Spatial dispersion of lesions as a surrogate biomarker for disability in multiple sclerosis

Many previous studies in multiple sclerosis (MS) have focused on the relationship between white matter lesion volume and clinical parameters, but few have investigated the independent contribution of the spatial dispersion of lesions to patient disability. In this study, we examine the ability of four different measures of lesion dispersion including one connectedness-based measure (compactness), one region-based measure (ratio of lesion convex hull to brain volume) and two distance-based measures (Euclidean distance from a fixed point and pair-wise Euclidean distances) to act as potential surrogate markers of disability. We use a set of T2-weighted and proton density-weighted MRIs of 24 MS patients, collected from a single selected scanning site participating in an MS clinical trial. For each patient, clinical status is available in the form of expanded disability status scale (EDSS) a standard measure of disability in MS. We segment all white matter lesions in each scan with a semi-automatic method to produce binary images of lesion voxels, quantify their spatial dispersion using the defined measures, then perform a statistical analysis to compare the dispersion values to EDSS and total lesion volume. We use linear and rank correlations to investigate the relationships between lesion dispersion, EDSS, and total lesion volume, and regression analysis to investigate whether there is a potentially meaningful relationship between lesion dispersion and EDSS, independent of total lesion volume. Our results show that one distance based measure, Euclidean distance from a fixed point, correlates with EDSS more strongly than total lesion volume (r = 0.57 vs. r = 0.47 for Pearson correlation), and has predictive value that is at least partly independent of lesion volume. The results suggest that for any two given patients with similar lesion loads, the one with greater dispersion would tend to have greater disability, but further experiments with larger data sets are requir- d to confirm these findings.