Noninvasive Atherosclerotic Phenotyping: The Next Frontier into Understanding the Pathobiology of Coronary Artery Disease.

PURPOSE OF REVIEW: Despite recent advances, coronary artery disease remains one of the leading causes of mortality worldwide. Noninvasive imaging allows atherosclerotic phenotyping by measurement of plaque burden, morphology, activity and inflammation, which has the potential to refine patient risk stratification and guide personalized therapy. This review describes the current and emerging roles of advanced noninvasive cardiovascular imaging methods for the assessment of coronary artery disease. RECENT FINDINGS: Cardiac computed tomography enables comprehensive, noninvasive imaging of the coronary vasculature, and is used to assess luminal stenoses, coronary calcifications, and distinct adverse plaque characteristics, helping to identify patients prone to future events. Novel software tools, implementing artificial intelligence solutions, can automatically quantify and characterize atherosclerotic plaque from standard computed tomography datasets. These quantitative imaging biomarkers have been shown to improve patient risk stratification beyond clinical risk scores and current clinical interpretation of cardiac computed tomography. In addition, noninvasive molecular imaging in higher risk patients can be used to assess plaque activity and plaque thrombosis. Noninvasive imaging allows unique insight into the burden, morphology and activity of atherosclerotic coronary plaques. Such phenotyping of atherosclerosis can potentially improve individual patient risk prediction, and in the near future has the potential for clinical implementation.

Deep Learning-Enabled Quantification of 99mTc-Pyrophosphate SPECT/CT for Cardiac Amyloidosis.

Transthyretin cardiac amyloidosis (ATTR CA) is increasingly recognized as a cause of heart failure in older patients, with 99mTc-pyrophosphate imaging frequently used to establish the diagnosis. Visual interpretation of SPECT images is the gold standard for interpretation but is inherently subjective. Manual quantitation of SPECT myocardial 99mTc-pyrophosphate activity is time-consuming and not performed clinically. We evaluated a deep learning approach for fully automated volumetric quantitation of 99mTc-pyrophosphate using segmentation of coregistered anatomic structures from CT attenuation maps. Methods: Patients who underwent SPECT/CT 99mTc-pyrophosphate imaging for suspected ATTR CA were included. Diagnosis of ATTR CA was determined using standard criteria. Cardiac chambers and myocardium were segmented from CT attenuation maps using a foundational deep learning model and then applied to attenuation-corrected SPECT images to quantify radiotracer activity. We evaluated the diagnostic accuracy of target-to-background ratio (TBR), cardiac pyrophosphate activity (CPA), and volume of involvement (VOI) using the area under the receiver operating characteristic curve (AUC). We then evaluated associations with the composite outcome of cardiovascular death or heart failure hospitalization. Results: In total, 299 patients were included (median age, 76 y), with ATTR CA diagnosed in 83 (27.8%) patients. CPA (AUC, 0.989; 95% CI, 0.974-1.00) and VOI (AUC, 0.988; 95% CI, 0.973-1.00) had the highest prediction performance for ATTR CA. The next highest AUC was for TBR (AUC, 0.979; 95% CI, 0.964-0.995). The AUC for CPA was significantly higher than that for heart-to-contralateral ratio (AUC, 0.975; 95% CI, 0.952-0.998; P = 0.046). Twenty-three patients with ATTR CA experienced cardiovascular death or heart failure hospitalization. All methods for establishing TBR, CPA, and VOI were associated with an increased risk of events after adjustment for age, with hazard ratios ranging from 1.41 to 1.84 per SD increase. Conclusion: Deep learning segmentation of coregistered CT attenuation maps is not affected by the pattern of radiotracer uptake and allows for fully automatic quantification of hot-spot SPECT imaging such as 99mTc-pyrophosphate. This approach can be used to accurately identify patients with ATTR CA and may play a role in risk prediction.

Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction.

