دورية أكاديمية
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Predicting cerebrovascular age and its clinical relevance: Modeling using 3D morphological features of brain vessels.

التفاصيل البيبلوغرافية
العنوان: Predicting cerebrovascular age and its clinical relevance: Modeling using 3D morphological features of brain vessels.
المؤلفون: Cho HH; Department of Electronics Engineering, Incheon National University, Incheon, South Korea., Kim J; Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea., Na I; Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea., Song HN; Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea., Choi JU; Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.; Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, South Korea., Baek IY; Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea., Lee JE; Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea., Chung JW; Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea., Kim CK; Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, South Korea., Oh K; Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, South Korea., Bang OY; Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea., Kim GM; Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea., Seo WK; Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.; Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, South Korea., Park H; Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea.; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea.
المصدر: Heliyon [Heliyon] 2024 Jun 04; Vol. 10 (11), pp. e32375. Date of Electronic Publication: 2024 Jun 04 (Print Publication: 2024).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Elsevier Ltd Country of Publication: England NLM ID: 101672560 Publication Model: eCollection Cited Medium: Print ISSN: 2405-8440 (Print) Linking ISSN: 24058440 NLM ISO Abbreviation: Heliyon Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: London : Elsevier Ltd, [2015]-
مستخلص: Aging manifests as many phenotypes, among which age-related changes in brain vessels are important, but underexplored. Thus, in the present study, we constructed a model to predict age using cerebrovascular morphological features, further assessing their clinical relevance using a novel pipeline. Age prediction models were first developed using data from a normal cohort (n = 1181), after which their relevance was tested in two stroke cohorts (n = 564 and n = 455). Our novel pipeline adapted an existing framework to compute generic vessel features for brain vessels, resulting in 126 morphological features. We further built various machine learning models to predict age using only clinical factors, only brain vessel features, and a combination of both. We further assessed deviation from healthy aging using the age gap and explored its clinical relevance by correlating the predicted age and age gap with various risk factors. The models constructed using only brain vessel features and those combining clinical factors with vessel features were better predictors of age than the clinical factor-only model (r = 0.37, 0.48, and 0.26, respectively). Predicted age was associated with many known clinical factors, and the associations were stronger for the age gap in the normal cohort. The age gap was also associated with important factors in the pooled cohort atherosclerotic cardiovascular disease risk score and white matter hyperintensity measurements. Cerebrovascular age, computed using the morphological features of brain vessels, could serve as a potential individualized marker for the early detection of various cerebrovascular diseases.
Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(© 2024 The Authors.)
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فهرسة مساهمة: Keywords: Age prediction; Cardiovascular disease; Cerebrovascular morphology; Machine learning; Personalized marker; Risk factors
تواريخ الأحداث: Date Created: 20240701 Latest Revision: 20240702
رمز التحديث: 20240702
مُعرف محوري في PubMed: PMC11214500
DOI: 10.1016/j.heliyon.2024.e32375
PMID: 38947444
قاعدة البيانات: MEDLINE
الوصف
تدمد:2405-8440
DOI:10.1016/j.heliyon.2024.e32375