Coronary Segments Research Articles

A Novel Reconstruction Technique to Reduce Stair-Step Artifacts in Sequential Mode Coronary CT Angiography.

Prospective electrocardiography-triggering is one of the most commonly used cardiac computed tomography (CT) scan modes but can be susceptible to stair-step artifacts in the transition areas of an acquisition over multiple cardiac cycles. We evaluated a novel reconstruction algorithm to reduce the occurrence and severity of such artifacts in sequential coronary CT angiography. In this institutional review board-approved, retrospective study, 50 consecutive patients (16 females; mean age, 58.9 ± 15.2) were included who underwent coronary CT angiography on a dual-source photon-counting detector CT in the sequential ultra-high-resolution mode with a detector collimation of 120 × 0.2 mm. Each scan was reconstructed without (hereafter called standard reconstruction) and with the novel ZeeFree reconstruction algorithm, which aims to minimize stair-step artifacts. The presence and extent of stair-step artifacts were rated by 2 independent, blinded readers on a 4-point discrete visual scale. The relationship between the occurrences of artifacts was correlated with the average and variability of heart rate and with patient characteristics. A total of 504 coronary segments were included into the analyses. In standard reconstructions, reader 1 reported stair-step artifacts in 40/504 (7.9%) segments, from which 12/504 led to nondiagnostic image quality (2.4% of all segments). Reader 2 reported 56/504 (11.1%) stair-step artifacts, from which 11/504 lead to nondiagnostic image quality (2.2% of all segments). With the ZeeFree algorithm, 9/12 (75%) and 8/11 (73%) of the nondiagnostic segments improved to a diagnostic quality for readers 1 and 2, respectively. The ZeeFree reconstruction algorithm significantly reduced the frequency and extent of stair-step artifacts compared with standard reconstructions for both readers ( P < 0.001, each). Heart rate variability and body mass index were significantly related to the occurrence of stair-step artifacts ( P < 0.05). Our study demonstrates the feasibility and effectiveness of a novel reconstruction algorithm leading to a significant reduction of stair-step artifacts and, hence, a reduction of coronary segments with a nondiagnostic image quality in sequential ultra-high-resolution coronary photon-counting detector CT angiography.

Endovascular rotational atherectomy for multivessel coronary calcification involving the main trunk of left coronary artery and the left anterior descending artery

The revascularization strategy for severe calcification is one of the most pressing problems of modern percutaneous coronary interventions, since it is observed more and more often and significantly complicates traditional endovascular treatment. Currently, the method of choice for severe coronary calcification is rotational atherectomy, which allows modification of atheromatous mass with an increase in the vessel lumen to allow satisfactory balloon angioplasty and stenting. This article presents a case of a patient at very high cardiovascular risk with a burdened coronary history and severe clinical symptoms of exertional angina while receiving the optimal multiagent therapy. The examination revealed a multivessel coronary calcification, but the patient was denied coronary artery bypass grafting due to high perioperative risks. SYNTAX (Synergy between Percutaneous Coronary Intervention with TAXUS and Cardiac Surgery score) score I – 25,5 points, SYNTAX score II — 40,4% and 70,1% for percutaneous coronary intervention and coronary bypass surgery, respectively, European System for Cardiac Operative Risk Evaluation (EuroSCORE II) — 4,95%. Attempts at traditional percutaneous intervention were unsuccessful and the patient underwent endovascular rotational atherectomy followed by stenting of affected coronary segments, against the background of which there was an improvement in exercise tolerance with a decrease in angina class.

A fully automated deep learning approach for coronary artery segmentation and comprehensive characterization.

