Machine learning of native T1 mapping radiomics for classification of hypertrophic cardiomyopathy phenotypes.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
08 12 2021
08 12 2021
Historique:
received:
30
05
2021
accepted:
08
11
2021
entrez:
9
12
2021
pubmed:
10
12
2021
medline:
28
1
2022
Statut:
epublish
Résumé
We explored whether radiomic features from T1 maps by cardiac magnetic resonance (CMR) could enhance the diagnostic value of T1 mapping in distinguishing health from disease and classifying cardiac disease phenotypes. A total of 149 patients (n = 30 with no heart disease, n = 30 with LVH, n = 61 with hypertrophic cardiomyopathy (HCM) and n = 28 with cardiac amyloidosis) undergoing a CMR scan were included in this study. We extracted a total of 850 radiomic features and explored their value in disease classification. We applied principal component analysis and unsupervised clustering in exploratory analysis, and then machine learning for feature selection of the best radiomic features that maximized the diagnostic value for cardiac disease classification. The first three principal components of the T1 radiomics were distinctively correlated with cardiac disease type. Unsupervised hierarchical clustering of the population by myocardial T1 radiomics was significantly associated with myocardial disease type (chi
Identifiants
pubmed: 34880319
doi: 10.1038/s41598-021-02971-z
pii: 10.1038/s41598-021-02971-z
pmc: PMC8654857
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
23596Informations de copyright
© 2021. The Author(s).
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