Relationship Between Quantitative Adverse Plaque Features From Coronary Computed Tomography Angiography and Downstream Impaired Myocardial Flow Reserve by 13N-Ammonia Positron Emission Tomography: A Pilot Study

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.

Coronary Atherosclerosis Phenotypes in Focal and Diffuse Disease

BACKGROUND

The interplay between coronary hemodynamics and plaque characteristics remains poorly understood.

OBJECTIVES

The aim of this study was to compare atherosclerotic plaque phenotypes between focal and diffuse coronary artery disease (CAD) defined by coronary hemodynamics.

METHODS

This multicenter, prospective, single-arm study was conducted in 5 countries. Patients with functionally significant lesions based on an invasive fractional flow reserve ≤0.80 were included. Plaque analysis was performed by using coronary computed tomography angiography and optical coherence tomography. CAD patterns were assessed using motorized fractional flow reserve pullbacks and quantified by pullback pressure gradient (PPG). Focal and diffuse CAD was defined according to the median PPG value.

RESULTS

A total of 117 patients (120 vessels) were included. The median PPG was 0.66 (IQR: 0.54-0.75). According to coronary computed tomography angiography analysis, plaque burden was higher in patients with focal CAD (87% ± 8% focal vs 82% ± 10% diffuse; P = 0.003). Calcifications were significantly more prevalent in patients with diffuse CAD (Agatston score per vessel: 51 [IQR: 11-204] focal vs 158 [IQR: 52-341] diffuse; P = 0.024). According to optical coherence tomography analysis, patients with focal CAD had a significantly higher prevalence of circumferential lipid-rich plaque (37% focal vs 4% diffuse; P = 0.001) and thin-cap fibroatheroma (TCFA) (47% focal vs 10% diffuse; P = 0.002). Focal disease defined by PPG predicted the presence of TCFA with an area under the curve of 0.73 (95% CI: 0.58-0.87).

CONCLUSIONS

Atherosclerotic plaque phenotypes associate with intracoronary hemodynamics. Focal CAD had a higher plaque burden and was predominantly lipid-rich with a high prevalence of TCFA, whereas calcifications were more prevalent in diffuse CAD. (Precise Percutaneous Coronary Intervention Plan [P3]; NCT03782688).

Machine learning based ischemia-specific stenosis prediction: A Chinese multicenter coronary CT angiography study

OBJECTIVES

To evaluate the performance of coronary computed tomography angiography (CCTA) derived characteristics including CT derived fractional flow reserve (CT-FFR) with FFR as a reference standard in identifying the lesion-specific ischemia by machine learning (ML) algorithms.

METHODS

The retrospective analysis enrolled 596 vessels in 462 patients (mean age, 61 years ± 11 [SD]; 71.4 % men) with suspected coronary artery disease who underwent CCTA and invasive FFR. The data were divided into training cohort, internal validation cohort, external validation cohorts 1 and 2 according to participating centers. All CCTA-derived parameters, which contained 10 qualitative and 33 quantitative plaque parameters, were collected to establish ML model. The Boruta and unsupervised clustering algorithm were implemented to select important and non-redundant parameters. Finally, the eight features with the highest mean importance were included for further ML model establishment and decision tree building. Five models were built to predict lesion-specific ischemia: stenosis degree from CCTA, CT-FFR, ΔCT-FFR, ML model and nested model.

RESULTS

Low-attenuation plaque, bend and lesion length were the main predictors of ischemia-specific lesions. Of 5 models, the ML model showed favorable discrimination for ischemia-specific lesions in the training and three validation sets (area under the curve [95 % confidence interval], 0.93 [0.90-0.96], 0.86 [0.79-0.94], 0.88 [0.83-0.94], and 0.90 [0.84-0.96], respectively). The nested model which combined the ML model and CT-FFR showed better diagnostic efficacy (AUC [95 %CI], 0.96 [0.94-0.99], 0.92 [0.86-0.99], 0.92 [0.86-0.99] and 0.94 [0.91-0.98], respectively; all P < 0.05), and net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were significantly higher than CT-FFR alone.

CONCLUSIONS

Comprehensive CCTA-derived multiparameter model could better predict the ischemia-specific lesions by ML algorithms compared to stenosis degree from CTA, CT-FFR and ΔCT-FFR. Decision tree can be used to predict myocardial ischemia effectively.

Relationship between impaired myocardial blood flow by positron emission tomography and low-attenuation plaque burden and pericoronary adipose tissue attenuation from coronary computed tomography: From the prospective PACIFIC trial

BACKGROUND

Positron emission tomography (PET) is the clinical gold standard for quantifying myocardial blood flow (MBF). Pericoronary adipose tissue (PCAT) attenuation may detect vascular inflammation indirectly. We examined the relationship between MBF by PET and plaque burden and PCAT on coronary CT angiography (CCTA).

METHODS

This post hoc analysis of the PACIFIC trial included 208 patients with suspected coronary artery disease (CAD) who underwent [15O]H2O PET and CCTA. Low-attenuation plaque (LAP, < 30HU), non-calcified plaque (NCP), and PCAT attenuation were measured by CCTA.

RESULTS

In 582 vessels, 211 (36.3%) had impaired per-vessel hyperemic MBF (≤ 2.30 mL/min/g). In multivariable analysis, LAP burden was independently and consistently associated with impaired hyperemic MBF (P = 0.016); over NCP burden (P = 0.997). Addition of LAP burden improved predictive performance for impaired hyperemic MBF from a model with CAD severity and calcified plaque burden (P < 0.001). There was no correlation between PCAT attenuation and hyperemic MBF (r = - 0.11), and PCAT attenuation was not associated with impaired hyperemic MBF in univariable or multivariable analysis of all vessels (P > 0.1).

CONCLUSION

In patients with stable CAD, LAP burden was independently associated with impaired hyperemic MBF and a stronger predictor of impaired hyperemic MBF than NCP burden. There was no association between PCAT attenuation and hyperemic MBF.

