Literature DB >> 25906184

MR Classification System Based on Axial Images for Cervical Compressive Myelopathy.

Ja Yeon You1, Joon Woo Lee1, Eugene Lee1, Guen Young Lee1, Jin S Yeom1, Heung Sik Kang1.   

Abstract

PURPOSE: To propose a new magnetic resonance (MR) classification system based on axial images for cervical compressive myelopathy (CCM) (Ax-CCM system), to assess the interobserver agreement with the Ax-CCM system, and to evaluate the relationship between Ax-CCM patterns and the baseline severity of CCM and the subsequent surgical outcome.
MATERIALS AND METHODS: The institutional review board approved this retrospective study. A total of 202 patients (male-to-female ratio, 128:84; mean age ± standard deviation, 56.3 years ± 11.9; age range, 24-83 years) with CCM who underwent preoperative MR imaging and decompression surgery were retrospectively evaluated. The Ax-CCM pattern was based on the margin and extent of intramedullary hyperintensity on axial T2-weighted images, as follows; type 0 = normal, type 1 = diffuse, type 2 = fuzzy focal, and type 3 = discrete focal. Interobserver variability was analyzed by using the intraclass correlation coefficient across three readers. The modified Japanese Orthopedic Association (JOA) score and the postoperative improvement (good vs little improvement) were evaluated according to the Ax-CCM pattern by using one-way analysis of variance, the χ(2) test, and the Fisher exact test.
RESULTS: The intraclass correlation coefficient for the Ax-CCM system was 0.83. The preoperative JOA score was significantly different according to Ax-CCM pattern across all readers (P < .05), with the type 2 pattern showing the worst preoperative JOA score (mean, 11.6 ± 3.1 for readers A and C and 11.7 ± 2.9 for reader B). The proportion of good improvement was significantly lower with the type 2 pattern (27 of 72 patients, 37%) than with the other patterns (64 of 123 patients, 52%) (P = .034).
CONCLUSION: The Ax-CCM system showed good interobserver agreement, and the type 2 pattern was correlated with poor preoperative neurologic status and less postoperative improvement. (©) RSNA, 2015.

Entities:  

Mesh:

Year:  2015        PMID: 25906184     DOI: 10.1148/radiol.2015142384

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  9 in total

1.  Impact of cervical stenosis on multiple sclerosis lesion distribution in the spinal cord.

Authors:  Daniel Gratch; David Do; Pouya Khankhanian; Matthew Schindler; J Eric Schmitt; Joseph R Berger
Journal:  Mult Scler Relat Disord       Date:  2020-07-20       Impact factor: 4.339

2.  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

3.  MRI Atlas-Based Measurement of Spinal Cord Injury Predicts Outcome in Acute Flaccid Myelitis.

Authors:  D B McCoy; J F Talbott; Michael Wilson; M D Mamlouk; J Cohen-Adad; Mark Wilson; J Narvid
Journal:  AJNR Am J Neuroradiol       Date:  2016-12-15       Impact factor: 3.825

4.  Kinetic DTI of the cervical spine: diffusivity changes in healthy subjects.

Authors:  Félix P Kuhn; Antoine Feydy; Nathalie Launay; Marie-Martine Lefevre-Colau; Serge Poiraudeau; Sébastien Laporte; Marc A Maier; Pavel Lindberg
Journal:  Neuroradiology       Date:  2016-06-08       Impact factor: 2.804

5.  Outcome Measures and Variables Affecting Prognosis of Cervical Spondylotic Myelopathy: WFNS Spine Committee Recommendations.

Authors:  Mehmet Zileli; Shradha Maheshwari; Shashank Sharad Kale; Kanwaljeet Garg; Sajesh K Menon; Jutty Parthiban
Journal:  Neurospine       Date:  2019-09-30

6.  Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T2*-weighted images of cervical spondylotic myelopathy.

Authors:  Meng-Ze Zhang; Han-Qiang Ou-Yang; Liang Jiang; Chun-Jie Wang; Jian-Fang Liu; Dan Jin; Ming Ni; Xiao-Guang Liu; Ning Lang; Hui-Shu Yuan
Journal:  JOR Spine       Date:  2021-11-13

7.  The effect of extracorporeal shock wave therapy in acute traumatic spinal cord injury on motor and sensory function within 6 months post-injury: a study protocol for a two-arm three-stage adaptive, prospective, multi-center, randomized, blinded, placebo-controlled clinical trial.

Authors:  Iris Leister; Rainer Mittermayr; Georg Mattiassich; Ludwig Aigner; Thomas Haider; Lukas Machegger; Harald Kindermann; Anja Grazer-Horacek; Johannes Holfeld; Wolfgang Schaden
Journal:  Trials       Date:  2022-04-01       Impact factor: 2.279

8.  Degenerative cervical myelopathy: Neuroradiological, neurophysiological and clinical correlations in 27 consecutive cases.

Authors:  C Soda; G Squintani; M Teli; N Marchesini; U M Ricci; A D'Amico; F Basaldella; E Concon; V Tramontano; S Romito; N Tommasi; G Pinna; F Sala
Journal:  Brain Spine       Date:  2022-07-08

9.  Magnetic resonance imaging and dynamic X-ray's correlations with dynamic electrophysiological findings in cervical spondylotic myelopathy: a retrospective cohort study.

Authors:  Zhengran Yu; Kaiyuan Lin; Jiacheng Chen; Kuan-Hung Chen; Wei Guo; Yuhu Dai; Yuguang Chen; Xuenong Zou; Xinsheng Peng
Journal:  BMC Neurol       Date:  2020-10-06       Impact factor: 2.474

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.