Evidence map›Paper›PMID 35250439›Full record

ArticleFrontiers in neuroscience2022

MRI-Based Classification of Neuropsychiatric Systemic Lupus Erythematosus Patients With Self-Supervised Contrastive Learning.

Francesca Inglese, Minseon Kim, Gerda M Steup-Beekman, Tom W J Huizinga, Mark A van Buchem, Jeroen de Bresser, Dae-Shik Kim, Itamar Ronen

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 5 pooled it
2.9field-weighted citation impact, top 9% of its field
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

17 citing papers in PubMed, 5 syntheses or guidelines pooled it, 22 citations in OpenAlex.

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  6. Review
  7. [Classification of systemic lupus erythematosus resting-state functional magnetic resonance imaging data based on the end-to-end model].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026
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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

8 authors at 2 institutions in 2 countries.

Francesca IngleseDepartment of Radiology, Leiden University Medical Center, Leiden, Netherlands.
Minseon KimSchool of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.
Gerda M Steup-BeekmanDepartment of Rheumatology, Leiden University Medical Center, Leiden, Netherlands.
Tom W J HuizingaDepartment of Rheumatology, Leiden University Medical Center, Leiden, Netherlands.
Mark A van BuchemDepartment of Radiology, Leiden University Medical Center, Leiden, Netherlands.
Jeroen de BresserDepartment of Radiology, Leiden University Medical Center, Leiden, Netherlands.
Dae-Shik KimSchool of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.
Itamar RonenDepartment of Radiology, Leiden University Medical Center, Leiden, Netherlands.
Leiden University Medical Center · NLKorea Advanced Institute of Science and Technology · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introduction/purposeSystemic lupus erythematosus (SLE) is a chronic auto-immune disease with a broad spectrum of clinical presentations, including heterogeneous neuropsychiatric (NP) syndromes. Structural brain abnormalities are commonly found in SLE and NPSLE, but their role in diagnosis is limited, and their usefulness in distinguishing between NPSLE patients and patients in which the NP symptoms are not primarily attributed to SLE (non-NPSLE) is non-existent. Self-supervised contrastive learning algorithms proved to be useful in classification tasks in rare diseases with limited number of datasets. Our aim was to apply self-supervised contrastive learning on T SUBJECTS AND

methodsWe used 3T MRI T

resultsIn the 6 trials, between 79% and 83% of the patients were correctly classified as NPSLE or non-NPSLE. For a qualitative evaluation of spatial distribution of the common features found in both groups, Gradient-weighted Class Activation Maps (Grad-CAM) were examined. Thresholded Grad-CAM maps show areas of common features identified for the NPSLE cohort, while no such communality was found for the non-NPSLE group. DISCUSSION/

conclusionThe self-supervised contrastive learning model was effective in capturing common brain MRI features from a limited but well-defined cohort of SLE patients with NP symptoms. The interpretation of the Grad-CAM results is not straightforward, but indicates involvement of the lateral and third ventricles, periventricular white matter and basal cisterns. We believe that the common features found in the NPSLE population in this study indicate a combination of tissue loss, local atrophy and to some extent that of periventricular white matter lesions, which are commonly found in NPSLE patients and appear hypointense on T

Indexed as

cohort studiesmagnetic resonance imagingneuroimagingSystemic Lupus Erythematosusunsupervised machine learning

Identifiers

PMID35250439
PMCPMC8889016
OpenAlexW4225788449

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.