From CT to artificial intelligence for complex assessment of plaque-associated risk

Effect of variability of mechanical properties on the predictive capabilities of vulnerable coronary plaques

BACKGROUND AND OBJECTIVE

Coronary plaque rupture is a precipitating event responsible for two thirds of myocardial infarctions. Currently, the risk of plaque rupture is computed based on demographic, clinical, and image-based adverse features. However, using these features the absolute event rate per single higher-risk lesion remains low. This work studies the power of a novel framework based on biomechanical markers accounting for material uncertainty to stratify vulnerable and non-vulnerable coronary plaques.

METHODS

Virtual histology intravascular ultrasounds from 55 patients, 29 affected by acute coronary syndrome and 26 affected by stable angina pectoris, were included in this study. Two-dimensional vessel cross-sections for finite element modeling (10 sections per plaque) incorporating plaque structure (medial tissue, loose matrix, lipid core and calcification) were reconstructed. A Montecarlo finite element analysis was performed on each section to account for material variability on three biomechanical markers: peak plaque structural stress at diastolic and systolic pressure, and peak plaque stress difference between systolic and diastolic pressures, together with the luminal pressure. Machine learning decision tree classifiers were trained on 75% of the dataset and tested on the remaining 25% with a combination of feature selection techniques. Performance against classification trees based on geometric markers (i.e., luminal, external elastic membrane and plaque areas) was also performed.

RESULTS

Our results indicate that the plaque structural stress outperforms the classification capacity of the combined geometric markers only (0.82 vs 0.51 area under curve) when accounting for uncertainty in material parameters. Furthermore, the results suggest that the combination of the peak plaque structural stress at diastolic and systolic pressures with the maximum plaque structural stress difference between systolic and diastolic pressures together with the systolic pressure and the diastolic to systolic pressure gradient is a robust classifier for coronary plaques when the intrinsic variability in material parameters is considered (area under curve equal to [0.91-0.93]).

CONCLUSION

In summary, our results emphasize that peak plaque structural stress in combination with the patient's luminal pressure is a potential classifier of plaque vulnerability as it independently considers stress in all directions and incorporates total geometric and compositional features of atherosclerotic plaques.

Enhancing cardiovascular risk stratification: Radiomics of coronary plaque and perivascular adipose tissue - Current insights and future perspectives

Radiomics, the quantitative extraction and mining of features from radiological images, has recently emerged as a promising source of non-invasive image-based cardiovascular biomarkers, potentially revolutionizing diagnostics and risk assessment. This review explores its application within coronary plaques and pericoronary adipose tissue, particularly focusing on plaque characterization and cardiac events prediction. By shedding light on the current state-of-the-art, achievements, and prospective avenues, this review contributes to a deeper understanding of the evolving landscape of radiomics in the context of coronary arteries. Finally, open challenges and existing gaps are emphasized to underscore the need for future efforts aimed at ensuring the robustness and reliability of radiomics studies, facilitating their clinical translation.

Stenosis Detection and Quantification of Coronary Artery Using Machine Learning and Deep Learning

The aim of this review is to introduce some applications of artificial intelligence (AI) algorithms for the detection and quantification of coronary stenosis using computed tomography angiography (CTA). The realization of automatic/semi-automatic stenosis detection and quantification includes the following steps: vessel central axis extraction, vessel segmentation, stenosis detection, and quantification. Many new AI techniques, such as machine learning and deep learning, have been widely used in medical image segmentation and stenosis detection. This review also summarizes the recent progress regarding coronary stenosis detection and quantification, and discusses the development trends in this field. Through evaluation and comparison, researchers can better understand the research frontier in related fields, compare the advantages and disadvantages of various methods, and better optimize the new technologies. Machine learning and deep learning will promote the process of automatic detection and quantification of coronary artery stenosis. However, the machine learning and the deep learning methods need a large amount of data, so they also face some challenges because of the lack of professional image annotations (manually add labels by experts).

Patient-level explainable machine learning to predict major adverse cardiovascular events from SPECT MPI and CCTA imaging

BACKGROUND

Machine learning (ML) has shown promise in improving the risk prediction in non-invasive cardiovascular imaging, including SPECT MPI and coronary CT angiography. However, most algorithms used remain black boxes to clinicians in how they compute their predictions. Furthermore, objective consideration of the multitude of available clinical data, along with the visual and quantitative assessments from CCTA and SPECT, are critical for optimal patient risk stratification. We aim to provide an explainable ML approach to predict MACE using clinical, CCTA, and SPECT data.

