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

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 Collins, Cheryl Hemingway, Sridar Narayanan, Jeremy Chataway, Declan Chard, Juan Eugenio Iglesias, Frederik Barkhof, Geoff J. M. Parker, Neil P. Oxtoby, Yael Hacohen, Alan Thompson, Daniel C. Alexander, Olga Ciccarelli and Arman Eshaghi
Additional contact information
Philipp Goebl: University College London
Jed Wingrove: University College London
Omar Abdelmannan: University College London
Barbara Brito Vega: 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
Douglas L. Arnold: McGill University
D. Louis Collins: McGill University
Cheryl Hemingway: UCL
Sridar Narayanan: McGill University
Jeremy Chataway: University College London
Declan Chard: University College London
Juan Eugenio Iglesias: University College London
Frederik Barkhof: University College London
Geoff J. M. Parker: University College London
Neil P. Oxtoby: University College London
Yael Hacohen: University College London
Alan Thompson: University College London
Daniel C. Alexander: University College London
Olga Ciccarelli: University College London
Arman Eshaghi: University College London

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-58274-8

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