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Predicting multiple sclerosis from radiologically isolated syndrome using generative artificial intelligence.

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Paper

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The authors' code

License · 21 lines · 1 KB · MIT

  1. MIT License
  3. Copyright (c) 2026 Geodaisics-code
  5. Permission is hereby granted, free of charge, to any person obtaining a copy
  6. of this software and associated documentation files (the "Software"), to deal
  7. in the Software without restriction, including without limitation the rights
  8. to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  9. copies of the Software, and to permit persons to whom the Software is
  10. furnished to do so, subject to the following conditions:
  12. The above copyright notice and this permission notice shall be included in all
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  15. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  16. IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  17. FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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  21. SOFTWARE.

LICENSE at commit ee34796, under MIT · at the source

Overview

Authors: Christine Lebrun-Frenay1,2, Felix Renard3, Lydiane Mondot1,2, Cassandre Landes-Chateau1, Adeline Stewart3, Mikael Cohen1,2, Darin T. Okuda4,5, Arnaud Attyé3
ORCID iDs: Christine Lebrun-Frenay, Felix Renard, Adeline Stewart, Mikael Cohen, Darin T. Okuda
  1. UR2CA-URRIS, Université Nice Côte d’Azur, Nice, France
  2. CRCSEP Nice, Département de Neurologie CHU de Nice Pasteur 2, Nice, France
  3. GeodAIsics. Biopolis - La Tronche, France
  4. Department of Neurology, Neuroinnovation Program and Multiple Sclerosis and Neuroimmunology Imaging Program, The University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
  5. Peter O’Donnell Brain Institute, The University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
Institutions: Université Côte d'Azur (France); Centre Hospitalier Universitaire de Nice (France); Hôpital Pasteur (France); Unité de Recherche Clinique Côte d'Azur; The University of Texas Southwestern Medical Center (United States)
Journal: PLOS digital health, volume 5, issue 4, article e0001374
Dates: received 16 May 2025; accepted 3 April 2026; published online 24 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pdig.0001374 · PMID 42030332 · PMCID PMC13108761 · OpenAlex W7155514758
Open access: gold, a free copy (OpenAlex)
Status: empty repository
Categories: structural MRI / diffusion (modality), human (organism), multiple sclerosis (population), clinical / translational (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, fMRI & imaging
Journal subjects: Medicine and Health Sciences, Clinical Medicine, Clinical Immunology, Autoimmune Diseases, Multiple Sclerosis, Biology and Life Sciences, Immunology, Medical Conditions, Demyelinating Disorders, Neurology, Neurodegenerative Diseases, Epidemiology, Medical Risk Factors, Diagnostic Medicine, Diagnostic Radiology, Magnetic Resonance Imaging, Research and Analysis Methods, Imaging Techniques, Radiology and Imaging, Biochemistry, Biomarkers, Signs and Symptoms, Lesions, Developmental Biology, Twins, Anatomy, Nervous System, Central Nervous System, Neuroanatomy, Spinal Cord, Neuroscience
Topic: Multiple Sclerosis Research Studies (Pathology and Forensic Medicine, Medicine), according to OpenAlex
Citations: not cited yet (Europe PMC); 33 references in the paper

Abstract

Radiologically Isolated Syndrome (RIS) is characterized by incidental MRI findings indicative of multiple sclerosis (MS) in asymptomatic individuals. Factors such as younger age, positive cerebrospinal fluid biomarkers, and specific lesion locations have been previously linked to a higher risk of conversion from RIS to clinical MS. Predicting which individuals will develop clinical MS remains challenging. Based on widely available cross-sectional patient studies, unsupervised machine learning has been proposed to uncover MRI-driven MS phenotypes with distinct temporal progression patterns. We evaluated whether an unsupervised artificial intelligence framework based on generative manifold learning could stratify RIS patients by conversion risk. BrainGML-MS analyzed imaging biomarkers and generated individualized digital twins from MRI data. We studied 152 RIS individuals (32 converters, RIS-C), 152 MS patients, and 152 healthy controls. The model identified four RIS clusters with distinct five-year conversion risks ranging from 10% to 39%. The brain age gap increased progressively from healthy controls to RIS non-converters, RIS-C, and MS. RIS converters showed greater structural atrophy and greater similarity to MS profiles. These findings indicate that MRI-derived brain aging biomarkers and structural deviations measured at the first RIS scan may improve early risk stratification and support clinical decision-making in preclinical MS.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above.

Geodaisics-code/ScoRIS

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: ee34796c51eaa9c0002fab60a3d6976e2b00450f, 17 February 2026
Size: 4 files, 0 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
2 files
At the source: github.com/Geodaisics-code/ScoRIS

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 0 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data Availability

We used 3D T1-weighted MRI data from nine publicly available databases covering the entire lifespan. https://github.com/volBrain/AssemblyNet. The study protocol is available on protocols.io (DOI: dx.doi.org/10.17504/protocols.io.kxygx826zv8j/v1). Reduced-space manifold coordinates, clustering outputs, and associated clinical metadata are available in the companion GitHub repository: https://github.com/Geodaisics-code/ScoRIS. For proprietary reasons, the code for BrainGML-MS is not publicly available. While the full BrainGML-MS software is not publicly distributed due to regulatory and proprietary constraints, all preprocessing steps, feature definitions, and model parameters are described in sufficient detail to enable independent methodological replication. Derived data supporting the findings are available from the corresponding author upon reasonable request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 31 references.

Cite

This paper

Lebrun-Frenay, C., Renard, F., Mondot, L., Landes-Chateau, C., Stewart, A., Cohen, M., Okuda, D. T., & Attyé, A. (2026). Predicting multiple sclerosis from radiologically isolated syndrome using generative artificial intelligence. PLOS digital health, 5(4), e0001374. https://doi.org/10.1371/journal.pdig.0001374

BibTeX

@article{lebrunfrenay2026predicting,
author = {Lebrun-Frenay, Christine and Renard, Felix and Mondot, Lydiane and Landes-Chateau, Cassandre and Stewart, Adeline and Cohen, Mikael and Okuda, Darin T. and Attyé, Arnaud},
title = {{Predicting multiple sclerosis from radiologically isolated syndrome using generative artificial intelligence}},
journal = {PLOS digital health},
year = {2026},
month = apr,
volume = {5},
number = {4},
pages = {e0001374},
publisher = {PLOS},
issn = {2767-3170},
doi = {10.1371/journal.pdig.0001374},
url = {https://doi.org/10.1371/journal.pdig.0001374},
pmid = {42030332},
pmcid = {PMC13108761}
}

RIS

TY - JOUR
AU - Lebrun-Frenay, Christine
AU - Renard, Felix
AU - Mondot, Lydiane
AU - Landes-Chateau, Cassandre
AU - Stewart, Adeline
AU - Cohen, Mikael
AU - Okuda, Darin T.
AU - Attyé, Arnaud
TI - Predicting multiple sclerosis from radiologically isolated syndrome using generative artificial intelligence
T2 - PLOS digital health
J2 - PLOS Digit Health
PY - 2026
DA - 2026/04/24
VL - 5
IS - 4
SP - e0001374
SN - 2767-3170
PB - PLOS
DO - 10.1371/journal.pdig.0001374
UR - https://doi.org/10.1371/journal.pdig.0001374
LA - en
ER -

CSL-JSON

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