Evidence map›Paper›PMID 40880618›Full record

ArticleMultiple sclerosis journal - experimental, translational and clinical

Diffusion tensor imaging in the SPRINT-MS clinical trial: Advancing trial methodology.

Ken Sakaie, Mengke Du, Nancy Obuchowski, Mark J Lowe, Jian Lin, Robert J Fox

Abstract read
In one paragraph

Article in Multiple sclerosis journal - experimental, translational and clinical. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Ken SakaieDiagnostics Institute Cleveland Clinic, Cleveland, OH, USA.ORCID https://orcid.org/0000-0002-5633-4494
Nancy ObuchowskiDepartment of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.ORCID https://orcid.org/0000-0003-1891-7477
Jian LinDiagnostics Institute Cleveland Clinic, Cleveland, OH, USA.ORCID https://orcid.org/0000-0002-3574-6035
Robert J FoxMellen Center for Multiple Sclerosis, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA.ORCID https://orcid.org/0000-0002-4263-3717

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: SPRINT-MS was a placebo-controlled phase 2 trial of ibudilast in secondary and primary progressive multiple sclerosis. The trial included multimodal imaging to assess brain tissue integrity. This contribution focuses on improved analysis methods of diffusion tensor imaging to refine its application in clinical trials. Objective: Reassess diffusion tensor imaging from the SPRINT-MS trial. Methods: Postprocessing incorporated corrections for bulk motion, eddy current distortion, outlier replacement, and intra-volume movement. The ICBM-DTI-81 white matter parcellation map was coregistered into native space. Six unilateral and 21 bilateral regions of interest were identified. Median radial diffusivity was the primary outcome measure for this analysis. A linear mixed-effects model was used to assess the interaction between time and treatment for the outcome measure with Holm correction for multiple comparisons. Results: Radial diffusivity in the cingulum and cerebellar peduncles showed a significant difference in rate of change between treatment and placebo groups (2.7-7.4 × 10 Conclusion: Our results suggest that the diffusion tensor imaging of the cingulum and cerebellar peduncles may be useful target outcome metrics in neuroprotective trials in progressive multiple sclerosis.

Indexed as

clinical trialsdiffusion tensor imagingmagnetic resonance imagingProgressive multiple sclerosis

Identifiers

PMID40880618
PMCPMC12381466

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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.