Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association.

Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed and developed. However, the processes involved in implementing AI in cardiovascular imaging are highly diverse, varying by imaging modality, patient subtype, features to be extracted and analyzed, and clinical application. This article establishes a framework that defines value from an organizational perspective, followed by value chain analysis to identify the activities in which AI might produce the greatest incremental value creation. The various perspectives that should be considered are highlighted, including clinicians, imagers, hospitals, patients, and payers. Integrating the perspectives of all health care stakeholders is critical for creating value and ensuring the successful deployment of AI tools in a real-world setting. Different AI tools are summarized, along with the unique aspects of AI applications to various cardiac imaging modalities, including cardiac computed tomography, magnetic resonance imaging, and positron emission tomography. AI is applicable and has the potential to add value to cardiovascular imaging at every step along the patient journey, from selecting the more appropriate test to optimizing image acquisition and analysis, interpreting the results for classification and diagnosis, and predicting the risk for major adverse cardiac events.

Downward myocardial creep during stress PET imaging is inversely associated with mortality.

PURPOSE: The myocardial creep is a phenomenon in which the heart moves from its original position during stress-dynamic PET myocardial perfusion imaging (MPI) that can confound myocardial blood flow measurements. Therefore, myocardial motion correction is important to obtain reliable myocardial flow quantification. However, the clinical importance of the magnitude of myocardial creep has not been explored. We aimed to explore the prognostic value of myocardial creep quantified by an automated motion correction algorithm beyond traditional PET-MPI imaging variables. METHODS: Consecutive patients undergoing regadenoson rest-stress [82Rb]Cl PET-MPI were included. A newly developed 3D motion correction algorithm quantified myocardial creep, the maximum motion at stress during the first pass (60 s), in each direction. All-cause mortality (ACM) served as the primary endpoint. RESULTS: A total of 4,276 patients (median age 71 years; 60% male) were analyzed, and 1,007 ACM events were documented during a 5-year median follow-up. Processing time for automatic motion correction was < 12 s per patient. Myocardial creep in the superior to inferior (downward) direction was greater than the other directions (median, 4.2 mm vs. 1.3-1.7 mm). Annual mortality rates adjusted for age and sex were reduced with a larger downward creep, with a 4.2-fold ratio between the first (0 mm motion) and 10th decile (11 mm motion) (mortality, 7.9% vs. 1.9%/year). Downward creep was associated with lower ACM after full adjustment for clinical and imaging parameters (adjusted hazard ratio, 0.93; 95%CI, 0.91-0.95; p < 0.001). Adding downward creep to the standard PET-MPI imaging model significantly improved ACM prediction (area under the receiver operating characteristics curve, 0.790 vs. 0.775; p < 0.001), but other directions did not (p > 0.5). CONCLUSIONS: Downward myocardial creep during regadenoson stress carries additional information for the prediction of ACM beyond conventional flow and perfusion PET-MPI. This novel imaging biomarker is quantified automatically and rapidly from stress dynamic PET-MPI.

Sex differences in coronary atherosclerotic plaque activity using 18F-sodium fluoride positron emission tomography.

INTRODUCTION: There are sex differences in the extent, severity, and outcomes of coronary artery disease. We aimed to assess the influence of sex on coronary atherosclerotic plaque activity measured using coronary 18F-sodium fluoride (18F-NaF) positron emission tomography (PET), and to determine whether 18F-NaF PET has prognostic value in both women and men. METHODS: In a post-hoc analysis of observational cohort studies of patients with coronary atherosclerosis who had undergone 18F-NaF PET CT angiography, we compared the coronary microcalcification activity (CMA) in women and men. RESULTS: Baseline 18F-NaF PET CT angiography was available in 999 participants (151 (15%) women) with 4282 patient-years of follow-up. Compared to men, women had lower coronary calcium scores (116 [interquartile range, 27-434] versus 205 [51-571] Agatston units; p = 0.002) and CMA values (0.0 [0.0-1.12] versus 0.53 [0.0-2.54], p = 0.01). Following matching for plaque burden by coronary calcium scores and clinical comorbidities, there was no sex-related difference in CMA values (0.0 [0.0-1.12] versus 0.0 [0.0-1.23], p = 0.21) and similar proportions of women and men had no 18F-NaF uptake (53.0% (n = 80) and 48.3% (n = 73); p = 0.42), or CMA values > 1.56 (21.8% (n = 33) and 21.8% (n = 33); p = 1.00). Over a median follow-up of 4.5 [4.0-6.0] years, myocardial infarction occurred in 6.6% of women (n = 10) and 7.8% of men (n = 66). Coronary microcalcification activity greater than 0 was associated with a similarly increased risk of myocardial infarction in both women (HR: 3.83; 95% CI:1.10-18.49; p = 0.04) and men (HR: 5.29; 95% CI:2.28-12.28; p < 0.001). CONCLUSION: Although men present with more coronary atherosclerotic plaque than women, increased plaque activity is a strong predictor of future myocardial infarction regardless of sex.

