ArticleAJNR. American journal of neuroradiology2018
An Automated Statistical Technique for Counting Distinct Multiple Sclerosis Lesions.
Article in AJNR. American journal of neuroradiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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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.
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Who cites it
14 citing papers in PubMed.
- UNISELF: A unified network with instance normalization and self-ensembled lesion fusion for multiple sclerosis lesion segmentation.Medical image analysis · 2026Article
- The Utility of Intravoxel Incoherent Motion Metrics in Assessing Disability in Relapsing-Remitting Multiple Sclerosis.Diagnostics (Basel, Switzerland) · 2025Article
- Increased White Matter Aerobic Glycolysis in Multiple Sclerosis.Annals of neurology · 2025Article
- Automated segmentation of multiple sclerosis lesions, paramagnetic rims, and central vein sign on MRI provides reliable diagnostic biomarkers.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Imaging chronic active lesions in multiple sclerosis: a consensus statement.Brain : a journal of neurology · 2024Review
- Stopping Interferon Beta 1b Does Not Influence the Risk of Disability Accrual in Non-Active SPMS: Results from an Italian Real-World Study.International journal of environmental research and public health · 2022Article
- Predictive MRI Biomarkers in MS-A Critical Review.Medicina (Kaunas, Lithuania) · 2022Review
- Stronger Microstructural Damage Revealed in Multiple Sclerosis Lesions With Central Vein Sign by Quantitative Gradient Echo MRI.Journal of central nervous system disease · 2022Article
- Central vein sign: A diagnostic biomarker in multiple sclerosis (CAVS-MS) study protocol for a prospective multicenter trial.NeuroImage. Clinical · 2021Article
- Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis.Scientific reports · 2020Article
- TAPAS: A Thresholding Approach for Probability Map Automatic Segmentation in Multiple Sclerosis.NeuroImage. Clinical · 2020Article
- Do All Patients with Multiple Sclerosis Benefit from the Use of Contrast on Serial Follow-Up MR Imaging? A Retrospective Analysis.AJNR. American journal of neuroradiology · 2018Article
- Automated Integration of Multimodal MRI for the Probabilistic Detection of the Central Vein Sign in White Matter Lesions.AJNR. American journal of neuroradiology · 2018Article
- Multicenter validation of automated detection of paramagnetic rim lesions on brain MRI in multiple sclerosis.Journal of neuroimaging : official journal of the American Society of NeuroimagingArticle
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
background and purposeLesion load is a common biomarker in multiple sclerosis, yet it has historically shown modest association with clinical outcome. Lesion count, which encapsulates the natural history of lesion formation and is thought to provide complementary information, is difficult to assess in patients with confluent (ie, spatially overlapping) lesions. We introduce a statistical technique for cross-sectionally counting pathologically distinct lesions. MATERIALS AND
methodsMR imaging was used to assess the probability of a lesion at each location. The texture of this map was quantified using a novel technique, and clusters resembling the center of a lesion were counted. Validity compared with a criterion standard count was demonstrated in 60 subjects observed longitudinally, and reliability was determined using 14 scans of a clinically stable subject acquired at 7 sites.
resultsThe proposed count and the criterion standard count were highly correlated (
conclusionsThis study introduces a novel technique for counting pathologically distinct lesions using cross-sectional data and demonstrates its ability to recover obscured longitudinal information. The proposed count allows more accurate estimation of lesion size, which correlated more closely with disability scores than either lesion load or lesion count alone.
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Registered trials
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.