An automated statistical technique for counting distinct multiple sclerosis lesions can recover aspects of lesion history and provide relevant disease information

Abstract

BackgroundLesion load is a common biomarker in multiple sclerosis, yet it has historically shown modest associations with clinical outcomes. Lesion count, which encapsulates the natural history of lesion formation and is thought to provide complementary information, is difficult to assess in patients with confluent (i.e. spatially overlapping) lesions. We introduce a statistical technique for cross-sectionally counting pathologically distinct lesions.\n\nMethodsMRI is used to assess the probability of lesion at each location. The texture of this map is quantified using a novel technique, and clusters resembling the center of a lesion are counted.\n\nResultsValidity was demonstrated by comparing the proposed count to a gold-standard count in 60 subjects observed longitudinally. The counts were highly correlated (r = .97, p .40). Reliability was determined using 14 scans of a clinically stable subject acquired at 7 sites, and variability of lesion count was equivalent to that of lesion load. Accounting for lesion load and age, lesion count was negatively associated (t58 = -2.73, p .40) or lesion count (r = -.12, p > .30) alone.\n\nConclusionThese findings demonstrate that it is possible to recover important aspects of the natural history of lesion formation without longitudinal data, and suggest that lesion size provides complementary information about disease.\n\nGrant SupportThe project described was supported in part by the NIH grants R01 NS085211, R21 NS093349, and R01 NS094456 from the National Institute of Neurological Disorders and Stroke (NINDS). The study was also supported by the Intramural Research Program of NINDS and the Race to Erase MS Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.