Plaque Imaging Research Articles

Prediction of cardiovascular events after carotid endarterectomy using pathological images and clinical data.

Carotid endarterectomy (CEA) is a surgical treatment for carotid artery stenosis. After CEA, some patients experience cardiovascular events (myocardial infarction, stroke, etc.); however, the prognostic factor has yet to be revealed. Therefore, this study explores the predictive factors in pathological images and predicts cardiovascular events within one year after CEA using pathological images of carotid plaques and patients' clinical data. This paper proposes a two-step method to predict the prognosis of CEA patients. The proposed method first computes the pathological risk score using an anomaly detection model trained using pathological images of patients without cardiovascular events. By concatenating the obtained image-based risk score with a patient's clinical data, a statistical machine learning-based classifier predicts the patient's prognosis. We evaluate the proposed method on a dataset containing 120 patients without cardiovascular events and 21 patients with events. The combination of autoencoder as the anomaly detection model and XGBoost as the classification model obtained the best results: area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, and F1-score were 81.9%, 84.1%, 79.1%, 86.3%, and 76.6%, respectively. These values were superior to those obtained using pathological images or clinical data alone. We showed the feasibility of predicting CEA patient's long-term prognosis using pathological images and clinical data. Our results revealed some histopathological features related to cardiovascular events: plaque hemorrhage (thrombus), lymphocytic infiltration, and hemosiderin deposition, which will contribute to developing preventive treatment methods for plaque development and progression.

Diagnostic Methods of Atherosclerotic Plaque and the Assessment of Its Prognostic Significance—A Narrative Review

Cardiovascular diseases (CVD) are a leading cause of death. The most notable cause of CVD is an atherosclerotic plaque. The aim of this review is to provide an overview of different diagnostic methods for atherosclerotic plaque relevant to the assessment of cardiovascular risk. The methods can be divided into invasive and non-invasive. This review focuses on non-invasive with attention paid to ultrasonography, contrast-enhanced ultrasonography, intravascular ultrasonography, and assessment of intima-media complex, coronary computed tomography angiography, and magnetic resonance. In the review, we discuss a number of Artificial Intelligence technologies that support plaque imaging.

Initial results of the Hyperion IIDPET insert for simultaneous PET-MRI applied to atherosclerotic plaque imaging in New-Zealand white rabbits.

In preclinical research, in vivo imaging of mice and rats is more
common than any other animal species, since their physiopathology is very well-
known and many genetically altered disease models exist. Animal studies based on
small rodents are usually performed using dedicated preclinical imaging systems
with high spatial resolution. For studies that require animal models such as mini-
pigs or New-Zealand White (NZW) rabbits, imaging systems with larger bore
sizes are required. In case of hybrid imaging using Positron Emission Tomography
(PET) and Magnetic Resonance Imaging (MRI), clinical systems have to be used,
as these animal models do not typically fi t in preclinical simultaneous PET-MRI
scanners.
Approach. In this paper, we present initial imaging results obtained with the
Hyperion IID PET insert which can accommodate NZW rabbits when combined
with a large volume MRI RF coil. First, we developed a rabbit-sized image
quality phantom of comparable size to a NZW rabbit in order to evaluate the
PET imaging performance of the insert under high count rates. For this phantom,
radioactive spheres with inner diameters between 3.95 and 7.86 mm were visible
in a warm background with a tracer activity ratio of 4.1 to 1 and with a total
18-F activity in the phantom of 58MBq at measurement start. Second, we performed
simultaneous PET-MR imaging of atherosclerotic plaques in a rabbit in vivo using
a single injection containing 18-F-FDG for detection of infl ammatory activity,
and Gd-ESMA for visualization of the aortic vessel wall and plaques with MRI.
Main results. The fused PET-MR images reveal 18-F-FDG uptake within an
active plaques with plaque thicknesses in the sub-millimeter range. Histology
showed colocalization of 18-F-FDG uptake with macrophages in the aortic vessel wall lesions. 
Significance. Our initial results demonstrate that this PET insert
is a promising system for simultaneous high-resolution PET-MR atherosclerotic
plaque imaging studies in NZW rabbits.