METHODS

Consecutive patients who underwent clinically indicated CCTA and SPECT myocardial imaging for suspected CAD were included and followed up for MACEs. A MACE was defined as a composite outcome that included all-cause mortality, myocardial infarction, or late revascularization. We employed an Automated Machine Learning (AutoML) approach to predict MACE using clinical, CCTA, and SPECT data. Various mainstream models with different sets of hyperparameters have been explored, and critical predictors of risk are obtained using explainable techniques on the global and patient levels. Ten-fold cross-validation was used in training and evaluating the AutoML model.

RESULTS

A total of 956 patients were included (mean age 61.1 ±14.2 years, 54% men, 89% hypertension, 81% diabetes, 84% dyslipidemia). Obstructive CAD on CCTA and ischemia on SPECT were observed in 14% of patients, and 11% experienced MACE. ML prediction's sensitivity, specificity, and accuracy in predicting a MACE were 69.61%, 99.77%, and 96.54%, respectively. The top 10 global predictive features included 8 CCTA attributes (segment involvement score, number of vessels with severe plaque ≥70, ≥50% stenosis in the left marginal coronary artery, calcified plaque, ≥50% stenosis in the left circumflex coronary artery, plaque type in the left marginal coronary artery, stenosis degree in the second obtuse marginal of the left circumflex artery, and stenosis category in the marginals of the left circumflex artery) and 2 clinical features (past medical history of MI or left bundle branch block, being an ever smoker).

CONCLUSION

ML can accurately predict risk of developing a MACE in patients suspected of CAD undergoing SPECT MPI and CCTA. ML feature-ranking can also show, at a sample- as well as at a patient-level, which features are key in making such a prediction.

Artificial Intelligence in Coronary CT Angiography: Current Status and Future Prospects

Coronary heart disease (CHD) is the leading cause of mortality in the world. Early detection and treatment of CHD are crucial. Currently, coronary CT angiography (CCTA) has been the prior choice for CHD screening and diagnosis, but it cannot meet the clinical needs in terms of examination quality, the accuracy of reporting, and the accuracy of prognosis analysis. In recent years, artificial intelligence (AI) has developed rapidly in the field of medicine; it played a key role in auxiliary diagnosis, disease mechanism analysis, and prognosis assessment, including a series of studies related to CHD. In this article, the application and research status of AI in CCTA were summarized and the prospects of this field were also described.

Artificial Intelligence (Enhanced Super-Resolution Generative Adversarial Network) for Calcium Deblooming in Coronary Computed Tomography Angiography: A Feasibility Study

Background: The presence of heavy calcification in the coronary artery always presents a challenge for coronary computed tomography angiography (CCTA) in assessing the degree of coronary stenosis due to blooming artifacts associated with calcified plaques. Our study purpose was to use an advanced artificial intelligence (enhanced super-resolution generative adversarial network [ESRGAN]) model to suppress the blooming artifact in CCTA and determine its effect on improving the diagnostic performance of CCTA in calcified plaques. Methods: A total of 184 calcified plaques from 50 patients who underwent both CCTA and invasive coronary angiography (ICA) were analysed with measurements of coronary lumen on the original CCTA, and three sets of ESRGAN-processed images including ESRGAN-high-resolution (ESRGAN-HR), ESRGAN-average and ESRGAN-median with ICA as the reference method for determining sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Results: ESRGAN-processed images improved the specificity and PPV at all three coronary arteries (LAD-left anterior descending, LCx-left circumflex and RCA-right coronary artery) compared to original CCTA with ESRGAN-median resulting in the highest values being 41.0% (95% confidence interval [CI]: 30%, 52.7%) and 26.9% (95% CI: 22.9%, 31.4%) at LAD; 41.7% (95% CI: 22.1%, 63.4%) and 36.4% (95% CI: 28.9%, 44.5%) at LCx; 55% (95% CI: 38.5%, 70.7%) and 47.1% (95% CI: 38.7%, 55.6%) at RCA; while corresponding values for original CCTA were 21.8% (95% CI: 13.2%, 32.6%) and 22.8% (95% CI: 20.8%, 24.9%); 12.5% (95% CI: 2.6%, 32.4%) and 27.6% (95% CI: 24.7%, 30.7%); 17.5% (95% CI: 7.3%, 32.8%) and 32.7% (95% CI: 29.6%, 35.9%) at LAD, LCx and RCA, respectively. There was no significant effect on sensitivity and NPV between the original CCTA and ESRGAN-processed images at all three coronary arteries. The area under the receiver operating characteristic curve was the highest with ESRGAN-median images at the RCA level with values being 0.76 (95% CI: 0.64, 0.89), 0.81 (95% CI: 0.69, 0.93), 0.82 (95% CI: 0.71, 0.94) and 0.86 (95% CI: 0.76, 0.96) corresponding to original CCTA and ESRGAN-HR, average and median images, respectively. Conclusions: This feasibility study shows the potential value of ESRGAN-processed images in improving the diagnostic value of CCTA for patients with calcified plaques.

