Predicting major adverse cardiovascular events within 3 years by optimization of radiomics model derived from pericoronary adipose tissue on coronary computed tomography angiography: a case-control study.

BACKGROUND: Coronary inflammation induces changes in pericoronary adipose tissue (PCAT) can be detected by coronary computed tomography angiography (CCTA). Our aim was to investigate whether different PCAT radiomics model based on CCTA could improve the prediction of major adverse cardiovascular events (MACE) within 3 years. METHODS: This retrospective study included 141 consecutive patients with MACE and matched to patients with non-MACE (n = 141). Patients were randomly assigned into training and test datasets at a ratio of 8:2. After the robust radiomics features were selected by using the Spearman correlation analysis and the least absolute shrinkage and selection operator, radiomics models were built based on different machine learning algorithms. The clinical model was then calculated according to independent clinical risk factors. Finally, an overall model was established using the radiomics features and the clinical factors. Performance of the models was evaluated for discrimination degree, calibration degree, and clinical usefulness. RESULTS: The diagnostic performance of the PCAT model was superior to that of the RCA-model, LAD-model, and LCX-model alone, with AUCs of 0.723, 0.675, 0.664, and 0.623, respectively. The overall model showed superior diagnostic performance than that of the PCAT-model and Cli-model, with AUCs of 0.797, 0.723, and 0.706, respectively. Calibration curve showed good fitness of the overall model, and decision curve analyze demonstrated that the model provides greater clinical benefit. CONCLUSION: The CCTA-based PCAT radiomics features of three major coronary arteries have the potential to be used as a predictor for MACE. The overall model incorporating the radiomics features and clinical factors offered significantly higher discrimination ability for MACE than using radiomics or clinical factors alone.

COACT: Coronary artery centerline tracker.

BACKGROUND: The curved planar reformation (CPR) technique is one of the most commonly used methods in clinical practice to locate coronary arteries in medical images. PURPOSE: The artery centerline is the cornerstone for the generation of the CPR image. Here, we describe the development of a new fully automatic artery centerline tracker with the aim of increasing the efficiency and accuracy of the process. METHODS: We propose a COronary artery Centerline Tracker (COACT) framework which consists of an ostium point finder (OPFinder) model, an intersection point detector (IPDetector) model and a set of centerline tracking strategies. The output of OPFinder is the ostium points. The function of the IPDetector is to predict the intersections of a sample sphere and the centerlines. The centerline tracking process starts from two ostium points detected by the OPFinder, and combines the results of the IPDetector with a series of strategies to gradually reconstruct the coronary artery centerline tree. RESULTS: Two coronary CT angiography (CCTA) datasets were used to validate the models. Dataset1 contains 160 cases (32 for test and 128 for training) and dataset2 contains 70 cases (20 for test and 50 for training). The results show that the average distance between the ostium points predicted by the OPFinder and the manually annotated ostium points was 0.88 mm, which is similar to the differences between the results obtained by two observers (0.85 mm). For the IPDetector, the average overlap of the predicted and ground truth intersection points was 97.82% and this is also close to the inter-observer agreement of 98.50%. For the entire coronary centerline tree, the overlap between the results obtained by COACT and the gold standard was 94.33%, which is slightly lower than the inter-observer agreement, 98.39%. CONCLUSIONS: We have developed a fully automatic centerline tracking method for CCTA scans and achieved a satisfactory result. The proposed algorithms are also incorporated in the medical image analysis platform TIMESlice (https://slice-doc.netlify.app) for further studies.

Noncontrast cardiac computed tomography-derived mitral annular calcification scores in mitral valve disease.

