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. 2021 Feb 25;3(1):e200512. doi: 10.1148/ryct.2021200512

Figure 9:

Coronary CT angiography–based radiomics and machine learning (ML) for identifying patients with myocardial infarction. A, Performance of ML models for patients with myocardial infarction. A model integrating clinical data, pericoronary adipose tissue (PCAT) attenuation, and PCAT radiomic features (area under the receiver operating characteristic curve [AUC], 0.87) outperformed a model with clinical data and PCAT attenuation (AUC, 0.77; P = .001) and clinical data alone (AUC, 0.87 vs 0.76; P < .001). B, Textural features of PCAT at coronary CT angiography were highest ranked in the final radiomics-based model. HDL-C = high-density lipoprotein cholesterol, hs-CRP = high-sensitivity C-reactive protein, LDL-C = low-density lipoprotein cholesterol. (Reprinted, with permission, from reference 56.)

Coronary CT angiography–based radiomics and machine learning (ML) for identifying patients with myocardial infarction. A, Performance of ML models for patients with myocardial infarction. A model integrating clinical data, pericoronary adipose tissue (PCAT) attenuation, and PCAT radiomic features (area under the receiver operating characteristic curve [AUC], 0.87) outperformed a model with clinical data and PCAT attenuation (AUC, 0.77; P = .001) and clinical data alone (AUC, 0.87 vs 0.76; P < .001). B, Textural features of PCAT at coronary CT angiography were highest ranked in the final radiomics-based model. HDL-C = high-density lipoprotein cholesterol, hs-CRP = high-sensitivity C-reactive protein, LDL-C = low-density lipoprotein cholesterol. (Reprinted, with permission, from reference 56.)