The evolution of carotid atherosclerotic plaque composition: a prospective study

Abstract Background The rupture of carotid atherosclerotic plaque is one of the major causes of ischemic stroke, and largely determined by the composition of the plaque. To date, most studies on carotid plaque composition focused on individual plaque components, whilst the interplay between different components within the same plaque may be a critical driving force for plaque evolution and rupture. Objectives To determine the interplay between carotid plaque components over time and describe the dynamics of plaque composition with a specific focus on age- and sex-specific changes in a population-based setting. Methods From a population-based cohort in Netherlands, 802 participants (mean age ± SD 68 ± 8 years, 43% women) with subclinical carotid atherosclerosis underwent repeat magnetic resonance imaging (MRI) of carotid plaque (first scan between 2007 and 2012, second scan between 2014 to 2017). We evaluated carotid plaque composition, including calcification, lipid-rich necrotic core (LRNC) and intraplaque hemorrhage (IPH). To assess the relationship between the incidence of one specific component and baseline plaque composition, plaques with this specific component present at the baseline were excluded. Generalized estimation equation models were used with adjustments for baseline plaque size and cardiovascular risk factors. We demonstrated the evolution of plaque composition along age and by sex. Results Baseline presence of calcification in carotid plaque is independently associated with a higher incidence of IPH (adjusted odds ratio: 2.25; 95% confidence interval: 1.40 to 3.63). No associations were found between baseline plaque composition and the new onset of calcification or LRNC. The proportion of complex plaques with multiple components significantly increased at follow-up (Figure 1a). The plaque composition dramatically evolved over time and the proportion of plaques with two or more components started with 10% around 55 years and increased to over 50% after 70 years of age (Figure 1b). Although the age distribution was comparable between men and women, men were more likely to have and develop plaques with multiple vulnerable plaque components such as IPH (Figure 2). Conclusions A higher incidence of IPH was found in plaques with baseline calcification. The pre-existing plaque composition strongly determines how the plaque will evolve over time. Given the interplay between individual components, the overall plaque composition exhibits significant age- and sex- specific characteristics.

C-C motif chemokine ligand 3 is associated with atherosclerotic plaque vulnerability and increased risk of mortality

Abstract Background and Purpose Chemokines play a crucial role in the inflammatory process underlying atherosclerotic plaque formation, progression and subsequent cardiovascular events. However, the specific role of C-C motif chemokine ligand 3 (CCL3) in atherosclerosis remains largely unexplored. This study aims to explore associations between intra-plaque levels of CCL3 and plaque vulnerability as well as to investigate the prognostic role of circulating CCL3 levels in patients undergoing carotid endarterectomy. Methods The CCL3 protein expression was measured using a proximity extension assay in 207 human carotid plaques and 558 plasma samples from patients undergoing carotid endarterectomy, all enrolled in the Carotid Plaque Imaging Project (CPIP) cohort. Protein expression is quantified as normalized protein expression (NPX), an arbitrary unit on a log2-scale. Plaque components and extracellular matrix including smooth muscle cells, macrophages, neutral lipids, intraplaque hemorrhage, collagen (by histology), and matrix metalloproteinases (assessed biochemically), were quantified. Plaque vulnerability index was calculated as the sum percentage of plaque areas stained for lipids, macrophages and hemorrhage, divided by the sum percentage of smooth muscle cells and collagen. Patients were followed up to 10.2 years to record cardiovascular events and mortality. Results Plaque and circulating levels of CCL3 were higher in symptomatic patients compared to asymptomatic patients (plaque, median NPX (interquartile range, IQR): 18.2 (17.4–19.1) vs 17.2 (16.4–18.0), P<0.001; plasma, median NPX (IQR): 1.78 (1.42–2.21) vs 1.56 (1.28–1.97), P<0.001). Plaque levels of CCL3 showed positive correlations with neutral lipids, macrophages, intraplaque hemorrhage, plaque vulnerability index and matrix metalloproteinases including MMP1, MMP2, MMP9, MMP10 and TIMP1, but inversely with smooth muscle cells. Among 511 patients with complete follow-up and circulating CCL3 measures, 144 (28.2%) patients developed cardiovascular events and 123 (24.1%) patients died. Elevated circulating CCL3 levels were associated with increased risk of cardiovascular events (hazard ratio (HR) per standard deviation (SD) = 1.40, 95% CI 1.06–1.84) and mortality (HR per SD=1.77, 95% CI 1.34–2.33), respectively. Higher levels of circulating CCL3 remained independently associated with an increased risk of mortality (HR per SD=1.50, 95% CI 1.09–2.04) even after adjustment for potential confounders. Conclusions Plaque levels of CCL3 are associated with plaque vulnerability features. Circulating levels of CCL3 predict higher risk of cardiovascular events and mortality among carotid endarterectomy patients.

