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Published 2023 | Version v1
Journal article

Predicting coronary plaque progression with conventional plaque parameters and radiomics features derived from coronary CT angiography

  • 1. Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, 210029, Nanjing, Jiangsu (China)
  • 2. Department of Radiology, Chaoyang Hospital, Capital Medical University, 100020, Beijing (China)
  • 3. Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No.218 Jixi Road, 230022, Hefei, Anhui (China)
  • 4. Siemens Healthineers, 201318, Shanghai (China)
  • 5. Department of Radiology, The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, 42 Baiziting, 210009, Nanjing, Jiangsu (China)

Description

To determine the value of combining conventional plaque parameters and radiomics features derived from coronary computed tomography angiography (CCTA) for predicting coronary plaque progression. Clinical data and CCTA images of 400 patients who underwent at least two CCTA examinations between January 2009 and August 2020 were analyzed retrospectively. Diameter stenosis, total plaque volume and burden, calcified plaque volume and burden, noncalcified plaque volume and burden (NCPB), pericoronary fat attenuation index (FAI), and other conventional plaque parameters were recorded. The patients were assigned to a training cohort (n = 280) and a validation cohort (n = 120) in a 7:3 ratio using a stratified random splitting method. The area under the receiver operating characteristics curve (AUC) was used to evaluate the predictive abilities of conventional parameters (model 1), radiomics features (model 2), and their combination (model 3). FAI and NCPB were identified as independent risk factors for coronary plaque progression in the training cohort. Both model 2 (training cohort AUC: 0.814, p < 0.001; validation cohort AUC: 0.729, p = 0.288) and model 3 (training cohort AUC: 0.824, p < 0.001; validation cohort AUC: 0.758, p = 0.042) had better diagnostic performances in predicting plaque progression than model 1 (training cohort AUC: 0.646; validation cohort AUC: 0.654). Moreover, model 3 was slightly higher than model 2, although not statistically significant. The combination of conventional coronary plaque parameters and CCTA-derived radiomics features had a better ability to predict plaque progression than conventional parameters alone. The conventional coronary plaque characteristics such as noncalcified plaque burden, pericoronary fat attenuation index, and radiomics features derived from CCTA can identify plaques prone to progression, which is helpful for further clinical decision-making of coronary artery disease.

Additional details

Identifiers

Publishing Information

Journal Title
European Radiology (Internet)
Journal Volume
33
Journal Issue
12
Journal Page Range
p. 8513-8520
ISSN
1432-1084
CODEN
EURAE3