ArticleAJNR. American journal of neuroradiology2017
Multivariate Analysis of MRI Biomarkers for Predicting Neurologic Impairment in Cervical Spinal Cord Injury.
Article in AJNR. American journal of neuroradiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 24 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The application of artificial intelligence in the acute and sub-acute phases of spinal cord injury- a systematic review.Spinal cord · 2026Pooled it
- A functional outcome prediction model of acute traumatic spinal cord injury based on extreme gradient boost.Journal of orthopaedic surgery and research · 2022Trial
- Intramedullary hemorrhage and early motor recovery as predictors of neurological recovery after acute cervical spinal cord injury.Acta neurologica Belgica · 2026Article
- Combined plasma and imaging biomarkers as predictors of neurological outcome in cervical traumatic spinal cord injury.BMC neurology · 2026Article
- Lesion characteristics are associated with bowel, bladder, and overall independence following cervical spinal cord injury.The journal of spinal cord medicine · 2025Article
- Modeling trajectories of routine blood tests as dynamic biomarkers for outcome in spinal cord injury.NPJ digital medicine · 2025Article
- Incidence and risk factors of dysphagia after cervical laminoplasty.Scientific reports · 2025Observational
- An integrative nomogram based on MRI radiomics and clinical characteristics for prognosis prediction in cervical spinal cord Injury.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2025Article
- Multicenter study on predicting postoperative upper limb muscle strength improvement in cervical spinal cord injury patients using radiomics and deep learning.Scientific reports · 2025Article
- MRI variables and peripheral inflammatory response biomarkers predict severity and prognosis in patients with acute cervical traumatic spinal cord injury.BMC musculoskeletal disorders · 2024Article
- A deep learning approach for cervical cord injury severity determination through axial and sagittal magnetic resonance imaging segmentation and classification.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2024Article
- Extent of Traumatic Spinal Cord Injury Is Lesion Level Dependent and Predictive of Recovery: A Multicenter Neuroimaging Study.Journal of neurotrauma · 2024Observational
- The Role of Magnetic Resonance Imaging and Computed Tomography in Spinal Cord Injury.Life (Basel, Switzerland) · 2023Review
- The influence of conventional TThe journal of spinal cord medicine · 2023Article
- A Systematic Review of Scoring System Based on Magnetic Resonance Imaging Parameters to Predict Outcome in Cervical Spinal Cord Injury.Spine surgery and related research · 2023Review
- Using hierarchical unsupervised learning to integrate and reduce multi-level and multi-paraspinal muscle MRI data in relation to low back pain.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2022Article
- Improving Diagnostic Workup Following Traumatic Spinal Cord Injury: Advances in Biomarkers.Current neurology and neuroscience reports · 2021Review
- Reproducible analysis of disease space via principal components using the novel R package syndRomics.eLife · 2021Article
- Acute spinal subdural hematoma: A case report of spontaneous recovery from paraplegia.Medicine · 2020Article
- XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury.Neurotrauma reports · 2020Article
Corrections and comments
- Erratum issuedErratum.2023
Authors and funding
11 authors.
Funding
Abstract
background and purposeAcute 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
methodsWe 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.
resultsNonlinear 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.
conclusionsThis 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.
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