Literature DB >> 28007771

Multivariate Analysis of MRI Biomarkers for Predicting Neurologic Impairment in Cervical Spinal Cord Injury.

J Haefeli1,2, M C Mabray3, W D Whetstone4,2, S S Dhall1,2, J Z Pan5,2, P Upadhyayula1,2, G T Manley1,2, J C Bresnahan1,2, M S Beattie1,2, A R Ferguson6,2,7, J F Talbott3,2.   

Abstract

BACKGROUND AND
PURPOSE: Acute markers of spinal cord injury are essential for both diagnostic and prognostic purposes. The goal of this study was to assess the relationship between early MR imaging biomarkers after acute cervical spinal cord injury and to evaluate their predictive validity of neurologic impairment.
MATERIALS AND METHODS: We performed a retrospective cohort study of 95 patients with acute spinal cord injury and preoperative MR imaging within 24 hours of injury. The American Spinal Injury Association Impairment Scale was used as our primary outcome measure to define neurologic impairment. We assessed several MR imaging features of injury, including axial grade (Brain and Spinal Injury Center score), sagittal grade, length of injury, maximum canal compromise, and maximum spinal cord compression. Data-driven nonlinear principal component analysis was followed by correlation and optimal-scaled multiple variable regression to predict neurologic impairment.
RESULTS: Nonlinear principal component analysis identified 2 clusters of MR imaging variables related to 1) measures of intrinsic cord signal abnormality and 2) measures of extrinsic cord compression. Neurologic impairment was best accounted for by MR imaging measures of intrinsic cord signal abnormality, with axial grade representing the most accurate predictor of short-term impairment, even when correcting for surgical decompression and degree of cord compression.
CONCLUSIONS: This study demonstrates the utility of applying nonlinear principal component analysis for defining the relationship between MR imaging biomarkers in a complex clinical syndrome of cervical spinal cord injury. Of the assessed imaging biomarkers, the intrinsic measures of cord signal abnormality were most predictive of neurologic impairment in acute spinal cord injury, highlighting the value of axial T2 MR imaging.
© 2017 by American Journal of Neuroradiology.

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Year:  2016        PMID: 28007771      PMCID: PMC5671488          DOI: 10.3174/ajnr.A5021

Source DB:  PubMed          Journal:  AJNR Am J Neuroradiol        ISSN: 0195-6108            Impact factor:   3.825


  41 in total

1.  Acute traumatic central cord syndrome: magnetic resonance imaging and clinical observations.

Authors:  Frédéric Collignon; Didier Martin; Jacques Lénelle; Achille Stevenaert
Journal:  J Neurosurg       Date:  2002-01       Impact factor: 5.115

Review 2.  Common data elements for spinal cord injury clinical research: a National Institute for Neurological Disorders and Stroke project.

Authors:  F Biering-Sørensen; S Alai; K Anderson; S Charlifue; Y Chen; M DeVivo; A E Flanders; L Jones; N Kleitman; A Lans; V K Noonan; J Odenkirchen; J Steeves; K Tansey; E Widerström-Noga; L B Jakeman
Journal:  Spinal Cord       Date:  2015-02-10       Impact factor: 2.772

Review 3.  Translating state-of-the-art spinal cord MRI techniques to clinical use: A systematic review of clinical studies utilizing DTI, MT, MWF, MRS, and fMRI.

Authors:  Allan R Martin; Izabela Aleksanderek; Julien Cohen-Adad; Zenovia Tarmohamed; Lindsay Tetreault; Nathaniel Smith; David W Cadotte; Adrian Crawley; Howard Ginsberg; David J Mikulis; Michael G Fehlings
Journal:  Neuroimage Clin       Date:  2015-12-04       Impact factor: 4.881

4.  Principal Component Analysis of Diffusion Tensor Images to Determine White Matter Injury Patterns Underlying Postconcussive Headache.

Authors:  A Ghodadra; L Alhilali; S Fakhran
Journal:  AJNR Am J Neuroradiol       Date:  2015-09-24       Impact factor: 3.825

5.  Magnetic resonance imaging of acute cervical spine trauma. Correlation with severity of neurologic injury.

Authors:  D M Schaefer; A Flanders; B E Northrup; H T Doan; J L Osterholm
Journal:  Spine (Phila Pa 1976)       Date:  1989-10       Impact factor: 3.468

6.  Diffusion tensor imaging as a predictor of locomotor function after experimental spinal cord injury and recovery.

Authors:  Brian J Kelley; Noam Y Harel; Chang-Yeon Kim; Xenophon Papademetris; Daniel Coman; Xingxing Wang; Omar Hasan; Adam Kaufman; Ronen Globinsky; Lawrence H Staib; William B J Cafferty; Fahmeed Hyder; Stephen M Strittmatter
Journal:  J Neurotrauma       Date:  2014-07-08       Impact factor: 5.269

Review 7.  Translational spinal cord injury research: preclinical guidelines and challenges.