Coronary computed tomography angiography (CCTA) allows detailed assessment of early markers associated with coronary artery disease (CAD), such as coronary artery calcium (CAC) and tortuosity (CorT). However, their analysis can be time-demanding and biased. We present a fully automated pipeline that performs (i) coronary artery segmentation and (ii) CAC and CorT objective analysis. Our method exploits supervised learning for the segmentation of the lumen, and then, CAC and CorT are automatically quantified. 281 manually annotated CCTA images were used to train a two-stage U-Net-based architecture. The first stage employed a 2.5D U-Net trained on axial, coronal, and sagittal slices for preliminary segmentation, while the second stage utilized a multichannel 3D U-Net for refinement. Then, a geometric post-processing was implemented: vessel centerlines were extracted, and tortuosity score was quantified as the count of branches with three or more bends with change in direction forming an angle >45°. CAC scoring relied on image attenuation. CAC was detected by setting a patient specific threshold, then a region growing algorithm was applied for refinement. The application of the complete pipeline required <5 min per patient. The model trained for coronary segmentation yielded a Dice score of 0.896 and a mean surface distance of 1.027 mm compared to the reference ground truth. Tracts that presented stenosis were correctly segmented. The vessel tortuosity significantly increased locally, moving from proximal, to distal regions (p < 0.001). Calcium volume score exhibited an opposite trend (p < 0.001), with larger plaques in the proximal regions. Volume score was lower in patients with a higher tortuosity score (p < 0.001). Our results suggest a linked negative correlation between tortuosity and calcific plaque formation. We implemented a fast and objective tool, suitable for population studies, that can help clinician in the quantification of CAC and various coronary morphological parameters, which is helpful for CAD risk assessment.

Data-driven models for the prediction of coronary atherosclerotic plaque progression/regression

Coronary artery disease is defined by the existence of atherosclerotic plaque on the arterial wall, which can cause blood flow impairment, or plaque rupture, and ultimately lead to myocardial ischemia. Intravascular ultrasound (IVUS) imaging can provide a detailed characterization of lumen and vessel features, and so plaque burden, in coronary vessels. Prediction of the regions in a vascular segment where plaque burden can either increase (progression) or decrease (regression) following a certain therapy, has remained an elusive major milestone in cardiology. Studies like IBIS-4 showed an association between plaque burden regression and high-intensity rosuvastatin therapy over 13 months. Nevertheless, it has not been possible to predict if a patient would respond in a favorable/adverse fashion to such a treatment. This work aims to (i) Develop a framework that processes lumen and vessel cross-sectional contours and extracts geometric descriptors from baseline and follow-up IVUS pullbacks; and to (ii) Develop, train, and validate a machine learning model based on baseline/follow-up IVUS datasets that predicts future percent of atheroma volume changes in coronary vascular segments using only baseline information, i.e. geometric features and clinical data. This is a post hoc analysis, revisiting the IBIS-4 study. We employed 140 arteries, from 81 patients, for which expert delineation of lumen and vessel contours were available at baseline and 13-month follow-up. Contour data from baseline and follow-up pullbacks were co-registered and then processed to extract several frame-wise features, e.g. areas, plaque burden, eccentricity, etc. Each pullback was divided into regions of interest (ROIs), following different criteria. Frame-wise features were condensed into region-wise markers using tools from statistics, signal processing, and information theory. Finally, a stratified 5-fold cross-validation strategy (20 repetitions) was used to train/validate an XGBoost regression models. A feature selection method before the model training was also applied. When the models were trained/validated on ROI defined by the difference between follow-up and baseline plaque burden, the average accuracy and Mathews correlation coefficient were 0.70 and 0.41 respectively. Using a ROI partition criterion based only on the baseline’s plaque burden resulted in averages of 0.60 accuracy and 0.23 Mathews correlation coefficient. An XGBoost model was capable of predicting plaque progression/regression changes in coronary vascular segments of patients treated with rosuvastatin therapy in 13 months. The proposed method, first of its kind, successfully managed to address the problem of stratification of patients at risk of coronary plaque progression, using IVUS images and standard patient clinical data.

Coronary vessel segmentation in coronary angiography with a multi-scale U-shaped transformer incorporating boundary aggregation and topology preservation.