Residual Risk in Non-ST-Segment Elevation Acute Coronary Syndrome: Quantitative Plaque Analysis at Coronary CT Angiography

Background Lipid-rich plaques detected with intravascular imaging are associated with adverse cardiovascular events in patients with non-ST-segment elevation (NSTE) acute coronary syndrome (ACS). But evidence about the prognostic implication of coronary CT angiography (CCTA) in NSTE ACS is limited. Purpose To assess whether quantitative variables at CCTA that reflect lipid content in nonrevascularized plaques in individuals with NSTE ACS might be predictors of subsequent nonrevascularized plaque-related major adverse cardiovascular events (MACEs). Materials and Methods In this multicenter prospective cohort study, from November 2017 to January 2019, individuals diagnosed with NSTE ACS (excluding those at very high risk) were enrolled and underwent CCTA before invasive coronary angiography (ICA) within 1 day. Lipid core was defined as areas with attenuation less than 30 HU in plaques. MACEs were defined as cardiac death, myocardial infarction, hospitalization for unstable angina, and revascularization. Participants were followed up at 6 months, 12 months, and annually thereafter for at least 3 years (ending by July 2022). Multivariable analysis using Cox proportional hazards regression models was performed to determine the association between lipid core burden, lipid core volume, and future nonrevascularized plaque-related MACEs at both the participant and plaque levels. Results A total of 342 participants (mean age, 57.9 years ± 11.1 [SD]; 263 male) were included for analysis with a median follow-up period of 4.0 years (IQR, 3.6-4.4 years). The 4-year nonrevascularized plaque-related MACE rate was 23.9% (95% CI: 19.1, 28.5). Lipid core burden (hazard ratio [HR], 12.6; 95% CI: 4.6, 34.3) was an independent predictor at the participant level, with an optimum threshold of 2.8%. Lipid core burden (HR, 12.1; 95% CI: 6.6, 22.3) and volume (HR, 11.0; 95% CI: 6.5, 18.4) were independent predictors at the plaque level, with an optimum threshold of 7.2% and 10.1 mm3, respectively. Conclusion In NSTE ACS, quantitative analysis of plaque lipid content at CCTA independently predicted participants and plaques at higher risk for future nonrevascularized plaque-related MACEs. Chinese Clinical Trial Registry no. ChiCTR1800018661 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Tavakoli and Duman in this issue.

Computed Tomography-derived Characterization of Pericoronary, Epicardial, and Paracardial Adipose Tissue and Its Association With Myocardial Ischemia as Assessed by Computed Fractional Flow Reserve

BACKGROUND

Increased pericoronary adipose tissue (PCAT) attenuation derived from coronary computed tomography (CT) angiography (CTA) relates to coronary inflammation and cardiac mortality. We aimed to investigate the association between CT-derived characterization of different cardiac fat compartments and myocardial ischemia as assessed by computed fractional flow reserve (FFRCT).

METHODS

In all, 133 patients (median 64 y, 74% male) with coronary artery disease (CAD) underwent CTA including FFRCT measurement followed by invasive FFR assessment (FFRINVASIVE). CT attenuation and volume of PCAT were quantified around the proximal right coronary artery (RCA), left anterior descending artery (LAD), and left circumflex artery (LCX). Epicardial adipose tissue (EAT) and paracardial adipose tissue (PAT; all intrathoracic adipose tissue outside the pericardium) were quantified in noncontrast cardiac CT datasets.

RESULTS

Median FFRCT was 0.86 [0.79, 0.91] and median FFRINVASIVE was 0.87 [0.81, 0.93]. Subjects with the presence of myocardial ischemia (n=26) defined by an FFRCT-threshold of ≤0.75 showed significantly higher RCA PCAT attenuation than individuals without myocardial ischemia (n=107) (-75.1±10.8 vs. -81.1±10.6 HU, P=0.011). In multivariable analysis adjusted for age, body mass index, sex and risk factors, increased RCA PCAT attenuation remained a significant predictor of myocardial ischemia. Between individuals with myocardial ischemia compared with individuals without myocardial ischemia, there was no significant difference in the volume and CT attenuation of EAT and PAT or in the PCAT volume of RCA, LAD, and LCX.

CONCLUSIONS

Increased RCA PCAT attenuation is associated with the presence of myocardial ischemia as assessed by FFR, while PCAT volume, EAT, and PAT are not.

Machine Learning From Quantitative Coronary Computed Tomography Angiography Predicts Fractional Flow Reserve-Defined Ischemia and Impaired Myocardial Blood Flow

BACKGROUND

A pathophysiological interplay exists between plaque morphology and coronary physiology. Machine learning (ML) is increasingly being applied to coronary computed tomography angiography (CCTA) for cardiovascular risk stratification. We sought to assess the performance of a ML score integrating CCTA-based quantitative plaque features for predicting vessel-specific ischemia by invasive fractional flow reserve (FFR) and impaired myocardial blood flow (MBF) by positron emission tomography (PET).

METHODS

This post-hoc analysis of the PACIFIC trial (Prospective Comparison of Cardiac Positron Emission Tomography/Computed Tomography [CT]' Single Photon Emission Computed Tomography/CT Perfusion Imaging and CT Coronary Angiography with Invasive Coronary Angiography) included 208 patients with suspected coronary artery disease who prospectively underwent CCTA' [15O]H2O PET, and invasive FFR. Plaque quantification from CCTA was performed using semiautomated software. An ML algorithm trained on the prospective NXT trial (484 vessels) was used to develop a ML score for the prediction of ischemia (FFR≤0.80), which was then evaluated in 581 vessels from the PACIFIC trial. Thereafter, the ML score was applied for predicting impaired hyperemic MBF (≤2.30 mL/min per g) from corresponding PET scans. The performance of the ML score was compared with CCTA reads and noninvasive FFR derived from CCTA (FFRCT).

RESULTS

One hundred thirty-nine (23.9%) vessels had FFR-defined ischemia, and 195 (33.6%) vessels had impaired hyperemic MBF. For the prediction of FFR-defined ischemia, the ML score yielded an area under the receiver-operating characteristic curve of 0.92, which was significantly higher than that of visual stenosis grade (0.84; P<0.001) and comparable with that of FFRCT (0.93; P=0.34). Quantitative percent diameter stenosis and low-density noncalcified plaque volume had the greatest ML feature importance for predicting FFR-defined ischemia. When applied for impaired MBF prediction, the ML score exhibited an area under the receiver-operating characteristic curve of 0.80; significantly higher than visual stenosis grade (area under the receiver-operating characteristic curve 0.74; P=0.02) and comparable with FFRCT (area under the receiver-operating characteristic curve 0.77; P=0.16).

CONCLUSIONS

An externally validated ML score integrating CCTA-based quantitative plaque features accurately predicts FFR-defined ischemia and impaired MBF by PET, performing superiorly to standard CCTA stenosis evaluation and comparably to FFRCT.

Association of coronary artery calcium score with qualitatively and quantitatively assessed adverse plaque on coronary CT angiography in the SCOT-HEART trial

AIMS

Coronary artery calcification is a marker of cardiovascular risk, but its association with qualitatively and quantitatively assessed plaque subtypes is unknown.

