RT Journal Article
SR Electronic
T1 An Automated Statistical Technique for Counting Distinct Multiple Sclerosis Lesions
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
DO 10.3174/ajnr.A5556
A1 J.D. Dworkin
A1 K.A. Linn
A1 I. Oguz
A1 G.M. Fleishman
A1 R. Bakshi
A1 G. Nair
A1 P.A. Calabresi
A1 R.G. Henry
A1 J. Oh
A1 N. Papinutto
A1 D. Pelletier
A1 W. Rooney
A1 W. Stern
A1 N.L. Sicotte
A1 D.S. Reich
A1 R.T. Shinohara
A1 the North American Imaging in Multiple Sclerosis Cooperative
YR 2018
UL http://www.ajnr.org/content/early/2018/02/22/ajnr.A5556.abstract
AB 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