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.

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.

Coronary inflammation on chest computed tomography and COVID-19 mortality.

OBJECTIVES: The main factors associated with coronavirus disease-19 (COVID-19) mortality are age, comorbidities, pattern of inflammatory response, and SARS-CoV-2 lineage involved in infection. However, the clinical course of the disease is extremely heterogeneous, and reliable biomarkers predicting adverse prognosis are lacking. Our aim was to elucidate the prognostic role of a novel marker of coronary artery disease inflammation, peri-coronary adipose tissue attenuation (PCAT), available from high-resolution chest computed tomography (HRCT) in COVID-19 patients with severe disease requiring hospitalization. METHODS: Two distinct groups of patients were admitted to Parma University Hospital in Italy with COVID-19 in March 2020 and March 2021 (first- and third-wave peaks of the COVID-19 pandemic in Italy, with the prevalence of wild-type and B.1.1.7 SARS-CoV-2 lineage, respectively) were retrospectively enrolled. The primary endpoint was in-hospital mortality. Demographic, clinical, laboratory, HRCT data, and coronary artery HRCT features (coronary calcium score and PCAT attenuation) were collected to show which variables were associated with mortality. RESULTS: Among the 769 patients enrolled, 555 (72%) were discharged alive, and 214 (28%) died. In multivariable logistic regression analysis age (p < 0.001), number of chronic illnesses (p < 0.001), smoking habit (p = 0.006), P/F ratio (p = 0.001), platelet count (p = 0.002), blood creatinine (p < 0.001), non-invasive mechanical ventilation (p < 0.001), HRCT visual score (p < 0.001), and PCAT (p < 0.001), but not the calcium score, were independently associated with in-hospital mortality. CONCLUSION: Coronary inflammation, measured with PCAT on non-triggered HRCT, appeared to be independently associated with higher mortality in patients with severe COVID-19, while the pre-existent coronary atherosclerotic burden was not associated with adverse outcomes after adjustment for covariates. CLINICAL RELEVANCE STATEMENT: The current study demonstrates that a relatively simple measurement, peri-coronary adipose tissue attenuation (PCAT), available ex-post from standard high-resolution computed tomography, is strongly and independently associated with in-hospital mortality. KEY POINTS: • Coronary inflammation can be measured by the attenuation of peri-coronary adipose tissue (PCAT) on high-resolution CT (HRCT) without contrast media. • PCAT is strongly and independently associated with in-hospital mortality in SARS-CoV-2 patients. • PCAT might be considered an independent prognostic marker in COVID-19 patients if confirmed in other studies.

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.

Noninvasive In Vivo Thrombus Imaging in Patients With Ischemic Stroke or Transient Ischemic Attack-Brief Report.

