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Artificial intelligence (AI) is likely to revolutionize the way medical images are analysed and has the potential to improve the identification and analysis of vulnerable or high-risk atherosclerotic plaques in coronary arteries, leading to advances in the treatment of coronary artery disease. However, coronary plaque analysis is challenging owing to cardiac and respiratory motion, as well as the small size of cardiovascular structures. Moreover, the analysis of coronary imaging data is time-consuming, can be performed only by clinicians with dedicated cardiovascular imaging training, and is subject to considerable interreader and intrareader variability. AI has the potential to improve the assessment of images of vulnerable plaque in coronary arteries, but requires robust development, testing and validation. Combining human expertise with AI might facilitate the reliable and valid interpretation of images obtained using CT, MRI, PET, intravascular ultrasonography and optical coherence tomography. In this Roadmap, we review existing evidence on the application of AI to the imaging of vulnerable plaque in coronary arteries and provide consensus recommendations developed by an interdisciplinary group of experts on AI and non-invasive and invasive coronary imaging. We also outline future requirements of AI technology to address bias, uncertainty, explainability and generalizability, which are all essential for the acceptance of AI and its clinical utility in handling the anticipated growing volume of coronary imaging procedures.In this Roadmap, Föllmer et al. summarize the evidence for the application of artificial intelligence (AI) technology to the imaging of vulnerable plaques in coronary arteries and discuss the current and future approaches to addressing the limitations of AI-guided coronary plaque imaging, such as bias, uncertainty and generalizability.

Vascular calcification is a complex biological process that is a hallmark of atherosclerosis. While macrocalcification confers plaque stability, microcalcification is a key feature of high-risk atheroma and is associated with increased morbidity and mortality. Positron emission tomography and X-ray computed tomography (PET/CT) imaging of atherosclerosis using 18 F-sodium fluoride ( 18 F-NaF) has the potential to identify pathologically high-risk nascent microcalcification. However, the precise molecular mechanism of 18 F-NaF vascular uptake is still unknown. Here we use electron microscopy, autoradiography, histology and preclinical and clinical PET/CT to analyse 18 F-NaF binding. We show that 18 F-NaF adsorbs to calcified deposits within plaque with high affinity and is selective and specific. 18 F-NaF PET/CT imaging can distinguish between areas of macro- and microcalcification. This is the only currently available clinical imaging platform that can non-invasively detect microcalcification in active unstable atherosclerosis. The use of 18 F-NaF may foster new approaches to developing treatments for vascular calcification. Atherosclerotic plaques with macrocalcification are stable, whereas microcalcification is a key feature of rupture-prone plaques. Here the authors show that 18 F-NaF PET/CT imaging can distinguish between macro- and microcalcification providing a potential, non-invasive imaging technique to identify patients with high-risk atheroma.

The use of non-invasive imaging to identify ruptured or high-risk coronary atherosclerotic plaques would represent a major clinical advance for prevention and treatment of coronary artery disease. We used combined PET and CT to identify ruptured and high-risk atherosclerotic plaques using the radioactive tracers 18F-sodium fluoride (18F-NaF) and 18F-fluorodeoxyglucose (18F-FDG). In this prospective clinical trial, patients with myocardial infarction (n=40) and stable angina (n=40) underwent 18F-NaF and 18F-FDG PET-CT, and invasive coronary angiography. 18F-NaF uptake was compared with histology in carotid endarterectomy specimens from patients with symptomatic carotid disease, and with intravascular ultrasound in patients with stable angina. The primary endpoint was the comparison of 18F-fluoride tissue-to-background ratios of culprit and non-culprit coronary plaques of patients with acute myocardial infarction. In 37 (93%) patients with myocardial infarction, the highest coronary 18F-NaF uptake was seen in the culprit plaque (median maximum tissue-to-background ratio: culprit 1·66 [IQR 1·40–2·25] vs highest non-culprit 1·24 [1·06–1·38], p<0·0001). By contrast, coronary 18F-FDG uptake was commonly obscured by myocardial uptake and where discernible, there were no differences between culprit and non-culprit plaques (1·71 [1·40–2·13] vs 1·58 [1·28–2·01], p=0·34). Marked 18F-NaF uptake occurred at the site of all carotid plaque ruptures and was associated with histological evidence of active calcification, macrophage infiltration, apoptosis, and necrosis. 18 (45%) patients with stable angina had plaques with focal 18F-NaF uptake (maximum tissue-to-background ratio 1·90 [IQR 1·61–2·17]) that were associated with more high-risk features on intravascular ultrasound than those without uptake: positive remodelling (remodelling index 1·12 [1·09–1·19] vs 1·01 [0·94–1·06]; p=0·0004), microcalcification (73% vs 21%, p=0·002), and necrotic core (25% [21–29] vs 18% [14–22], p=0·001). 18F-NaF PET-CT is the first non-invasive imaging method to identify and localise ruptured and high-risk coronary plaque. Future studies are needed to establish whether this method can improve the management and treatment of patients with coronary artery disease. Chief Scientist Office Scotland and British Heart Foundation.