Authors:  Paul J Reier; Michael A Lane; Edward D Hall; Y D Teng; Dena R Howland
Journal:  Handb Clin Neurol       Date:  2012

8.  Acute spinal cord injury: MR imaging at 1.5 T.

Authors:  M V Kulkarni; C B McArdle; D Kopanicky; M Miner; H B Cotler; K F Lee; J H Harris
Journal:  Radiology       Date:  1987-09       Impact factor: 11.105

Review 9.  Does early decompression improve neurological outcome of spinal cord injured patients? Appraisal of the literature using a meta-analytical approach.

Authors:  G La Rosa; A Conti; S Cardali; F Cacciola; F Tomasello
Journal:  Spinal Cord       Date:  2004-09       Impact factor: 2.772

10.  MRI investigation of the sensorimotor cortex and the corticospinal tract after acute spinal cord injury: a prospective longitudinal study.

Authors:  Patrick Freund; Nikolaus Weiskopf; John Ashburner; Katharina Wolf; Reto Sutter; Daniel R Altmann; Karl Friston; Alan Thompson; Armin Curt
Journal:  Lancet Neurol       Date:  2013-07-02       Impact factor: 44.182

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  9 in total

1.  Convolutional Neural Network-Based Automated Segmentation of the Spinal Cord and Contusion Injury: Deep Learning Biomarker Correlates of Motor Impairment in Acute Spinal Cord Injury.

Authors:  D B McCoy; S M Dupont; C Gros; J Cohen-Adad; R J Huie; A Ferguson; X Duong-Fernandez; L H Thomas; V Singh; J Narvid; L Pascual; N Kyritsis; M S Beattie; J C Bresnahan; S Dhall; W Whetstone; J F Talbott
Journal:  AJNR Am J Neuroradiol       Date:  2019-03-28       Impact factor: 3.825

2.  A Clinical Practice Guideline for the Management of Patients With Acute Spinal Cord Injury: Recommendations on the Role of Baseline Magnetic Resonance Imaging in Clinical Decision Making and Outcome Prediction.

Authors:  Michael G Fehlings; Allan R Martin; Lindsay A Tetreault; Bizhan Aarabi; Paul Anderson; Paul M Arnold; Darrel Brodke; Anthony S Burns; Kazuhiro Chiba; Joseph R Dettori; Julio C Furlan; Gregory Hawryluk; Langston T Holly; Susan Howley; Tara Jeji; Sukhvinder Kalsi-Ryan; Mark Kotter; Shekar Kurpad; Brian K Kwon; Ralph J Marino; Eric Massicotte; Geno Merli; James W Middleton; Hiroaki Nakashima; Narihito Nagoshi; Katherine Palmieri; Anoushka Singh; Andrea C Skelly; Eve C Tsai; Alexander Vaccaro; Jefferson R Wilson; Albert Yee; James S Harrop
Journal:  Global Spine J       Date:  2017-09-05

3.  Acute spinal subdural hematoma: A case report of spontaneous recovery from paraplegia.

Authors:  Kazuya Yokota; Osamu Kawano; Hironari Kaneyama; Takeshi Maeda; Yasuharu Nakashima
Journal:  Medicine (Baltimore)       Date:  2020-05       Impact factor: 1.889

Review 4.  Guidelines for the conduct of clinical trials in spinal cord injury: Neuroimaging biomarkers.

Authors:  Maryam Seif; Claudia Am Gandini Wheeler-Kingshott; Julien Cohen-Adad; Adam E Flanders; Patrick Freund
Journal:  Spinal Cord       Date:  2019-07-02       Impact factor: 2.772

5.  Reproducible analysis of disease space via principal components using the novel R package syndRomics.

Authors:  Abel Torres-Espín; Austin Chou; J Russell Huie; Nikos Kyritsis; Pavan S Upadhyayula; Adam R Ferguson
Journal:  Elife       Date:  2021-01-14       Impact factor: 8.140

6.  A functional outcome prediction model of acute traumatic spinal cord injury based on extreme gradient boost.

Authors:  Zhan Sizheng; Huang Boxuan; Xue Feng; Zhang Dianying
Journal:  J Orthop Surg Res       Date:  2022-10-12       Impact factor: 2.677

Review 7.  Improving Diagnostic Workup Following Traumatic Spinal Cord Injury: Advances in Biomarkers.

Authors:  Simon Schading; Tim M Emmenegger; Patrick Freund
Journal:  Curr Neurol Neurosci Rep       Date:  2021-07-16       Impact factor: 5.081

8.  XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury.

Authors:  Tomoo Inoue; Daisuke Ichikawa; Taro Ueno; Maxwell Cheong; Takashi Inoue; William D Whetstone; Toshiki Endo; Kuniyasu Nizuma; Teiji Tominaga
Journal:  Neurotrauma Rep       Date:  2020-07-23

9.  Using hierarchical unsupervised learning to integrate and reduce multi-level and multi-paraspinal muscle MRI data in relation to low back pain.

Authors:  Abel Torres-Espin; Anastasia Keller; Gabriel T A Johnson; Aaron J Fields; Roland Krug; Adam R Ferguson; Alan R Hargens; Conor W O'Neill; Jeffrey C Lotz; Jeannie F Bailey
Journal:  Eur Spine J       Date:  2022-03-25       Impact factor: 2.721

  9 in total

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