Coronary vessel segmentation plays a pivotal role in automating the auxiliary diagnosis of coronary heart disease. The continuity and boundary accuracy of the segmented vessels directly affect the subsequent processing. Notably, during segmentation, vessels with severe stenosis can easily cause boundary errors and breakage, resulting in isolated islands. To address these issues, we propose a novel multi-scale U-shaped transformer with boundary aggregation and topology preservation (UT-BTNet) for coronary vessel segmentation in coronary angiography. Specifically, considering the characteristics of coronary vessels, we first develop the UT-BTNet for coronary vessels segmentation, which combines the advantages of a convolutional neural networks (CNN) and a transformer, and is able to effectively extract the local and global features of angiographic images. Secondly, we innovatively employ boundary loss and topological loss in two stages, in addition to the traditional losses. In the first stage, boundary loss is adopted, which has the effect of boundary aggregation. In the second stage, topological loss is applied to preserve the topology of the vessels, after the network converges. In the experiment, in addition to the two metrics of Dice and intersection over union (IoU), we specifically propose two metrics of boundary intersection over union (BIoU) and Betti error to evaluate boundary accuracy and the continuity of segmentation results. The results show that the Dice is 0.9291, the IoU is 0.8687, the BIoU is 0.5094, and the Betti error is 0.3400. Compared with the other state-of-the-art methods, UT-BTNet achieves better segmentation results, while ensuring the continuity and boundary accuracy of the vessels, indicating its potential clinical value.

Image quality assessment of coronary artery segments using ultra-high resolution dual source photon-counting detector computed tomography

PurposeThe study is intended to assess the image quality of ultra-high resolution (UHR) coronary computed tomography angiography (CCTA) performed on dual source photon-counting detector CT (PCD-CT). MethodConsecutive patients, who underwent clinically indicated CCTA on PCD-CT (UHR 120x 0.2 mm collimation), were included. CCTA images were reconstructed at 0.2 mm slice thickness with Bv40, Bv44, Bv48 and Bv56 kernels and quantum iterative reconstruction level 4. Contrast-to-noise (CNR) and signal-to-noise ratios (SNR) were quantified from contrast-enhanced blood and subcutaneous adipose tissue. All reconstructions were scored per coronary segment (18-segment model) for presence, image quality, motion artefacts, stack artefacts, plaque presence and composition, and stenosis degree. Image quality was scored by two independent observers. ResultsSixty patients were included (median age 62 [25th – 75th percentile: 53–67] years, 45% male, median calcium score 62 [0–217]). The mean heart rate during scanning was 71 ± 11 bpm. Median CTDIvol was 19 [16–22]mGy and median DLP 243 [198–327]mGy.cm. The SNR was 9.3 ± 2.3 and the CNR was 11.7 ± 2.6. Of the potential 1080 coronary segments (60 patients x 18 segments), 255/256 (reader1/reader2) segments could not be assessed for being absent or non-evaluable due to size. Both readers scored 85% of the segments as excellent or very good (Intraclass Correlation Coefficient: 0.88 (95% CI: 0.87–0.90). Motion artefacts were present in 45(5%) segments, stack artefacts in 60(7%) segments and metal artefacts in 9(1%) segments. ConclusionUHR dual-source PCD-CT CCTA provides excellent or very good image quality in 85% of coronary segments at relatively high heart rates at moderate radiation dose with only limited stack artefacts.

Dataset for Automatic Region-based Coronary Artery Disease Diagnostics Using X-Ray Angiography Images

X-ray coronary angiography is the most common tool for the diagnosis and treatment of coronary artery disease. It involves the injection of contrast agents into coronary vessels using a catheter to highlight the coronary vessel structure. Typically, multiple 2D X-ray projections are recorded from different angles to improve visualization. Recent advances in the development of deep-learning-based tools promise significant improvement in diagnosing and treating coronary artery disease. However, the limited public availability of annotated X-ray coronary angiography image datasets presents a challenge for objective assessment and comparison of existing tools and the development of novel methods. To address this challenge, we introduce a novel ARCADE dataset with 2 objectives: coronary vessel classification and stenosis detection. Each objective contains 1500 expert-labeled X-ray coronary angiography images representing: i) coronary artery segments; and ii) the locations of stenotic plaques. These datasets will serve as a benchmark for developing new methods and assessing existing approaches for the automated diagnosis and risk assessment of coronary artery disease.

