Identification of pathology-confirmed vulnerable atherosclerotic lesions by coronary computed tomography angiography using radiomics analysis.
Eur Radiol
; 32(6): 4003-4013, 2022 Jun.
Article
em En
| MEDLINE
| ID: mdl-35171348
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.Palavras-chave
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Doença da Artéria Coronariana
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Aterosclerose
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Placa Aterosclerótica
Tipo de estudo:
Diagnostic_studies
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Observational_studies
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Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article