; B De Leener; J Cohen-Adad; D W Cadotte; S Kalsi-Ryan; S F Lange; L Tetreault; A Nouri; A Crawley; D J Mikulis; H Ginsberg; M G Fehlings
Journal:  AJNR Am J Neuroradiol       Date:  2017-04-20       Impact factor: 3.825

7.  Machine Learning for the Prediction of Cervical Spondylotic Myelopathy: A Post Hoc Pilot Study of 28 Participants.

Authors:  Benjamin S Hopkins; Kenneth A Weber; Kartik Kesavabhotla; Monica Paliwal; Donald R Cantrell; Zachary A Smith
Journal:  World Neurosurg       Date:  2019-03-25       Impact factor: 2.104

Review 8.  Application of magnetic resonance imaging in cervical spondylotic myelopathy.

Authors:  Chuan Zhang; Sushant K Das; Dong-Jun Yang; Han-Feng Yang
Journal:  World J Radiol       Date:  2014-10-28

9.  Correlation between degree of subvoxel spinal cord compression measured with super-resolution tract density imaging and neurological impairment in cervical spondylotic myelopathy.

Authors:  Benjamin M Ellingson; Noriko Salamon; Davis C Woodworth; Langston T Holly
Journal:  J Neurosurg Spine       Date:  2015-03-06

10.  Magnetic Resonance Imaging Biomarker of Axon Loss Reflects Cervical Spondylotic Myelopathy Severity.

Authors:  Rory K J Murphy; Peng Sun; Junqian Xu; Yong Wang; Samir Sullivan; Paul Gamble; Joanne Wagner; Neill N Wright; Ian G Dorward; Daniel Riew; Paul Santiago; Michael P Kelly; Kathryn Trinkaus; Wilson Z Ray; Sheng-Kwei Song
Journal:  Spine (Phila Pa 1976)       Date:  2016-05       Impact factor: 3.468

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