Evidence map›Paper›PMID 42903003›Full record

ArticleInternational journal of MS care2025

New/Enlarging T2 Lesions in a Progressive Multiple Sclerosis Trial Cohort: Computer vs Human Detection.

Alexandra J White, Stephen E Jones, Jay Costantini, Douglas L Arnold, Colm Elliott, Robert J Fox

Abstract read
In one paragraph

Article in International journal of MS care, 2025. 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.

Alexandra J WhiteCleveland Clinic Lerner College of Medicine, Cleveland Clinic, Cleveland, OH.ORCID https://orcid.org/0000-0003-4704-6578
Stephen E JonesCleveland Clinic Imaging Institute, Cleveland Clinic, Cleveland, OH.
Jay CostantiniCleveland Clinic Imaging Institute, Cleveland Clinic, Cleveland, OH.
Douglas L ArnoldNeuroRx Research, Montreal, Quebec, Canada.
Colm ElliottNeuroRx Research, Montreal, Quebec, Canada.
Robert J FoxMellen Center for Multiple Sclerosis Treatment and Research, Cleveland Clinic Neurological Institute, Cleveland Clinic, Cleveland, OH.ORCID https://orcid.org/0000-0002-4263-3717

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Although detection of new/enlarging multiple sclerosis (MS) lesions is a key metric in clinical practice and research, little is known about how human and automated detection methods compare. We compared findings made by a neuroradiologist to simulate routine practice to those made with a computer-aided technique as used in a clinical trial. Methods: MRIs from a 96-week, progressive MS clinical trial with 255 participants were evaluated for new/enlarging T2 lesions by both a neuroradiologist and a semiautomated method. Readings from 887 paired scans were compared using a paired Results: The semiautomated method identified new/enlarging lesions on 19.7% of the paired scans, while the neuroradiologist identified lesions on 5.7%. Of the 185 paired scans with new/enlarging T2 lesions by either method, the semiautomated method detected a mean of 3.4 new lesions, while the neuroradiologist detected 0.4. Overall κ was 0.18 (poor agreement); the κ of only scans with new/enlarging lesions by either method was -0.06. When scans were categorized as active or inactive, κ was 0.18. Unblinded neuroradiologist overread of selected discordant scans found more lesions when using advanced radiology tools such as coregistration. Conclusions: A large discordance was found between new/enlarging MS lesions identified by a neuroradiologist and a semiautomated identification method. These findings may explain clinical trials reporting new lesions in patients on highly effective therapies, while they are less common in clinical practice. These findings have implications for treatment decision algorithms using MRI. Advanced radiology tools such as coregistration may improve lesion detection in routine clinical practice.

Indexed as

diagnostic accuracymachine learningT2 lesions

Identifiers

PMID42903003
PMCPMC13629443

What OpenQuestion holds

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Registered trials

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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.