BACKGROUND AND AIMS: Mitral annular calcification (MAC) by computed tomography (CT) is reported as an independent predictor of poor outcomes. However, it currently remains unclear if quantitative MAC parameters provide more value for mitral valve disease (MVD) management, therefore, we examined the prognostic value of MAC scores using noncontrast cardiac-CT in MVD patients. METHODS: Between January 2020 and December 2021, we prospectively enrolled 300 consecutive patients with MVD (MAC-present = 80 and MAC-absent = 220) undergoing preoperative cardiac-CT and mitral valve (MV) surgery. Noncontrast cardiac-CT images were used to qualitatively detect MAC (present or absent) and evaluate MAC scores. For analyses, we also collected baseline clinical data, intraoperative conversion (from MV repair to MV replacement), and follow-up arrhythmia data. RESULTS: Compared with the MAC-absent group, MAC-present patients were older (62 ± 7 vs. 58 ± 9 years, p < .001), mostly women (55% vs. 39.5%, p = .017), and also had aortic valve calcification (57.5% vs. 23.2%, p < .001), mitral stenosis (82.5% vs. 61.8%, p < .001), atrial fibrillation (30% vs. 11.8%, p < .001), and larger left atrial end-diastolic dimension (LADD, 49 [44-56] versus 46 [41-50], p = .001]. Furthermore, MAC-present patients underwent more MV replacements (61.8% vs. 82.5%, p = .001) and experienced a higher intraoperative conversion prevalence (11.8% vs. 61.3%, p < .001). Multiple logistic regression analyses showed that the female gender (odds ratio [OR]/95% confidence interval [CI]/p = 2.001/1.042-3.841/0.037) and MAC scores (OR/95% CI/p = 10.153/4.434-23.253/p < .001) were independent predictors of intraoperative conversion. During a follow-up of 263 ± 134 days, MAC-present patients had more arrhythmias (42.5% vs. 9.5%, p < .001). Also, MAC-scores (hazard ratio [HR]/95% CI/p = 6.841/3.322-14.089/p < .001) and LADD (HR/95% CI/p = 1.039/1.018-1.060/p < .001) were independently associated with arrhythmias by Cox regression analyses. CONCLUSIONS: Noncontrast cardiac CT-derived MAC-scores showed a high risk for intraoperative conversion and follow-up arrhythmias in MVD-patients.

The role of epicardial and pericoronary adipose tissue radiomics in identifying patients with non-ST-segment elevation myocardial infarction from unstable angina.

Objectives: This study aimed to ascertain if the radiomics features of epicardial adipose tissue (EAT) and pericoronary adipose tissue (PCAT) based on coronary computed tomography angiography (CCTA) could identify non-ST-segment elevation myocardial infarction (NSTEMI) from unstable angina (UA). Materials and methods: This retrospective case-control study included 108 patients with NSTEMI and 108 controls with UA. All patients were separated into training cohort (n = 116), internal validation cohort 1 (n = 50), and internal validation cohort 2 (n = 50) based on the time order of admission. The internal validation cohort 1 used the same scanner and scan parameters as the training cohort, while the internal validation cohort 2 used different canners and scan parameters than the training cohort. The EAT and PCAT radiomics features selected by maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were adopted to build logistic regression models. Finally, we developed an EAT radiomics model, three vessel-based (right coronary artery [RCA], left anterior descending artery [LAD], and left circumflex artery [LCX]) PCAT radiomics models, and a combined model by combining the three PCAT radiomics models. Discrimination, calibration, and clinical application were employed to assess the performance of all models. Results: Eight radiomics features of EAT, sixteen of RCA-PCAT, fifteen of LAD-PCAT, and eighteen of LCX-PCAT were selected and used to construct radiomics models. The area under the curves (AUCs) of the EAT, RCA-PCAT, LAD-PCAT, LCX-PCAT and the combined models were 0.708 (95% CI: 0.614-0.802), 0.833 (95% CI:0.759-0.906), 0.720 (95% CI:0.628-0.813), 0.713 (95% CI:0.619-0.807), 0.889 (95% CI:0.832-0.946) in the training cohort, 0.693 (95% CI:0.546-0.840), 0.837 (95% CI: 0.729-0.945), 0.766 (95% CI: 0.625-0.907), 0.675 (95% CI: 0.521-0.829), 0.898 (95% CI: 0.802-0.993) in the internal validation cohort 1, and 0.691 (0.535-0.847), 0.822 (0.701-0.944), 0.760 (0.621-0.899), 0.674 (0.517-0.830), 0.866 (0.769-0.963) in the internal validation cohort 2, respectively. Conclusion: Compared with the RCA-PCAT radiomics model, the EAT radiomics model had a limited ability to discriminate between NSTEMI and UA. The combination of the three vessel-based PCAT radiomics may have the potential to distinguish between NSTEMI and UA.

Radiomics Signatures of Carotid Plaque on Computed Tomography Angiography : An Approach to Identify Symptomatic Plaques.