BACKGROUND: 18F-GP1 is a novel positron-emitting radiotracer that is highly specific for activated platelets and thrombus. In a proof-of-concept study, we aimed to determine its potential clinical application in establishing the role and origin of thrombus in ischemic stroke. METHODS: Eleven patients with recent ischemic stroke (n=9) or transient ischemic attack (n=2) underwent 18F-GP1 positron emission tomography and computed tomography angiography at a median of 11 (range, 2-21) days from symptom onset. 18F-GP1 uptake (maximum target-to-background ratio) was assessed in the carotid arteries and brain. RESULTS: 18F-GP1 uptake was identified in 10 of 11 patients: 4 in the carotid arteries only, 3 in the brain only, and 3 in both the brain and carotid arteries. In those with carotid uptake, 4 participants had >50% stenosis and 3 had nonstenotic disease. One case had bilateral stenotic disease (>70%), but only the culprit carotid artery demonstrated 18F-GP1 uptake. The average uptake was higher in the culprit (median maximum target-to-background ratio, 1.55 [interquartile range, 1.26-1.82]) compared with the contralateral nonculprit carotid artery (maximum target-to-background ratio, 1.22 [1.19-1.6]). In those with brain 18F-GP1 uptake (maximum target-to-background ratio, 6.45 [4.89-7.65]), areas of acute infarction on computed tomography correlated with brain 18F-GP1 uptake in 6 cases. Ex vivo autoradiography of postmortem infarcted brain tissue showed focal uptake corresponding to intraluminal thrombus within the culprit vessel and downstream microvasculature. There was also evidence of diffuse uptake within some of the infarcted brain tissue reflecting parenchymal petechial hemorrhage. CONCLUSIONS: 18F-GP1 positron emission tomography and computed tomography angiography is a novel noninvasive method of identifying in vivo cerebrovascular thrombosis, which holds major promise in understanding the role and origin of thrombosis in stroke. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT03943966.

Latent Coronary Plaque Morphology From Computed Tomography Angiography, Molecular Disease Activity on Positron Emission Tomography, and Clinical Outcomes.

BACKGROUND: Assessments of coronary disease activity with 18F-sodium fluoride positron emission tomography and radiomics-based precision coronary plaque phenotyping derived from coronary computed tomography angiography may enhance risk stratification in patients with coronary artery disease. We sought to investigate whether the prognostic information provided by these 2 approaches is complementary in the prediction of myocardial infarction. METHODS: Patients with known coronary artery disease underwent coronary 18F-sodium fluoride positron emission tomography and coronary computed tomography angiography on a hybrid positron emission tomography/computed tomography scanner. Coronary 18F-NaF uptake was determined by the coronary microcalcification activity. We performed quantitative plaque analysis of coronary computed tomography angiography datasets and extracted 1103 radiomic features for each plaque. Using weighted correlation network analysis, we derived latent morphological features of coronary lesions which were aggregated to patient-level radiomics nomograms to predict myocardial infarction. RESULTS: Among 260 patients with established coronary artery disease (age, 65±9 years; 83% men), 179 (69%) participants showed increased coronary 18F-NaF activity (coronary microcalcification activity>0). Over 53 (40-59) months of follow-up, 18 patients had a myocardial infarction. Using weighted correlation network analysis, we derived 15 distinct eigen radiomic features representing latent morphological coronary plaque patterns in an unsupervised fashion. Following adjustments for calcified, noncalcified, and low-density noncalcified plaque volumes and 18F-NaF coronary microcalcification activity, 4 radiomic features remained independent predictors of myocardial infarction (hazard ratio, 1.46 [95% CI, 1.03-2.08]; P=0.03; hazard ratio, 1.62 [95% CI, 1.04-2.54]; P=0.02; hazard ratio, 1.49 [95% CI, 1.07-2.06]; P=0.01; and hazard ratio, 1.50 (95% CI, 1.05-2.13); P=0.02). CONCLUSIONS: In patients with established coronary artery disease, latent coronary plaque morphological features, quantitative plaque volumes, and disease activity on 18F-sodium fluoride positron emission tomography are additive predictors of myocardial infarction.

Rapid three-dimensional quantification of high-intensity plaques from coronary atherosclerosis T1-weighted characterization to predict periprocedural myocardial injury.

