TY - JOUR T1 - An Automated Statistical Technique for Counting Distinct Multiple Sclerosis Lesions JF - American Journal of Neuroradiology JO - Am. J. Neuroradiol. DO - 10.3174/ajnr.A5556 AU - J.D. Dworkin AU - K.A. Linn AU - I. Oguz AU - G.M. Fleishman AU - R. Bakshi AU - G. Nair AU - P.A. Calabresi AU - R.G. Henry AU - J. Oh AU - N. Papinutto AU - D. Pelletier AU - W. Rooney AU - W. Stern AU - N.L. Sicotte AU - D.S. Reich AU - R.T. Shinohara AU - the North American Imaging in Multiple Sclerosis Cooperative Y1 - 2018/02/22 UR - http://www.ajnr.org/content/early/2018/02/22/ajnr.A5556.abstract N2 - BACKGROUND AND PURPOSE: Lesion load is a common biomarker in multiple sclerosis, yet it has historically shown modest association with clinical outcome. 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 (ie, spatially overlapping) lesions. We introduce a statistical technique for cross-sectionally counting pathologically distinct lesions.MATERIALS AND METHODS: MR imaging was used to assess the probability of a lesion at each location. The texture of this map was quantified using a novel technique, and clusters resembling the center of a lesion were counted. Validity compared with a criterion standard count was demonstrated in 60 subjects observed longitudinally, and reliability was determined using 14 scans of a clinically stable subject acquired at 7 sites.RESULTS: The proposed count and the criterion standard count were highly correlated (r = 0.97, P < .001) and not significantly different (t59 = −.83, P = .41), and the variability of the proposed count across repeat scans was equivalent to that of lesion load. After accounting for lesion load and age, lesion count was negatively associated (t58 = −2.73, P < .01) with the Expanded Disability Status Scale. Average lesion size had a higher association with the Expanded Disability Status Scale (r = 0.35, P < .01) than lesion load (r = 0.10, P = .44) or lesion count (r = −.12, P = .36) alone.CONCLUSIONS: This study introduces a novel technique for counting pathologically distinct lesions using cross-sectional data and demonstrates its ability to recover obscured longitudinal information. The proposed count allows more accurate estimation of lesion size, which correlated more closely with disability scores than either lesion load or lesion count alone.CCcount based on the standard connected-components techniqueCGcriterion standard countCPcount based on the technique proposed in this studyCVcoefficient of variationEDSSExpanded Disability Status ScaleEDDSavgaverage of the EDSS scores over all visits for each subject in the National Institute of Neurological Disorders and Stroke longitudinal studyNAIMSNorth American Imaging in Multiple SclerosisOASISAutomated Statistical Inference for Segmentation ER -