Current State and Future Perspectives of Artificial Intelligence for Automated Coronary Angiography Imaging Analysis in Patients with Ischemic Heart Disease

Purpose of Review

Artificial intelligence (AI) applications in (interventional) cardiology continue to emerge. This review summarizes the current state and future perspectives of AI for automated imaging analysis in invasive coronary angiography (ICA).

Recent Findings

Recently, 12 studies on AI for automated imaging analysis In ICA have been published. In these studies, machine learning (ML) models have been developed for frame selection, segmentation, lesion assessment, and functional assessment of coronary flow. These ML models have been developed on monocenter datasets (in range 31–14,509 patients) and showed moderate to good performance. However, only three ML models were externally validated.

Summary

Given the current pace of AI developments for the analysis of ICA, less-invasive, objective, and automated diagnosis of CAD can be expected in the near future. Further research on this technology in the catheterization laboratory may assist and improve treatment allocation, risk stratification, and cath lab logistics by integrating ICA analysis with other clinical characteristics.

Functional Coronary Artery Assessment: a Systematic Literature Review

Cardiovascular diseases represent the number one cause of death in the world, including the most common disorders in the heart's health, namely coronary artery disease (CAD). CAD is mainly caused by fat accumulated in the arteries' internal walls, creating an atherosclerotic plaque that impacts the blood flow functional behavior. Anatomical plaque characteristics are essential but not sufficient for a complete functional assessment of CAD. In fact, plaque analysis and visual inspection alone have proven insufficient to determine the lesion severity and hemodynamic repercussion. Furthermore, the fractional flow reserve (FFR) exam, which is considered the gold standard for stenosis functional impair determination, is invasive and contains several limitations. Such a panorama evidences the need for new techniques applied to image exams to improve CAD functional assessment. In this article, we perform a systematic literature review on emerging methods determining CAD significance, thus delivering a unique base for comparing these methods, qualitatively and quantitatively. Our goal is to guide further studies with evidence from the most promising methods, highlighting the benefits from both areas. We summarize benchmarks, metrics for evaluation, and challenges already faced, thus shedding light on the requirements for a valid, meaningful, and accepted technique for functional assessment evaluation. We create a base of comparison based on quantitative and qualitative indicators and highlight the most relevant geometrical metrics that correlate with lesion significance. Finally, we point out future benchmarks based on recent literature.

Predicting haemodynamic significance of coronary stenosis with radiomics-based pericoronary adipose tissue characteristics

AIM

To investigate the diagnostic performance of the radiomics features of pericoronary adipose tissue (PCAT) in determining haemodynamically significant coronary artery stenosis as evaluated by fractional flow reserve (FFR).

MATERIALS AND METHODS

A total of 92 patients with clinically suspected coronary artery disease who underwent coronary computed tomography (CT) angiography (CCTA), invasive coronary angiography (ICA), and FFR examination within 1 month were included retrospectively, and 121 lesions were randomly assigned to the training and testing set. Based on manual segmentation of PCAT, 1,116 radiomics features were computed. After radiomics robustness assessment and feature selection, radiomics models were established using the different machine-learning algorithms. The area under the receiver operating characteristic (ROC) curve (AUC) and net reclassification index (NRI) were analysed to compare the discrimination and reclassification abilities of radiomics models.

RESULTS

Two radiomics features were selected after exclusions, and both were significantly higher in coronary arteries with FFR ≤0.8 than those with FFR >0.8. ROC analysis showed that the combination of CCTA and decision tree radiomics model achieved significantly higher diagnostic performance (AUC: 0.812) than CCTA alone (AUC: 0.599, p=0.015). Furthermore, the NRI of the combined model was 0.820 and 0.775 in the training and testing sets, respectively, suggesting the radiomics features of PCAT had were effective in classifying the haemodynamic significance of coronary stenosis.

CONCLUSIONS

Adding PCAT radiomics features to CCTA enabled identification of haemodynamically significant coronary artery stenosis.

Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review

Background

Artificial intelligence (AI) technology has been increasingly developed and studied in cardiac imaging. This systematic review summarizes the latest progress of image segmentation, quantification, and the clinical application of AI in evaluating cardiac adipose tissue.

