Machine learning of native T1 mapping radiomics for classification of hypertrophic cardiomyopathy phenotypes.

ABSTRACT: 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 component

SUBMITTER: Antonopoulos AS 

PROVIDER: S-EPMC8654857 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature