An Automated Statistical Technique for Counting Distinct Multiple Sclerosis Lesions

AJNR. American Journal of Neuroradiology
J D DworkinNorth American Imaging in Multiple Sclerosis Cooperative

Abstract

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. 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. 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....Continue Reading

References

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Citations

May 20, 2020·Scientific Reports·Aaron CarassIpek Oguz
Sep 15, 2018·AJNR. American Journal of Neuroradiology·J D DworkinR T Shinohara
Mar 22, 2021·Neurología : publicación oficial de la Sociedad Española de Neurología·S LlufriuÀ Rovira
Sep 29, 2019·Multiple Sclerosis and Related Disorders·Patrick VermerschPierre Labauge
Jun 16, 2021·Journal of Neuroimaging : Official Journal of the American Society of Neuroimaging·R Davis HolmesCornelia Laule
Aug 17, 2021·Neurological Research·Sibel Guler
Nov 20, 2021·Acta Neurologica Scandinavica·Lara ChaabanSalem Hannoun

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