In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.

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 ( 68 Ga-DOTATATE) has recently been proposed as a more specific marker of arterial wall inflammation than 18 F-fluorodeoxyglucose ( 18 F-FDG). This study set out to investigate whether 68 Ga-DOTATATE uptake is amenable to therapeutic intervention in individuals with type 2 diabetes. Methods Individuals aged >50 years with type 2 diabetes underwent 68 Ga-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 68 Ga-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, HbA 1c 55±12 mmol/mol [7.2%±3.2%]) completed both 68 Ga-DOTATATE PET/CT scans. The maximum TBR was −31% (95% CI −50, −12) lower in the coronary arteries, and bone marrow and splenic 68 Ga-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 68 Ga-DOTATATE uptake across the cardio–haematopoietic axis was lower after statin therapy in individuals with type 2 diabetes. Therefore, 68 Ga-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 Graphical Abstract

ObjectiveCT quantification of aortic valve calcification (CT-AVC) is useful in the assessment of aortic stenosis severity. Our objective was to assess its ability to track aortic stenosis progression compared with echocardiography.MethodsSubjects were recruited in two cohorts: (1) a reproducibility cohort where patients underwent repeat CT-AVC or echocardiography within 4 weeks and (2) a disease progression cohort where patients underwent annual CT-AVC and/or echocardiography. Cohen’s d-statistic (d) was computed from the ratio of annualised progression and measurement repeatability and used to estimate group sizes required to detect annualised changes in CT-AVC and echocardiography.ResultsA total of 33 (age 71±8) and 81 participants (age 72±8) were recruited to the reproducibility and progression cohorts, respectively. Ten CT scans (16%) were excluded from the progression cohort due to non-diagnostic image quality. Scan-rescan reproducibility was excellent for CT-AVC (limits of agreement −12% to 10 %, intraclass correlation (ICC) 0.99), peak velocity (−7% to +17%; ICC 0.92) mean gradient (−25% to 27%, ICC 0.96) and dimensionless index (−11% to +15%; ICC 0.98). Repeat measurements of aortic valve area (AVA) were less reliable (−44% to +28%, ICC 0.85).CT-AVC progressed by 152 (65–375) AU/year. For echocardiography, the median annual change in peak velocity was 0.1 (0.0–0.3) m/s/year, mean gradient 2 (0–4) mm Hg/year and AVA −0.1 (−0.2–0.0) cm2/year. Cohen’s d-statistic was more than double for CT-AVC (d=3.12) than each echocardiographic measure (peak velocity d=0.71 ; mean gradient d=0.66; AVA d=0.59, dimensionless index d=1.41).ConclusionCT-AVC is reproducible and demonstrates larger increases over time normalised to measurement repeatability compared with echocardiographic measures.

Aortic stenosis (AS) is the most common valvular heart disease characterized by progressive calcific degeneration of the aortic valve leaflets, ultimately leading to severe left ventricular (LV) outflow obstruction. Accurate assessment of AS severity is critical for appropriate clinical decision-making. While echocardiography remains the cornerstone of AS evaluation, it can yield discordant results in around a quarter of patients often leading to diagnostic uncertainty. In patients with discordant echocardiography, non-contrast computed tomography aortic valve calcium scoring (CT-AVC) has emerged as a powerful adjunctive modality that offers load-independent, highly reproducible quantification of valvular calcification and a complementary assessment of AS severity. In addition, contrast-enhanced computed tomography (CT) has become the gold standard for preprocedural assessment in transcatheter aortic valve implantation (TAVI) patients, providing a comprehensive anatomical visualization of the cardiovascular system which is essential for optimal planning. It allows for precise measurement of the aortic annulus, identification of coronary artery origins, assessment of peripheral vascular access, and evaluation of the aortic root and ascending aorta. This detailed anatomical assessment facilitates accurate prosthesis sizing and selection, reducing the risk of complications such as paravalvular leak and coronary artery obstruction. Advanced CT imaging techniques are being developed to further advance the assessment of patients with AS. In particular, contrast-enhanced CT techniques allow for the quantification of both fibrotic and calcific aortic valve thickening, while CT-derived strain imaging and extracellular volume (ECV) quantification provide a better assessment of the myocardial remodeling response (Figure 1).

