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Detection of Coronary Artery Disease using Epicardial Adipose Tissue Radiomics in Non-contrast Computed Tomography
Session:
Comunicações Orais - Sessão 17 -Síndromes Coronárias Crónicas
Speaker:
Fábio Sousa Nunes
Congress:
CPC 2023
Topic:
E. Coronary Artery Disease, Acute Coronary Syndromes, Acute Cardiac Care
Theme:
12. Coronary Artery Disease (Chronic)
Subtheme:
12.8 Coronary Artery Disease - Other
Session Type:
Comunicações Orais
FP Number:
---
Authors:
Fábio Sousa Nunes; Carolina Santos; Wilson Ferreira; Mónica Carvalho; João Pedrosa; Miguel Coimbra; Ricardo Ladeiras Lopes; Rita Faria; Nuno Ferreira; Luís Vouga; Jennifer Mancio; Ricardo Fontes Carvalho
Abstract
<p><strong>Introduction</strong>: Dysfunctional epicardial adipose tissue (EAT) is an active player in the pathophysiology of atherosclerosis. EAT can be quantified noninvasively by computed tomography (CT) and its volume and attenuation have been investigated as imaging biomarkers of coronary artery disease (CAD). Radiomic analysis allows thorough phenotyping of adipose tissue which has the potential of capturing the underlying tissue biology. The objective was to characterize the CTradiomic profile of EAT associated with coronary atherosclerosis and to derive the EAT radioproteomic signature of CAD.<br /> <strong>Methods</strong>: We extracted radiomic features from the EAT in non-contrast CT images of 192 patients from the EPICHEART study (NCT03280433) to build a machine learning model to discriminate patients with CAD (i.e., >50% stenosis in invasive angiography) from patients without CAD. Among the 1037 extracted radiomic features, we performed features selection to identify the best performing features for CAD classification and build a radiomic signature of CAD. Subsequently, a multivariate XGBoost model was trained using the entire dataset in a 6-fold stratified cross-validation. Furthermore, in a nested-case-control group of 21 patients with EAT proteomics, a spearman correlation was performed to determine the association between the EAT radiomics and proteomics of CAD.<br /> <strong>Results</strong>: CAD patients showed accumulation of EAT with higher median gray level values and heterogeneous texture in non-contrast CT images. This phenotype was correlated with upregulation of pro-calcifying (Annexin-A2), pro-inflammatory (IGHM) and adipocyte fatty acid transport (FABP4) proteins. EAT radiomic signature of CAD added to calcium score (CCS) improved the performance of CCS alone and provided an area under the curve of 0.81 (95% CI: 0.69-0.93), sensitivity of 0.83, negative predictive value of 0.87, F1 score of 0.77 and accuracy score of 0.79 (Figure 1).<br /> <strong>Conclusions</strong>: In non-contrast CT images, radiomic profiling of EAT detected significant EAT gray-level and texture differences between patients with and without CAD. This EAT radiomic phenotype was correlated with upregulation of inflammatory, calcifying and fatty acid import proteins and when added to CCS improved the detection of CAD, supporting CT radiomics interpretability and its potential diagnostic applications.<br /> </p>
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