Synergistically Activated Aggregation-Induced Emission Probe for Precise In Situ Staining of Lipids in Atherosclerotic Plaques.

Visualizing the localization and distribution of lipids within arteries is crucial for studying atherosclerosis. However, existing lipid-specific probes face challenges such as strong hydrophobicity and nonspecific staining of lipophilic organelles or tissues, making them impractical for the precise identification of atherosclerotic plaques. To address this issue, we design a synergistically activated probe, Cbz-Lys-Lys-TPEB, which responds to cathepsin B (CTB) and H2O2 for the in situ generation of aggregation-induced emission luminogens (AIEgens). This enables specific staining of lipids within arteries and precise imaging of atherosclerotic plaques. The probe combines a tetraphenylethene building block with a hydrophilic peptide sequence (Cbz-Lys-Lys) and phenylboric acid module, providing excellent water solubility and fluorescence quenching in a molecular dispersion state. Upon interaction with H2O2 and CTB within plaques, the hydrophilic Cbz-Lys-Lys-TPEB probe is specifically cleaved and converted into hydrophobic AIEgens, leading to rapid aggregation and significant fluorescence enhancement. Interestingly, the in situ-liberated AIEgens display distinct lipid binding ability, effectively tracking the location and distribution of lipids in plaques. This synergistic target-activated AIEgen liberation strategy demonstrates significant feasibility for the reliable and accurate identification of atherosclerotic plaques, holding tremendous potential for clinical diagnosis and risk stratification of atherosclerosis.

Engineering Protein-Nanoparticle Hybrids as Targeted Contrast Agents.

Iron oxide nanoparticles (IONPs) have shown great promise in biomedical applications, particularly as MRI contrast agents due to their magnetic properties and biocompatibility. Although several IONPs have been approved by regulatory agencies as MRI contrast agents, their primary application as negative contrast agents limits their usage. Additionally, there is an emerging need for the development of molecular contrast agents that can specifically target biomarkers, enabling more accurate and sensitive diagnostics. To address these challenges, we exploited the engineerability of proteins to stabilize IONPs with tailored magnetic properties, creating protein-stabilized iron oxide nanoparticles (Prot-IONPs) and leveraged the chemical diversity of proteins to functionalize Prot-IONPs with targeting moieties. As a proof-of-concept, we used alendronate (Ald) to target atherosclerotic plaques in the aorta. Simple protein functionalization allowed targeting while maintaining the stability and relaxation properties of the Prot-IONPs. Prot-IONPs-Ald successfully enabled positive contrast imaging of atherosclerotic plaques in vivo in an atherosclerotic mouse model (ApoE-/- mice on a high-fat diet). This study demonstrates the potential of engineering protein-nanoparticle hybrids as versatile platforms for developing targeted in vivo MRI contrast agents.

Isotope Encoded chemical Imaging Identifies Amyloid Plaque Age Dependent Structural Maturation, Synaptic Loss, and Increased Toxicity.