Gadolinium-based contrast media are widely used in cardiovascular MRI to identify and to highlight the intravascular and extracellular space. After gadolinium, manganese has the second highest paramagnetic moment and was one of the first MRI contrast agents assessed in humans. Over the last 50 years, manganese-enhanced MRI (MEMRI) has emerged as a complementary approach enabling intracellular myocardial contrast imaging that can identify functional myocardium through its ability to act as a calcium analogue. Early progress was limited by its potential to cause myocardial depression. To overcome this problem, two clinical formulations of manganese were developed using either chelation (manganese dipyridoxyl diphosphate) or coadministration with a calcium compound (EVP1001-1, Eagle Vision Pharmaceuticals). Preclinical studies have demonstrated the efficacy of MEMRI in quantifying myocardial infarction and detecting myocardial viability as well as tracking altered contractility and calcium handling in cardiomyopathy. Recent clinical data suggest that MEMRI has exciting potential in the quantification of myocardial viability in ischaemic cardiomyopathy, the early detection of abnormalities in myocardial calcium handling, and ultimately, in the development of novel therapies for myocardial infarction or heart failure by actively quantifying viable myocardium. The stage is now set for wider clinical translational study of this novel and promising non-invasive imaging modality.

ObjectivesIntraplaque angiogenesis and inflammation are key promoters of atherosclerosis and are mediated by the alpha-V beta-3 (αvβ3) integrin pathway. We investigated the applicability of the αvβ3-integrin receptor-selective positron emission tomography (PET) radiotracer 18F-fluciclatide in assessing human aortic atherosclerosis.MethodsVascular 18F-fluciclatide binding was evaluated using ex vivo analysis of carotid endarterectomy samples with autoradiography and immunohistochemistry, and in vivo kinetic modelling following radiotracer administration. Forty-six subjects with a spectrum of atherosclerotic disease categorised as stable (n=27) or unstable (n=19; recent myocardial infarction) underwent PET and CT imaging of the thorax after administration of 229 (IQR 217–237) MBq 18F-fluciclatide. Thoracic aortic 18F-fluciclatide uptake was quantified on fused PET-CT images and corrected for blood-pool activity using the maximum tissue-to-background ratio (TBRmax). Aortic atherosclerotic burden was quantified by CT wall thickness, plaque volume and calcium scoring.Results18F-Fluciclatide uptake co-localised with regions of increased αvβ3 integrin expression, and markers of inflammation and angiogenesis. 18F-Fluciclatide vascular uptake was confirmed in vivo using kinetic modelling, and on static imaging correlated with measures of aortic atherosclerotic burden: wall thickness (r=0.57, p=0.001), total plaque volume (r=0.56, p=0.001) and aortic CT calcium score (r=0.37, p=0.01). Patients with recent myocardial infarction had greater aortic 18F-fluciclatide uptake than those with stable disease (TBRmax 1.29 vs 1.21, p=0.02).ConclusionsIn vivo expression of αvβ3 integrin in human aortic atheroma is associated with plaque burden and is increased in patients with recent myocardial infarction. Quantification of αvβ3 integrin expression with 18F-fluciclatide PET has potential to assess plaque vulnerability and disease activity in atherosclerosis.

Pressure overload initiates a series of alterations in the human heart that predate macroscopic organ-level remodeling and downstream heart failure. We study aortic stenosis through integrated proteomic, tissue transcriptomic, and genetic methods to prioritize targets causal in human heart failure. First, we identify the circulating proteome of cardiac remodeling in aortic stenosis, specifying known and previously-unknown mediators of fibrosis, hypertrophy, and oxidative stress, several associated with interstitial fibrosis in a separate cohort ( N  = 145). These signatures are strongly related to clinical outcomes in aortic stenosis ( N  = 802) and in broader at-risk populations in the UK Biobank ( N  = 36,668). We next map this remodeling proteome to myocardial transcription in patients with and without aortic stenosis through single-nuclear transcriptomics, observing broad differential expression of genes encoding this remodeling proteome, featuring fibrosis pathways and metabolic-inflammatory signaling. Finally, integrating our circulating and tissue-specific results with modern genetic approaches, we implicate several targets as causal in heart failure. Pressure overload in the heart, such as from aortic stenosis, triggers early molecular changes before visible damage occurs. Here, the authors show that combining proteomics, transcriptomics, and genetic data reveals key drivers of heart failure, highlighting potential targets for treatment.