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Updated: Oct 3, 2025
Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans
Published on: August 28, 2018
Identification of pathology-confirmed vulnerable atherosclerotic lesions by coronary computed tomography angiography
Xiang-Nan Li1, Wei-Hua Yin1, Yang Sun2
1Department of Radiology, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, #167 Bei-Li-Shi Street, Xi-Cheng District, Beijing, 100037, China.
Insights
Radiomics-based machine learning (ML) models significantly improve the identification of vulnerable coronary artery lesions compared to traditional methods. These advanced models show superior diagnostic ability in assessing coronary plaque vulnerability using coronary computed tomographic angiography (CCTA).
Area of Science:
- Cardiology
- Radiology
- Artificial Intelligence
Background:
- Coronary artery disease (CAD) poses a significant risk, with vulnerable plaques being key indicators of potential cardiac events.
- Accurate identification of vulnerable coronary lesions is crucial for effective patient management and treatment strategies.
- Conventional diagnostic methods using coronary computed tomographic angiography (CCTA) have limitations in precisely assessing plaque vulnerability.
Purpose of the Study:
- To evaluate the diagnostic performance of radiomics-based machine learning (ML) models in identifying vulnerable coronary artery lesions.
- To compare the efficacy of radiomics-ML models against conventional CCTA-based diagnostic approaches for plaque vulnerability.
- To determine if advanced ML techniques can enhance the detection of high-risk coronary plaques.
Main Methods:
- A retrospective analysis of 36 heart transplant recipients with CAD was conducted.
- 1184 radiomic features were extracted from CCTA images of 350 plaques, with eight ML models developed.
- Model performance was validated on an independent set of 196 plaques and compared to conventional CCTA feature analysis using AUC.
Main Results:
- Radiomics-ML models demonstrated excellent diagnostic accuracy in cross-validation (AUC: 0.900).
- In the validation group, radiomics-ML models achieved a higher AUC (0.782) compared to conventional CCTA features (AUC: 0.656).
- The radiomics-based approach significantly improved the diagnostic ability, particularly in sensitivity, for detecting vulnerable coronary lesions.
Conclusions:
- Radiomics-based ML models offer superior diagnostic ability for assessing coronary plaque vulnerability compared to conventional CCTA features.
- CCTA holds significant potential for diagnosing vulnerable coronary artery lesions when augmented by radiomics and ML.
- These findings suggest that radiomics-ML models can substantially improve the accuracy of vulnerability diagnosis in CAD patients.
Objectives:
To explore whether radiomics-based machine learning (ML) models could outperform conventional diagnostic methods at identifying vulnerable lesions on coronary computed tomographic angiography (CCTA).
Methods:
In this retrospective study, 36 heart transplant recipients with coronary heart disease (CAD) and end-stage heart failure were included. Pathological cross-section samples of 350 plaques were collected and coregistered to patients' preoperative CCTA images. A total of 1184 radiomic features were extracted from CCTA images. Through feature selection and stratified fivefold cross-validation, we derived eight radiomics-based ML models for lesion vulnerability prediction. An independent set of 196 plaques from another 8 CAD patients who underwent heart transplants was collected to validate radiomics-based ML models' diagnostic accuracy against conventional CCTA feature-based diagnosis (presence of at least 2 high-risk plaque features). The performance of the prediction models was assessed by the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (CI).
Results:
The training group used to develop radiomics-based ML models contained 200/350 (57.1%) vulnerable plaques and the external validation group was composed of 67.3% (132/196) vulnerable plaques. The radiomics-based ML model based on eight radiomic features showed excellent cross-validation diagnostic accuracy (AUC: 0.900 ± 0.033). In the validation group, diagnosis based on conventional CCTA features demonstrated moderate performance (AUC: 0.656 [95% CI: 0.593 -0.718]), while the radiomics-based ML model showed higher diagnostic ability (0.782 [95% CI: 0.710 -0.846]).
Conclusions:
Radiomics-based ML models showed better diagnostic ability than the conventional CCTA features at assessing coronary plaque vulnerability.
Key Points:
• CCTA has great potential in the diagnosis of vulnerable coronary artery lesions. • Radiomics model built through CCTA could discriminate coronary vulnerable lesions in good diagnostic ability. • Radiomics model could improve the ability of vulnerability diagnosis against traditional CCTA method, sensitivity especially.
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