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Comparing Lesion Volume Dynamics Between Multiple Sclerosis and Neuromyelitis Optica Spectrum Disorder During Remission Using Machine-Learning Segmentation

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dc.contributor.authorHong, Shaun G.-
dc.contributor.authorKim, Ki Hoon-
dc.contributor.authorKang, You-Ri-
dc.contributor.authorHyun, Jae-Won-
dc.contributor.authorKim, Su-Hyun-
dc.contributor.authorKim, Ho Jin-
dc.date.accessioned2025-12-03T08:18:30Z-
dc.date.available2025-12-03T08:18:30Z-
dc.date.created2025-11-21-
dc.date.issued2025-09-
dc.identifier.issn1738-6586-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/209426-
dc.description.abstractBackground and Purpose Multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) are inflammatory demyelinating conditions of the central nervous system that have distinct pathological mechanisms. There is a paucity of studies comparing the accumulation of subclinical lesions between MS and NMOSD, especially during the clinical remission period. Recent advances in neuroimaging techniques, those particularly involving the use of machine learning (ML) methods for lesion segmentation, have provided new opportunities to quantitatively assess the volumes of brain lesions and how they change over time. In this study, we aimed to use ML-based lesion segmentation methods to measure differences in lesion volumes and their changes during the remission period between patients with MS and NMOSD. Methods This study included a retrospective cohort of 31 patients with MS and patients with 30 aquaporin-4-positive (AQP4(+)) NMOSD from the National Cancer Center registry. Serial 3D brain magnetic resonance imaging (MRI) scans obtained during the interattack period were analyzed using ML-based segmentation. MRI data preprocessing included alignment, distortion correction, and normalization, with lesion mapping and statistical analyses determining changes in lesion volumes. Results The MS patients exhibited significant increases in the median lesion volume (from 3,493 mm(3) to 4,430 mm(3), p<0.001), indicating ongoing subclinical activity without clinical relapses. In contrast, the NMOSD patients showed no significant change in the median lesion volume (from 640 mm3 to 930 mm(3), p=0.129), supporting an attack-dependent disease course. The lesion volume increased by 193 mm(3)/year in the MS group, compared with only 25 mm(3)/ year in the NMOSD group (p=0.017). Conclusions These findings highlight the distinct pathogenic processes of the two conditions and hence the need for specialized therapeutic and monitoring strategies for patients with MS and AQP4(+) NMOSD.-
dc.languageEnglish-
dc.publisherKorean Neurological Association-
dc.relation.isPartOfJOURNAL OF CLINICAL NEUROLOGY-
dc.relation.isPartOfJOURNAL OF CLINICAL NEUROLOGY-
dc.titleComparing Lesion Volume Dynamics Between Multiple Sclerosis and Neuromyelitis Optica Spectrum Disorder During Remission Using Machine-Learning Segmentation-
dc.typeArticle-
dc.contributor.googleauthorHong, Shaun G.-
dc.contributor.googleauthorKim, Ki Hoon-
dc.contributor.googleauthorKang, You-Ri-
dc.contributor.googleauthorHyun, Jae-Won-
dc.contributor.googleauthorKim, Su-Hyun-
dc.contributor.googleauthorKim, Ho Jin-
dc.identifier.doi10.3988/jcn.2025.0199-
dc.relation.journalcodeJ01327-
dc.identifier.eissn2005-5013-
dc.identifier.pmid40878307-
dc.subject.keywordmultiple sclerosis-
dc.subject.keywordsubclinical activity-
dc.subject.keywordneuromyelitis optica spectrum disorder-
dc.subject.keywordmachine learning-
dc.contributor.affiliatedAuthorKim, Ki Hoon-
dc.identifier.scopusid2-s2.0-105015872161-
dc.identifier.wosid001578754600010-
dc.citation.volume21-
dc.citation.number5-
dc.citation.startPage433-
dc.citation.endPage438-
dc.identifier.bibliographicCitationJOURNAL OF CLINICAL NEUROLOGY, Vol.21(5) : 433-438, 2025-09-
dc.identifier.rimsid90156-
dc.type.rimsART-
dc.description.journalClass1-
dc.description.journalClass1-
dc.subject.keywordAuthormultiple sclerosis-
dc.subject.keywordAuthorsubclinical activity-
dc.subject.keywordAuthorneuromyelitis optica spectrum disorder-
dc.subject.keywordAuthormachine learning-
dc.subject.keywordPlusMRI-
dc.subject.keywordPlusNORMALIZATION-
dc.subject.keywordPlusREGISTRATION-
dc.type.docTypeArticle-
dc.identifier.kciidART003232540-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.description.journalRegisteredClasskci-
dc.relation.journalWebOfScienceCategoryClinical Neurology-
dc.relation.journalResearchAreaNeurosciences & Neurology-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Neurology (신경과학교실) > 1. Journal Papers

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