Background: While low-dose computed tomography scans are traditionally used for attenuation correction in hybrid myocardial perfusion imaging (MPI), they also contain additional anatomic and pathologic information not utilized in clinical assessment. We seek to uncover the full potential of these scans utilizing a holistic artificial intelligence (AI)-driven image framework for image assessment. Methods: Patients with SPECT/CT MPI from 4 REFINE SPECT registry sites were studied. A multi-structure model segmented 33 structures and quantified 15 radiomics features for each on CT attenuation correction (CTAC) scans. Coronary artery calcium and epicardial adipose tissue scores were obtained from separate deep-learning models. Normal standard quantitative MPI features were derived by clinical software. Extreme Gradient Boosting derived all-cause mortality risk scores from SPECT, CT, stress test, and clinical features utilizing a 10-fold cross-validation regimen to separate training from testing data. The performance of the models for the prediction of all-cause mortality was evaluated using area under the receiver-operating characteristic curves (AUCs). Results: Of 10,480 patients, 5,745 (54.8%) were male, and median age was 65 (interquartile range [IQR] 57-73) years. During the median follow-up of 2.9 years (1.6-4.0), 651 (6.2%) patients died. The AUC for mortality prediction of the model (combining CTAC, MPI, and clinical data) was 0.80 (95% confidence interval [0.74-0.87]), which was higher than that of an AI CTAC model (0.78 [0.71-0.85]), and AI hybrid model (0.79 [0.72-0.86]) incorporating CTAC and MPI data (p<0.001 for all). Conclusion: In patients with normal perfusion, the comprehensive model (0.76 [0.65-0.86]) had significantly better performance than the AI CTAC (0.72 [0.61-0.83]) and AI hybrid (0.73 [0.62-0.84]) models (p<0.001, for all).CTAC significantly enhances AI risk stratification with MPI SPECT/CT beyond its primary role - attenuation correction. A comprehensive multimodality approach can significantly improve mortality prediction compared to MPI information alone in patients undergoing cardiac SPECT/CT.

Computed Tomography Angiography Characteristics of Thin-Cap Fibroatheroma in Patients With Diabetes.

BACKGROUND: It was recently reported that thin-cap fibroatheroma (TCFA) detected by optical coherence tomography was an independent predictor of future cardiac events in patients with diabetes. However, the clinical usefulness of this finding is limited by the invasive nature of optical coherence tomography. Computed tomography angiography (CTA) characteristics of TCFA have not been systematically studied. The aim of this study was to investigate CTA characteristics of TCFA in patients with diabetes. METHODS AND RESULTS: Patients with diabetes who underwent preintervention CTA and optical coherence tomography were included. Qualitative and quantitative analyses were performed for plaques on CTA. TCFA was assessed by optical coherence tomography. Among 366 plaques in 145 patients with diabetes, 111 plaques had TCFA. The prevalence of positive remodeling (74.8% versus 50.6%, P<0.001), low attenuation plaque (63.1% versus 33.7%, P<0.001), napkin-ring sign (32.4% versus 11.0%, P<0.001), and spotty calcification (55.0% versus 34.9%, P<0.001) was significantly higher in TCFA than in non-TCFA. Low-density noncalcified plaque volume (25.4 versus 15.7 mm3, P<0.001) and remodeling index (1.30 versus 1.20, P=0.002) were higher in TCFA than in non-TCFA. The presence of napkin-ring sign, spotty calcification, high low-density noncalcified plaque volume, and high remodeling index were independent predictors of TCFA. When all 4 predictors were present, the probability of TCFA increased to 82.4%. CONCLUSIONS: The combined qualitative and quantitative plaque analysis of CTA may be helpful in identifying TCFA in patients with diabetes. REGISTRATION INFORMATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT04523194.

Application of Quantitative Assessment of Coronary Atherosclerosis by Coronary Computed Tomographic Angiography.