It is of critical importance to our understanding of Alzheimer's disease (AD) pathology to determine how key pathological factors are interconnected and implicated in nerve cell death, clinical symptoms, and disease progression. The formation of extracellular beta-amyloid (Aβ) plaques is the major pathological hallmark of AD and Aβ has been suggested to be a critical inducer of AD, driving disease pathogenesis. Exactly how Aβ plaque formation begins and how ongoing plaque deposition proceeds and initiates subsequent neurotoxic mechanisms is not well understood. The primary aim of our research is to elucidate the biochemical processes underlying early Aβ plaque formation in brain tissue. We recently introduced a chemical imaging paradigm based on mass spectrometry imaging (MSI) and metabolic isotope labelling to follow stable isotope labelling kinetics (iSILK) in vivo to track the in vivo build-up and deposition of Aβ. Herein, knock-in Aβ mouse models ( App NL-F ) that develop Aβ pathology gradually are metabolically labeled with stable isotopes. This chemical imaging approach timestamps amyloid plaques during the period of initial deposition allowing the fate of aggregating Aβ species from before and during the earliest events of plaque pathology through plaque maturation to be tracked. To identify the molecular and cellular response to plaque maturation, we integrated iSILK with single plaque transcriptomics performed on adjacent tissue sections. This enabled changes in gene expression to be tracked as a function of plaque age (as encoded in the Aβ peptide isotopologue pattern) distinct from changes due to the chronological age or pathological severity. This approach identified that plaque age correlates negatively with gene expression patterns associated with synaptic function as early as in 10-month-old animals but persists into 18 months. Finally, we integrated hyperspectral confocal microscopy into our multiomic approach to image amyloid structural isomers, revealing a positive correlation between plaque age and amyloid structural maturity. This analysis identified three categories of plaques, each with a distinct impact on the surrounding microenvironment. Here, we identified that older, more compact plaques were associated with the most significant synapse loss and toxicity. These data show how isotope-encoded MS imaging can be used to delineate Aβ toxicity dynamics in vivo. Moreover, we show for the first time a functional integration of dynamic MSI, structural plaque imaging and whole genome-wide spatial transcriptomics at the single plaque level. This multiomic approach offers an unprecedented combination of temporal and spatial resolution enabling a description of the earliest events of precipitating amyloid pathology and how Aβ modulates synaptotoxic mechanisms.

DeepPlaq: Dental plaque indexing based on deep neural networks.

The selection of treatment for dental plaque is closely related to the condition of the plaque on different teeth. This study validated the ability of CNN models in assessing the dental plaque indices. In 70 (20 male and 50 female) healthy adults (18 to 55 years old), frontal and lateral view intraoral images (210) of plaque disclosing agent stained permanent and deciduous dentitions were obtained. A three-stage method was employed, where the You Look Only Once version 8 (YOLOv8) model was first used to detect the target teeth, followed by the prompt-based Segment Anything Model (SAM) segmentation algorithm to segment teeth. A new single-tooth dataset consisting of 1400 photographs was obtained after applying a two-stage method. Finally, a multi-class classification model DeepPlaq was trained and evaluated on the accuracy of dental plaque indexing based on the Quigley-Hein Index (QHI) scoring system. Classification performance was measured using accuracy, recall, precision, and F1-score. The teeth detector exhibited an accuracy (mean average precision, mAP) of approximately 0.941 ± 0.005 in identifying teeth with plaque disclosing agents. The maximum accuracy attained in the plaque indexing through DeepPlaq was 0.84 (probability that DeepPlaq scored identical to experts), and the smallest average scoring error was less than 0.25 on a 0 to 5 scale for scoring. A three-stage approach demonstrated excellent performance in detecting and segmenting target teeth, and DeepPlaq model also showed strong performance in assessing dental plaque indices. Application of artificial intelligence to the evaluation of dental plaque distribution could enhance diagnostic accuracy and treatment efficiency and accuracy.

Recent Advancement of Indocyanine Green Based Nanotheranostics for Imaging and Therapy of Coronary Atherosclerosis.

Atherosclerosis is a vascular intima condition in which any part of the circulatory system is affected, including the aorta and coronary arteries. Indocyanine green (ICG), a theranostic compound approved by the FDA, has shown promise in the treatment of coronary atherosclerosis after incorporation into nanoplatforms. By integration of ICG with targeting agents such as peptides or antibodies, it is feasible to increase its concentration in damaged arteries, hence increasing atherosclerosis detection. Nanotheranostics offers cutting-edge techniques for the clinical diagnosis and therapy of atherosclerotic plaques. Combining the optical properties of ICG with those of nanocarriers enables the improved imaging of atherosclerotic plaques and targeted therapeutic interventions. Several ICG-based nanotheranostics platforms have been developed such as polymeric nanoparticles, iron oxide nanoparticles, biomimetic systems, liposomes, peptide-based systems, etc. Theranostics for atherosclerosis diagnosis use magnetic resonance imaging (MRI), computed tomography (CT), near-infrared fluorescence (NIRF) imaging, photoacoustic/ultrasound imaging, positron emission tomography (PET), and single photon emission computed tomography (SPECT) imaging techniques. In addition to imaging, there is growing interest in employing ICG to treat atherosclerosis. In this review, we provide a conceptual explanation of ICG-based nanotheranostics for the imaging and therapy of coronary atherosclerosis. Moreover, advancements in imaging modalities such as MRI, CT, PET, SPECT, and ultrasound/photoacoustic have been discussed. Furthermore, we highlight the applications of ICG for coronary atherosclerosis.