METHODS AND RESULTS

In this post-hoc analysis, computed tomography (CT) images and 5-year clinical outcomes were assessed in SCOT-HEART trial participants. Agatston coronary artery calcium score (CACS) was measured on non-contrast CT and was stratified as zero (0 Agatston units, AU), minimal (1-9 AU), low (10-99 AU), moderate (100-399 AU), high (400-999 AU), and very high (≥1000 AU). Adverse plaques were investigated by qualitative (visual categorization of positive remodelling, low-attenuation plaque, spotty calcification, and napkin ring sign) and quantitative (calcified, non-calcified, low-attenuation, and total plaque burden; Autoplaque) assessments. Of 1769 patients, 36% had a zero, 9% minimal, 20% low, 17% moderate, 10% high, and 8% very high CACS. Amongst patients with a zero CACS, 14% had non-obstructive disease, 2% had obstructive disease, 2% had visually assessed adverse plaques, and 13% had low-attenuation plaque burden >4%. Non-calcified and low-attenuation plaque burden increased between patients with zero, minimal, and low CACS (P < 0.001), but there was no statistically significant difference between those with medium, high, and very high CACS. Myocardial infarction occurred in 41 patients, 10% of whom had zero CACS. CACS >1000 AU and low-attenuation plaque burden were the only predictors of myocardial infarction, independent of obstructive disease, and 10-year cardiovascular risk score.

CONCLUSION

In patients with stable chest pain, zero CACS is associated with a good but not perfect prognosis, and CACS cannot rule out obstructive coronary artery disease, non-obstructive plaque, or adverse plaque phenotypes, including low-attenuation plaque.

Myocardial Flow Reserve in Coronary Artery Disease with Low Attenuation Plaque: Coronary CTA and 13N-ammonia PET Assessments

RATIONALE AND OBJECTIVES

Physiological measurements from coronary angiography show that coronary stenosis with necrotic core plaque reduces coronary flow reserve (CFR). Myocardial flow reserve (MFR) estimated by ¹³N-ammonia PET (NH₃-PET) is a different index from CFR. Low attenuation plaque (LAP) on coronary CTA (CCTA) contains necrotic core, but the link between LAP and MFR has not been elucidated. We aimed to investigate the influence of LAP on MFR in coronary artery disease (CAD).

MATERIALS AND METHODS

The study included 105 consecutive patients who underwent NH₃-PET and CCTA within 3 months. Nonevaluable coronary arteries due to severe calcification and stent implants were excluded. Finally, 290 major vessels were retrospectively analyzed. Coronary arteries were divided into mild (1%-49%), moderate (50%-69% stenosis), and severe (≥70% stenosis) groups. Coronary plaques were classified either LAP (including soft tissue CT value <30 HU) or completely classified plaques. MFR for the major vessels were calculated and MFR <2.0 was considered a significant decrease. Comparison of MFR between territories with and without LAP, and the effect of plaque characteristics on MFR was analyzed.

RESULTS

MFR was significantly lower for territories with LAP than with calcified plaques or no plaque (2.1 ± 0.7, 2.4 ± 0.7, and 2.3 ± 0.7; p < 0.05). There was no difference between calcified plaque and no plaque territories (p = 0.79). Multivariate logistic analysis for plaque characteristics and stenosis severity revealed that LAP and severe stenosis were independent predictors for territories with MFR <2.0 with odds ratios of 3.1 (95% confidence interval, 1.2-8.1) and 3.0 (95% confidence interval, 1.7-5.3).

CONCLUSION

LAP reduced MFR compared with calcified plaque or no plaque in CAD. LAP is an independent predictor of the territory with MFR <2.0.

Artificial Intelligence in Cardiovascular Atherosclerosis Imaging

At present, artificial intelligence (AI) has already been applied in cardiovascular imaging (e.g., image segmentation, automated measurements, and eventually, automated diagnosis) and it has been propelled to the forefront of cardiovascular medical imaging research. In this review, we presented the current status of artificial intelligence applied to image analysis of coronary atherosclerotic plaques, covering multiple areas from plaque component analysis (e.g., identification of plaque properties, identification of vulnerable plaque, detection of myocardial function, and risk prediction) to risk prediction. Additionally, we discuss the current evidence, strengths, limitations, and future directions for AI in cardiac imaging of atherosclerotic plaques, as well as lessons that can be learned from other areas. The continuous development of computer science and technology may further promote the development of this field.

Predictive Ability of Machine-Learning Methods for Vitamin D Deficiency Prediction by Anthropometric Parameters

Background: Vitamin D deficiency affects the general population and is very common among elderly Europeans. This study compared different supervised learning algorithms in a cohort of Spanish individuals aged 35–75 years to predict which anthropometric parameter was most strongly associated with vitamin D deficiency. Methods: A total of 501 participants were recruited by simple random sampling with replacement (reference population: 43,946). The analyzed anthropometric parameters were waist circumference (WC), body mass index (BMI), waist-to-height ratio (WHtR), body roundness index (BRI), visceral adiposity index (VAI), and the Clinical University of Navarra body adiposity estimator (CUN-BAE) for body fat percentage. Results: All the anthropometric indices were associated, in males, with vitamin D deficiency (p < 0.01 for the entire sample) after controlling for possible confounding factors, except for CUN-BAE, which was the only parameter that showed a correlation in females. Conclusions: The capacity of anthropometric parameters to predict vitamin D deficiency differed according to sex; thus, WC, BMI, WHtR, VAI, and BRI were most useful for prediction in males, while CUN-BAE was more useful in females. The naïve Bayes approach for machine learning showed the best area under the curve with WC, BMI, WHtR, and BRI, while the logistic regression model did so in VAI and CUN-BAE.

The Emergence of Artificial Intelligence in Cardiology: Current and Future Applications

Artificial intelligence technology is emerging as a promising entity in cardiovascular medicine, potentially improving diagnosis and patient care. In this article, we review the literature on artificial intelligence and its utility in cardiology. We provide a detailed description of concepts of artificial intelligence tools like machine learning, deep learning, and cognitive computing. This review discusses the current evidence, application, prospects, and limitations of artificial intelligence in cardiology.

Sex-Specific Computed Tomography Coronary Plaque Characterization and Risk of Myocardial Infarction

OBJECTIVES

This study was designed to investigate whether coronary computed tomography angiography assessments of coronary plaque might explain differences in the prognosis of men and women presenting with chest pain.

BACKGROUND

Important sex differences exist in coronary artery disease. Women presenting with chest pain have different risk factors, symptoms, prevalence of coronary artery disease and prognosis compared to men.

METHODS

Within a multicenter randomized controlled trial, we explored sex differences in stenosis, adverse plaque characteristics (positive remodeling, low-attenuation plaque, spotty calcification, or napkin ring sign) and quantitative assessment of total, calcified, noncalcified and low-attenuation plaque burden.

RESULTS

Of the 1,769 participants who underwent coronary computed tomography angiography, 772 (43%) were female. Women were more likely to have normal coronary arteries and less likely to have adverse plaque characteristics (p < 0.001 for all). They had lower total, calcified, noncalcified, and low-attenuation plaque burdens (p < 0.001 for all) and were less likely to have a low-attenuation plaque burden >4% (41% vs. 59%; p < 0.001). Over a median follow-up of 4.7 years, myocardial infarction (MI) occurred in 11 women (1.4%) and 30 men (3%). In those who had MI, women had similar total, noncalcified, and low-attenuation plaque burdens as men, but men had higher calcified plaque burden. Low-attenuation plaque burden predicted MI (hazard ratio: 1.60; 95% confidence interval: 1.10 to 2.34; p = 0.015), independent of calcium score, obstructive disease, cardiovascular risk score, and sex.

