Identification of patients with acute myocardial infarction based on coronary CT angiography: the value of pericoronary adipose tissue radiomics

作者：Si, Nuo; Shi, Ke; Li, Na; Dong, Xiaolin; Zhu, Chentao; Guo, Yan; Hu, Jiesi; Cui, Jingjing; Yang, Fan; Zhang, Tong^*

来源：European Radiology, 2022, 32(10): 6868-6877.

DOI：10.1007/s00330-022-08812-5

• Computed tomography angiography
• Myocardial infarction
• Inflammation
• Adipose tissue
• Radiomics

Objective To determine whether radiomics analysis of pericoronary adipose tissue (PCAT) captured by coronary computed tomography angiography (CCTA) could discriminate acute myocardial infarction (MI) from unstable angina (UA). Methods In a single-center retrospective case-control study, patients with acute MI (n = 105) were matched to patients with UA (n = 105) and all patients were randomly divided into training and validation cohorts with a ratio of 7:3. Fat attenuation index (FAI) and PCAT radiomics features selected by Max-Relevance and Min-Redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) around the proximal three major epicardial coronary vessels (LAD [left anterior descending artery], LCx [left circumflex artery], and RCA [right coronary artery]) were used to build logistic regression models. Finally, a FAI model, three radiomics models of PCAT (LAD, LCx, and RCA), and a combined model that used the scores of these independent models were constructed. The performance of the models was evaluated by identification, calibration, and clinical application. Results In training and validation cohorts, compared with the FAI model (AUC = 0.53, 0.50), the combined model achieved superior performance (AUC = 0.97, 0.95) while there was a significant difference of AUC between two models (p < 0.05). The calibration curves of the combined model demonstrated the smallest Brier score loss. Decision curve analysis suggested that the combined model provided higher clinical benefit than the FAI model. Conclusions The CCTA-based radiomics phenotype of PCAT outperforms the FAI model in discriminating acute MI from UA. The combination of PCAT radiomics and FAI could further enhance the performance of acute MI identification.

• 单位
  1; 哈尔滨医科大学