1. Myocardial tissue phenotyping by radiomic features of native T1 maps and machine learning enhances disease detection and classification
- Author
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E Oikonomou, Dimitrios Tousoulis, Andreas Angelopoulos, Kostas Tsioufis, M Kanoupaki, M Boutsikou, Alexis Antonopoulos, Raad H. Mohiaddin, Spyridon Simantiris, Georgios Lazaros, and C. Vlachopoulos
- Subjects
Disease detection ,Myocardial tissue ,business.industry ,Medicine ,Computational biology ,Cardiology and Cardiovascular Medicine ,business - Abstract
Background Myocardial T1 mapping by cardiac magnetic resonance (CMR) is a useful technique to detect diffuse myocardial fibrosis, but a major limitation of T1 mapping is the significant overlap in native T1 values between health and disease. Purpose We explored whether radiomic features from T1 maps could enhance the diagnostic value of T1 mapping in distinguishing health from disease and classifying cardiac disease phenotypes. Methods In a total of 149 patients (n=30 with no evidence of heart disease, n=30 with LVH of various etiologies, n=61 with hypertrophic cardiomyopathy (HCM) and n=28 with cardiac amyloidosis) undergoing a CMR scan for various indications were included in this study. In addition to measuring native myocardial T1 values from T1 maps, we extracted a total of 843 radiomic features of myocardial texture and explored their value in disease classification. Results We first demonstrated that T1 mapping images are a rich source of extractable, quantifiable data. The first three principal components of the T1 radiomics were significantly and 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 Conclusions We have shown that specific imaging patterns in myocardial native T1 maps are linked to features of cardiac disease and we have provided for the first-time evidence that radiomic phenotyping can be used to enhance the diagnostic yield of native T1 mapping for myocardial disease detection and classification. Funding Acknowledgement Type of funding sources: None.
- Published
- 2021
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