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Enabling new insights from old scans by repurposing clinical MRI archives for multiple sclerosis research

Author

Listed:
  • Philipp Goebl

    (University College London
    University College London)

  • Jed Wingrove

    (University College London)

  • Omar Abdelmannan

    (University College London)

  • Barbara Brito Vega

    (University College London
    University College London)

  • Jonathan Stutters

    (University College London)

  • Silvia Da Graca Ramos

    (University College London)

  • Owain Kenway

    (University College London)

  • Thomas Rossor

    (Guy’s and St Thomas’ NHS Foundation Trust)

  • Evangeline Wassmer

    (Birmingham Children’s Hospital
    Aston University)

  • Douglas L. Arnold

    (McGill University)

  • D. Louis Collins

    (McGill University)

  • Cheryl Hemingway

    (UCL)

  • Sridar Narayanan

    (McGill University)

  • Jeremy Chataway

    (University College London
    National Institute for Health Research University College London Hospitals Biomedical Research Centre (BRC))

  • Declan Chard

    (University College London
    National Institute for Health Research University College London Hospitals Biomedical Research Centre (BRC))

  • Juan Eugenio Iglesias

    (University College London
    Harvard Medical School
    Massachusetts Institute of Technology)

  • Frederik Barkhof

    (University College London
    University College London
    Vrije Universiteit)

  • Geoff J. M. Parker

    (University College London
    University College London
    Bioxydyn Limited)

  • Neil P. Oxtoby

    (University College London)

  • Yael Hacohen

    (University College London)

  • Alan Thompson

    (University College London
    National Institute for Health Research University College London Hospitals Biomedical Research Centre (BRC))

  • Daniel C. Alexander

    (University College London
    University College London)

  • Olga Ciccarelli

    (University College London
    National Institute for Health Research University College London Hospitals Biomedical Research Centre (BRC))

  • Arman Eshaghi

    (University College London
    University College London)

Abstract

Magnetic resonance imaging (MRI) biomarkers are vital for multiple sclerosis (MS) clinical research and trials but quantifying them requires multi-contrast protocols and limits the use of abundant single-contrast hospital archives. We developed MindGlide, a deep learning model to extract brain region and white matter lesion volumes from any single MRI contrast. We trained MindGlide on 4247 brain MRI scans from 2934 MS patients across 592 scanners, and externally validated it using 14,952 scans from 1,001 patients in two clinical trials (primary-progressive MS and secondary-progressive MS trials) and a routine-care MS dataset. The model outperformed two state-of-the-art models when tested against expert-labelled lesion volumes. In clinical trials, MindGlide detected treatment effects on T2-lesion accrual and cortical and deep grey matter volume loss. In routine-care data, T2-lesion volume increased with moderate-efficacy treatment but remained stable with high-efficacy treatment. MindGlide uniquely enables quantitative analysis of archival single-contrast MRIs, unlocking insights from untapped hospital datasets.

Suggested Citation

  • Philipp Goebl & Jed Wingrove & Omar Abdelmannan & Barbara Brito Vega & Jonathan Stutters & Silvia Da Graca Ramos & Owain Kenway & Thomas Rossor & Evangeline Wassmer & Douglas L. Arnold & D. Louis Coll, 2025. "Enabling new insights from old scans by repurposing clinical MRI archives for multiple sclerosis research," Nature Communications, Nature, vol. 16(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58274-8
    DOI: 10.1038/s41467-025-58274-8
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    References listed on IDEAS

    as
    1. Arman Eshaghi & Alexandra L. Young & Peter A. Wijeratne & Ferran Prados & Douglas L. Arnold & Sridar Narayanan & Charles R. G. Guttmann & Frederik Barkhof & Daniel C. Alexander & Alan J. Thompson & De, 2021. "Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    2. Michela Antonelli & Annika Reinke & Spyridon Bakas & Keyvan Farahani & Annette Kopp-Schneider & Bennett A. Landman & Geert Litjens & Bjoern Menze & Olaf Ronneberger & Ronald M. Summers & Bram Ginneken, 2022. "The Medical Segmentation Decathlon," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    3. Arman Eshaghi & Alexandra L. Young & Peter A. Wijeratne & Ferran Prados & Douglas L. Arnold & Sridar Narayanan & Charles R. G. Guttmann & Frederik Barkhof & Daniel C. Alexander & Alan J. Thompson & De, 2021. "Author Correction: Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data," Nature Communications, Nature, vol. 12(1), pages 1-3, December.
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