Evidence map›Paper›PMID 40947502›Full record

ArticleJournal of neuroimaging : official journal of the American Society of Neuroimaging

Association of Spinal Cord Radiomic Features and Disability in Multiple Sclerosis.

Jeffrey Lambe, Nicolas R Thompson, Yadi Li, Kunio Nakamura, Daniel Ontaneda

Abstract read
In one paragraph

Article in Journal of neuroimaging : official journal of the American Society of Neuroimaging. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Association of Spinal Cord Radiomic Features and Disability in Multiple Sclerosis.Journal of neuroimaging : official journal of the American Society of Neuroimaging
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jeffrey LambeMellen Center for Multiple Sclerosis Treatment and Research, Neurology Department, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, Ohio, USA.ORCID https://orcid.org/0000-0002-3306-0988
Nicolas R ThompsonDepartment of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio, USA.
Yadi LiDepartment of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio, USA.
Kunio NakamuraDepartment of Biomedical Engineering, Cleveland Clinic Research, Cleveland Clinic, Cleveland, Ohio, USA.ORCID https://orcid.org/0000-0002-7833-8138
Daniel OntanedaMellen Center for Multiple Sclerosis Treatment and Research, Neurology Department, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, Ohio, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

background and purposeSpinal cord pathology underpins disability accumulation in people with multiple sclerosis (pwMS). Visual inspection of spinal cord magentic resonance imaging (MRI) often fails to reliably detect injury. Radiomics analyzes signal intensities in images to identify pathological changes that may be imperceptible to the human eye. This study evaluated the application of radiomics to spinal cord MRI to distinguish subgroups of pwMS and disability correlations.

methodsRadiomic features were extracted from upper cervical cord coverage on cross-sectional 3.0T brain noncontrasted T1-weighted MRI scans in pwMS and healthy controls (HCs). Ninety-three radiomic features-predominantly gray-level matrices-were extracted using Pyradiomics, with pixel heterogeneity considered to reflect neuroaxonal pathology. T2 lesion and brain substructure volumes were segmented from 3D fluid-attenuated inversion recovery and magnetization-prepared rapid gradient-echo sequences using an in-house 2.5D U-Net convolutional neural network to encapsulate neuroinflammation and neurodegeneration. Cervical cross-sectional area (C1-C3) was measured using in-house atlas-based segmentation. Imaging features were compared between pwMS and HCs, and pwMS by phenotype (relapsing vs. progressive), age, and race. Associations of imaging features with Patient-Determined Disease Steps (PDDS) were examined.

resultsAmong 2966 pwMS and 41 HCs, we identified radiomic features distinguishing pwMS from HCs, and pwMS by phenotype, age, and race. Radiomic features exhibited stronger correlations with PDDS than conventional MRI measures.

conclusionsRadiomics identified pathological changes in pwMS in varying stages of the disease course that are undetectable by conventional spinal cord MRI. Radiomics may increase the yield of spinal cord MRI in pwMS and serve as biomarkers predicting disability worsening.

Indexed as

Magnetic Resonance ImagingMultiple SclerosisSpinal CordAdultBrainCross-Sectional StudiesDisability EvaluationFemaleHumansMaleMiddle AgedRadiomicsdisabilityMRImultiple sclerosisradiomicsspinal cord

Identifiers

PMID40947502
PMCPMC12434151

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.