Methods

We exhaustively searched PubMed and the Web of Science for publications prior to 30 April 2021. The search included eligible studies that used AI for image analysis of epicardial adipose tissue (EAT) or pericoronary adipose tissue (PCAT). The risk of bias and concerns regarding applicability were assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool.

Results

Of the 140 initially identified citation records, 19 high-quality studies were eligible for this systematic review, including 15 (79%) on the image segmentation and quantification of EAT or PCAT and 4 (21%) on the clinical application of EAT or PCAT in cardiovascular diseases. All 19 included studies were rated as low risk of bias in terms of flow and timing, reference standards, and the index test and as having low concern of applicability in terms of reference standards and patient selection, but 16 (84%) studies did not conduct external validation.

Conclusion

AI technology can provide accurate and quicker methods to segment and quantify EAT and PCAT images and shows potential value in the diagnosis and risk prediction of cardiovascular diseases. AI is expected to expand the value of cardiac adipose tissue imaging.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41824-021-00107-0.

Site-specific Phenotype of Atherosclerotic Lesions According to Their Location Within the Coronary Tree – a CCTA-based Study of Vulnerable Plaques

Background: The evaluation of site-specific phenotype according to the topographic location of atherosclerotic lesions within the coronary tree has not been studied so far. The present study is based on the premise that the location of coronary plaques can influence their composition and degree of vulnerability. Aim: To evaluate different phenotypes of vulnerable coronary plaques across the three major coronary arteries in terms of composition, morphology, and degree of vulnerability, in patients with chest pain and low-to-intermediate probability of coronary artery disease, using coronary computed tomography angiography (CCTA) and a complex plaque analysis. Material and methods: This was a cross-sectional study on 75 subjects undergoing CCTA for chest pain, who presented at least one vulnerable coronary plaque (VP), defined as the presence of ≥1 CT vulnerability marker (low attenuation plaque, napkin-ring sign, spotty calcifications, positive remodeling). The study included per plaque analysis of 90 vulnerable coronary lesions identified in various locations within the coronary tree as follows: n = 30 VPs in the left anterior descending artery (LAD), n = 30 VPs in the circumflex artery (CXA), and n = 30 VPs in the right coronary artery (RCA). Results: The RCA exhibited significantly longer VPs (p = 0.001), with the largest volume (p = 0.0007) compared to those arising from the LAD and CXA. Vulnerable plaques located in the LAD exhibited a significantly more calcified phenotype (calcified volume: LAD – 44.07 ± 63.90 mm3 vs. CXA – 12.40 ± 19.65 mm3 vs. RCA – 33.69 ± 34.38 mm3, p = 0.002). Plaques from the RCA presented a more non-calcified phenotype, with the largest non-calcified (p = 0.002), lipid rich (p = 0.0005), and fibrotic volumes (p = 0.003). Low-attenuation plaques were most frequent in the RCA (p = 0.0009), while the highest vulnerability degree was present in lesions located in the LAD, which presented the highest number of vulnerability markers per plaque (p = 0.01). Conclusions: Vulnerable plaques arising from the right coronary artery are longer, more vo-luminous and with larger lipid and non-calcified content, whereas those located in the left anterior descending artery present a higher volume of calcium, but also a higher degree of vulnerability. The least vulnerable lesions were present in the circumflex artery.

The Radiologist as a Gatekeeper in Chest Pain

Chest pain is a symptom that can be found in life-threatening conditions such as acute coronary syndrome (ACS). Those patients requiring invasive coronary angiography treatment or surgery should be identified. Often the clinical setting and laboratory tests are not sufficient to rule out a coronary or aortic syndrome. Cardiac radiological imaging has evolved in recent years both in magnetic resonance (MR) and in computed tomography (CT). CT, in particular, due to its temporal and spatial resolution, the quickness of the examination, and the availability of scanners, is suitable for the evaluation of these patients. In particular, the latest-generation CT scanners allow the exclusion of diagnoses such as coronary artery disease and aortic pathology, thereby reducing the patient's stay in hospital and safely selecting patients by distinguishing those who do not need further treatment from those who will need more- or less-invasive therapies. CT additionally reduces costs by improving long-term patient outcome. The limitations related to patient characteristics and those related to radiation exposure are weakening with the improvement of CT technology.