CONCLUSIONS

Women presenting with stable chest pain have less atherosclerotic plaque of all subtypes compared to men and a lower risk of subsequent MI. However, quantitative low-attenuation plaque is as strong a predictor of subsequent MI in women as in men. (Scottish Computed Tomography of the HEART Trial [SCOT-HEART]; NCT01149590).

Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease

ABSTRACT

Rapid technological advances in non-invasive imaging, coupled with the availability of large data sets and the expansion of computational models and power, have revolutionized the role of imaging in medicine. Non-invasive imaging is the pillar of modern cardiovascular diagnostics, with modalities such as cardiac computed tomography (CT) now recognized as first-line options for cardiovascular risk stratification and the assessment of stable or even unstable patients. To date, cardiovascular imaging has lagged behind other fields, such as oncology, in the clinical translational of artificial intelligence (AI)-based approaches. We hereby review the current status of AI in non-invasive cardiovascular imaging, using cardiac CT as a running example of how novel machine learning (ML)-based radiomic approaches can improve clinical care. The integration of ML, deep learning, and radiomic methods has revealed direct links between tissue imaging phenotyping and tissue biology, with important clinical implications. More specifically, we discuss the current evidence, strengths, limitations, and future directions for AI in cardiac imaging and CT, as well as lessons that can be learned from other areas. Finally, we propose a scientific framework in order to ensure the clinical and scientific validity of future studies in this novel, yet highly promising field. Still in its infancy, AI-based cardiovascular imaging has a lot to offer to both the patients and their doctors as it catalyzes the transition towards a more precise phenotyping of cardiovascular disease.

Reproducibility of quantitative plaque measurement in advanced coronary artery disease

BACKGROUND

The ability to characterize and to quantify the extent of coronary artery disease has the potential to improve the prognostic capability of coronary computed tomography angiography. Although reproducible techniques have been described in those with mild coronary disease, this has yet to be assessed in patients with advanced disease.

METHODS

Twenty patients with known multivessel disease underwent repeated computed tomography coronary angiography, 2 weeks apart. Coronary artery segments were analysed using semi-automated software by two trained observers to determine intraobserver, interobserver and interscan reproducibility.

RESULTS

Overall, 149 coronary arterial segments were analysed. There was excellent intraobserver and interobserver agreement for all plaque volume measurements (Lin's coefficient 0.95 to 1.0). There were no substantial interscan differences (P ​> ​0.05 for all) for total (2063 ​± ​1246 ​mm3, mean of differences -35.6 ​mm3), non-calcified (1795 ​± ​910 ​mm3, mean of differences -4.3 ​mm3), calcified (298 ​± ​425 ​mm3, mean of differences -31.3 ​mm3) and low-attenuation (13 ​± ​13 ​mm3, mean of differences -2.6 ​mm3) plaque volumes. Interscan agreement was highest for total and noncalcified plaque volumes. Calcified and low-attenuation plaque (-236.6 to 174 ​mm3 and -15.8 to 10.5 ​mm3 respectively) had relatively wider 95% limits of agreement reflecting the lower absolute plaque volumes.

CONCLUSION

In the presence of advanced coronary disease, semi-automated plaque quantification provides excellent reproducibility, particularly for total and non-calcified plaque volumes. This approach has major potential to assess change in disease over time and optimize risk stratification in patients with established coronary artery disease.

Value of semiquantitative assessment of high-risk plaque features on coronary CT angiography over stenosis in selection of studies for FFRct

INTRODUCTION

The degree of stenosis on coronary CT angiography (CCTA) guides referral for CT-derived flow reserve (FFRct). We sought to assess whether semiquantitative assessment of high-risk plaque (HRP) features on CCTA improves selection of studies for FFRct over stenosis assessment alone.

METHODS

Per-vessel FFRct was computed in 1,395 vessels of 836 patients undergoing CCTA with 25-99% maximal stenosis. By consensus analysis, stenosis severity was graded as 25-49%, 50-69%, 70-89%, and 90-99%. HRPs including low attenuation plaque (LAP), positive remodeling (PR), and spotty calcification (SC) were assessed in lesions with maximal stenosis. Lesion FFRct was measured distal to the lesion with maximal stenosis, and FFRct<0.80 was defined as abnormal. Association of HRP and abnormal lesion FFRct was evaluated by univariable and multivariable logistic regression models.

RESULTS

The frequency of abnormal lesion FFRct increased with increase of stenosis severity across each stenosis category (25-49%:6%; 50-69%:30%; 70-89%:54%; 90-99%:91%, p ​< ​0.001). Univariable analysis demonstrated that stenosis severity, LAP, and PR were predictive of abnormal lesion FFRct, while SC was not. In multivariable analyses considering stenosis severity, presence of PR, LAP, and PR and/or LAP were independently associated with abnormal FFRct: Odds ratio 1.58, 1.68, and 1.53, respectively (p ​< ​0.02 for all). The presence of PR and/or LAP increased the frequency of abnormal FFRct with mild stenosis (p ​< ​0.05) with a similar trend with 70-89% stenosis. The combination of 2 HRP (LAP and PR) identified more lesions with FFR < 0.80 than only 1 HRP.

CONCLUSIONS

Semiquantitative visual assessment of high-risk plaque features may improve the selection of studies for FFRct.

Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT

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.

Prevalence and disease features of myocardial ischemia with non-obstructive coronary arteries: Insights from a dynamic CT myocardial perfusion imaging study

BACKGROUND

Ischemia with non-obstructive coronary arteries (INOCA) is not uncommon in clinical practice. However, the incidence and imaging characteristics of INOCA on dynamic CT myocardial perfusion imaging (CT-MPI) remains unclear. We aimed to investigate the prevalence and disease features of INOCA as evaluated by dynamic CT-MPI + coronary CT angiography (CCTA).

METHODS

Patients with suspected chronic coronary syndrome and intermediate-to-high pre-test probability of obstructive CAD (according to updated Diamond and Forrester Chest Pain Prediction Rule) were referred for dynamic CT-MPI + CCTA and retrospectively included. Various parameters, including myocardial blood flow (MBF) and high-risk plaque (HRP) features, were measured. INOCA was diagnosed if patients were revealed to have myocardial ischemia and absence of obstructive stenosis.

