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Novel Estimation of Penumbra Zone Based on Infarct Growth Using Machine Learning Techniques in Acute Ischemic Stroke

DC FieldValueLanguage
dc.contributor.author남효석-
dc.date.accessioned2021-05-21T17:06:24Z-
dc.date.available2021-05-21T17:06:24Z-
dc.date.issued2020-06-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/182707-
dc.description.abstractWhile the penumbra zone is traditionally assessed based on perfusion-diffusion mismatch, it can be assessed based on machine learning (ML) prediction of infarct growth. The purpose of this work was to develop and validate an ML method for the prediction of infarct growth distribution and volume, in cases of successful (SR) and unsuccessful recanalization (UR). Pre-treatment perfusion-weighted, diffusion-weighted imaging (DWI) data, and final infarct lesions annotated from day-7 DWI from patients with middle cerebral artery occlusion were utilized to develop and validate two ML models for prediction of tissue fate. SR and UR models were developed from data in patients with modified treatment in cerebral infarction (mTICI) scores of 2b-3 and 0-2a, respectively. When compared to manual infarct annotation, ML-based infarct volume predictions resulted in an intraclass correlation coefficient (ICC) of 0.73 (95% CI = 0.31-0.91, p < 0.01) for UR, and an ICC of 0.87 (95% CI = 0.73-0.94, p < 0.001) for SR. Favorable outcomes for mismatch presence and absence in SR were 50% and 36%, respectively, while they were 61%, 56%, and 25%, respectively, for the low, intermediate, and high infarct growth groups. The presented method can offer novel and alternative insights into selecting patients for recanalization therapy and predicting functional outcome.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherMDPI AG-
dc.relation.isPartOfJOURNAL OF CLINICAL MEDICINE-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleNovel Estimation of Penumbra Zone Based on Infarct Growth Using Machine Learning Techniques in Acute Ischemic Stroke-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Neurology (신경과학교실)-
dc.contributor.googleauthorYoon-Chul Kim-
dc.contributor.googleauthorHyung Jun Kim-
dc.contributor.googleauthorJong-Won Chung-
dc.contributor.googleauthorIn Gyeong Kim-
dc.contributor.googleauthorMin Jung Seong-
dc.contributor.googleauthorKeon Ha Kim-
dc.contributor.googleauthorPyoung Jeon-
dc.contributor.googleauthorHyo Suk Nam-
dc.contributor.googleauthorWoo-Keun Seo-
dc.contributor.googleauthorGyeong-Moon Kim-
dc.contributor.googleauthorOh Young Bang-
dc.identifier.doi10.3390/jcm9061977-
dc.contributor.localIdA01273-
dc.relation.journalcodeJ03556-
dc.identifier.eissn2077-0383-
dc.identifier.pmid32599812-
dc.subject.keywordcerebral infarction-
dc.subject.keywordischemia-
dc.subject.keywordmachine learning-
dc.subject.keywordstroke-
dc.contributor.alternativeNameNam, Hyo Suk-
dc.contributor.affiliatedAuthor남효석-
dc.citation.volume9-
dc.citation.number6-
dc.citation.startPage1977-
dc.identifier.bibliographicCitationJOURNAL OF CLINICAL MEDICINE, Vol.9(6) : 1977, 2020-06-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Neurology (신경과학교실) > 1. Journal Papers

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