ArticleScientific reports2022
Tract-based white matter hyperintensity patterns in patients with systemic lupus erythematosus using an unsupervised machine learning approach.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Artificial Intelligence-based detection of neuropsychiatric lupus: an exploratory meta-analysis of neuroimaging and multimodal biomarker models.Clinical and experimental medicine · 2026Pooled it
- Machine learning models identify prognostic factors in systemic lupus erythematosus patients with epstein-barr virus infection.Clinical rheumatology · 2026Article
- Characterizing white matter hyperintensities in myotonic dystrophy type 1 through IVIM derived metrics.Scientific reports · 2025Article
- Microorganisms as Potential Accelerators of Speed Breeding: Mechanisms and Knowledge Gaps.Plants (Basel, Switzerland) · 2025Review
- Current imaging applications, radiomics, and machine learning modalities of CNS demyelinating disorders and its mimickers.Journal of neurology · 2025Review
- Can conventional brain MRI support the attribution process in neuropsychiatric SLE? A multicentre retrospective study.Lupus science & medicine · 2025Article
- A cross-sectional study on white matter hyperitensity in patients at the initial diagnosis of neuropsychiatric SLE: Correlation with Clinical and Laboratory Findings.Clinical rheumatology · 2025Article
- Serotonin Syndrome-Mimicking Manifestations in a Patient with Systemic Lupus Erythematosus.Journal of clinical medicine · 2024Article
- Systemic lupus in the era of machine learning medicine.Lupus science & medicine · 2024Review
- Application of Machine Learning Models in Systemic Lupus Erythematosus.International journal of molecular sciences · 2023Review
Corrections and comments
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Authors and funding
15 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Currently, little is known about the spatial distribution of white matter hyperintensities (WMH) in the brain of patients with Systemic Lupus erythematosus (SLE). Previous lesion markers, such as number and volume, ignore the strategic location of WMH. The goal of this work was to develop a fully-automated method to identify predominant patterns of WMH across WM tracts based on cluster analysis. A total of 221 SLE patients with and without neuropsychiatric symptoms from two different sites were included in this study. WMH segmentations and lesion locations were acquired automatically. Cluster analysis was performed on the WMH distribution in 20 WM tracts. Our pipeline identified five distinct clusters with predominant involvement of the forceps major, forceps minor, as well as right and left anterior thalamic radiations and the right inferior fronto-occipital fasciculus. The patterns of the affected WM tracts were consistent over the SLE subtypes and sites. Our approach revealed distinct and robust tract-based WMH patterns within SLE patients. This method could provide a basis, to link the location of WMH with clinical symptoms. Furthermore, it could be used for other diseases characterized by presence of WMH to investigate both the clinical relevance of WMH and underlying pathomechanism in the brain.
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