Evidence map›Paper›PMID 39410780›Full record

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

Multicenter validation of automated detection of paramagnetic rim lesions on brain MRI in multiple sclerosis.

Luyun Chen, Zheng Ren, Kelly A Clark, Carolyn Lou, Fang Liu, Quy Cao, Abigail R Manning, Melissa L Martin, Elaina Luskin, Carly M O'Donnell and 19 more

Abstract readMulticenter StudyValidation Study
PubMed Full text at PMC Publisher
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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Multiple sclerosis: 2024 update.Free neuropathology · 2025
    Review
  4. Review
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

29 authors.

Luyun ChenPenn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.ORCID https://orcid.org/0009-0002-4495-4685
Zheng RenPenn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Kelly A ClarkPenn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.ORCID https://orcid.org/0000-0002-3583-0966
Carolyn LouPfizer Inc., New York, New York, USA.
Fang LiuPenn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Quy CaoPenn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Abigail R ManningPenn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Melissa L MartinPenn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Elaina LuskinDepartment of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Carly M O'DonnellPenn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Christina J AzevedoDepartment of Neurology, University of Southern California, Los Angeles, California, USA.
Peter A CalabresiDepartment of Neurology, Johns Hopkins University, Baltimore, Maryland, USA.
Leorah FreemanDepartment of Neurology, Dell Medical School, The University of Texas at Austin, Austin, Texas, USA.
Roland G HenryUCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, California, USA.
Erin E LongbrakeDepartment of Neurology, Yale University, New Haven, Connecticut, USA.
Jiwon OhDivision of Neurology, Department of Medicine, St. Michael's Hospital, University of Toronto, Toronto, Ontario, Canada.
Nico PapinuttoUCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, California, USA.ORCID https://orcid.org/0000-0003-2184-4782
Michel BilelloDepartment of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Jae W SongDepartment of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.ORCID https://orcid.org/0000-0002-3127-6427
Marwa KaiseyDepartment of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Nancy L SicotteDepartment of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Daniel S ReichTranslational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA.
Andrew J SolomonDepartment of Neurological Sciences, Larner College of Medicine at the University of Vermont, Burlington, Vermont, USA.
Daniel OntanedaMellen Center for Multiple Sclerosis, Cleveland Clinic, Cleveland, Ohio, USA.
Pascal SatiDepartment of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA.ORCID https://orcid.org/0000-0002-6763-0125
Martina AbsintaDepartment of Neurology, Johns Hopkins University, Baltimore, Maryland, USA.
Matthew K SchindlerDepartment of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.ORCID https://orcid.org/0000-0003-0157-5669
Russell T ShinoharaPenn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
NAIMS Cooperative

Funding

Central Vein Sign: A Diagnostic Biomarker in Multiple SclerosisU01NS116776 · NINDS · CLEVELAND CLINIC LERNER COM-CWRU · PI ONTANEDA, DANIEL, SATI, PASCAL · 2020 to 2024
$7.1M
Advanced Statistical Analytics of MRI in MSR01NS112274 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI SHINOHARA, RUSSELL TAKESHI · 2020 to 2024
$2.8M
Abata TherapeuticsBiogenBristol Meyers SquibbCariplo Foundation #1677Cedars-Sinai Precision HealthConrad N. Hilton Foundation #17313EMD SeronoFRRB Early Career Award #1750327GenentechInternational Progressive MS alliance 21NS037Myelin Repair FoundationNational Multiple Sclerosis SocietyNIH HHS R01NS112274NIH HHS U01NS116776NIH/NINDSNINDS NIH HHS R01 NS112274NINDS NIH HHS U01 NS116776PCORIQuanterixRace to Erase MS FoundationRoche Foundation for Independent ResearchSanofiSanofi-Genzyme
6 · The paper itself

Abstract

background and purposeParamagnetic rim lesions (PRLs) are an MRI biomarker of chronic inflammation in people with multiple sclerosis (MS). PRLs may aid in the diagnosis and prognosis of MS. However, manual identification of PRLs is time-consuming and prone to poor interrater reliability. To address these challenges, the Automated Paramagnetic Rim Lesion (APRL) algorithm was developed to automate PRL detection. The primary objective of this study is to evaluate the accuracy of APRL for detecting PRLs in a multicenter setting.

methodsWe applied APRL to a multicenter dataset, which included 3-Tesla MRI acquired in 92 participants (43 with MS, 14 with clinically isolated syndrome [CIS]/radiologically isolated syndrome [RIS], 35 without RIS/CIS/MS). Subsequently, we assessed APRL's performance by comparing its results with manual PRL assessments carried out by a team of trained raters.

resultsAmong the 92 participants, expert raters identified 5637 white matter lesions and 148 PRLs. The automated segmentation method successfully captured 115 (78%) of the manually identified PRLs. Within these 115 identified lesions, APRL differentiated between manually identified PRLs and non-PRLs with an area under the curve (AUC) of .73 (95% confidence interval [CI]: [.68, .78]). At the subject level, the count of APRL-identified PRLs predicted MS diagnosis with an AUC of .69 (95% CI: [.57, .81]).

conclusionOur study demonstrated APRL's capability to differentiate between PRLs and lesions without paramagnetic rims in a multicenter study. Automated identification of PRLs offers greater efficiency over manual identification and could facilitate large-scale assessments of PRLs in clinical trials.

Indexed as

AlgorithmsBrainMagnetic Resonance ImagingMultiple SclerosisAdultFemaleHumansImage Interpretation, Computer-AssistedMaleMiddle AgedPattern Recognition, AutomatedReproducibility of ResultsSensitivity and SpecificityWhite Matterautomated detectionmultiple sclerosisparamagnetic rim lesion

Identifiers

PMID39410780
PMCPMC12370289

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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.