Repurposing Clinical MRI Archives for Multiple Sclerosis Research with a Flexible, Single-Modality Approach: New Insights from Old Scans

In multiple sclerosis (MS), magnetic resonance imaging (MRI) biomarkers are critical for research in diagnosis, prognosis and assessing treatment efficacy. Traditionally, extracting relevant biomarkers of disease activity and neurodegeneration requires multimodal MRI protocols, limiting the use of the already existing vast amount of incomplete or single-modality MRI data which are acquired in clinical settings. We developed MindGlide, a deep learning model that extracts volums of brain regions and lesion from a single MRI modality, simplifying analysis and enabling the use of heterogeneous clinical archives. We trained MindGlide on a dataset of 4,247 brain MRI scans from 2,934 MS patients across 592 MRI scanners and validated it on 14,952 brain MRI scans from 1001 patients from three unseen external validation cohorts including 161 adolescent patients. Using dice scores, we demonstrated that MindGlide accurately estimated white matter lesion, cortical, and deep grey matter volumes. These volumes correlated with disability (Expanded Disability Status Scale, absolute correlation coefficients 0.1-0.2, p<0.05), and MindGlide outperformed an established tool in this regard. MindGlide robustly detected treatment effects across clinical trials, including disease activity and neurodegeneration (as shown by lesion accrual and brain tissue loss, respectively), even when analysing MRI modalities not traditionally used for such detailed measurements. Our results indicate the potential to indirectly reduce scan time and drug development costs in clinical trials while directly transforming the utility of retrospective analysis of real-world data acquired in clinical settings. As a consequence, scan time will be reduced and, in turn, the cost of trials.