BACKGROUND: High-intensity plaque (HIP) on magnetic resonance imaging (MRI) has been documented as a powerful predictor of periprocedural myocardial injury (PMI) following percutaneous coronary intervention (PCI). Despite the recent proposal of three-dimensional HIP quantification to enhance the predictive capability, the conventional pulse sequence, which necessitates the separate acquisition of anatomical reference images, hinders accurate three-dimensional segmentation along the coronary vasculature. Coronary atherosclerosis T1-weighted characterization (CATCH) enables the simultaneous acquisition of inherently coregistered dark-blood plaque and bright-blood coronary artery images. We aimed to develop a novel HIP quantification approach using CATCH and to ascertain its superior predictive performance compared to the conventional two-dimensional assessment based on plaque-to-myocardium signal intensity ratio (PMR). METHODS: In this prospective study, CATCH MRI was conducted before elective stent implantation in 137 lesions from 125 patients. On CATCH images, dedicated software automatically generated tubular three-dimensional volumes of interest on the dark-blood plaque images along the coronary vasculature, based on the precisely matched bright-blood coronary artery images, and subsequently computed PMR and HIP volume (HIPvol). Specifically, HIPvol was calculated as the volume of voxels with signal intensity exceeding that of the myocardium, weighted by their respective signal intensities. PMI was defined as post-PCI cardiac troponin-T > 5 × the upper reference limit. RESULTS: The entire analysis process was completed within 3 min per lesion. PMI occurred in 44 lesions. Based on the receiver operating characteristic curve analysis, HIPvol outperformed PMR for predicting PMI (C-statistics, 0.870 [95% CI, 0.805-0.936] vs. 0.787 [95% CI, 0.706-0.868]; p = 0.001). This result was primarily driven by the higher sensitivity HIPvol offered: 0.886 (95% CI, 0.754-0.962) vs. 0.750 for PMR (95% CI, 0.597-0.868; p = 0.034). Multivariable analysis identified HIPvol as an independent predictor of PMI (odds ratio, 1.15 per 10-µL increase; 95% CI, 1.01-1.30, p = 0.035). CONCLUSIONS: Our semi-automated method of analyzing coronary plaque using CATCH MRI provided rapid HIP quantification. Three-dimensional assessment using this approach had a better ability to predict PMI than conventional two-dimensional assessment.

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 .

Atorvastatin lowers 68Ga-DOTATATE uptake in coronary arteries, bone marrow and spleen in individuals with type 2 diabetes.

AIMS/HYPOTHESIS: Inflammation is a core component of residual cardiovascular risk in type 2 diabetes. With new anti-inflammatory therapeutics entering the field, accurate markers to evaluate their effectiveness in reducing cardiovascular disease are paramount. Gallium-68-labelled DOTATATE (68Ga-DOTATATE) has recently been proposed as a more specific marker of arterial wall inflammation than 18F-fluorodeoxyglucose (18F-FDG). This study set out to investigate whether 68Ga-DOTATATE uptake is amenable to therapeutic intervention in individuals with type 2 diabetes. METHODS: Individuals aged >50 years with type 2 diabetes underwent 68Ga-DOTATATE positron emission tomography (PET)/computed tomography (CT) at baseline and after 3 months treatment with atorvastatin 40 mg once daily. Primary outcome was the difference in coronary 68Ga-DOTATATE uptake, expressed as target-to-background ratio (TBR). The secondary outcome was difference in bone marrow and splenic uptake, expressed as the standardised uptake value (SUV). RESULTS: Twenty-two individuals with type 2 diabetes (mean age 63.2±6.4 years, 82% male, LDL-cholesterol 3.42±0.81 mmol/l, HbA1c 55±12 mmol/mol [7.2%±3.2%]) completed both 68Ga-DOTATATE PET/CT scans. The maximum TBR was -31% (95% CI -50, -12) lower in the coronary arteries, and bone marrow and splenic 68Ga-DOTATATE uptake was also significantly lower post statin treatment, with a mean percentage reduction of -15% (95% CI -27, -4) and -17% (95% CI -32, -2), respectively. CONCLUSIONS/INTERPRETATION: 68Ga-DOTATATE uptake across the cardio-haematopoietic axis was lower after statin therapy in individuals with type 2 diabetes. Therefore, 68Ga-DOTATATE is promising as a metric for vascular and haematopoietic inflammation in intervention studies using anti-inflammatory therapeutics in individuals with type 2 diabetes. TRIAL REGISTRATION: ClinicalTrials.gov NCT05730634.

Coronary Artery and Cardiac Disease in Patients With Type 2 Myocardial Infarction: A Prospective Cohort Study.

