4.7 Article

Prediction of acute coronary syndrome within 3 years using radiomics signature of pericoronary adipose tissue based on coronary computed tomography angiography

Journal

EUROPEAN RADIOLOGY
Volume 32, Issue 2, Pages 1256-1266

Publisher

SPRINGER
DOI: 10.1007/s00330-021-08109-z

Keywords

Computed tomography angiography; Adipose tissue; Inflammation; Acute coronary syndrome; Radiomics

Funding

  1. National Natural Science Foundation [82071920, 81901741, 81871435]
  2. Key Research & Development Plan of Liaoning Province [2020JH2/10300037]
  3. 345 Talent Project in Shengjing Hospital of China Medical University

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The study evaluated whether radiomics signature of PCAT based on CCTA could improve the prediction of future ACS within 3 years. Radiomics score outperformed plaque score in identifying patients with future ACS, while the improvement of integrated score was non-significant. The study suggests that CCTA-based radiomics signature of PCAT has the potential to predict the occurrence of subsequent ACS within 3 years.
Objectives To evaluate whether radiomics signature of pericoronary adipose tissue (PCAT) based on coronary computed tomography angiography (CCTA) could improve the prediction of future acute coronary syndrome (ACS) within 3 years. Methods We designed a retrospective case-control study that patients with ACS (n = 90) were well matched to patients with no cardiac events (n = 1496) during 3 years follow-up, then which were randomly divided into training and test datasets with a ratio of 3:1. A total of 107 radiomics features were extracted from PCAT surrounding lesions and 14 conventional plaque characteristics were analyzed. Radiomics score, plaque score, and integrated score were respectively calculated via a linear combination of the selected features, and their performance was evaluated with discrimination, calibration, and clinical application. Results Radiomics score achieved superior performance in identifying patients with future ACS within 3 years in both training and test datasets (AUC = 0.826, 0.811) compared with plaque score (AUC = 0.699, 0.640), with a significant difference of AUC between two scores in the training dataset (p = 0.009); while the improvement of integrated score discriminating capability (AUC = 0.838, 0.826) was non-significant. The calibration curves of three predictive models demonstrated a good fitness respectively (all p > 0.05). Decision curve analysis suggested that integrated score added more clinical benefit than plaque score. Stratified analysis revealed that the performance of three predictive models was not affected by tube voltage, CT version, different sites of hospital. Conclusion CCTA-based radiomics signature of PCAT could have the potential to predict the occurrence of subsequent ACS. Radiomics-based integrated score significantly outperformed plaque score in identifying future ACS within 3 years.

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