Adult-onset vanishing white-matter disease (VWM) is a rare autosomal recessive disease with neurological symptoms such as ataxia and paraparesis, showing extensive white-matter hyperintensities (WMH) on magnetic resonance (MR) imaging. Besides symptom-specific scores like the International Cooperative Ataxia Rating Scale (ICARS), there is no established tool to monitor disease progression. Because of extensive WMH, visual comparison of MR images is challenging. Here, we report the results of an automated method of segmentation to detect alterations in T2-weighted fluid-attenuated-inversion-recovery (FLAIR) sequences in a one-year follow-up study of a clinically stable patient with genetically diagnosed VWM. Signal alterations in MR imaging were quantified with a recently published WMH segmentation method by means of extreme value distribution (EVD). Our analysis revealed progressive FLAIR alterations of 5.84% in the course of one year, whereas no significant WMH change could be detected in a stable multiple sclerosis (MS) control group. This result demonstrates that automated EVD-based segmentation allows a precise and rapid quantification of extensive FLAIR alterations like in VWM and might be a powerful tool for the clinical and scientific monitoring of degenerative white-matter diseases and potential therapeutic interventions.
Keywords: Extreme value distribution; automated segmentation; vanishing white-matter disease; white-matter hyperintensities.