Fluorescence and photoacoustic (FL/PA) dual-modal probe: Responsive to reactive oxygen species (ROS) for atherosclerotic plaque imaging

Accurate and early detection of atherosclerosis (AS) is imperative for their effective treatment. However, fluorescence probes for efficient diagnosis of AS often encounter insufficient deep tissue penetration, which hinders the reliable assessment of plaque vulnerability. In this work, a reactive oxygen species (ROS) activated near-infrared (NIR) fluorescence and photoacoustic (FL/PA) dual model probe TPA-QO-B is developed by conjugating two chromophores (TPA-QI and O–OH) and ROS-specific group phenylboronic acid ester. The incorporation of ROS-specific group not only induces blue shift in absorbance, but also inhibits the ICT process of TPA-QO-OH, resulting an ignorable initial FL/PA signal. ROS triggers the convertion of TPA-QO-B to TPA-QO-OH, resulting in the concurrent amplification of FL/PA signal. The exceptional selectivity of TPA-QO-B towards ROS makes it effectively distinguish AS mice from the healthy. The NIR emission can achieve a tissue penetration imaging depth of 0.3 cm. Moreover, its PA775 signal possesses the capability to penetrate tissues up to a thickness of 0.8 cm, ensuring deep in vivo imaging of AS model mice in early stage. The ROS-triggered FL/PA dual signal amplification strategy improves the accuracy and addresses the deep tissue penetration problem simultaneously, providing a promising tool for in vivo tracking biomarkers in life science and preclinical applications.

Fractal geometry of culprit coronary plaque images within optical coherence tomography in patients with acute coronary syndrome vs stable angina pectoris.

The main cause of acute coronary syndrome (ACS) is plaque rupture and thrombus formation. However, it has not been fairly successful to identify vulnerable plaque to rupture using conventional parameters of intravascular imaging modalities. Fractal analysis is one of the mathematical models to examine geometrical features of picture image using a specific parameter called as fractal dimension (FD) which suggests geometric complexity of the image. This study examined FD of the optical coherence tomography (OCT)-derived images of the culprit plaque in patients with ACS vs stable angina pectoris (SAP) to evaluate the feasibility of FD for identifying vulnerable coronary plaques prone to provoke ACS distinguished from stable plaques only provoking SAP. We examined 65 cases (34 ACS patients, 31 SAP patients) in which the culprit lesion was imaged by OCT before percutaneous coronary intervention in patients with ACS and SAP. The culprit plaque lesion in the ACS group had a significantly larger mean lipid arc (203.8 ± 39.4° vs 152.3 ± 34.5°, p < 0.001) and a larger lipid plaque length (12.6 ± 5.1mm vs 7.7 ± 2.7mm, p < 0.001) and a thinner fibrous cap thickness (75.3 ± 22.3μm vs 134.8 ± 53.2μm, p < 0.001) than those in the SAP group. The prevalence of OCT-derived macrophage infiltration (Mph) in the entire culprit coronary vessel as well as that of the OCT-derived thin-cap fibroatheroma (TCFA) at the culprit lesion were significantly greater in the ACS group than those in the SAP group, respectively (Mph: 61.8% vs 35.5%, p = 0.048; TCFA: 44.1% vs 6.4%, p < 0.001). The FD of culprit plaque in the ACS group was significantly greater than in the SAP group (2.401 ± 0.073 vs 2.341 ± 0.051, p < 0.001). In multivariate regression analysis, the presence of Mph was a significant determinant of FD (regression coefficient estimate 0.049, CI 0.018-0.079, p = 0.002). The FD of OCT-derived image of culprit coronary plaque in the ACS group was significantly greater than that in the SAP group, indicating that the culprit plaque in ACS were structurally more complex. Therefore, fractal analysis of coronary OCT images might be clinically useful for identifying coronary plaques prone to provoke ACS.