BACKGROUND: Type 2 myocardial infarction is caused by myocardial oxygen supply-demand imbalance, and its diagnosis is increasingly common with the advent of high-sensitivity cardiac troponin assays. Although this diagnosis is associated with poor outcomes, widespread uncertainty and confusion remain among clinicians as to how to investigate and manage this heterogeneous group of patients with type 2 myocardial infarction. METHODS: In a prospective cohort study, 8064 consecutive patients with increased cardiac troponin concentrations were screened to identify patients with type 2 myocardial infarction. We excluded patients with frailty or renal or hepatic failure. All study participants underwent coronary (invasive or computed tomography angiography) and cardiac (magnetic resonance or echocardiography) imaging, and the underlying causes of infarction were independently adjudicated. The primary outcome was the prevalence of coronary artery disease. RESULTS: In 100 patients with a provisional diagnosis of type 2 myocardial infarction (median age, 65 years [interquartile range, 55-74 years]; 43% women), coronary and cardiac imaging reclassified the diagnosis in 7 patients: type 1 or 4b myocardial infarction in 5 and acute myocardial injury in 2 patients. In those with type 2 myocardial infarction, median cardiac troponin I concentrations were 195 ng/L (interquartile range, 62-760 ng/L) at presentation and 1165 ng/L (interquartile range, 277-3782 ng/L) on repeat testing. The prevalence of coronary artery disease was 68% (63 of 93), which was obstructive in 30% (28 of 93). Infarct-pattern late gadolinium enhancement or regional wall motion abnormalities were observed in 42% (39 of 93), and left ventricular systolic dysfunction was seen in 34% (32 of 93). Only 10 patients had both normal coronary and normal cardiac imaging. Coronary artery disease and left ventricular systolic dysfunction were previously unrecognized in 60% (38 of 63) and 84% (27 of 32), respectively, with only 33% (21 of 63) and 19% (6 of 32) on evidence-based treatments. CONCLUSIONS: Systematic coronary and cardiac imaging of patients with type 2 myocardial infarction identified coronary artery disease in two-thirds and left ventricular systolic dysfunction in one-third of patients. Unrecognized and untreated coronary or cardiac disease is seen in most patients with type 2 myocardial infarction, presenting opportunities for initiation of evidence-based treatments with major potential to improve clinical outcomes. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT03338504.

Noninvasive Plaque Imaging to Accelerate Coronary Artery Disease Drug Development.

Coronary artery disease (CAD) remains the leading cause of adult mortality globally. Targeting known modifiable risk factors has had substantial benefit, but there remains a need for new approaches. Improvements in invasive and noninvasive imaging techniques have enabled an increasing recognition of distinct quantitative phenotypes of coronary atherosclerosis that are prognostically relevant. There are marked differences in plaque phenotype, from the high-risk, lipid-rich, thin-capped atheroma to the low-risk, quiescent, eccentric, nonobstructive calcified plaque. Such distinct phenotypes reflect different pathophysiologic pathways and are associated with different risks for acute ischemic events. Noninvasive coronary imaging techniques, such as computed tomography, positron emission tomography, and coronary magnetic resonance imaging, have major potential to accelerate cardiovascular drug development, which has been affected by the high costs and protracted timelines of cardiovascular outcome trials. This may be achieved through enrichment of high-risk phenotypes with higher event rates or as primary end points of drug efficacy, at least in phase 2 trials, in a manner historically performed through intravascular coronary imaging studies. Herein, we provide a comprehensive review of the current technology available and its application in clinical trials, including implications for sample size requirements, as well as potential limitations. In its effort to accelerate drug development, the US Food and Drug Administration has approved surrogate end points for 120 conditions, but not for CAD. There are robust data showing the beneficial effects of drugs, including statins, on CAD progression and plaque stabilization in a manner that correlates with established clinical end points of mortality and major adverse cardiovascular events. This, together with a clear mechanistic rationale for using imaging as a surrogate CAD end point, makes it timely for CAD imaging end points to be considered. We discuss the importance of global consensus on these imaging end points and protocols and partnership with regulatory bodies to build a more informed, sustainable staged pathway for novel therapies.

Incremental prognostic value of stress phase entropy over standard PET myocardial perfusion imaging variables.

