Evidence map›Paper›PMID 37142431›Full record

ArticleAJNR. American journal of neuroradiology2023

Evaluation of the Statistical Detection of Change Algorithm for Screening Patients with MS with New Lesion Activity on Longitudinal Brain MRI.

M Homssi, E M Sweeney, E Demmon, W Mannheim, M Sakirsky, Y Wang, S A Gauthier, A Gupta, T D Nguyen

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PubMed Full text at PMC Publisher
In one paragraph

Article in AJNR. American journal of neuroradiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

M HomssiFrom the Department of Radiology (M.H., Y.W., A.G., T.D.N.).ORCID 0000-0002-4616-9819
E M SweeneyPenn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology, and Informatics (E.M.S.), University of Pennsylvania, Philadelphia, Pennsylvania.ORCID 0000-0002-4094-7421
E DemmonDepartment of Neurology (E.D., W.M., M.S., S.A.G.).ORCID 0000-0003-2304-0366
W MannheimDepartment of Neurology (E.D., W.M., M.S., S.A.G.).ORCID 0000-0003-1064-3950
M SakirskyDepartment of Neurology (E.D., W.M., M.S., S.A.G.).ORCID 0000-0002-6338-3825
Y WangFrom the Department of Radiology (M.H., Y.W., A.G., T.D.N.).ORCID 0000-0003-1404-8526
S A GauthierDepartment of Neurology (E.D., W.M., M.S., S.A.G.).ORCID 0000-0002-4015-1781
A GuptaFrom the Department of Radiology (M.H., Y.W., A.G., T.D.N.).ORCID 0000-0003-2816-0340
T D NguyenFrom the Department of Radiology (M.H., Y.W., A.G., T.D.N.) tdn2001@med.cornell.edu.ORCID 0000-0002-1411-7694

Funding

No-Gd MRI for Monitoring Disease Status in Multiple SclerosisR01NS105144 · NINDS · WEILL MEDICAL COLL OF CORNELL UNIV · PI GAUTHIER, SUSAN A, GUPTA, AJAY · 2018 to 2022
$2.8M
Multiple Sclerosis Lesion Magnetic Susceptibility ActivityR01NS090464 · NINDS · WEILL MEDICAL COLL OF CORNELL UNIV · PI WANG, YI · 2015 to 2020
$2.3M
Identifying mechanisms of tissue injury in MS lesions: a multi-modality imaging approachR01NS104283 · NINDS · WEILL MEDICAL COLL OF CORNELL UNIV · PI GAUTHIER, SUSAN A · 2018 to 2022
$2.2M
NINDS NIH HHS R01 NS090464NINDS NIH HHS R01 NS104283NINDS NIH HHS R01 NS105144
6 · The paper itself

Abstract

background and purposeIdentification of new MS lesions on longitudinal MR imaging by human readers is time-consuming and prone to error. Our objective was to evaluate the improvement in the performance of subject-level detection by readers when assisted by the automated statistical detection of change algorithm. MATERIALS AND

methodsA total of 200 patients with MS with a mean interscan interval of 13.2 (SD, 2.4) months were included. Statistical detection of change was applied to the baseline and follow-up FLAIR images to detect potential new lesions for confirmation by readers (Reader + statistical detection of change method). This method was compared with readers operating in the clinical workflow (Reader method) for a subject-level detection of new lesions.

resultsReader + statistical detection of change found 30 subjects (15.0%) with at least 1 new lesion, while Reader detected 16 subjects (8.0%). As a subject-level screening tool, statistical detection of change achieved a perfect sensitivity of 1.00 (95% CI, 0.88-1.00) and a moderate specificity of 0.67 (95% CI, 0.59-0.74). The agreement on a subject level was 0.91 (95% CI, 0.87-0.95) between Reader + statistical detection of change and Reader, and 0.72 (95% CI, 0.66-0.78) between Reader + statistical detection of change and statistical detection of change.

conclusionsThe statistical detection of change algorithm can serve as a time-saving screening tool to assist human readers in verifying 3D FLAIR images of patients with MS with suspected new lesions. Our promising results warrant further evaluation of statistical detection of change in prospective multireader clinical studies.

Indexed as

Magnetic Resonance ImagingNeuroimagingAlgorithmsBrainHumansProspective Studies

Identifiers

PMID37142431
PMCPMC10249703

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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.