An Unsupervised Learning Tool for Plaque Tissue Characterization in Histopathological Images.

Stroke is the second leading cause of death and a major cause of disability around the world, and the development of atherosclerotic plaques in the carotid arteries is generally considered the leading cause of severe cerebrovascular events. In recent years, new reports have reinforced the role of an accurate histopathological analysis of carotid plaques to perform the stratification of affected patients and proceed to the correct prevention of complications. This work proposes applying an unsupervised learning approach to analyze complex whole-slide images (WSIs) of atherosclerotic carotid plaques to allow a simple and fast examination of their most relevant features. All the code developed for the present analysis is freely available. The proposed method offers qualitative and quantitative tools to assist pathologists in examining the complexity of whole-slide images of carotid atherosclerotic plaques more effectively. Nevertheless, future studies using supervised methods should provide evidence of the correspondence between the clusters estimated using the proposed textural-based approach and the regions manually annotated by expert pathologists.

Diagnostic accuracy of carotid plaque magnetic resonance imaging compared to histopathology in symptomatic carotid artery stenosis

IntroductionVulnerable plaques have been shown to predict ipsilateral cerebral ischemic events and identifying them leads to appropriate secondary stroke prevention strategies. We evaluated the diagnostic accuracy of MR carotid plaque imaging in identifying plaque vulnerability when compared with histopathological findings in patients with symptomatic carotid stenosis who underwent carotid endarterectomy (CEA). MethodsA prospective cohort of forty-five consecutive patients with moderate to severe symptomatic carotid stenosis who underwent CEA at a tertiary Indian hospital had 3 T MRI plaque imaging with multi-parametric protocol between November 2021 and December 2022. Images were analyzed by a vascular radiologist blinded to histopathological data. High-risk plaque characteristics such as lipid rich necrotic core (LRNC), intraplaque hemorrhage (IPH), thin fibrous cap and ulceration were assessed and correlated with histopathological findings as per American Heart Association (AHA) classification using Cohen’s kappa statistics to obtain diagnostic accuracies. ResultsOf the 45 patients, 38(84 %) were males. The mean age was 65 ± 7.7 years and mean duration to CEA from the most recent event was 57 days (57 ± 46 days). A significant correlation between MR plaque imaging and histopathology was noted for IPH (sensitivity-91 %, specificity-86 %, κ = 0.774, p < 0.001), LRNC (sensitivity-92.1 %, specificity-85.7 %, κ = 0.697, p < 0.001), and plaque ulceration (sensitivity-84.6 %, specificity-78.1 %, κ = 0.563, p < 0.001). MRI had an overall sensitivity and specificity of 92.3 % and 84.2 % respectively (κ = 0.77, p < 0.001) in discriminating high risk plaques. ConclusionMR plaque imaging shows a very good correlation with histopathology and can identify unstable high-risk plaques with high accuracy. This may have implication in selection of patients for carotid revascularization in symptomatic carotid stenosis.

Plaque Characteristics Derived from Intravascular Optical Coherence Tomography That Predict Cardiovascular Death.

This study aimed to investigate whether plaque characteristics derived from intravascular optical coherence tomography (IVOCT) could predict a long-term cardiovascular (CV) death. This study was a single-center, retrospective study on 104 patients who had undergone IVOCT-guided percutaneous coronary intervention. Plaque characterization was performed using Optical Coherence TOmography PlaqUe and Stent (OCTOPUS) software developed by our group. A total of 31 plaque features, including lesion length, lumen, calcium, fibrous cap (FC), and vulnerable plaque features (e.g., microchannel), were computed from the baseline IVOCT images. The discriminatory power for predicting CV death was determined using univariate/multivariate logistic regressions. Of 104 patients, CV death was identified in 24 patients (23.1%). Univariate logistic regression revealed that lesion length, calcium angle, calcium thickness, FC angle, FC area, and FC surface area were significantly associated with CV death (p < 0.05). In the multivariate logistic analysis, only the FC surface area (OR 2.38, CI 0.98-5.83, p < 0.05) was identified as a significant determinant for CV death, highlighting the importance of the 3D lesion analysis. The AUC of FC surface area for predicting CV death was 0.851 (95% CI 0.800-0.927, p < 0.05). Patients with CV death had distinct plaque characteristics (i.e., large FC surface area) in IVOCT. Studies such as this one might someday lead to recommendations for pharmaceutical and interventional approaches.

