Abstract
<jats:p>Background: White matter hyperintensities (WMH) represent the most visible manifestation of cerebral small vessel disease and of white matter pathology more broadly, yet empirical evidence points to a brain tissue injury extending beyond radiologically detectable lesions on fluid-attenuated inversion recovery (FLAIR) MRI. We present RADAR-WMH (Relaxometry And Diffusion Analysis for Radiological WMH), a multicontrast MRI machine learning framework that characterises white matter pathology through tissue microstructural information rather than lesion contrast alone. Methods: RADAR-WMH was trained on quantitative relaxometry and diffusion-weighted MRI acquired in community-dwelling participants (mean age 59.6 years [SD 22.4], 60.8% women, n=148) using a LightGBM classifier integrating local, textural, and anatomical features at the voxel level. Biological validity was assessed through longitudinal analyses and associations with age, cardio-vascular risk, and cognitive performance in independent cohorts. Results: RADAR-WMH achieved segmentation performance comparable to state-of-the-art FLAIR-based approaches without requiring FLAIR or T1-weighted data. Mean diffusivity was the most influential feature for lesion classification. Beyond FLAIR-defined WMH, RADAR-WMH identified tissue pathology extending outside lesion borders characterised by myelin loss, axonal injury, and increased extracellular water. These microstructural signatures persisted over follow-up and showed stronger association with age, systolic blood pressure, and cognitive performance than corresponding tissue properties restricted to FLAIR-defined WMH extent. Conclusions: RADAR-WMH reveals a significant burden of biologically meaningful white matter injury that remains invisible to FLAIR-defined WMH segmentation. By capturing microstructural pathology linked to vascular risk, cognitive decline, and lesion evolution, RADAR-WMH may provide more sensitive markers of cerebral small vessel disease than FLAIR-visible WMH alone.</jats:p>