Coronary computed tomography angiography (CCTA) has emerged as a pivotal tool for diagnosing and risk-stratifying patients with suspected coronary artery disease (CAD). Recent advancements in image analysis and artificial intelligence (AI) techniques have enabled the comprehensive quantitative analysis of coronary atherosclerosis. Fully quantitative assessments of coronary stenosis and lumen attenuation have improved the accuracy of assessing stenosis severity and predicting hemodynamically significant lesions. In addition to stenosis evaluation, quantitative plaque analysis plays a crucial role in predicting and monitoring CAD progression. Studies have demonstrated that the quantitative assessment of plaque subtypes based on CT attenuation provides a nuanced understanding of plaque characteristics and their association with cardiovascular events. Quantitative analysis of serial CCTA scans offers a unique perspective on the impact of medical therapies on plaque modification. However, challenges such as time-intensive analyses and variability in software platforms still need to be addressed for broader clinical implementation. The paradigm of CCTA has shifted towards comprehensive quantitative plaque analysis facilitated by technological advancements. As these methods continue to evolve, their integration into routine clinical practice has the potential to enhance risk assessment and guide individualized patient management. This article reviews the evolving landscape of quantitative plaque analysis in CCTA and explores its applications and limitations.

Coronary Atherosclerotic Plaque Activity and Risk of Myocardial Infarction.

BACKGROUND: Total coronary atherosclerotic plaque activity across the entire coronary arterial tree is associated with patient-level clinical outcomes. OBJECTIVES: We aimed to investigate whether vessel-level coronary atherosclerotic plaque activity is associated with vessel-level myocardial infarction. METHODS: In this secondary analysis of an international multicenter study of patients with recent myocardial infarction and multivessel coronary artery disease, we assessed vessel-level coronary atherosclerotic plaque activity using coronary 18F-sodium fluoride positron emission tomography to identify vessel-level myocardial infarction. RESULTS: Increased 18F-sodium fluoride uptake was found in 679 of 2,094 coronary arteries and 414 of 691 patients. Myocardial infarction occurred in 24 (4%) vessels with increased coronary atherosclerotic plaque activity and in 25 (2%) vessels without increased coronary atherosclerotic plaque activity (HR: 2.08; 95% CI: 1.16-3.72; P = 0.013). This association was not demonstrable in those treated with coronary revascularization (HR: 1.02; 95% CI: 0.47-2.25) but was notable in untreated vessels (HR: 3.86; 95% CI: 1.63-9.10; Pinteraction = 0.024). Increased coronary atherosclerotic plaque activity in multiple coronary arteries was associated with heightened patient-level risk of cardiac death or myocardial infarction (HR: 2.43; 95% CI: 1.37-4.30; P = 0.002) as well as first (HR: 2.19; 95% CI: 1.18-4.06; P = 0.013) and total (HR: 2.50; 95% CI: 1.42-4.39; P = 0.002) myocardial infarctions. CONCLUSIONS: In patients with recent myocardial infarction and multivessel coronary artery disease, coronary atherosclerotic plaque activity prognosticates individual coronary arteries and patients at risk for myocardial infarction.

Epicardial fat volume is associated with primary coronary slow-flow phenomenon in patients with severe aortic stenosis undergoing transcatheter valve implantation.

BACKGROUND: Primary coronary slow flow (CSF) is defined as delayed opacification of the distal epicardial vasculature during coronary angiography in the absence of relevant coronary artery stenoses. Microvascular disease is thought to be the underlying cause of this pathology. Epicardial fat tissue (EFT) is an active endocrine organ directly surrounding the coronary arteries that provides pro-inflammatory factors to the adjacent tissue by paracrine and vasocrine mechanisms. The aim of the present study was to investigate a potential association between EFT and primary CSF and whether EFT can predict the presence of primary CSF. METHODS: Between 2016 and 2017, n = 88 patients with high-grade aortic stenosis who were planned for transcatheter aortic valve implantation (TAVI) were included in this retrospective study. EFT volume was measured by pre-TAVI computed tomography (CT) using dedicated software. The presence of primary CSF was defined based on the TIMI frame count from the pre-TAVI coronary angiograms. RESULTS: Thirty-nine of 88 TAVI patients had CSF (44.3%). EFT volume was markedly higher in patients with CSF (142 ml [IQR 107-180] vs. 113 ml [IQR 89-147]; p = 0.009) and was strongly associated with the presence of CSF (OR 1.012 [95%CI 1.002-1.021]; p = 0.014). After adjustment, EFT volume was still an independent predictor of CSF (OR 1.016 [95%CI 1.004-1.026]; p = 0.009). CONCLUSION: Primary CSF was independently associated with increased EFT volume. Further studies are needed to validate this finding and elucidate whether a causal relationship exists.