Rapid automated lumen segmentation of coronary optical coherence tomography images followed by 3D reconstruction of coronary arteries.

Optical coherence tomography has emerged as an important intracoronary imaging technique for coronary artery disease diagnosis as it produces high-resolution cross-sectional images of luminal and plaque morphology. Precise and fast lumen segmentation is essential for efficient OCT morphometric analysis. However, due to the presence of various image artifacts, including side branches, luminal blood artifacts, and complicated lesions, this remains a challenging task. Our research study proposes a rapid automatic segmentation method that utilizes nonuniform rational B-spline to connect limited pixel points and identify the edges of the OCT lumen. The proposed method suppresses image noise and accurately extracts the lumen border with a high correlation to ground truth images based on the area, minimal diameter, and maximal diameter. We evaluated the method using 3300 OCT frames from 10 patients and found that it achieved favorable results. The average time taken for automatic segmentation by the proposed method is 0.17s per frame. Additionally, the proposed method includes seamless vessel reconstruction following the lumen segmentation. The developed automated system provides an accurate, efficient, robust, and user-friendly platform for coronary lumen segmentation and reconstruction, which can pave the way for improved assessment of the coronary artery lumen morphology.

Coronary artery segmentation in CCTA images based on multi-scale feature learning.

Coronary artery segmentation is a prerequisite in computer-aided diagnosis of Coronary Artery Disease (CAD). However, segmentation of coronary arteries in Coronary Computed Tomography Angiography (CCTA) images faces several challenges. The current segmentation approaches are unable to effectively address these challenges and existing problems such as the need for manual interaction or low segmentation accuracy. A Multi-scale Feature Learning and Rectification (MFLR) network is proposed to tackle the challenges and achieve automatic and accurate segmentation of coronary arteries. The MFLR network introduces a multi-scale feature extraction module in the encoder to effectively capture contextual information under different receptive fields. In the decoder, a feature correction and fusion module is proposed, which employs high-level features containing multi-scale information to correct and guide low-level features, achieving fusion between the two-level features to further improve segmentation performance. The MFLR network achieved the best performance on the dice similarity coefficient, Jaccard index, Recall, F1-score, and 95% Hausdorff distance, for both in-house and public datasets. Experimental results demonstrate the superiority and good generalization ability of the MFLR approach. This study contributes to the accurate diagnosis and treatment of CAD, and it also informs other segmentation applications in medicine.

Ensembled-SAMs for Enhanced Small Coronary Artery Segmentation in CCTA Images.

Accurate coronary artery segmentation is crucial for quantitative analysis of coronary arteries in noninvasive coronary computed tomography angiography (CCTA) images. However, current segmentation algorithms often have unsatisfactory recall due to the small size and complex morphology of coronary arteries, particularly in the distal segments. To address this issue, we introduce a new fully automated method named Ensembled-SAMs, which harnesses the strengths of the Segment Anything Model (SAM) and the no-new-U-Net (nnU-Net). First, noisy bounding box prompts are automatically generated by a vesselness algorithm that highlights the tubular structures in the CCTA images. These noisy prompts are then used to fine-tune the SAM and its two variants separately. The SAM variants introduce a classification head in their mask decoder to alleviate the false positives. In addition, an nnU-Net segmentation network is trained from scratch. Finally, the outputs of the SAMs and the nnU-Net are strategically aggregated to obtain the final segmentation result. Experiments on both a self-built dataset and the public Automated Segmentation of Coronary Arteries (ASOCA) challenge dataset demonstrate that the proposed Ensembled-SAMs outperforms the state-of-the-arts, achieving precise segmentation of coronary arteries, with particular enhancement in delineating small coronary artery segments.