Imaging Cardiovascular Inflammation in the COVID-19 Era

Cardiac complications are among the most frequent extrapulmonary manifestations of COVID-19 and are associated with high mortality rates. Moreover, positive SARS-CoV-2 patients with underlying cardiovascular disease are more likely to require intensive care and are at higher risk of death. The underlying mechanism for myocardial injury is multifaceted, in which the severe inflammatory response causes myocardial inflammation, coronary plaque destabilization, acute thrombotic events, and ischemia. Cardiac magnetic resonance (CMR) imaging is the non-invasive method of choice for identifying myocardial injury, and it is able to differentiate between underlying causes in various and often challenging clinical scenarios. Multimodal imaging protocols that incorporate CMR and computed tomography provide a complex evaluation for both respiratory and cardiovascular complications of SARS-CoV2 infection. This, in relation to biological evaluation of systemic inflammation, can guide appropriate therapeutic management in every stage of the disease. The use of artificial intelligence can further improve the diagnostic accuracy of these imaging techniques, thus enabling risk stratification and evaluation of prognosis. The present manuscript aims to review the current knowledge on the possible modalities for imaging COVID-related myocardial inflammation or post-COVID coronary inflammation and atherosclerosis.

Machine Learning Quantitation of Cardiovascular and Cerebrovascular Disease: A Systematic Review of Clinical Applications

Research into machine learning (ML) for clinical vascular analysis, such as those useful for stroke and coronary artery disease, varies greatly between imaging modalities and vascular regions. Limited accessibility to large diverse patient imaging datasets, as well as a lack of transparency in specific methods, are obstacles to further development. This paper reviews the current status of quantitative vascular ML, identifying advantages and disadvantages common to all imaging modalities. Literature from the past 8 years was systematically collected from MEDLINE^® and Scopus database searches in January 2021. Papers satisfying all search criteria, including a minimum of 50 patients, were further analysed and extracted of relevant data, for a total of 47 publications. Current ML image segmentation, disease risk prediction, and pathology quantitation methods have shown sensitivities and specificities over 70%, compared to expert manual analysis or invasive quantitation. Despite this, inconsistencies in methodology and the reporting of results have prevented inter-model comparison, impeding the identification of approaches with the greatest potential. The clinical potential of this technology has been well demonstrated in Computed Tomography of coronary artery disease, but remains practically limited in other modalities and body regions, particularly due to a lack of routine invasive reference measurements and patient datasets.

The Napkin-Ring Sign – the Story Behind Invasive Coronary Angiography

Coronary artery disease (CAD) represents one of the leading causes of morbidity and mortality across Europe. Most of the patients do not experience any warning sign before the coronary event develops, therefore screening this group of patients is essential to prevent major cardiac events. Coronary computed tomography angiography (CCTA) offers a noninvasive approach of the coronary arteries, providing information not only on the presence and severity of the coronary stenosis, but is also able to characterize the structure of the coronary wall. CCTA allows complex evaluation of the extension of CAD, and by assessing the structure of the atherosclerotic plaque, it can identify its degree of vulnerability. The napkin-ring sign (NRS) represents a ring-like attenuation of the non-calcified portion of the coronary lesion and has a high specificity (96–100%) for the identification of thin cap fibroatheroma (TCFA) or culprit lesion in acute coronary syndromes (ACS). It is also an independent predictor for ACS events and the strongest predictor for future ACS. Modern CCTA can provide submillimeter isotropic spatial resolution. Thus, CT attenuation-based tissue interpretation enables the assessment of total coronary plaque burden and individual plaque components, with a similar accuracy as intravascular ultrasoud-based investigations. This review aims to present the important role of CCTA as a potent screening tool for patients with CAD, and the current evidences in the detection and quantification of vulnerable plaques.

Artificial Intelligence in Coronary Computed Tomography Angiography: From Anatomy to Prognosis

Cardiac computed tomography angiography (CCTA) is widely used as a diagnostic tool for evaluation of coronary artery disease (CAD). Despite the excellent capability to rule-out CAD, CCTA may overestimate the degree of stenosis; furthermore, CCTA analysis can be time consuming, often requiring advanced postprocessing techniques. In consideration of the most recent ESC guidelines on CAD management, which will likely increase CCTA volume over the next years, new tools are necessary to shorten reporting time and improve the accuracy for the detection of ischemia-inducing coronary lesions. The application of artificial intelligence (AI) may provide a helpful tool in CCTA, improving the evaluation and quantification of coronary stenosis, plaque characterization, and assessment of myocardial ischemia. Furthermore, in comparison with existing risk scores, machine-learning algorithms can better predict the outcome utilizing both imaging findings and clinical parameters. Medical AI is moving from the research field to daily clinical practice, and with the increasing number of CCTA examinations, AI will be extensively utilized in cardiac imaging. This review is aimed at illustrating the state of the art in AI-based CCTA applications and future clinical scenarios.