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Background Epicardial adipose tissue (EAT) is a visceral fat deposit within the pericardial sac which surrounds the heart myocardium and coronary arteries. EAT volume has been demonstrated to be strongly associated with the development and prognosis of cardiovascular diseases, but its measurement is subjective and challenging in practice. Purpose To develop a deep-learning approach for automated segmentation of EAT from routine CCTA scans, that could assist clinical interpretation of CCTA. Methods A deep-learning method using a 3D Residual-U-Net neural network architecture for 3D volumetric segmentation of CCTA data was created. The network was trained on a diverse sample of 1900 CCTAs, each manually segmented by a single expert, drawn from the UK sites of the Oxford Risk Factors And Non-invasive imaging (ORFAN) Study. Three iterations of feedback learning were used to fine tune the algorithm for the segmentation of the whole heart within the bounds of the pericardium. In each iteration, the machine analysed sets of 100–250 unannotated CCTAs unseen by the machine which were then corrected by experts. EAT volumes were calculated by automated thresholding of adipose tissue (−190HU through −30HU) from within the bound of the pericardial segment (Figure 1). The network was then applied to 817 unseen CCTAs from US sites of the ORFAN Study. These scans were also segmented for ground truth by two experts blind to all other data. Comparisons between machine vs expert total pericardial volume and EAT volume were made using Lin's concordance correlation coefficient (CCC). The algorithm was then applied externally in 1588 CCTAs from the SCOTHEART trial (UK), and the EAT volume was automatically calculated for each case. Cross-sectional associations between standardised EAT volumes and prevalent AF and CAD were performed. Results Within both the internal (UK ORFAN sites) and external (USA ORFAN sites) validation cohorts correlation between human and machine segmented total pericardium and EAT was excellent, with CCC of 0.97 for both volumes (external validation cohort shown in Figure 2A). Utilising SCOTHEART CCTAs with automatically segmented EAT volumes, a multivariable-adjusted logistic regression model accounting for risk factors of age, sex, BMI, hypertension, diabetes mellitus, valvular disease, and previous heart surgery found that EAT volumes were significantly associated with prevalent AF, with odds ratio (OR) per 1 SD increase of EAT volume of 1.20 (95% CI, 1.06 to 1.44; P=0.03). A similar model for prevalent CAD, adjusted for age, sex, BMI, hypertension, non-HDL cholesterol, diabetes mellitus, and coronary artery calcium score resulted in an OR per 1 SD increase of EAT volume of 1.26 (95% CI, 1.10 to 1.45; P=0.001) (Figure 2B). Conclusion Highly accurate, reproducible, and instantaneous EAT volume quantification is possible utilising deep-learning detection of the whole human heart within the pericardial sac. Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): British Heart FoundationNational Institute for Health Research - Oxford University Hospitals Biomedical Research Centre Figure 1Figure 2

AimsCoronary computed tomography angiography (CCTA) is a first-line modality in the investigation of suspected coronary artery disease (CAD). Mapping of perivascular fat attenuation index (FAI) on routine CCTA enables the non-invasive detection of coronary artery inflammation by quantifying spatial changes in perivascular fat composition. We now report the performance of a new medical device, CaRi-Heart®, which integrates standardized FAI mapping together with clinical risk factors and plaque metrics to provide individualized cardiovascular risk prediction.Methods and resultsThe study included 3912 consecutive patients undergoing CCTA as part of clinical care in the USA (n = 2040) and Europe (n = 1872). These cohorts were used to generate age-specific nomograms and percentile curves as reference maps for the standardized interpretation of FAI. The first output of CaRi-Heart® is the FAI-Score of each coronary artery, which provides a measure of coronary inflammation adjusted for technical, biological, and anatomical characteristics. FAI-Score is then incorporated into a risk prediction algorithm together with clinical risk factors and CCTA-derived coronary plaque metrics to generate the CaRi-Heart® Risk that predicts the likelihood of a fatal cardiac event at 8 years. CaRi-Heart® Risk was trained in the US population and its performance was validated externally in the European population. It improved risk discrimination over a clinical risk factor-based model [Δ(C-statistic) of 0.085, P = 0.01 in the US Cohort and 0.149, P < 0.001 in the European cohort] and had a consistent net clinical benefit on decision curve analysis above a baseline traditional risk factor-based model across the spectrum of cardiac risk.ConclusionMapping of perivascular FAI on CCTA enables the non-invasive detection of coronary artery inflammation by quantifying spatial changes in perivascular fat composition. We now report the performance of a new medical device, CaRi-Heart®, which allows standardized measurement of coronary inflammation by calculating the FAI-Score of each coronary artery. The CaRi-Heart® device provides a reliable prediction of the patient's absolute risk for a fatal cardiac event by incorporating traditional cardiovascular risk factors along with comprehensive CCTA coronary plaque and perivascular adipose tissue phenotyping. This integration advances the prognostic utility of CCTA for individual patients and paves the way for its use as a dual diagnostic and prognostic tool among patients referred for CCTA.

Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): UKRI, British Heart Foundation Background Inflammation in the coronaries induces macroscopic changes in perivascular adipose tissue composition, detectable by the pericoronary Fat Radiomic Profile (FRP) on coronary computed tomography angiography (CCTA). Purpose To assess the ability of FRP to stratify cardiac risk in patients with Coronary Artery Calcium (CAC) score below 100 following routine CCTA. Methods 1,575 participants from the CCTA arm of the SCOT-HEART trial (NCT01149590) eligible for image analysis were included. Pericoronary FRP mapping was performed in perivascular adipose tissue segmentations around the proximal sites of the right and left coronary arteries, as previously validated. We first tested the prognostic value of FRP in the sub-cohort of patients with CAC Results In total, 1,032 patients (53.9% female sex) were found with low CAC score (CAC Conclusion In individuals with low CAC scores the Fat Radiomic Profile biormarker significantly improves risk prediction for adverse clinical events beyond the current state-of-the-art. Non-invasive profiling of pericoronary adipose tissue using CCTA-derived FRP captures irreversible changes in perivascular adipose tissue composition associated with chronic vascular inflammation and atherosclerotic disease, and can supplement the traditional anatomical assessment of the coronary vasculature with a functional marker of disease activity.

Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): British Heart Foundation OnBehalf the CRISP-CT study investigators Introduction Non-invasive detection of coronary inflammation has the potential to improve targeting of cardiovascular prevention strategies. Coronary computed tomography angiography (CCTA)-based fat attenuation indexing (FAI) is a novel, validated measure of coronary inflammation that captures spatial changes in perivascular fat composition induced by coronary inflammation. However, to ensure accuracy, FAI values must be carefully standardized for technical, anatomical and biological factors before they are introduced for risk stratification in clinical practice. Purpose To validate the prognostic value of FAI-Score, the output of a new, artificial intelligence (AI)-powered algorithm, that harmonizes the FAI values of each coronary artery to generate a standardized metric of coronary inflammation for clinical use. Methods The study included 3912 individuals from the CRISP-CT study (2040 from the United States and 1872 from Europe) with a mean age of 55.7 (± standard deviation 13.7 years), 1608 (41.1%) of whom were women; all underwent clinically indicated CCTA. FAI-Score was measured at baseline in the proximal right coronary (RCA), left anterior descending (LAD) and left circumflex arteries (LCX) using the CaRi-Heart V1.1 platform (panel A). Percentile curves were created for the FAI-score of the RCA, LAD and LCX across different age and sex strata. Their prognostic value was further assessed in Cox models adjusted for hypertension, hyperlipidemia, diabetes mellitus, smoking, high-risk plaque features and the modified Duke prognostic coronary artery disease index. Results Over a median follow-up period of 5.6 years there were a total of 74 cardiac deaths. FAI-Score percentile curves were created to provide a population reference map for FAI-Score phenotyping of the three main coronary territories; LAD (panels B, C), RCA and LCX. There was a stepwise association between higher FAI-Score percentiles and the prospective risk of cardiac mortality across all three vessels analysed. When compared to individuals below the 50th percentile, those in the 90th percentile or higher had a 3.5-fold (LAD, panel D), 5.5-fold (RCA, panel E) and 2-fold (LCX, panel F) higher adjusted risk of cardiac mortality. Conclusion Coronary FAI-Score percentile curves provide a reference map for coronary inflammatory burden and enable standardized interpretation of perivascular FAI mapping on CCTA. Individuals in the top percentile curves have an increased cardiovascular risk independent of traditional risk factors and existing atherosclerotic changes. Abstract Figure.

Background Coronary computed tomography angiography (CCTA) provides useful information regarding cardiovascular risk assessment. Key aspects of coronary biology induce changes in perivascular adipose tissue composition, detectable by the pericoronary Fat Radiomic Profile (FRP) index. Purpose We assessed the ability of FRP to further stratify cardiac risk in patients with Coronary Artery Calcium (CAC) scoring below 100 following routine CCTA. Methods The study population consisted of 1,575 participants from the CCTA arm of the SCOT-HEART trial (NCT01149590) with images available and eligible for analysis. Pericoronary FRP mapping was performed in perivascular adipose tissue segmentations around the proximal sites of the right and left coronary arteries, as previously validated. The prognostic potential of FRP was initially tested in a sub-cohort, consisting of patients with Coronary Artery Calcium (CAC) score lower than 100. Further analysis was performed after sub-grouping based on the absence of high risk plaque (HRP) features and obstructive coronary artery disease (CAD). The association with future incidence of major adverse cardiac events (MACE: cardiac mortality or non-fatal myocardial infarction) or a composite endpoint of MACE ± late revascularization (MACE-ReVasc) was assessed using adjusted Cox regression models [adjusted for age, sex, systolic blood pressure (SBP), diabetes mellitus (DM), body mass index (BMI), smoking, CAD (≥50% stenosis), total cholesterol, high-density lipoprotein (HDL), and HRP features]. Results Two-thirds (66%) of the study population were at low-risk according to the CAC score (CAC Conclusion The Fat Radiomic Profile biormarker significantly improves risk prediction for adverse clinical events beyond the current state-of-the-art in individuals with low CAC scores. Non-invasive profiling of pericoronary adipose tissue using CCTA-derived FRP captures irreversible changes in perivascular adipose tissue composition associated with chronic vascular inflammation and atherosclerotic disease, and can improve risk stratification and clinical decision making in low-risk populations. Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): British Heart Foundation, National Institute of Health Research, Oxford Biomedical Research Centre