RESULTS

314 patients were finally included. 20 patients (6.4%) were observed to have myocardial ischemia without obstructive stenosis. In addition, 138 patients (43.9%) had normal or near normal findings, 101 patients (32.2%) had obstructive stenosis without myocardial ischemia and 55 patients (17.5%) had obstructive stenosis with myocardial ischemia. Compared with patients with normal/near normal findings, patients with INOCA showed a higher prevalence of positive remodeling (40.0% vs. 17.4%, p = 0.04). In patients with obstructive stenosis, the mean age, calcium score and incidence of spotty calcification, positive remodeling as well as HRPs were significantly higher than those in patients with INOCA (p < 0.05 for all).

CONCLUSIONS

The overall prevalence of INOCA was low in patients with suspected chronic coronary syndrome. HRPs were less frequently presented in patients with INOCA, compared with patients having obstructive coronary stenosis.

Relationship between changes in pericoronary adipose tissue attenuation and coronary plaque burden quantified from coronary computed tomography angiography

AIMS

Increased attenuation of pericoronary adipose tissue (PCAT) around the proximal right coronary artery (RCA) from coronary computed tomography angiography (CTA) has been shown to be associated with coronary inflammation and improved prediction of cardiac death over plaque features. Our aim was to investigate whether PCAT CT attenuation is related to progression of coronary plaque burden.

METHODS AND RESULTS

We analysed CTA studies of 111 stable patients (age 59.2 ± 9.8 years, 77% male) who underwent sequential CTA (3.4 ± 1.6 years between scans) with identical acquisition protocols. Total plaque (TP), calcified plaque (CP), non-calcified plaque (NCP), and low-density non-calcified plaque (LD-NCP) volumes and corresponding burden (plaque volume × 100%/vessel volume) were quantified using semi-automated software. PCAT CT attenuation (HU) was measured around the proximal RCA, the most standardized method for PCAT analysis. Patients with an increase in NCP burden (n = 51) showed an increase in PCAT attenuation, whereas patients with a decrease in NCP burden (n = 60) showed a decrease {4.4 [95% confidence interval (CI) 2.6-6.2] vs. -2.78 (95% CI -4.6 to -1.0) HU, P < 0.0001}. Changes in PCAT attenuation correlated with changes in the burden of NCP (r = 0.55, P < 0.001) and LD-NCP (r = 0.24, P = 0.01); but not CP burden (P = 0.3). Increased baseline PCAT attenuation ≥-75 HU was independently associated with increase in NCP (odds ratio 3.07, 95% CI 1.4-7.0; P < 0.008) and TP burden on follow-up CTA.

CONCLUSION

PCAT attenuation measured from routine CTA is related to the progression of NCP and TP burden. This imaging biomarker may help to identify patients at increased risk of high-risk plaque progression and allow monitoring of beneficial changes from medical therapy.

Prediction of revascularization by coronary CT angiography using a machine learning ischemia risk score

OBJECTIVES

The machine learning ischemia risk score (ML-IRS) is a machine learning-based algorithm designed to identify hemodynamically significant coronary disease using quantitative coronary computed tomography angiography (CCTA). The purpose of this study was to examine whether the ML-IRS can predict revascularization in patients referred for invasive coronary angiography (ICA) after CCTA.

METHODS

This study was a post hoc analysis of a prospective dual-center registry of sequential patients undergoing CCTA followed by ICA within 3 months, referred from inpatient, outpatient, and emergency department settings (n = 352, age 63 ± 10 years, 68% male). The primary outcome was revascularization by either percutaneous coronary revascularization or coronary artery bypass grafting. Blinded readers performed semi-automated quantitative coronary plaque analysis. The ML-IRS was automatically computed. Relationships between clinical risk factors, coronary plaque features, and ML-IRS with revascularization were examined.

RESULTS

The study cohort consisted of 352 subjects with 1056 analyzable vessels. The ML-IRS ranged between 0 and 81% with a median of 18.7% (6.4-34.8). Revascularization was performed in 26% of vessels. Vessels receiving revascularization had higher ML-IRS (33.6% (21.1-55.0) versus 13.0% (4.5-29.1), p < 0.0001), as well as higher contrast density difference, and total, non-calcified, calcified, and low-density plaque burden. ML-IRS, when added to a traditional risk model based on clinical data and stenosis to predict revascularization, resulted in increased area under the curve from 0.69 (95% CI: 0.65-0.72) to 0.78 (95% CI: 0.75-0.81) (p < 0.0001), with an overall continuous net reclassification improvement of 0.636 (95% CI: 0.503-0.769; p < 0.0001).

CONCLUSIONS

ML-IRS from quantitative coronary CT angiography improved the prediction of future revascularization and can potentially identify patients likely to receive revascularization if referred to cardiac catheterization.

KEY POINTS

• Machine learning ischemia risk from quantitative coronary CT angiography was significantly higher in patients who received revascularization versus those who did not receive revascularization. • The machine learning ischemia risk score was significantly higher in patients with invasive fractional flow ≤ 0.8 versus those with > 0.8. • The machine learning ischemia risk score improved the prediction of future revascularization significantly when added to a standard prediction model including stenosis.

Diagnostic value of comprehensive on-site and off-site coronary CT angiography for identifying hemodynamically obstructive coronary artery disease

BACKGROUND

This study aimed to investigate the diagnostic value of comprehensive on-site coronary computed tomography angiography (CCTA) using stenosis and plaque measures and subtended myocardial mass (V_sub) for fractional flow reserve (FFR) defined hemodynamically obstructive coronary artery disease (CAD). Additionally, the incremental diagnostic value of off-site CT-derived FFR (FFR_CT) was assessed.

METHODS

Prospectively enrolled patients underwent CCTA followed by invasive FFR interrogation of all major coronary arteries. Vessels with ≥30% stenosis were included for analysis. On-site CCTA assessment included qualitative and quantitative stenosis (visual grading and minimal lumen area, MLA) and plaque measures (characteristics and volumes), and V_sub. Diagnostic value of comprehensive on-site CCTA assessment was tested by comparing area under the curves (AUC). In vessels with available FFR_CT, the incremental value of off-site FFR_CT was tested.

RESULTS

In 236 vessels (132 patients), MLA, positive remodeling, non-calcified plaque volume, and V_sub were independent on-site CCTA predictors for hemodynamically obstructive CAD (p < 0.05 for all). V_sub/MLA² outperformed all these on-site CCTA parameters (AUC = 0.85) and V_sub was incremental to all other CCTA predictors (p = 0.02). In subgroup analysis (n = 194 vessels), diagnostic performance of FFR_CT and V_sub/MLA² was similar (AUC 0.89 and 0.85 respectively, p = 0.25). Furthermore, diagnostic performance significantly albeit minimally increased when FFR_CT was added to on-site CCTA assessment (ΔAUC = 0.03, p = 0.02).

