TY - JOUR
TI - Machine learning of native T1 mapping radiomics for classification of
hypertrophic cardiomyopathy phenotypes
AU - Antonopoulos, Alexios S.
AU - Boutsikou, Maria
AU - Simantiris, Spyridon
AU - and Angelopoulos, Andreas
AU - Lazaros, George
AU - Panagiotopoulos,
AU - Ioannis
AU - Oikonomou, Evangelos
AU - Kanoupaki, Mikela
AU - Tousoulis,
AU - Dimitris
AU - Mohiaddin, Raad H.
AU - Tsioufis, Konstantinos and
AU - Vlachopoulos, Charalambos
JO - Scientific Reports
PY - 2021
VL - 11
TODO - 1
SP - null
PB - NATURE PORTFOLIO
SN - 2045-2322
TODO - 10.1038/s41598-021-02971-z
TODO - null
TODO - 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(2) = 55.98, p
< 0.0001). After feature selection, internal validation and external
testing, a model of T1 radiomics had good diagnostic performance (AUC
0.753) for multinomial classification of disease phenotype (normal vs.
LVH vs. HCM vs. cardiac amyloid). A subset of six radiomic features
outperformed mean native T1 values for classification between myocardial
health vs. disease and HCM phenocopies (AUC of T1 vs. radiomics model,
for normal: 0.549 vs. 0.888; for LVH: 0.645 vs. 0.790; for HCM 0.541 vs.
0.638; and for cardiac amyloid 0.769 vs. 0.840). We show that myocardial
texture assessed by native T1 maps is linked to features of cardiac
disease. Myocardial radiomic phenotyping could enhance the diagnostic
yield of T1 mapping for myocardial disease detection and classification.
ER -