IVOCT Coronary Artery Calcification Plaque Segmentation Based on Convolutional Neural Network

IVOCT Coronary Artery Calcification Plaque Segmentation Based on Convolutional Neural Network

Coronary artery angiography image vessel segmentation method based on feature pyramid network

Coronary artery angiography image vessel segmentation method based on feature pyramid network

Comparison between diaphragmatic-navigated and self-navigated coronary magnetic resonance angiography at 3T in pediatric patients with congenital coronary artery anomalies.

Coronary magnetic resonance angiography (CMRA) is being increasingly used in pediatric patients with congenital coronary artery anomalies (CAAs). However, the data on the free-breathing self-navigation technique, which has the potential to simplify the acquisition plan with a high success rate at 3T, remain scarce. This study investigated the clinical application value of self-navigated (sNAV) CMRA at 3T in pediatric patients with suspected CAAs and compared it to conventional diaphragmatic-navigated (dNAV) CMRA. From April 2019 to March 2022, we enrolled 65 pediatric patients (38 males and 27 females; mean age 8.5±4.4 years) with suspected CAAs in this prospective study. All patients underwent both dNAV and sNAV sequences in random order with gradient recalled echo (GRE) sequence during free breathing, with 39 (20 males and 19 females; mean age 10.2±3.6 years) of them additionally undergoing coronary computed tomography angiography (CCTA) or invasive coronary angiography (ICA). We measured and compared the success rate, scan time, visual score of the 9 main coronary artery segments, vessel sharpness, and vessel length between the two sequences. The diagnostic accuracy was compared using CCTA or ICA as a reference. The success rate of sNAV-CMRA (65/65, 100%) was higher than that of dNAV-CMRA (61/65, 93.8%) (P<0.001), and the scan time of sNAV-CMRA (7.3±2.5 min) was significantly shorter than that of dNAV-CMRA (9.1±3.6 min) (P=0.002). The acquisition efficiency of dNAV-CMRA was 40.5%±12.9%, while for sNAV-CMRA, 100% acquisition efficiency was achieved. There was no significant difference in vessel length of any of the coronary arteries, visual score, or vessel sharpness of the left circumflex coronary artery (LCX) between the two sequences (all P values >0.050). The visual score and vessel sharpness of the right coronary artery and left anterior descending coronary artery (LAD) were significantly improved in dNAV-CMRA compared with sNAV-CMRA (all P values <0.050). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the detection of CAAs were not significantly different between the two sequences (all P values >0.050). Our findings demonstrated that both sNAV and dNAV in CMRA provide clinical application value in pediatric patients with CAAs and have similar diagnostic performance. Although the image quality of sNAV-CMRA is slightly inferior compared to that of dNAV-CMRA, sNAV-CMRA allows for a simpler scanning procedure.

CIDN: A context interactive deep network with edge-aware for X-ray angiography images segmentation

Accurate segmentation of X-ray angiography images is imperative for cardiovascular disease diagnosis. Despite significant strides in segmentation through deep learning, challenges persist in precisely delineating vessel edges. This paper introduces a Context Interactive Deep Network (CIDN) tailored for coronary vessel segmentation. CIDN integrates a Bio-inspired Attention Block (BAB) and a Multi-scale Interactive Block (MIB) to optimize the capture of spatial and edge information. The encoder-decoder facilitates optimal interaction between low-level features and high-level semantics. A compound loss function, amalgamating binary cross-entropy and active contour elasticity loss, enhances the recognition of intricate vessel edges. In experiments conducted on both public and private X-ray contrast images, the CIDN exhibits superior performance compared to the state-of-the-art Spatial Multi-scale Attention U-improved Network (SMAU-Net). Specifically, on dataset DCA1, the F1 value reached 0.7675, with an accuracy of 0.9795. Furthermore, on dataset JMA, the CIDN model achieved an F1 value of 0.8732, coupled with an accuracy of 0.9757. Accurate segmentation provides clinicians with precise vessel depictions, aiding in the identification of coronary stenosis and facilitating more informed diagnostic and therapeutic decisions.

Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look.

Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias. Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework. UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach.

Naturally Occurring Atherosclerosis Progression and In-stent Restenosis: Exploring Histomorphologic Associations Using Optical Coherence Tomography.

