ArticleBrain communications2025
Deep learning to predict progression independent of relapse activity at a first demyelinating event.
Article in Brain communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Relapse prediction and individualized treatment-effect modeling in relapsing multiple sclerosis: a systematic review.Frontiers in public health · 2026Pooled it
- Spider-MS: an individualized polyhedral prediction of multiple sclerosis prognosis.Brain : a journal of neurology · 2026Article
- [XVIII Post-ECTRIMS Meeting: Review of the New Developments Presented at the 2025 ECTRIMS Congress (Part I)].Revista de neurologia · 2026Article
- Assessing the Relative Importance of Imaging and Serum Biomarkers in Capturing Disability, Cognitive Impairment, and Clinical Progression in Multiple Sclerosis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
27 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Progression independent of relapse activity is the main cause of irreversible disability in multiple sclerosis and is strongly associated with older age at symptom onset. Early and accurate prediction, at symptom onset, of which patients are at highest risk of progression independent of relapses, is an unmet need. This study aimed to develop a deep learning survival model using only routine MRI acquired at the first demyelinating attack to predict the risk of progression independent of relapses, and assess its ability to improve classical age-adjusted predictions. We analysed a prospective cohort of patients under 50, clinically assessed within three months of symptom onset, with available MRI (T1- and T2-Fluid-Attenuated Inversion Recovery sequences). An independent early multiple sclerosis cohort (≤1 year from symptom onset) from the Multiple Sclerosis Partners Advancing Technology and Health Solutions database (
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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.