Mitochondria are the source of cellular energy production and are present in different types of cells. However, their function is especially important for the heart due to the high demands in energy which is achieved through oxidative phosphorylation. Mitochondria form large networks which regulate metabolism and the optimal function is achieved through the balance between mitochondrial fusion and mitochondrial fission. Moreover, mitochondrial function is upon quality control via the process of mitophagy which removes the damaged organelles. Mitochondrial dysfunction is associated with the development of numerous cardiac diseases such as atherosclerosis, ischemia-reperfusion (I/R) injury, hypertension, diabetes, cardiac hypertrophy and heart failure (HF), due to the uncontrolled production of reactive oxygen species (ROS). Therefore, early control of mitochondrial dysfunction is a crucial step in the therapy of cardiac diseases. A number of anti-oxidant molecules and medications have been used but the results are inconsistent among the studies. Eventually, the aim of future research is to design molecules which selectively target mitochondrial dysfunction and restore the capacity of cellular anti-oxidant enzymes.

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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 (chi2 = 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.

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