ArticleCommunications medicine2026
Quantifying cortical lesions in multiple sclerosis MRI datasets using multi-contrast post-processing and deep learning.
Article in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The trial behind it
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Who cites it
3 citing papers in PubMed.
- A comparative study of deep learning for cortical lesion MRI segmentation with explainability analysis in multiple sclerosis.NeuroImage. Clinical · 2026Article
- Benchmarking and Explaining Deep Learning Cortical Lesion MRI Segmentation in Multiple Sclerosis.ArXiv · 2025Article
- Neuroimaging phenotype characterization of early aggressive and late severe multiple sclerosis: a case-control study.Brain communications · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMultiple sclerosis (MS) is a chronic neurological disease affecting both white and gray matter of the central nervous system. Despite the well-established involvement of cortical lesions in MS, feasibility limitations in their visualization on typical magnetic resonance imaging (MRI) protocols prevent their evaluation in nearly all clinical trials. Recently, several post-processing methods, including synthetic contrasts and artificial intelligence (AI)-based approaches, have shown potential for enhancing cortical lesion detection on conventional MRI data. These methods have the potential to reanalyze existing clinical-trial data to answer key mechanistic questions about both MS development and about treatment effects.
methodsWe sought to evaluate the feasibility of combining and extending existing methods into a unified framework for analysis using the data from the large, multicenter, phase 3 ORATORIO trial (full n = 732, age=44.6 ± 8.0; development subset n = 80, age=46.6 ± 7.1). We specifically evaluated three of the most promising of them - fluid-attenuated inversion recovery squared (FLAIR
resultsAt baseline, we detected 14.8 + /-20.72 lesions per participant, with 86.0% true positive rate and 8.4% false positive rate across subjects for blinded MMCLE, using simultaneous review of all contrasts as the reference. High reproducibility was observed across field strengths and acquisition types (ICC 88.8-92.5%).
conclusionsWe confirmed that cortical lesions can be clearly visualized and quantified with these methods. Using deep learning, we also confirmed that the simultaneous use of multiple contrasts improves quantification.
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Registered trials
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