Another extra-coronary application of coronary computed tomography angiography: quantification of pericardial and intra-thoracic adipose tissue. Can it define the cardiovascular risk?

2008 ◽  
Vol 2 (5) ◽  
pp. 296-297
Author(s):  
Eric Alexanderson ◽  
Aloha Meave
Author(s):  
Alexios S Antonopoulos ◽  
Andreas Angelopoulos ◽  
Konstantinos Tsioufis ◽  
Charalambos Antoniades ◽  
Dimitris Tousoulis

Abstract Current cardiovascular risk stratification by use of clinical risk score systems or plasma biomarkers is good but less than satisfactory in identifying patients at residual risk for coronary events. Recent clinical evidence puts now further emphasis on the role of coronary anatomy assessment by coronary computed tomography angiography (CCTA) for the management of patients with stable ischaemic heart disease. Available computed tomography (CT) technology allows the quantification of plaque burden, identification of high-risk plaques, or the functional assessment of coronary lesions for ischaemia detection and revascularization for refractory angina symptoms. The current CT armamentum is also further enhanced by perivascular Fat Attenuation Index (FAI), a non-invasive metric of coronary inflammation, which allows for the first time the direct quantification of the residual vascular inflammatory burden. Machine learning and radiomic features’ extraction and spectral CT for tissue characterization are also expected to maximize the diagnostic and prognostic yield of CCTA. The combination of anatomical, functional, and biological information on coronary circulation by CCTA offers a unique toolkit for the risk stratification of patients, and patient selection for targeted aggressive prevention strategies. We hereby provide a review of the current state-of-the art in the field and discuss how integrating the full capacities of CCTA into clinical care pathways opens new opportunities for the tailored management of coronary artery disease.


Author(s):  
Tom Finck ◽  
Antonija Stojanovic ◽  
Albrecht Will ◽  
Eva Hendrich ◽  
Stefan Martinoff ◽  
...  

Abstract Aims To investigate the incremental prognostic value of morphological plaque features beyond clinical risk and coronary stenosis levels. Although associated with the degree of coronary stenosis, most cardiac events occur on the basis of ruptured non-obstructive plaques and consecutive vessel thrombosis. As such, identification of vulnerable plaques is paramount for cardiovascular risk prediction and treatment decisions. Methods and results A total of 1615 patients with suspected but not previously diagnosed coronary artery disease (CAD) were examined by coronary computed tomography angiography and morphological plaque features were assessed. Mean follow-up was 10.5 (interquartile range 9.2–11.4) years. Cox proportional hazards analysis was used for the composite endpoint of cardiac death and non-fatal myocardial infarction. The study endpoint was reached in 51 patients (36 cardiac deaths, 15 non-fatal myocardial infarctions). In addition to quantitative parameters (presence of any calcified/non-calcified plaque or elevated plaque load), morphologic plaque features such as a spotty or gross calcification pattern and napkin-ring sign (NRS) were predictive for events. However, only spotty calcified plaques and NRS could confer additive prognostic value beyond clinical risk and coronary stenosis level. In a stepwise approach, endpoint prediction beyond clinical risk (Morise score) could be improved by inclusion of CAD severity (χ2 of 27.5, P < 0.001) and further discrimination for spotty calcified plaques (χ2 of 3.89, P = 0.049). Conclusion Improved cardiovascular risk prediction beyond clinical risk and coronary stenosis levels can be made by discriminating for the presence of spotty calcified plaques. Thus, an intensified prophylactic anti-atherosclerotic treatment appears to be warranted in patients with coronary plaques that show spotty calcifications.


Author(s):  
Jin Shang ◽  
Shaowei Ma ◽  
Yan Guo ◽  
Linlin Yang ◽  
Qian Zhang ◽  
...  

Abstract Objectives To evaluate whether radiomics signature of pericoronary adipose tissue (PCAT) based on coronary computed tomography angiography (CCTA) could improve the prediction of future acute coronary syndrome (ACS) within 3 years. Methods We designed a retrospective case-control study that patients with ACS (n = 90) were well matched to patients with no cardiac events (n = 1496) during 3 years follow-up, then which were randomly divided into training and test datasets with a ratio of 3:1. A total of 107 radiomics features were extracted from PCAT surrounding lesions and 14 conventional plaque characteristics were analyzed. Radiomics score, plaque score, and integrated score were respectively calculated via a linear combination of the selected features, and their performance was evaluated with discrimination, calibration, and clinical application. Results Radiomics score achieved superior performance in identifying patients with future ACS within 3 years in both training and test datasets (AUC = 0.826, 0.811) compared with plaque score (AUC = 0.699, 0.640), with a significant difference of AUC between two scores in the training dataset (p = 0.009); while the improvement of integrated score discriminating capability (AUC = 0.838, 0.826) was non-significant. The calibration curves of three predictive models demonstrated a good fitness respectively (all p > 0.05). Decision curve analysis suggested that integrated score added more clinical benefit than plaque score. Stratified analysis revealed that the performance of three predictive models was not affected by tube voltage, CT version, different sites of hospital. Conclusion CCTA-based radiomics signature of PCAT could have the potential to predict the occurrence of subsequent ACS. Radiomics-based integrated score significantly outperformed plaque score in identifying future ACS within 3 years. Key Points • Plaque score based on conventional plaque characteristics had certain limitations in the prediction of ACS. • Radiomics signature of PCAT surrounding plaques could have the potential to improve the predictive ability of subsequent ACS. • Radiomics-based integrated score significantly outperformed plaque score in the identification of future ACS within 3 years.


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