PURPOSE: To develop and validate a combined model incorporating conventional clinical and imaging characteristics and radiomics signatures based on head and neck computed tomography angiography (CTA) to assess plaque vulnerability. METHODS: We retrospectively analyzed 167 patients with carotid atherosclerosis who underwent head and neck CTA and brain magnetic resonance imaging (MRI) within 1 month. Clinical risk factors and conventional plaque characteristics were evaluated, and radiomic features were extracted from the carotid plaques. The conventional, radiomics and combined models were developed using fivefold cross-validation. Model performance was evaluated using receiver operating characteristic (ROC), calibration, and decision curve analyses. RESULTS: Patients were divided into symptomatic (n = 70) and asymptomatic (n = 97) groups based on MRI results. Homocysteine (odds ratio, OR 1.057; 95% confidence interval, CI 1.001-1.116), plaque ulceration (OR 6.106; 95% CI 1.933-19.287), and carotid rim sign (OR 3.285; 95% CI 1.203-8.969) were independently associated with symptomatic status and were used to construct the conventional model and s radiomic features were retained to establish the radiomics model. Radiomics scores incorporated with conventional characteristics were used to establish the combined model. The area under the ROC curve (AUC) of the combined model was 0.832, which outperformed the conventional (AUC = 0.767) and radiomics (AUC = 0.797) models. Calibration and decision curves analysis showed that the combined model was clinically useful. CONCLUSION: Radiomics signatures of carotid plaque on CTA can well predict plaque vulnerability, which may provide additional value to identify high-risk patients and improve outcomes.

Performance of Radiomics Models Based on Coronary Computed Tomography Angiography in Predicting The Risk of Major Adverse Cardiovascular Events Within 3 Years: A Comparison Between the Pericoronary Adipose Tissue Model and the Epicardial Adipose Tissue Model.

RATIONALE AND OBJECTIVES: To compare the prediction performance of the epicardial adipose tissue (EAT) and pericoronary adipose tissue (PCAT) radiomics models based on coronary computed tomography angiography for major adverse cardiovascular events (MACE) within 3 years. MATERIALS AND METHODS: Our study included 288 patients (144 with MACE and 144 without MACE within 3 years) by matching age, gender, body mass index, and medication intake. Patients were randomly assigned either to the training (n = 201) or validation cohort (n = 87). A total of 184 radiomics features were extracted from EAT and PCAT images. Spearman's rank correlation coefficient and the gradient boosting decision tree algorithm were performed for feature selection. Five models were established based on PCAT or EAT radiomics features and clinical factors, including PCAT, EAT, clinical, PCAT-clinical, and EAT-clinical model (MPCAT, MEAT, Mclinical, MPCAT-clinical, and MEAT-clinical). Receiver operating characteristic curves, calibration curves, and the decision curve analysis were plotted to evaluate the model performance. RESULTS: The MPCAT achieved an area under the curve (AUC) of 0.703 in the validation cohort, which was better than MEAT with AUC of 0.538. The MPCAT-clinical showed better performance (AUC = 0.781) in predicting MACE than the Mclinical (AUC = 0.748) or MEAT-clinical (AUC = 0.745). CONCLUSION: Our results showed that the PCAT was better than the EAT in both single modality and combined models, and the MPCAT-clinical had the most significant clinical value in predicting the occurrence of MACE within 3 years.

A hybrid boronate affinity probe for the selective detection of cis-diol containing compounds in tea beverages.

UiO-66-NH2 nanocomposite was post-modified with 4-mercaptophenylboronic acid (MPBA) by the method of in situ hybridization reaction. The hybrid boronate affinity material UiO-NH2 @P (TEPIC-co-MPBA) was characterized by scanning electron microscopy, X-ray diffraction and Fourier-transform infrared spectroscopy. The material was applied as fluorescent probe for the detection of cis-diol containing compounds based on the boronate affinity mechanism, and exhibited high specific selectively. The proposed method exhibited good linearity for the detection of catechol in the range of 0.50 to 8.00 µg ml-1 . The detection limit was 0.13 µg ml-1 . The tactic was successfully applied to analyze the total polyphenols in tea beverages for catechol, and relative recovery was in 98.86-106.00%. Therefore, this work provided a promising strategy for the recognition of cis-diol containing compounds.

Relationship between epicardial fat volume on cardiac CT and atherosclerosis severity in three-vessel coronary artery disease: a single-center cross-sectional study.