Layered plaque is associated with high levels of vascular inflammation and vulnerability in patients with stable angina pectoris.

Layered plaque, a signature of previous plaque destabilization and healing, is a known predictor for rapid plaque progression; however, the mechanism of which is unknown. The aim of the current study was to compare the level of vascular inflammation and plaque vulnerability in layered plaques to investigate possible mechanisms of rapid plaque progression. This is a retrospective, observational, single-center cohort study. Patients who underwent both coronary computed tomography angiography (CTA) and optical coherence tomography (OCT) for stable angina pectoris (SAP) were selected. Plaques were defined as any tissue (noncalcified, calcified, or mixed) within or adjacent to the lumen. Perivascular inflammation was measured by pericoronary adipose tissue (PCAT) attenuation at the plaque levels on CTA. Features of plaque vulnerability were assessed by OCT. Layered plaques were defined as plaques presenting one or more layers of different optical densities and a clear demarcation from underlying components on OCT. A total of 475 plaques from 195 patients who presented with SAP were included. Layered plaques (n = 241), compared with non-layered plaques (n = 234), had a higher level of vascular inflammation (-71.47 ± 10.74 HU vs. -73.69 ± 10.91 HU, P = 0.026) as well as a higher prevalence of the OCT features of plaque vulnerability, including lipid-rich plaque (83.8% vs. 66.7%, P < 0.001), thin-cap fibroatheroma (26.1% vs. 17.5%, P = 0.026), microvessels (61.8% vs. 34.6%, P < 0.001), and cholesterol crystals (38.6% vs. 25.6%, P = 0.003). Layered plaque was associated with a higher level of vascular inflammation and a higher prevalence of plaque vulnerability, which might play an important role in rapid plaque progression.Clinical trial registration: https://classic.clinicaltrials.gov/ct2/show/NCT04523194 .

Coronary plaque phenotype associated with positive remodeling.

BACKGROUND: Positive remodeling is an integral part of the vascular adaptation process during the development of atherosclerosis, which can be detected by coronary computed tomography angiography (CTA). METHODS: A total of 426 patients who underwent both coronary CTA and optical coherence tomography (OCT) were included. Four machine learning (ML) models, gradient boosting machine (GBM), random forest (RF), deep learning (DL), and support vector machine (SVM), were employed to detect specific plaque features. A total of 15 plaque features assessed by OCT were analyzed. The variable importance ranking was used to identify the features most closely associated with positive remodeling. RESULTS: In the variable importance ranking, lipid index and maximal calcification arc were consistently ranked high across all four ML models. Lipid index and maximal calcification arc were correlated with positive remodeling, showing pronounced influence at the lower range and diminishing influence at the higher range. Patients with more plaques with positive remodeling throughout their entire coronary trees had higher low-density lipoprotein cholesterol levels and were associated with a higher incidence of cardiovascular events during 5-year follow-up (Hazard ratio 2.10 [1.26-3.48], P â€‹= â€‹0.004). CONCLUSION: Greater lipid accumulation and less calcium burden were important features associated with positive remodeling in the coronary arteries. The number of coronary plaques with positive remodeling was associated with a higher incidence of cardiovascular events.

Pericardial fat volume is related to endothelial-mediated coronary blood flow in women with suspected coronary microvascular dysfunction. A report from the Women's Ischemia Syndrome Evaluation-Coronary Vascular Dysfunction (WISE-CVD) study.