PURPOSE: Phase analysis can assess left ventricular dyssynchrony. The independent prognostic value of phase variables over positron emission tomography myocardial perfusion imaging (PET-MPI) variables including myocardial flow reserve (MFR) has not been studied. The aim of this study was to explore the prognostic value of phase variables for predicting mortality over standard PET-MPI variables. METHODS: Consecutive patients who underwent pharmacological stress-rest 82Rb PET study were enrolled. All PET-MPI variables including phase variables (phase entropy, phase bandwidth, and phase standard deviation) were automatically obtained by QPET software (Cedars-Sinai, Los Angeles, CA). Cox proportional hazard analyses were used to assess associations with all-cause mortality (ACM). RESULTS: In a total of 3963 patients (median age 71 years; 57% male), 923 patients (23%) died during a median follow-up of 5 years. Annualized mortality rates increased with stress phase entropy, with a 4.6-fold difference between the lowest and highest decile groups of entropy (2.6 vs. 12.0%/year). Abnormal stress phase entropy (optimal cutoff value, 43.8%) stratified ACM risk in patients with normal and impaired MFR (both p < 0.001). Among three phase variables, only stress phase entropy was significantly associated with ACM after the adjustment of standard clinical and PET-MPI variables including MFR and stress-rest change of phase variables, whether modeled as binary variables (adjusted hazard ratio, 1.44 for abnormal entropy [> 43.8%]; 95%CI, 1.18-1.75; p < 0.001) or continuous variables (adjusted hazard ratio, 1.05 per 5% increase; 95%CI, 1.01-1.10; p = 0.030). The addition of stress phase entropy to the standard PET-MPI variables significantly improved the discriminatory power for ACM prediction (p < 0.001), but the other phase variables did not (p > 0.1). CONCLUSION: Stress phase entropy is independently and incrementally associated with ACM beyond standard PET-MPI variables including MFR. Phase entropy can be obtained automatically and included in clinical reporting of PET-MPI studies to improve patient risk prediction.

Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images.

PURPOSE: Artificial intelligence (AI) has high diagnostic accuracy for coronary artery disease (CAD) from myocardial perfusion imaging (MPI). However, when trained using high-risk populations (such as patients with correlating invasive testing), the disease probability can be overestimated due to selection bias. We evaluated different strategies for training AI models to improve the calibration (accurate estimate of disease probability), using external testing. METHODS: Deep learning was trained using 828 patients from 3 sites, with MPI and invasive angiography within 6 months. Perfusion was assessed using upright (U-TPD) and supine total perfusion deficit (S-TPD). AI training without data augmentation (model 1) was compared to training with augmentation (increased sampling) of patients without obstructive CAD (model 2), and patients without CAD and TPD < 2% (model 3). All models were tested in an external population of patients with invasive angiography within 6 months (n = 332) or low likelihood of CAD (n = 179). RESULTS: Model 3 achieved the best calibration (Brier score 0.104 vs 0.121, p < 0.01). Improvement in calibration was particularly evident in women (Brier score 0.084 vs 0.124, p < 0.01). In external testing (n = 511), the area under the receiver operating characteristic curve (AUC) was higher for model 3 (0.930), compared to U-TPD (AUC 0.897) and S-TPD (AUC 0.900, p < 0.01 for both). CONCLUSION: Training AI models with augmentation of low-risk patients can improve calibration of AI models developed to identify patients with CAD, allowing more accurate assignment of disease probability. This is particularly important in lower-risk populations and in women, where overestimation of disease probability could significantly influence down-stream patient management.

Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging.

PURPOSE: Patients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics. Unsupervised machine learning can identify new risk phenotypes in an unbiased fashion. We use cluster analysis to risk-stratify patients with known CAD undergoing single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). METHODS: From 37,298 patients in the REFINE SPECT registry, we identified 9221 patients with known coronary artery disease. Unsupervised machine learning was performed using clinical (23), acquisition (17), and image analysis (24) parameters from 4774 patients (internal cohort) and validated with 4447 patients (external cohort). Risk stratification for all-cause mortality was compared to stress total perfusion deficit (< 5%, 5-10%, ≥10%). RESULTS: Three clusters were identified, with patients in Cluster 3 having a higher body mass index, more diabetes mellitus and hypertension, and less likely to be male, have dyslipidemia, or undergo exercise stress imaging (p < 0.001 for all). In the external cohort, during median follow-up of 2.6 [0.14, 3.3] years, all-cause mortality occurred in 312 patients (7%). Cluster analysis provided better risk stratification for all-cause mortality (Cluster 3: hazard ratio (HR) 5.9, 95% confidence interval (CI) 4.0, 8.6, p < 0.001; Cluster 2: HR 3.3, 95% CI 2.5, 4.5, p < 0.001; Cluster 1, reference) compared to stress total perfusion deficit (≥10%: HR 1.9, 95% CI 1.5, 2.5 p < 0.001; < 5%: reference). CONCLUSIONS: Our unsupervised cluster analysis in patients with known CAD undergoing SPECT MPI identified three distinct phenotypic clusters and predicted all-cause mortality better than ischemia alone.