The mechanism of in-stent restenosis (ISR) remains elusive, and in-stent neoatherosclerosis (ISNA) may hold significant pathophysiologic implications. Nevertheless, the correlation between ISNA and the progression of untreated coronary segments affected by native atherosclerosis remains incompletely investigated. This study enrolled 225 patients diagnosed with coronary heart disease and multivessel disease. These patients underwent successful percutaneous coronary intervention and intraoperative placement of the drug-eluting stent, followed by optical coherence tomography assessment of the culprit stent. The mechanism of ISR was examined through qualitative and quantitative analysis of optical coherence tomography imaging. A significantly higher proportion of patients in the ISR with nontarget lesion progression (N-TLP) group exhibited lipid plaque formation compared with the ISR without N-TLP group (69.0% vs. 39.8%, P < 0.001). The incidence of thin-cap fibroatheroma (33.3% vs. 11.4%, P = 0.001) and ISNA (60.7% vs. 38.6%, P < 0.001) was markedly elevated in the ISR with N-TLP group compared with the ISR without N-TLP group. Regarding manifestations, heterogeneous hyperplasia was predominantly observed in the ISR with N-TLP group (76.2% vs. 38.6%, P < 0.001), whereas homogeneous hyperplasia was primarily presented in the ISR without N-TLP group (61.4% vs. 23.8%, P < 0.001). Patients displaying notable progression of naturally occurring atherosclerosis manifest histomorphologic features of ISR, primarily characterized by heterogeneous intimal hyperplasia and a higher prevalence of ISNA. In contrast, patients without substantial progression of naturally occurring atherosclerosis exhibit histomorphologic features of ISR primarily characterized by homogeneous intimal hyperplasia.

Mechanical wall stress and wall shear stress are associated with atherosclerosis development in non-calcified coronary segments

Background and aimsAtherosclerotic plaque onset and progression are known to be affected by local biomechanical factors. Whereas the role of wall shear stress (WSS) has been studied, the impact of another biomechanical factor, namely mechanical wall stress (MWS), remains poorly understood. In this study, we investigated the association of MWS, independently and combined with WSS, towards atherosclerosis in coronary arteries. MethodsThirty-four human coronary arteries were analyzed using near-infrared spectroscopy intravascular ultrasound (NIRS-IVUS) and optical coherence tomography (OCT) at baseline and after 12 months. Baseline WSS and MWS were calculated using computational models, and wall thickness (ΔWT) and lipid-rich necrotic core size (ΔLRNC) change were measured in non-calcified coronary segments. The arteries were further divided into 1.5 mm/45° sectors and categorized as plaque-free or plaque sectors. For each category, associations between the biomechanical factors (WSS & MWS) and changes in the coronary wall (ΔWT & ΔLRNC) were studied using linear mixed models. ResultsIn plaque-free sectors, higher MWS (p < 0.001) was associated with greater vessel wall growth. Plaque sectors demonstrated wall thickness reduction over time, likely due to the medical therapy, where higher levels of WSS and WMS, individually and combined, (p < 0.05) were associated with a greater reduction. Sectors with low MWS combined with high WSS demonstrated the highest LRNC increase (p < 0.01). ConclusionsIn this study, we investigated the association of the (largely-overlooked) biomechanical factor, MWS with coronary atherosclerosis, individually and combined with WSS. Our results demonstrated that both MWS and WSS significantly correlate with atherosclerotic plaque initiation and development.