Background: Coronary microvascular dysfunction is prevalent in women with signs and symptoms of ischemia but no obstructive coronary artery disease (CAD) and is associated with an adverse prognosis. Elevated pericardial fat volume predicts adverse cardiac events, but mechanistic pathways of the association are not well understood. Methods: 118 women enrolled in the NHLBI-sponsored Women's Ischemia Syndrome Evaluation-Coronary Vascular Dysfunction study with suspected coronary microvascular dysfunction but no obstructive CAD underwent adenosine stress 1.5 T cardiovascular magnetic resonance imaging (CMR) imaging and invasive coronary reactivity testing. Semi-quantitative myocardial perfusion reserve index (MPR) index was derived from perfusion images. Pericardial fat volume was measured by manually contouring the cardiac margins and adjacent adipose tissue on a single trans-axial HASTE slice at the level of the left main coronary artery origin and indexed to body surface-area. Simple standard deviation analysis obtained for continuous variables and frequency (percent) for categorical variables. The relationships between pericardial fat volume and coronary reactivity testing parameters were examined by correlation and multivariable regression analyses. Results: Women with suspected coronary microvascular dysfunction had a mean age of 55 ± 10 years, body mass index (BMI) of 28 ± 7 kg/m2, 44 % had a history of smoking, 63 % hypertension, 8 % diabetes, and 20 % dyslipidemia. CMR imaging-derived pericardial fat volume and coronary blood flow response to intracoronary acetylcholine (Δ CBF) were negatively correlated (r = -0.32, p = 0.0013). After adjustment for age, number of risk factors, high-density lipoprotein (HDL), and cold pressor diameter response, pericardial fat volume remained a significant predictor of Δ coronary blood flow (p = 0.04). There was no association with other coronary reactivity testing measures or CMRI derived MPR index. Conclusions: Among women with suspected coronary microvascular dysfunction but no obstructive CAD, pericardial fat volume appears to be related in a hypothesized adverse direction to coronary microvascular endothelial function. These results support further work confirming and extending these results to investigate pericardial fat volume as mechanistic pathway and potential treatment target for coronary microvascular dysfunction-related adverse events.Trial registration: clinicaltrials.govNCT00832702.

Interaction Between Risk Factors, Coronary Calcium, and CCTA Plaque Characteristics in Patients Age 18-45.

AIMS: The atherosclerotic profile and advanced plaque subtype burden in symptomatic patients ≤45 years old have not been established. This study aimed to assess the prevalence and predictors of coronary artery calcium (CAC), plaque subtypes, and plaque burden by coronary computed tomography angiography (CCTA) in symptomatic young patients. METHODS AND RESULTS: We included 907 symptomatic young patients (18-45 years) from Montefiore undergoing CCTA for chest pain evaluation. Prevalence and predictors of CAC, plaque subtypes, and burden were evaluated using semi-automated software. In the overall population (55% female and 44% Hispanic), 89% had CAC = 0. The likelihood of CAC or any plaque by CCTA increased with >3 risk factors (RF, OR 7.13 [2.14-23.7] and OR 10.26 [3.36-31.2], respectively). Any plaque by CCTA was present in 137 (15%); the strongest independent predictors were age ≥35 years (OR 3.62 [2.05-6.41]) and family history of premature CAD (FHx) (OR 2.76 [1.67-4.58]). Stenosis ≥50% was rare (1.8%), with 31% of those having CAC = 0. Significant non-calcified (NCP, 37.2%) and low-attenuation (LAP, 4.24%) plaque burdens were seen, even in those with non-obstructive stenosis. Among patients with CAC = 0, 5% had plaque, and the only predictor of exclusively non-calcified plaque was FHx (OR 2.29 [1.08-4.86]). CONCLUSIONS: In symptomatic young patients undergoing CCTA, the prevalence of CAC or any coronary atherosclerosis was not negligible, and the likelihood increased with RF burden. The presence of coronary stenosis ≥50% was rare and most often accompanied by CAC > 0 but there was a significant burden of NCP and LAP even within the non-obstructive group.

Self-reported exercise activity influences the relationship between coronary computed tomography angiographic finding and mortality.