CONCLUSIONS

In comprehensive on-site CCTA assessment, V_sub/MLA2 demonstrated greatest diagnostic value for hemodynamically obstructive CAD and V_sub was incremental to all evaluated CCTA indices. Additionally, adding FFR_CT only minimally increased diagnostic performance, demonstrating that on-site CCTA assessment is a reasonable alternative to FFR_CT.

Low-Attenuation Noncalcified Plaque on Coronary Computed Tomography Angiography Predicts Myocardial Infarction: Results From the Multicenter SCOT-HEART Trial (Scottish Computed Tomography of the HEART)

BACKGROUND

The future risk of myocardial infarction is commonly assessed using cardiovascular risk scores, coronary artery calcium score, or coronary artery stenosis severity. We assessed whether noncalcified low-attenuation plaque burden on coronary CT angiography (CCTA) might be a better predictor of the future risk of myocardial infarction.

METHODS

In a post hoc analysis of a multicenter randomized controlled trial of CCTA in patients with stable chest pain, we investigated the association between the future risk of fatal or nonfatal myocardial infarction and low-attenuation plaque burden (% plaque to vessel volume), cardiovascular risk score, coronary artery calcium score or obstructive coronary artery stenoses.

RESULTS

In 1769 patients (56% male; 58±10 years) followed up for a median 4.7 (interquartile interval, 4.0-5.7) years, low-attenuation plaque burden correlated weakly with cardiovascular risk score (r=0.34; P<0.001), strongly with coronary artery calcium score (r=0.62; P<0.001), and very strongly with the severity of luminal coronary stenosis (area stenosis, r=0.83; P<0.001). Low-attenuation plaque burden (7.5% [4.8-9.2] versus 4.1% [0-6.8]; P<0.001), coronary artery calcium score (336 [62-1064] versus 19 [0-217] Agatston units; P<0.001), and the presence of obstructive coronary artery disease (54% versus 25%; P<0.001) were all higher in the 41 patients who had fatal or nonfatal myocardial infarction. Low-attenuation plaque burden was the strongest predictor of myocardial infarction (adjusted hazard ratio, 1.60 (95% CI, 1.10-2.34) per doubling; P=0.014), irrespective of cardiovascular risk score, coronary artery calcium score, or coronary artery area stenosis. Patients with low-attenuation plaque burden greater than 4% were nearly 5 times more likely to have subsequent myocardial infarction (hazard ratio, 4.65; 95% CI, 2.06-10.5; P<0.001).

CONCLUSIONS

In patients presenting with stable chest pain, low-attenuation plaque burden is the strongest predictor of fatal or nonfatal myocardial infarction. These findings challenge the current perception of the supremacy of current classical risk predictors for myocardial infarction, including stenosis severity. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01149590.

Association between perivascular inflammation and downstream myocardial perfusion in patients with suspected coronary artery disease

Aims

To investigate the association between pericoronary adipose tissue (PCAT) computed tomography (CT) attenuation derived from coronary computed tomography angiography (CTA) and coronary flow reserve (CFR) by positron emission tomography (PET) in patients with suspected coronary artery disease (CAD).

Methods and results

PCAT CT attenuation was measured in proximal segments of all major epicardial coronary vessels of 105 patients with suspected CAD. We evaluated the relationship between PCAT CT attenuation and other quantitative/qualitative CT-derived anatomic parameters with CFR by PET. Overall, the mean age was 60 ± 12 years and 93% had intermediate pre-test probability of obstructive CAD. Obstructive CAD (≥50% stenosis) was detected in 37 (35.2%) patients and impaired CFR (&lt;2.0) in 32 (30.5%) patients. On a per-vessel analysis (315 vessels), obstructive CAD, non-calcified plaque volume, and PCAT CT attenuation were independently associated with CFR. In patients with coronary calcium score (CCS) &lt;100, those with high-PCAT CT attenuation presented significantly lower CFR values than those with low-PCAT CT attenuation (2.47 ± 0.95 vs. 3.13 ± 0.89, P = 0.003). Among those without obstructive CAD, CFR was significantly lower in patients with high-PCAT CT attenuation (2.51 ± 0.95 vs. 3.02 ± 0.84, P = 0.021).

Conclusion

Coronary perivascular inflammation by CTA was independently associated with downstream myocardial perfusion by PET. In patients with low CCS or without obstructive CAD, CFR was lower in the presence of higher perivascular inflammation. PCAT CT attenuation might help identifying myocardial ischaemia particularly among patients who are traditionally considered non-high risk for future cardiovascular events.

Diagnostic Value of Lesion-specific Measurement of Myocardial Blood Flow Using Hybrid PET/CT

BACKGROUND

We evaluated whether lesion-specific measurement of myocardial blood flow (MBF) and flow reserve (MFR) by hybrid imaging of myocardial perfusion positron emission tomography (PET) and coronary computed tomography (CT) can provide additional diagnostic value.

METHODS

Forty-three patients with stable angina underwent N-13 ammonia PET and coronary CT before invasive coronary angiography (CAG). The lesion-specific MBF was calculated from the average MBF of the myocardial segments downstream of a coronary stenosis using hybrid PET/CT images. The hyperemic MBF, resting MBF, and MFR were measured for the left anterior descending artery (LAD) using conventional and lesion-specific methods. The diagnostic accuracy was compared between the two methods for significant LAD stenoses (≥ 70% reference diameter on CAG).

RESULTS

There were 19 significant LAD stenoses. The sensitivity, specificity, negative predictive value, positive predictive value, and accuracy were 71%, 68%, 74%, 65%, and 70% for conventional hyperemic MBF (optimal cutoff = 2.15 mL/min/g), 79%, 63%, 74%, 65%, and 70% for conventional MFR (optimal cutoff = 1.82), 83%, 74%, 80%, 78%, and 80% for lesion-specific hyperemic MBF (optimal cutoff = 1.75 mL/min/g), and 79%, 79%, 83%, 75%, and 79% for lesion-specific MFR (optimal cutoff = 1.86), respectively. The lesion-specific measurement was more accurate and had a better linear correlation with anatomical stenosis severity for both hyperemic MBF and MFR.

CONCLUSIONS

Lesion-specific measurement using hybrid PET/CT imaging showed significant improvement in the diagnostic accuracy of PET-measured hyperemic MBF and MFR.

Machine learning in the integration of simple variables for identifying patients with myocardial ischemia

BACKGROUND

A significant number of variables are obtained when characterizing patients suspected with myocardial ischemia or at risk of MACE. Guidelines typically use a handful of them to support further workup or therapeutic decisions. However, it is likely that the numerous available predictors maintain intrinsic complex interrelations. Machine learning (ML) offers the possibility to elucidate complex patterns within data to optimize individual patient classification. We evaluated the feasibility and performance of ML in utilizing simple accessible clinical and functional variables for the identification of patients with ischemia or an elevated risk of MACE as determined through quantitative PET myocardial perfusion reserve (MPR).

