We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Identification of pathology-confirmed vulnerable atherosclerotic lesions by coronary computed tomography angiography using radiomics analysis.
- Authors
Li, Xiang-nan; Yin, Wei-hua; Sun, Yang; Kang, Han; Luo, Jie; Chen, Kuan; Hou, Zhi-hui; Gao, Yang; Ren, Xin-shuang; Yu, Yi-tong; An, Yun-qiang; Zhang, Yan; Wang, Hong-yue; Lu, Bin
- Abstract
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.
- Publication
European Radiology, 2022, Vol 32, Issue 6, p4003
- ISSN
0938-7994
- Publication type
Article
- DOI
10.1007/s00330-021-08518-0