AIM: Recent studies suggest that the application of exercise activity questionnaires, including the use of a single-item exercise question, can be additive to the prognostic efficacy of imaging findings. This study aims to evaluate the prognostic efficacy of exercise activity in patients undergoing coronary computed tomography angiography (CCTA). METHODS AND RESULTS: We assessed 9772 patients who underwent CCTA at a single center between 2007 and 2020. Patients were divided into 4 groups of physical activity as no exercise (n â€‹= â€‹1643, 17%), mild exercise (n â€‹= â€‹3156, 32%), moderate exercise (n â€‹= â€‹3542, 36%), and high exercise (n â€‹= â€‹1431,15%), based on a single-item self-reported questionnaire. Coronary stenosis was categorized as no (0%), non-obstructive (1-49%), borderline (50-69%), and obstructive (≥70%). During a median follow-up of 4.64 (IQR 1.53-7.89) years, 490 (7.6%) died. There was a stepwise inverse relationship between exercise activity and mortality (p â€‹< â€‹0.001). Compared with the high activity group, the no activity group had a 3-fold higher mortality risk (HR: 3.3, 95%CI (1.94-5.63), p â€‹< â€‹0.001) after adjustment for age, clinical risk factors, symptoms, and statin use. For any level of CCTA stenosis, mortality rates were inversely associated with the degree of patients' exercise activity. The risk of all-cause mortality was similar among the patients with obstructive stenosis with high exercise versus those with no coronary stenosis but no exercise activity (p â€‹= â€‹0.912). CONCLUSION: Physical activity as assessed by a single-item self-reported questionnaire is a strong stepwise inverse predictor of mortality risk among patients undergoing CCTA.

Predicting mortality from AI cardiac volumes mass and coronary calcium on chest computed tomography.

Chest computed tomography is one of the most common diagnostic tests, with 15 million scans performed annually in the United States. Coronary calcium can be visualized on these scans, but other measures of cardiac risk such as atrial and ventricular volumes have classically required administration of contrast. Here we show that a fully automated pipeline, incorporating two artificial intelligence models, automatically quantifies coronary calcium, left atrial volume, left ventricular mass, and other cardiac chamber volumes in 29,687 patients from three cohorts. The model processes chamber volumes and coronary artery calcium with an end-to-end time of ~18 s, while failing to segment only 0.1% of cases. Coronary calcium, left atrial volume, and left ventricular mass index are independently associated with all-cause and cardiovascular mortality and significantly improve risk classification compared to identification of abnormalities by a radiologist. This automated approach can be integrated into clinical workflows to improve identification of abnormalities and risk stratification, allowing physicians to improve clinical decision-making.

Thoracic aortic microcalcification activity in combined positron emission tomography and magnetic resonance imaging.

INTRODUCTION: Non-invasive detection of pathological changes in thoracic aortic disease remains an unmet clinical need particularly for patients with congenital heart disease. Positron emission tomography combined with magnetic resonance imaging (PET-MRI) could provide a valuable low-radiation method of aortic surveillance in high-risk groups. Quantification of aortic microcalcification activity using sodium [18F]fluoride holds promise in the assessment of thoracic aortopathies. We sought to evaluate aortic sodium [18F]fluoride uptake in PET-MRI using three methods of attenuation correction compared to positron emission tomography computed tomography (PET-CT) in patients with bicuspid aortic valve, METHODS: Thirty asymptomatic patients under surveillance for bicuspid aortic valve disease underwent sodium [18F]fluoride PET-CT and PET-MRI of the ascending thoracic aorta during a single visit. PET-MRI data were reconstructed using three iterations of attenuation correction (Dixon, radial gradient recalled echo with two [RadialVIBE-2] or four [RadialVIBE-4] tissue segmentation). Images were qualitatively and quantitatively analysed for aortic sodium [18F]fluoride uptake on PET-CT and PET-MRI. RESULTS: Aortic sodium [18F]fluoride uptake on PET-MRI was visually comparable with PET-CT using each reconstruction and total aortic standardised uptake values on PET-CT strongly correlated with each PET-MRI attenuation correction method (Dixon R = 0.70; RadialVIBE-2 R = 0.63; RadialVIBE-4 R = 0.64; p < 0.001 for all). Breathing related artefact between soft tissue and lung were detected using Dixon and RadialVIBE-4 but not RadialVIBE-2 reconstructions, with the presence of this artefact adjacent to the atria leading to variations in blood pool activity estimates. Consequently, quantitative agreements between radiotracer activity on PET-CT and PET-MRI were most consistent with RadialVIBE-2. CONCLUSION: Ascending aortic microcalcification analysis in PET-MRI is feasible with comparable findings to PET-CT. RadialVIBE-2 tissue attenuation correction correlates best with the reference standard of PET-CT and is less susceptible to artefact. There remain challenges in segmenting tissue types in PET-MRI reconstructions, and improved attenuation correction methods are required.

