Identification of patients with unstable angina based on coronary CT angiography: the application of pericoronary adipose tissue radiomics
ObjectiveTo explore whether radiomics analysis of pericoronary adipose tissue (PCAT) captured by coronary computed tomography angiography (CCTA) could discriminate unstable angina (UA) from stable angina (SA).MethodsIn this single-center retrospective case-control study, coronary CT images and clini...
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Cardiovascular Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2024.1462566/full |
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| author | Weisheng Zhan Yixin Li Hui Luo Jiang He Jiao Long Yang Xu Ying Yang |
| author_facet | Weisheng Zhan Yixin Li Hui Luo Jiang He Jiao Long Yang Xu Ying Yang |
| author_sort | Weisheng Zhan |
| collection | DOAJ |
| description | ObjectiveTo explore whether radiomics analysis of pericoronary adipose tissue (PCAT) captured by coronary computed tomography angiography (CCTA) could discriminate unstable angina (UA) from stable angina (SA).MethodsIn this single-center retrospective case-control study, coronary CT images and clinical data from 240 angina patients were collected and analyzed. Patients with unstable angina (n = 120) were well-matched with those having stable angina (n = 120). All patients were randomly divided into training (70%) and testing (30%) datasets. Automatic segmentation was performed on the pericoronary adipose tissue surrounding the proximal segments of the left anterior descending artery (LAD), left circumflex coronary artery (LCX), and right coronary artery (RCA). Corresponding radiomic features were extracted and selected, and the fat attenuation index (FAI) for these three vessels was quantified. Machine learning techniques were employed to construct the FAI and radiomic models. Multivariate logistic regression analysis was used to identify the most relevant clinical features, which were then combined with radiomic features to create clinical and integrated models. The performance of different models was compared in terms of area under the curve (AUC), calibration, clinical utility, and sensitivity.ResultsIn both training and validation cohorts, the integrated model (AUC = 0.87, 0.74) demonstrated superior discriminatory ability compared to the FAI model (AUC = 0.68, 0.51), clinical feature model (AUC = 0.84, 0.67), and radiomic model (AUC = 0.85, 0.73). The nomogram derived from the combined radiomic and clinical features exhibited excellent performance in diagnosing and predicting unstable angina. Calibration curves showed good fit for all four machine learning models. Decision curve analysis indicated that the integrated model provided better clinical benefit than the other three models.ConclusionsCCTA-based radiomics signature of PCAT is better than the FAI model in identifying unstable angina and stable angina. The integrated model constructed by combining radiomics and clinical features could further improve the diagnosis and differentiation ability of unstable angina. |
| format | Article |
| id | doaj-art-15548c9fa57142e8980cd65d4d6347c4 |
| institution | Kabale University |
| issn | 2297-055X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj-art-15548c9fa57142e8980cd65d4d6347c42024-12-12T06:18:28ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2024-12-011110.3389/fcvm.2024.14625661462566Identification of patients with unstable angina based on coronary CT angiography: the application of pericoronary adipose tissue radiomicsWeisheng Zhan0Yixin Li1Hui Luo2Jiang He3Jiao Long4Yang Xu5Ying Yang6Cardiovascular Medicine Department, Affiliated Hospital of North Sichuan Medical College, Nanchong, ChinaDigestive System Department, Affiliated Hospital of North Sichuan Medical College, Nanchong, ChinaThoracic Surgery Department, Nan Chong Center Hospital, Nanchong, ChinaCardiovascular Medicine Department, Affiliated Hospital of North Sichuan Medical College, Nanchong, ChinaCardiovascular Medicine Department, Affiliated Hospital of North Sichuan Medical College, Nanchong, ChinaDermatological Department, Nan Chong Center Hospital, Nanchong, ChinaCardiovascular Medicine Department, Affiliated Hospital of North Sichuan Medical College, Nanchong, ChinaObjectiveTo explore whether radiomics analysis of pericoronary adipose tissue (PCAT) captured by coronary computed tomography angiography (CCTA) could discriminate unstable angina (UA) from stable angina (SA).MethodsIn this single-center retrospective case-control study, coronary CT images and clinical data from 240 angina patients were collected and analyzed. Patients with unstable angina (n = 120) were well-matched with those having stable angina (n = 120). All patients were randomly divided into training (70%) and testing (30%) datasets. Automatic segmentation was performed on the pericoronary adipose tissue surrounding the proximal segments of the left anterior descending artery (LAD), left circumflex coronary artery (LCX), and right coronary artery (RCA). Corresponding radiomic features were extracted and selected, and the fat attenuation index (FAI) for these three vessels was quantified. Machine learning techniques were employed to construct the FAI and radiomic models. Multivariate logistic regression analysis was used to identify the most relevant clinical features, which were then combined with radiomic features to create clinical and integrated models. The performance of different models was compared in terms of area under the curve (AUC), calibration, clinical utility, and sensitivity.ResultsIn both training and validation cohorts, the integrated model (AUC = 0.87, 0.74) demonstrated superior discriminatory ability compared to the FAI model (AUC = 0.68, 0.51), clinical feature model (AUC = 0.84, 0.67), and radiomic model (AUC = 0.85, 0.73). The nomogram derived from the combined radiomic and clinical features exhibited excellent performance in diagnosing and predicting unstable angina. Calibration curves showed good fit for all four machine learning models. Decision curve analysis indicated that the integrated model provided better clinical benefit than the other three models.ConclusionsCCTA-based radiomics signature of PCAT is better than the FAI model in identifying unstable angina and stable angina. The integrated model constructed by combining radiomics and clinical features could further improve the diagnosis and differentiation ability of unstable angina.https://www.frontiersin.org/articles/10.3389/fcvm.2024.1462566/fullpericoronary adipose tissueradiomicscoronary computed tomography angiographycoronary heart diseasemachine learning |
| spellingShingle | Weisheng Zhan Yixin Li Hui Luo Jiang He Jiao Long Yang Xu Ying Yang Identification of patients with unstable angina based on coronary CT angiography: the application of pericoronary adipose tissue radiomics Frontiers in Cardiovascular Medicine pericoronary adipose tissue radiomics coronary computed tomography angiography coronary heart disease machine learning |
| title | Identification of patients with unstable angina based on coronary CT angiography: the application of pericoronary adipose tissue radiomics |
| title_full | Identification of patients with unstable angina based on coronary CT angiography: the application of pericoronary adipose tissue radiomics |
| title_fullStr | Identification of patients with unstable angina based on coronary CT angiography: the application of pericoronary adipose tissue radiomics |
| title_full_unstemmed | Identification of patients with unstable angina based on coronary CT angiography: the application of pericoronary adipose tissue radiomics |
| title_short | Identification of patients with unstable angina based on coronary CT angiography: the application of pericoronary adipose tissue radiomics |
| title_sort | identification of patients with unstable angina based on coronary ct angiography the application of pericoronary adipose tissue radiomics |
| topic | pericoronary adipose tissue radiomics coronary computed tomography angiography coronary heart disease machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2024.1462566/full |
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