Does Coronary Plaque Morphology Matter Beyond Plaque Burden?

PURPOSE OF REVIEW: Imaging of adverse coronary plaque features by coronary computed tomography angiography (CCTA) has advanced greatly and at a fast pace. We aim to describe the evolution, present and future in plaque analysis, and its value in comparison to plaque burden. RECENT FINDINGS: Recently, it has been demonstrated that in addition to plaque burden, quantitative and qualitative assessment of coronary plaque by CCTA can improve the prediction of future major adverse cardiovascular events in diverse coronary artery disease scenarios. The detection of high-risk non-obstructive coronary plaque can lead to higher use of preventive medical therapies such as statins and aspirin, help identify culprit plaque, and differentiate between myocardial infarction types. Even more, over traditional plaque burden, plaque analysis including pericoronary inflammation can potentially be useful tools for tracking disease progression and response to medical therapy. The identification of the higher risk phenotypes with plaque burden, plaque characteristics, or ideally both can allow the allocation of targeted therapies and potentially monitor response. Further observational data are now required to investigate these key issues in diverse populations, followed by rigorous randomized controlled trials.

Impact of virtual monoenergetic levels on coronary plaque volume components using photon-counting computed tomography.

OBJECTIVES: Virtual monoenergetic images (VMIs) from photon-counting CT (PCCT) may change quantitative coronary plaque volumes. We aimed to assess how plaque component volumes change with respect to VMIs. METHODS: Coronary CT angiography (CTA) images were acquired using a dual-source PCCT and VMIs were reconstructed between 40 and 180 keV in 10-keV increments. Polychromatic images at 120 kVp (T3D) were used as reference. Quantitative plaque analysis was performed on T3D images and segmentation masks were copied to VMI reconstructions. Calcified plaque (CP; > 350 Hounsfield units, HU), non-calcified plaque (NCP; 30 to 350 HU), and low-attenuation NCP (LAP; - 100 to 30 HU) volumes were calculated using fixed thresholds. RESULTS: We analyzed 51 plaques from 51 patients (67% male, mean age 65 ± 12 years). Average attenuation and contrast-to-noise ratio (CNR) decreased significantly with increasing keV levels, with similar values observed between T3D and 70 keV images (299 ± 209 vs. 303 ± 225 HU, p = 0.15 for mean HU; 15.5 ± 3.7 vs. 15.8 ± 3.5, p = 0.32 for CNR). Mean NCP volume was comparable between T3D and 100-180-keV reconstructions. There was a monotonic decrease in mean CP volume, with a significant difference between all VMIs and T3D (p < 0.05). LAP volume increased with increasing keV levels and all VMIs showed a significant difference compared to T3D, except for 50 keV (28.0 ± 30.8 mm3 and 28.6 ± 30.1 mm3, respectively, p = 0.63). CONCLUSIONS: Estimated coronary plaque volumes significantly differ between VMIs. Normalization protocols are needed to have comparable results between future studies, especially for LAP volume which is currently defined using a fixed HU threshold. CLINICAL RELEVANCE STATEMENT: Different virtual monoenergetic images from photon-counting CT alter attenuation values and therefore corresponding plaque component volumes. New clinical standards and protocols are required to determine the optimal thresholds to derive plaque volumes from photon-counting CT. KEY POINTS: • Utilizing different VMI energy levels from photon-counting CT for the analysis of coronary artery plaques leads to substantial changes in attenuation values and corresponding plaque component volumes. • Low-energy images (40-70 keV) improved contrast-to-noise ratio, however also increased image noise. • Normalization protocols are needed to have comparable results between future studies, especially for low-attenuation plaque volume which is currently defined using a fixed HU threshold.