METHODS

1,234 patients referred to Nitrogen-13 ammonia PET were analyzed. Demographic (4), clinical (8), and functional variables (9) were retrieved and input into a cross-validated ML workflow consisting of feature selection and modeling. Two PET-defined outcome variables were operationalized: (1) any myocardial ischemia (regional MPR < 2.0) and (2) an elevated risk of MACE (global MPR < 2.0). ROC curves were used to evaluate ML performance.

RESULTS

16 features were included for boosted ensemble ML. ML achieved an AUC of 0.72 and 0.71 in identifying patients with myocardial ischemia and with an elevated risk of MACE, respectively. ML performance was superior to logistic regression when the latter used the ESC guidelines risk models variables for both PET-defined labels (P < .001 and P = .01, respectively).

CONCLUSIONS

ML is feasible and applicable in the evaluation and utilization of simple and accessible predictors for the identification of patients who will present myocardial ischemia and an elevated risk of MACE in quantitative PET imaging.

Artificial Intelligence in Cardiovascular Medicine

PURPOSE OF REVIEW

The ripples of artificial intelligence are being felt in various sectors of human life. Machine learning, a subset of artificial intelligence, extracts information from large databases of information and is gaining traction in various fields of cardiology. In this review, we highlight noteworthy examples of machine learning utilization in echocardiography, nuclear cardiology, computed tomography, and magnetic resonance imaging over the past year.

RECENT FINDINGS

In the past year, machine learning (ML) has expanded its boundaries in cardiology with several positive results. Some studies have integrated clinical and imaging information to further augment the accuracy of these ML algorithms. All the studies mentioned in this review have clearly demonstrated superior results of ML in relation to conventional approaches for identifying obstructions or predicting major adverse events in reference to conventional approaches. As the influx of data arriving from gradually evolving technologies in health care and wearable devices continues to be more complex, ML may serve as the bridge to transcend the gap between health care and patients in the future. In order to facilitate a seamless transition between both, a few issues must be resolved for a successful implementation of ML in health care.

Artificial Intelligence in Cardiovascular Imaging: JACC State-of-the-Art Review

Data science is likely to lead to major changes in cardiovascular imaging. Problems with timing, efficiency, and missed diagnoses occur at all stages of the imaging chain. The application of artificial intelligence (AI) is dependent on robust data; the application of appropriate computational approaches and tools; and validation of its clinical application to image segmentation, automated measurements, and eventually, automated diagnosis. AI may reduce cost and improve value at the stages of image acquisition, interpretation, and decision-making. Moreover, the precision now possible with cardiovascular imaging, combined with "big data" from the electronic health record and pathology, is likely to better characterize disease and personalize therapy. This review summarizes recent promising applications of AI in cardiology and cardiac imaging, which potentially add value to patient care.

Myocardial Perfusion Imaging: A Brief Review of Nuclear and Nonnuclear Techniques and Comparative Evaluation of Recent Advances

Coronary artery disease (CAD) is the leading cause of morbidity and mortality worldwide. Invasive coronary angiography (ICA) is the gold standard for the evaluation of epicardial CAD. In the pathogenesis of the CAD, myocardial perfusion abnormalities are the first changes that appear followed by wall motion abnormalities, electrocardiogram changes, and angina. Myocardial perfusion imaging (MPI) demonstrates the cumulative effect of pathology at epicardial coronary arteries, small vessels, and endothelium. Thus, it evaluates the overall burden of ischemic heart disease (IHD). MPI is used noninvasively to diagnose early asymptomatic CAD or to know the functional significance of known CAD. There are evidence that early detection of myocardial perfusion abnormalities followed by aggressive intervention against cardiovascular risk factors may restore myocardial perfusion. This may lead to reduce morbidity and mortality. Various MPI modalities have been used to diagnose and define the severity of CAD. Cardiac myocardial perfusion single-photon emission computed tomography (myocardial perfusion scintigraphy [MPS]) has been in use since decades. Several newer modalities such as positron emission tomography, cardiac magnetic resonance imaging, computed tomography perfusion, and myocardial contrast echocardiography are developing utilizing the similar principle of MPS. We shall be reviewing briefly these modalities, their performance, comparison to each other, and with ICA.

Quantitative global plaque characteristics from coronary computed tomography angiography for the prediction of future cardiac mortality during long-term follow-up

Aims

Adverse plaque characteristics determined by coronary computed tomography angiography (CTA) have been associated with future cardiac events. Our aim was to investigate whether quantitative global per-patient plaque characteristics from coronary CTA can predict subsequent cardiac death during long-term follow-up.

Methods and results

Out of 2748 patients without prior history of coronary artery disease undergoing CTA with dual-source CT, 32 patients suffered cardiac death (mean follow-up of 5 ± 2 years). These patients were matched to 32 controls by age, gender, risk factors, and symptoms (total 64 patients, 59% male, age 69 ± 10 years). Coronary CTA data sets were analysed by semi-automated software to quantify plaque characteristics over the entire coronary tree, including total plaque volume, volumes of non-calcified plaque (NCP), low-density non-calcified plaque (LD-NCP, attenuation <30 Hounsfield units), calcified plaque (CP), and corresponding burden (plaque volume × 100%/vessel volume), as well as stenosis and contrast density difference (CDD, maximum percent difference in luminal attenuation/cross-sectional area compared to proximal cross-section). In patients who died from cardiac cause, NCP, LD-NCP, CP and total plaque volumes, quantitative stenosis, and CDD were significantly increased compared to controls (P < 0.025 for all). NCP > 146 mm³ [hazards ratio (HR) 2.24; 1.09-4.58; P = 0.027], LD-NCP > 10.6 mm³ (HR 2.26; 1.11-4.63; P = 0.025), total plaque volume > 179 mm³ (HR 2.30; 1.12-4.71; P = 0.022), and CDD > 35% in any vessel (HR 2.85;1.4-5.9; P = 0.005) were associated with increased risk of future cardiac death, when adjusted for segment involvement score.

Conclusion

Among quantitative global plaque characteristics, total, non-calcified, and low-density plaque volumes as well as CDD predict cardiac death in long-term follow-up.

Positron Emission Tomography in the Diagnosis and Management of Coronary Artery Disease

Cardiac positron emission tomography (PET) and positron emission tomography/computed tomography (PET/CT) are encouraging precise non-invasive imaging modalities that allow imaging of the cellular function of the heart, while other non-invasive cardiovascular imaging modalities are considered to be techniques for imaging the anatomy, morphology, structure, function and tissue characteristics. The role of cardiac PET has been growing rapidly and providing high diagnostic accuracy of coronary artery disease (CAD). Clinical cardiology has established PET as a criterion for the assessment of myocardial viability and is recommended for the proper management of reduced left ventricle (LV) function and ischemic cardiomyopathy. Hybrid PET/CT imaging has enabled simultaneous integration of the coronary anatomy with myocardial perfusion and metabolism and has improved characterization of dysfunctional areas in chronic CAD. Also, the availability of quantitative myocardial blood flow (MBF) evaluation with various PET perfusion tracers provides additional prognostic information and enhances the diagnostic performance of nuclear imaging.