Incidence of flow-limiting coronary artery stenoses in patients with diabetes - a coronary angiography natural history study

Abstract Background Diabetes is an established risk factor for the development of coronary atherosclerosis. We sought to assess the association between diabetes and the incidence of coronary artery atherosclerosis progression on coronary angiography. Methods Using the Swedish Coronary Angiography and Angioplasty Registry (SCAAR), we included all coronary artery segments with mild to no atherosclerosis defined as &amp;lt;50% luminal obstruction in patients undergoing a first-time coronary angiography in Sweden between 1989 and 2017. Patients with two or more vessel disease were excluded as were patients with a previous history of coronary artery disease. In patients with one vessel disease, the diseased vessel including all its branches were excluded. Patients were followed for 15 years until a clinically indicated re-angiography was done, death occurred or end of follow-up (April 2018). The primary outcome was plaque progression to flow-limiting coronary artery stenosis defined as ≥50% luminal obstruction on repeat angiography assessed using epidemiological tables, Kaplan Meier event rates, and Cox proportional hazard models. The importance of selected risk factors (smoking, hypertension, hyperlipidemia, and established coronary artery disease) was assessed. Results A total of 2 661 245 coronary segments in 248 736 patients were included in the study. Diabetes was present in 30 533 patients and 320 815 coronary artery segments. Kaplan-Meier event rates and incidence rates (IR) per 1000 segment-years were higher in patients with diabetes (5.5%, IR 2.78 [95% CI 2.70–2.85] vs. 3.0%, 1.44 [1.42–1.46]) resulting in an almost two-folded hazard ratio (HR) of plaque progression (HR 1.93 [95% CI 1.87– 2.00]). The anatomical distribution of plaque progression was similar between the two cohorts on all levels of analysis (coronary artery level, main artery and branches level, and coronary segment level artery). Diabetic patients with established coronary artery disease had a significantly higher risk of future events compared to those without clinically significant coronary artery disease. Conclusions Diabetes was associated with an almost twofold higher incidence rate of plaque progression but did not appear to alter the anatomical location of plaque development compared to the absence of diabetes. Once coronary artery disease is established, the rate of atherosclerotic events accelerates. Therefore, adopting aggressive primary prevention strategies is crucial to reduce the risk of adverse outcomes in diabetic patients.Event rates and anatomical distributionProgression of stenoses by risk factor

Formation of denser plasma fibrin clots is associated with coronary artery ectasias and aneurysms - preliminary results from the CARE-ANEURYSM study

Abstract Background Coronary artery ectasias and aneurysms (CAEAs) are defined as diffused or localized coronary artery dilations, that exceed diameter of the adjacent segment by at least 50%. In the Caucasian population, CAEAs are present in 2-5% of all-comer patients undergoing coronary angiography and are associated with increased risk of myocardial infarction and death. Purpose To determine whether CAEAs are associated with increased thrombin generation potential and unfavorably modified plasma fibrin clot properties. Methods In this prospective study 19 patients with CAEAs, 19 sex-, age-, fibrinogen- and diabetes mellitus-matched patients with coronary artery disease (CAD) but without CAEAs, and 20 healthy control subjects were assessed. Plasma fibrin clot permeability (Ks) reflecting an average pore size within fibrin network, plasma fibrinolytic potential expressed as clot lysis time (CLT), and thrombin generation capacity defined as endogenous thrombin potential (ETP) were assessed. The CARE-ANEURYSM study, founded by the Polish National Science Centre (no. UMO-2020/39/O/NZ5/02863), was approved by the Ethical Committee and conducted in accordance with the Declaration of Helsinki. Patients provided written informed consent. Results As many as 84.2% of CAEA patients were men, mean age was 69.6 years, and 73.7% individuals had CAD. On average, CAEA affected 3 coronary segments and exceeded vessels referral diameter by 2.15-fold. Patients with CAEA had 46.5% reduced Ks (median, 3.98 [3.30-4.63] vs 7.43 [6.15-8.88] x10-9cm2, p&amp;lt;0.001), a tendency to prolonged CLT (112 [103-116.5] vs 99 [89-114] min, p=0.07), and 51% higher ETP (1685 [1502-1786] vs 1119 [1025-1189] nM×min, p&amp;lt;0.001) compared to healthy control (Fig. 1A-C). Moreover, individuals with CAEA compared to CAD patients had 19.5% lower Ks (median 3.98 [3.30-4.63] vs 5.33 [4.86-6.78] x10-9cm2, p=0.016) and 21% longer CLT (112 [103-116.50] vs 92.52 [81.50-97.50] min, p=0.015) and (1685 [1502-1786] vs 1119 [1693-1966] nM×min, p&amp;lt;0.001) (Fig. 1A-C). In the CAEA group, Ks was associated with the CAEA maximal diameter (R=-0.595, p=0.007) and the ratio of CAEA maximal diameter to referral vessel diameter (R=0.743, p&amp;lt;0.001; Figu.1D). There were no associations between the number of coronary artery segments affected by the lesion and fibrin clot properties or ETP. However, CAEA patients in whom the ratio of the CAEA maximal diameter to the referral vessel diameter exceed 2 (n=9) had 24.5% lower Ks (3.45 [1.88-3.68] vs 4.57 [4.08-5.35] x10-9cm2, p=0.004) and 14.7% higher ETP (1779 [1685-1812] vs 1515 [1431-1674] nM×min, p=0.028) with no difference in CLT (p&amp;gt;0.05). Conclusion CAEAs are associated with denser fibrin clot structure and enhanced thrombin generation, which indicate that CAEA patients are characterized by prothrombotic state. Further studies are needed to elucidate whether prothrombotic clot phenotype may be associated with increased risk of thromboembolic events in CAEA patients.Figure 1.

