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Record W2606128815 · doi:10.3174/ajnr.a5162

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

2017· article· en· W2606128815 on OpenAlexafffund
Allan R. Martin, Benjamin De Leener, Julien Cohen‐Adad, David W. Cadotte, Sukhvinder Kalsi‐Ryan, Stefan Lange, Lindsay Tetreault, Aria Nouri, Adrian P. Crawley, David J. Mikulis, Howard J. Ginsberg, Michael G. Fehlings

Bibliographic record

VenueAmerican Journal of Neuroradiology · 2017
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsUniversity of TorontoUniversity Health NetworkUniversité de MontréalInstitut Universitaire de Gériatrie de MontréalPolytechnique Montréal
FundersCanadian Institutes of Health ResearchAOSpineRick Hansen InstituteChristopher and Dana Reeve Foundation
KeywordsMedicineNuclear medicineWhite matterFractional anisotropyMagnetic resonance imagingMagnetization transferRadiology

Abstract

fetched live from OpenAlex

<h3>BACKGROUND AND PURPOSE:</h3> T2*-weighted imaging provides sharp contrast between spinal cord GM and WM, allowing their segmentation and cross-sectional area measurement. Injured WM demonstrates T2*WI hyperintensity but requires normalization for quantitative use. We introduce T2*WI WM/GM signal-intensity ratio and compare it against cross-sectional area, the DTI metric fractional anisotropy, and magnetization transfer ratio in degenerative cervical myelopathy. <h3>MATERIALS AND METHODS:</h3> Fifty-eight patients with degenerative cervical myelopathy and 40 healthy subjects underwent 3T MR imaging, covering C1–C7. Metrics were automatically extracted at maximally compressed and uncompressed rostral/caudal levels. Normalized metrics were compared with <i>t</i> tests, area under the curve, and logistic regression. Relationships with clinical measures were analyzed by using Pearson correlation and multiple linear regression. <h3>RESULTS:</h3> The maximally compressed level cross-sectional area demonstrated superior differences (<i>P</i> = 1 × 10<sup>−13</sup>), diagnostic accuracy (area under the curve = 0.890), and univariate correlation with the modified Japanese Orthopedic Association score (0.66). T2*WI WM/GM showed strong differences (rostral: <i>P</i> = 8 × 10<sup>−7</sup>; maximally compressed level: <i>P</i> = 1 × 10<sup>−11</sup>; caudal: <i>P</i> = 1 × 10<sup>−4</sup>), correlations (modified Japanese Orthopedic Association score; rostral: −0.52; maximally compressed level: −0.59; caudal: −0.36), and diagnostic accuracy (rostral: 0.775; maximally compressed level: 0.860; caudal: 0.721), outperforming fractional anisotropy and magnetization transfer ratio in most comparisons and cross-sectional area at rostral/caudal levels. Rostral T2*WI WM/GM showed the strongest correlations with focal motor (−0.45) and sensory (−0.49) deficits and was the strongest independent predictor of the modified Japanese Orthopedic Association score (<i>P</i> = .01) and diagnosis (<i>P</i> = .02) in multivariate models (<i>R</i><sup>2</sup> = 0.59, <i>P</i> = 8 × 10<sup>−13</sup>; area under the curve = 0.954, respectively). <h3>CONCLUSIONS:</h3> T2*WI WM/GM shows promise as a novel biomarker of WM injury. It detects damage in compressed and uncompressed regions and contributes substantially to multivariate models for diagnosis and correlation with impairment. Our multiparametric approach overcomes limitations of individual measures, having the potential to improve diagnostics, monitor progression, and predict outcomes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.921

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.046
GPT teacher head0.356
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations92
Published2017
Admission routes2
Has abstractyes

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