Role of imaging techniques in the assessment of vulnerable plaques and the effectiveness of lipid-lowering therapy

The aim was to evaluate the potential of various imaging methods in the diagnosis of vulnerable coronary plaques and their stabilization using various lipid-lowering therapy regimens.The sources were searched in the PubMed database using the following keywords: "vulnerable (unstable) plaque" AND "PCSK9 inhibitors" AND "intravascular ultrasound" OR "optical coherence tomography" OR "computed tomography angiography". As a result, 8 original clinical trials were selected that corresponded to the review purpose. We assessed the results of following studies on plaque imaging and their regression with various lipid-lowering therapy regimens: GLAGVO, ODYSSEY J, PACMAN-AMI, HUYGENS, ALTAIR, ARCHITECT, etc. The results of most studies have established the advantages of a combined regimen of lipid-lowering therapy (statin+PCSK9 inhibitor) in reducing the rate of cardiovascular events in com­parison with statin monotherapy. The reduction in plaque volume ranged from 0,95% to 2,13% in patients receiving a combination of lipid-lowering drugs, while in monotherapy, it increased from 0,05% to 0,92% (GLAGOV, PACMAN-AMI). The changes of minimum fibrous cap thickness varied from 18,0-62,67 µm with combined therapy and 13,2-33,19 µm with monotherapy (PACMAN-AMI, Gao F). Lipid arc regression was 57,5о in combination therapy (statin+PCSK9 inhibitor) and 31,4о in statin monotherapy (HUYGENS). Imaging diagnostic methods makes it possible to identify vulnerable plaques, which helps in consideration of combination lipid-lowering therapy. In addition, plaque visualization makes it possible to evaluate the treatment effectiveness.

Benefits and Challenges of Drug-Coated Balloons in Peripheral Artery Disease: From Molecular Mechanisms to Clinical Practice.

Multiple clinical trials have reported favorable outcomes after drug-coated balloon therapy for peripheral artery disease in above-the-knee and below-the-knee lesions and in both de novo and in-stent restenosis. However, there are still insufficient data to identify and tackle the risk factors associated with a higher risk of restenosis, which is the primary concern for patients who are treated with an endovascular approach. A modern armamentarium, which includes improved lesion preparation techniques such as plaque modification balloons, mechanical atherectomy, intravascular lithotripsy, and imaging, is crucial for obtaining better long-term clinical outcomes. Moreover, a better understanding of the molecular properties of drug-coated balloons has led to improved devices that could tackle the shortcomings of previous generations. This comprehensive review focuses on drug-coated balloon technology as a tool to treat peripheral artery disease and the effects of the molecular mechanisms involved in preventing vascular restenosis.

Machine Learning Constructed Based on Patient Plaque and Clinical Features for Predicting Stent Malapposition: A Retrospective Study.

Stent malapposition (SM) following percutaneous coronary intervention (PCI) for myocardial infarction continues to present significant clinical challenges. In recent years, machine learning (ML) models have demonstrated potential in disease risk stratification and predictive modeling. ML models based on optical coherence tomography (OCT) imaging, laboratory tests, and clinical characteristics can predict the occurrence of SM. We studied 337 patients from the Affiliated Hospital of Zunyi Medical University, China, who had PCI and coronary OCT from May to October 2023. We employed nested cross-validation to partition patients into training and test sets. We developed five ML models: XGBoost, LR, RF, SVM, and NB based on calcification features. Performance was assessed using ROC curves. Lasso regression selected features from 46 clinical and 21 OCT imaging features, which were optimized with the five ML algorithms. In the prediction model based on calcification features, the XGBoost model and SVM model exhibited higher AUC values. Lasso regression identified five key features from clinical and imaging data. After incorporating selected features into the model for optimization, the AUC values of all algorithmic models showed significant improvements. The XGBoost model demonstrated the highest calibration accuracy. SHAP values revealed that the top five ranked features influencing the XGBoost model were calcification length, age, coronary dissection, lipid angle, and troponin. ML models developed using plaque imaging features and clinical characteristics can predict the occurrence of SM. ML models based on clinical and imaging features exhibited better performance.