Discovering pericoronary adipose tissue differences between normal and atherosclerotic coronary segments with radiomics analysis

Abstract Introduction Pericoronary adipose tissue (PCAT) extracted from coronary CT angiography (CCTA) is a novel marker for coronary inflammation. Establishing correlation between PCAT changes and the vascular lesion itself is essential for using PCAT to monitor clinical and subclinical disease status. Past studies focused on patient-level and vessel-level PCAT differences. However, lesion-level PCAT differences beyond mean attenuation values are not well understood. Purpose To investigate the differences between PCAT adjacent to atherosclerotic plaques and PCAT adjacent to normal coronary segments using radiomics analysis. Material and Methods This study enrolled 100 consecutive patients who underwent CCTA. A total of 446 plaques were successfully annotated in 89 patients. The PCAT of these 446 plaques and also their normal segment counterparts were extracted. Following prior literature, adipose tissue was defined as voxels with attenuation ranging -190 to -30 HU. PCAT was extracted within 3 mm distance from the vessel wall, along the middle 10 mm of each plaque. The PCAT of normal coronary artery segments were extracted both proximal and distal with distance D mm to the plaque with 10 mm length. We used 3 different distances to define the normal segments (D={10, 20, 30}). PyRadiomics was used to generate radiomics features and the random forest classifier from Scikit-Learn was used to distinguish PCAT extracted adjacent to atherosclerotic plaques and PCAT adjacent to normal coronary segments. To minimize bias due to vessel diameter or shape and focus on the texture of PCAT, we included only the First Order, GLCM, GLDM, GLRLM, GLSZM and NGTDM features (total 93 features) from PyRadiomics. We used 5-fold cross validation splitted at patient-level to develop the classifier. Result The radiomics model achieved acceptable discrimination for D=30 mm (mean AUROC = 0.70), borderline discrimination for D=20 mm (mean AUROC=0.68) and D=10 mm (mean AUROC = 0.60). These results suggest a trend toward greater differences between PCAT further away from the lesion compared with PCAT adjacent to the lesion. The feature "Gray Level Size Zone Matrix (GLSZM): Low Gray Level Zone Emphasis" had the highest importance in 4 out of 5 folds in our D = 30 mm experiment. The mean value of this feature is significantly different for the PCAT adjacent to the lesions compared to PCAT extracted from the normal segments (0.152 vs 0.166, p=0.001). Conclusion We investigated the difference between PCAT adjacent to atherosclerotic plaques and PCAT adjacent to normal coronary segments using radiomics analysis. We found greater discrimination when the normal segments were extracted further away from the lesion, suggesting PCAT changes due to atherosclerosis may exist beyond the length of the plaque. Moreover, "Gray Level Size Zone Matrix (GLSZM): Low Gray Level Zone Emphasis" could serve as a novel radiomic biomarker to correlate PCAT changes to atherosclerosis.