AI-Defined Cardiac Anatomy Improves Risk Stratification of Hybrid Perfusion Imaging.

BACKGROUND: Computed tomography attenuation correction (CTAC) improves perfusion quantification of hybrid myocardial perfusion imaging by correcting for attenuation artifacts. Artificial intelligence (AI) can automatically measure coronary artery calcium (CAC) from CTAC to improve risk prediction but could potentially derive additional anatomic features. OBJECTIVES: The authors evaluated AI-based derivation of cardiac anatomy from CTAC and assessed its added prognostic utility. METHODS: The authors considered consecutive patients without known coronary artery disease who underwent single-photon emission computed tomography/computed tomography (CT) myocardial perfusion imaging at 3 separate centers. Previously validated AI models were used to segment CAC and cardiac structures (left atrium, left ventricle, right atrium, right ventricular volume, and left ventricular [LV] mass) from CTAC. They evaluated associations with major adverse cardiovascular events (MACEs), which included death, myocardial infarction, unstable angina, or revascularization. RESULTS: In total, 7,613 patients were included with a median age of 64 years. During a median follow-up of 2.4 years (IQR: 1.3-3.4 years), MACEs occurred in 1,045 (13.7%) patients. Fully automated AI processing took an average of 6.2 ± 0.2 seconds for CAC and 15.8 ± 3.2 seconds for cardiac volumes and LV mass. Patients in the highest quartile of LV mass and left atrium, LV, right atrium, and right ventricular volume were at significantly increased risk of MACEs compared to patients in the lowest quartile, with HR ranging from 1.46 to 3.31. The addition of all CT-based volumes and CT-based LV mass improved the continuous net reclassification index by 23.1%. CONCLUSIONS: AI can automatically derive LV mass and cardiac chamber volumes from CT attenuation imaging, significantly improving cardiovascular risk assessment for hybrid perfusion imaging.

Association of epicardial adipose tissue with early structural and functional cardiac changes in Type 2 diabetes.

BACKGROUND: Dysregulated epicardial adipose tissue (EAT) may contribute to the development of heart failure in Type 2 diabetes (T2D). This study aimed to evaluate the associations between EAT volume and composition with imaging markers of subclinical cardiac dysfunction in people with T2D and no prevalent cardiovascular disease. METHODS: Prospective case-control study enrolling participants with and without T2D and no known cardiovascular disease. Two hundred and fifteen people with T2D (median age 63 years, 60 % male) and thirty-nine non-diabetics (median age 59 years, 62 % male) were included. Using computed tomography (CT), total EAT volume and mean CT attenuation, as well as, low attenuation (Hounsfield unit range -190 to -90) EAT volume were quantified by a deep learning method and volumes indexed to body surface area. Associations with cardiac magnetic resonance-derived left ventricular (LV) volumes and strain indices were assessed using linear regression. RESULTS: T2D participants had higher LV mass/volume ratio (median 0.89 g/mL [0.82-0.99] vs 0.79 g/mL [0.75-0.89]) and lower global longitudinal strain (GLS; 16.1 ± 2.3 % vs 17.2 ± 2.2 %). Total indexed EAT volume correlated inversely with mean CT attenuation. Low attenuation indexed EAT volume was 2-fold higher (18.8 cm3/m2 vs. 9.4 cm3/m2, p < 0.001) in T2D and independently associated with LV mass/volume ratio (ß = 0.002, p = 0.01) and GLS (ß = -0.03, p = 0.03). CONCLUSIONS: Higher EAT volumes seen in T2D are associated with a lower mean CT attenuation. Low attenuation indexed EAT volume is independently, but only weakly, associated with markers of subclinical cardiac dysfunction in T2D.

CT pericoronary adipose tissue density predicts coronary allograft vasculopathy and adverse clinical outcomes after cardiac transplantation.