Special Report on the Consensus QIBA Profile for Objective Analytical Validation of Non-calcified and High-risk Plaque and Other Biomarkers using Computed Tomography Angiography

Evidence is building in support of the clinical utility of atherosclerotic plaque imaging by computed tomography angiography (CTA). There is increasing organized activity to embrace non-calcified plaque (NCP) as a formally defined biomarker for clinical trials, and high-risk plaque (HRP) for clinical care, as the most relevant measures for the field to advance and worthy of community efforts to validate. Yet the ability to assess the quantitative performance of any given specific solution to make these measurements or classifications is not available. Vendors use differing definitions, assessment metrics, and validation data sets to describe their offerings without clinician users having the capability to make objective assessments of accuracy and precision and how this affects diagnostic confidence. The QIBA Profile for Atherosclerosis Biomarkers by Computed Tomography Angiography (CTA) was created by the Quantitative Imaging Biomarkers Alliance (QIBA) to improve objectivity and decrease the variability of noninvasive plaque phenotyping. The Profile provides claims on the accuracy and precision of plaque measures individually and when combined. Individual plaque morphology measurements are evaluated in terms of bias (accuracy), slope (consistency of the bias across the measurement range, needed for measurements of change), and variability. The multiparametric plaque stability phenotype is evaluated in terms of agreement with expert pathologists. The Profile is intended for a broad audience, including those engaged in discovery science, clinical trials, and patient care. This report provides a rationale and overview of the Profile claims and how to comply with the Profile in research and clinical practice. Summary StatementThis article summarizes objective means to validate the analytical performance of non-calcified plaque (NCP), other emerging plaque morphology measurements, and multiparametric histology-defined high-risk plaque (HRP), as outlined in the QIBA Profile for Atherosclerosis Biomarkers by CTA.

30-Day Risk Score for Mortality and Stroke in Patients with Carotid Artery Stenosis Using Artificial Intelligence Based Carotid Plaque Morphology

The gold standard for determining carotid artery stenosis intervention is based on a combination of percent stenosis and symptomatic status. Few studies have assessed plaque morphology as an additive tool for stroke prediction. Our goal was to create a predictive model and risk score for 30-day stroke and death inclusive of plaque morphology. Patients with a computed tomographic angiography head/neck between 2010 and 2021 at a single institution and a diagnosis of carotid artery stenosis were included in our analysis. Each computed tomography was used to create a three-dimensional image of carotid plaque based off image recognition software. A stepwise backward regression was used to select variables for inclusion in our prediction models. Model discrimination was assessed with area under the receiver operating characteristic curves (AUCs). Additionally, calibration was performed and the model with the least Akaike Information Criterion (AIC) was selected. The risk score was modeled from the Framingham Study. Primary outcome was mortality/stroke. We created 3 models to predict mortality/stroke from 366 patients: model A using only clinical variables, model B using only plaque morphology and model C using both clinical and plaque morphology variables. Model A used age, sex, peripheral arterial disease, hyperlipidemia, body mass index (BMI), chronic obstructive pulmonary disease (COPD), and history of transient ischemia attack (TIA)/stroke and had an AUC of 0.737 and AIC of 285.4. Model B used perivascular adipose tissue (PVAT) volume, lumen area, calcified volume, and target lesion length and had an AUC of 0.644 and AIC of 304.8. Finally, model C combined both clinical and software variables of age, sex, matrix volume, history of TIA/stroke, BMI, PVAT, lipid rich necrotic core, COPD and hyperlipidemia and had an AUC of 0.759 and an AIC of 277.6. Model C was the most predictive because it had the highest AUC and lowest AIC. Our study demonstrates that combining both clinical factors and plaque morphology creates the best predication of a patient's risk for all-cause mortality or stroke from carotid artery stenosis. Additionally, we found that for patients with even 3 points in our risk score model has a 20% chance of stroke/death. Further prospective studies are needed to validate our findings.