AI and Neurological Rare & Neuroimmune Diseases ── Multiple sclerosis ── 2026-10-22
Multiple sclerosis: treatment, trials and AI
- Abstract
Introduction: Magnetic resonance imaging (MRI) reveals macroscopic lesion burden in multiple sclerosis (MS) but correlates poorly with clinical phenotype, a phenomenon known as the clinico-radiological paradox. While diffusion tensor imaging (DTI) metrics assess microstructural damage, standard whole-lesion averaging may obscure microstructurally spared white matter within heterogeneous plaques.
Methods: We propose a fully automated, multimodal pipeline to identify and quantify reference-like intralesional segments (RLIS) within MS lesions (i.e., the cluster exhibiting maximum feature-space similarity to subject-specific, lesion-spared reference tissue). A modified 2.5D U-Net, developed and evaluated on a cohort of 75 MS patients (115 series), performed automated FLAIR lesion segmentation. In an independent cohort of 100 MS subjects, intralesional DTI metrics (FA and MD) were extracted along streamlines reconstructed via Constrained Spherical Deconvolution tractography. Unsupervised clustering algorithms, validated against a subject-specific, lesion-spared reference CST streamlines (internal capsule), identified operationally defined reference-like segments.
Results: The 2.5D U-Net achieved robust segmentation performance (DSC: 0.732, VC: 0.954), exhibited higher nominal metrics in a contextual benchmark comparison (DSC: 0.682, VC: 0.864). Across the clinical cohort, lesion count (11–131) and volume (0.54–57.3 mL) varied widely. Among the evaluated clustering methods, K-Means and Gaussian Mixture Models proved to be the most conservative, identifying median reference-like segment proportions of 26% and 22%, respectively. Crucially, the proportion of reference-like intralesional segments showed no significant monotonic association with macroscopic lesion volume or count.
Conclusion: This automated framework successfully integrates robust deep learning-based lesion segmentation with tract-specific analysis to quantify the proportion of reference-like intralesional segments. The observed dissociation between overall lesion burden and RLIS suggests that this index serves as a promising candidate metric for exploring microstructural features that may contribute to the clinico-radiological paradox.
Journal IF-equivalent: 3.6 (OpenAlex 2-year mean citedness, value as of 2026-10-21, retrieved 2026-10-22; not the official Clarivate IF)Reference: Toscano-Flores EA, Mireles-Ramírez MA, Guerrero-García JdJ, Alvarez-Padilla FJ. Revisiting the MS paradox: automated 2.5D FLAIR segmentation and DTI microstructural profiling reveals dissociation between lesion burden and reference-like intralesional tract profiles. Front Hum Neurosci. 2026 Oct 20;20:1901702. doi:10.3389/fnhum.2026.1901702.Checked: Abstract only