AIMS: To assess pericoronary adipose tissue (PCAT) density on Coronary Computed Tomography Angiography (CCTA) as a marker of inflammatory disease activity in coronary allograft vasculopathy (CAV). METHODS AND RESULTS: PCAT density, lesion volumes, and total vessel volume-to-myocardial mass ratio (V/M) were retrospectively measured in 126 CCTAs from 94 heart transplant patients (mean age 49 [SD 14.5] years, 40% female) who underwent imaging between 2010 to 2021; age and sex-matched controls; and patients with atherosclerosis. PCAT density was higher in transplant patients with CAV (n = 40; -73.0 HU [SD 9.3]) than without CAV (n = 86; -77.9 HU [SD 8.2]), and controls (n = 12; -86.2 HU [SD 5.4]), p < 0.01 for both. Unlike patients with atherosclerotic coronary artery disease (n = 32), CAV lesions were predominantly non-calcified, comprised of mostly fibrous or fibrofatty tissue. V/M was lower in patients with CAV than without (32.4 mm3/g [SD 9.7] vs. 41.4 mm3/g [SD 12.3], p < 0.0001). PCAT density and V/M improved the ability to predict CAV from AUC 0.75 to 0.85 when added to donor age and donor hypertension status (p < 0.0001). PCAT density above -66 HU was associated with a greater incidence of all-cause mortality (OR 18.0 [95%CI 3.25-99.6], p < 0.01) and the composite endpoint of death, CAV progression, acute rejection, and coronary revascularization (OR 7.47 [95%CI 1.8-31.6], p = 0.01) over 5.3 (SD 2.1) years. CONCLUSIONS: Heart transplant patients with CAV have higher PCAT density and lower V/M than those without. Increased PCAT density is associated with adverse clinical outcomes. These CCTA metrics could be useful for diagnosis and monitoring of CAV severity.

Reproducibility of artificial intelligence-enabled plaque measurements between systolic and diastolic phases from coronary computed tomography angiography.

OBJECTIVES: Current coronary CT angiography (CTA) guidelines suggest both end-systolic and mid-diastolic phases of the cardiac cycle can be used for CTA image acquisition. However, whether differences in the phase of the cardiac cycle influence coronary plaque measurements is not known. We aim to explore the potential impact of cardiac phases on quantitative plaque assessment. METHODS: We enrolled 39 consecutive patients (23 male, age 66.2 ± 11.5 years) who underwent CTA with dual-source CT with visually evident coronary atherosclerosis and with good image quality. End-systolic and mid- to late-diastolic phase images were reconstructed from the same CTA scan. Quantitative plaque and stenosis were analyzed in both systolic and diastolic images using artificial intelligence (AI)-enabled plaque analysis software (Autoplaque). RESULTS: Overall, 186 lesions from 39 patients were analyzed. There were excellent agreement and correlation between systolic and diastolic images for all plaque volume measurements (Lin's concordance coefficient ranging from 0.97 to 0.99; R ranging from 0.96 to 0.98). There were no substantial intrascan differences per patient between systolic and diastolic phases (p > 0.05 for all) for total (1017.1 ± 712.9 mm3 vs. 1014.7 ± 696.2 mm3), non-calcified (861.5 ± 553.7 mm3 vs. 856.5 ± 528.7 mm3), calcified (155.7 ± 229.3 mm3 vs. 158.2 ± 232.4 mm3), and low-density non-calcified plaque volume (151.4 ± 106.1 mm3 vs. 151.5 ± 101.5 mm3) and diameter stenosis (42.5 ± 18.4% vs 41.3 ± 15.1%). CONCLUSION: Excellent agreement and no substantial differences were observed in AI-enabled quantitative plaque measurements on CTA in systolic and diastolic images. Following further validation, standardized plaque measurements can be performed from CTA in systolic or diastolic cardiac phase. CLINICAL RELEVANCE STATEMENT: Quantitative plaque assessment using artificial intelligence-enabled plaque analysis software can provide standardized plaque quantification, regardless of cardiac phase. KEY POINTS: • The impact of different cardiac phases on coronary plaque measurements is unknown. • Plaque analysis using artificial intelligence-enabled software on systolic and diastolic CT angiography images shows excellent agreement. • Quantitative coronary artery plaque assessment can be performed regardless of cardiac phase.