Improvement in LDL is associated with decrease in non-calcified plaque volume on coronary CTA as measured by automated quantitative software

BACKGROUND

Computed tomography coronary angiography (CTA) can be used for assessment of plaque characteristics; however, quantitative assessment of changes in plaque composition in response to LDL lowering has not been performed with CTA. We sought to assess the association between LDL reduction and changes in plaque composition with quantitative CTA.

METHODS

Quantification of total, calcified, non-calcified and low-density non-calcified plaque volumes (TPV, CPV, NCPV and LD-NCPV) was performed using semi-automated software in 234 vessels from 116 consecutive patients (89 men, 60 ± 10 years) with baseline LDL>70 mg/dl. Significant reduction in LDL was defined as a decrease by >10% of baseline LDL. Changes (Δ) in plaque volumes between the second and baseline study were compared between patients with LDL reduction (n = 63) and those with no decrease in LDL (n = 53).

RESULTS

Median LDL at baseline was 98 mg/dl [interquartile range (IQR) 83-119 mg/dl] and median ΔLDL was -14 mg/dl (IQR -38 to 3 mg/dl). Mean interval between sequential CTA was 3.5 ± 1.6 years. TPV, NCPV, and LD-NCPV decreased in patients with a reduction in LDL compared to baseline; whereas, patients without reduction in LDL experienced an increase in TPV, NCPV and LD-NCPV. After adjusting for age, statin use, diabetes, baseline LDL and baseline TPV, reduction in LDL was associated with a decrease in TPV (P = 0.005), NCPV (P = 0.002) and LD-NCPV (P = 0.011) compared to patients without a reduction in LDL.

CONCLUSION

Reduction in LDL was associated with beneficial changes in the amount and composition of noncalcified plaque as measured using semi-automated quantitative software by CTA.

Reasons and implications of agreements and disagreements between coronary flow reserve, fractional flow reserve, and myocardial perfusion imaging

Information on coronary physiology and myocardial blood flow (MBF) in patients with suspected angina is increasingly important to inform treatment decisions. A number of different techniques including myocardial perfusion imaging (MPI), noninvasive estimation of MBF, and coronary flow reserve (CFR), as well as invasive methods for CFR and fractional flow reserve (FFR) are now readily available. However, despite their incorporation into contemporary guidelines, these techniques are still poorly understood and their interpretation to guide revascularization decisions is often inconsistent. In particular, these inconsistencies arise when there are discrepancies between the various techniques. The purpose of this article is therefore to review the basic principles, techniques, and clinical value of MPI, FFR, and CFR-with particular focus on interpreting their agreements and disagreements.

Role of new imaging modalities in pursuit of the vulnerable plaque and the vulnerable patient

Numerous biomarkers and imaging modalities were investigated during the past few decades to identify patients harboring plaques at high risk of rupturing and causing catastrophic events. The classical description of a vulnerable plaque included a large lipid core, covered by a thin fibrous cap and evidence of inflammation especially around the hinge points of the plaque. Unfortunately, the search has resulted to a large extent in a failure to accurately identify the site of a future event. In time the search focus switched to the vulnerable patient rather than the individual vulnerable plaques, but the debate continues as to the more appropriate approach to risk assessment. This review discusses the most recent developments in molecular, anatomical and functional imaging directed at identifying a patient at high-risk of coronary artery disease events.

Long-Term Prognostic Value of Coronary Computed Tomography Angiography

OPINION STATEMENT

Coronary CT angiography (CTA) is a highly accurate test for the diagnosis of coronary artery disease (CAD), with its use guided by numerous contemporary appropriate use criteria and clinical guidelines. Unique among non-invasive tests for CAD, coronary CTA provides direct visualization of coronary atherosclerosis for the assessment of angiographic stenosis, as well as validated measures of plaque vulnerability. Long-term studies now clearly demonstrate that the absence of CAD on coronary CTA identifies a patient that is at very low risk for future cardiovascular events. Conversely, the presence, location, and severity of CAD as measured on coronary CTA provide powerful prognostic information that is superior to traditional risk factors and other clinical variables. Observational studies and data obtained from clinical trials suggest that the anatomic information derived from coronary CTA significantly increases the utilization of statins and aspirin. Furthermore, these changes are associated with reductions in the risk for mortality, revascularizations, and incident myocardial infarctions among subjects with coronary atherosclerosis. As a result, current societal consensus statements have attempted to standardize coronary CTA reporting, to include incorporation of vulnerable plaque features and recommendations on the use of preventive therapies, such as statins, so to more consistently link important prognostic findings on coronary CTA to appropriate preventive and therapeutic interventions. Automated measures of total coronary plaque volume, machine learning, and CT-derived fractional flow reserve may further refine the prognostic accuracy of coronary CTA. Herein, we summarize recently published literature that reports the long-term (≥ 5 years of follow-up) prognostic usefulness of coronary CTA.

Updates on Stress Imaging Testing and Myocardial Viability With Advanced Imaging Modalities

OPINION STATEMENT

Non-invasive stress testing plays a key role in diagnosis and risk stratification in patients with coronary artery disease. Technical advances in CT, MRI, and PET have lead to increased utility of these modalities in myocardial perfusion imaging. The aim of the review is to provide a succinct update on CT, PET, and MRI for myocardial stress perfusion imaging.

Cardiac imaging: working towards fully-automated machine analysis & interpretation

INTRODUCTION

Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered: This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary: Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation.

Imaging of coronary atherosclerosis - evolution towards new treatment strategies

Coronary atherosclerosis and the precipitation of acute myocardial infarction are highly complex processes, which makes accurate risk prediction challenging. Rapid developments in invasive and noninvasive imaging technologies now provide us with detailed, exquisite images of the coronary vasculature that allow direct investigation of a wide range of these processes. These modalities include sophisticated assessments of luminal stenoses and myocardial perfusion, complemented by novel measures of the atherosclerotic plaque burden, adverse plaque characteristics, and disease activity. Together, they can provide comprehensive, individualized assessments of coronary atherosclerosis as it occurs in patients. Not only can this information provide important pathological insights, but it can also potentially be used to guide personalized treatment decisions. In this Review, we describe the latest advances in both established and emerging imaging techniques, focusing on the strengths and weakness of each approach. Moreover, we discuss how these technological advances might be translated from attractive images into novel imaging strategies and definite improvements in clinical risk prediction and patient outcomes. This process will not be easy, and the many potential barriers and difficulties are also reviewed.