BACKGROUND: The ideal treatment strategy for stable three-vessel coronary artery disease (CAD) patients are difficult to determine and for patients undergoing conservative treatment, imaging evidence of coronary atherosclerotic severity progression remains limited. Epicardial fat volume (EFV) on coronary CT angiography (CCTA) has been considered to be associated with coronary atherosclerosis. Therefore, this study aims to evaluate the relationship between EFV level and coronary atherosclerosis severity in three-vessel CAD. METHODS: This retrospective study enrolled 252 consecutive patients with three-vessel CAD and 252 normal control group participants who underwent CCTA between January 2018 and December 2019. A semi-automatic method was developed for EFV quantification on CCTA images, standardized by body surface area. Coronary atherosclerosis severity was evaluated and scored by the number of coronary arteries with ≥ 50% stenosis on coronary angiography. Patients were subdivided into groups on the basis of lesion severity: mild (score = 3 vessels, n = 85), moderate (3.5 vessels ≤ score < 4 vessels, n = 82), and severe (4 vessels ≤ score ≤ 7 vessels, n = 85). The independent sample t-test, analysis of variance, and logistic regression analysis were used to evaluate the associations between EFV level and severity of coronary atherosclerosis. RESULTS: Compared with normal controls, three-vessel CAD patients had significantly higher EFV level (65 ± 22 mL/m2 vs. 48 ± 19 mL/m2; P < 0.001). In patients with three-vessel CAD, there was a progressive decline in EFV level as the score of coronary atherosclerosis severity increased, especially in those patients with a body mass index (BMI) ≥ 25 kg/m2 (75 ± 21 mL/m2 vs. 72 ± 22 mL/m2 vs. 62 ± 17 mL/m2; P < 0.05). Multivariable regression analysis showed that both BMI (OR 3.40, 95% CI 2.00-5.78, P < 0.001) and the score of coronary atherosclerosis severity (OR 0.49, 95% CI 0.26-0.93, P < 0.05) were independently related to the change of EFV level. CONCLUSION: Three-vessel CAD patients do have higher EFV level than the normal controls. While, there may be an inverse relationship between EFV level and the severity of coronary atherosclerosis in patients with three-vessel CAD.

Assessing left atrial function in patients with atrial fibrillation and valvular heart disease using cardiovascular magnetic resonance imaging.

BACKGROUND: Atrial fibrillation (AF) is common arrhythmia in valvular heart disease (VHD) and is associated with adverse outcomes. HYPOTHESIS: To evaluate the left atrial (LA) function in patients with AF-VHD by cardiovascular magnetic resonance imaging feature tracking (CMR-FT) using LA strain (εs /εe /εa ) and their corresponding strain rate (SRs/SRe/SRa). METHODS: This was a retrospective cross-sectional inter-reader and intra-reader reproducibility conducted from July 1, 2020, to January 31, 2021. A total of 39 patients with AF-VHD (rheumatic heart valvular disease [RHVD] [n = 22], degenerative heart valvular disease [DHVD] [n = 17]) underwent MRI scans performed with drug-controlled heart rate before correcting the rhythm and valves through maze procedure. Fifteen participants with normal cardiac MRI were included as healthy control. εs /SRs, εe /SRe, and εa /SRa, corresponding to LA reservoir, conduit, and booster-pump function, were assessed using Feature Tracking software (CVI42 v5.12.1). RESULTS: Compared with healthy controls, LA global strain parameters (εs /εe /εa /SRs/SRe/SRa) were significantly decreased (all p < 0.001), while LA size and volume were increased in AF-VHD group (all p < 0.001). In the subgroup, RHVD group showed lower LA total ejection fraction (LATEF) and strain data than DHVD group (12.6% ± 3.3% vs. 19.4 ± 8.6, p = 0.001). Decreased LATEF was significantly related to altered LA strain and strain rate, especially in εs , εe , and SRs (Pearson/Spearman r/ρ = 0.856/0.837/0.562, respectively; all p < 0.001). Interstudy and intrastudy reproducibility were consistent for LA volumetry and strain parameters (intraclass correlation coefficient: 0.88-0.99). CONCLUSIONS: CMR-FT can be used to assess the LA strain parameters, and identify LA dysfunction and deformation noninvasively, which could be a helpful functional imaging biomarker in the clinical treatment of AF-VHD.

Risk Factors of Anterior Circulation Intracranial Aneurysm Rupture: Extracranial Carotid Artery Tortuosity and Aneurysm Morphologic Parameters.

Background: This study was conducted to explore the risk factors of anterior circulation intracranial aneurysm rupture based on extracranial carotid artery (ECA) tortuosity. Methods: This retrospective study, conducted from January 1, 2017, to March 1, 2021, collected and reviewed the clinical and imaging data of 308 patients with anterior circulation intracranial aneurysm [133 (43.2%) patients in the ruptured aneurysm group; 175 (56.8%) patients in the unruptured aneurysm group]. Computed tomography angiography (CTA) of the head and neck was used to determine the ECA tortuosity (normal, simple tortuosity, kink, coil) and the morphologic parameters of the aneurysms. The relationship of aneurysm rupture to ECA tortuosity and the morphologic parameters were analyzed. Results: After univariate analysis, kink, angle of flow inflow (FA), aspect ratio (AR), aneurysm length (L), the distance from the tortuosity to the aneurysm (distance), and size ratio (SR) were significantly correlated with anterior circulation intracranial aneurysm rupture (p < 0.05). Spearman correlation analysis showed that ECA tortuosity was correlated with FA and SR (p < 0.05). Multiple logistic analyses showed that FA [odds ratio (OR), 1.013; 95% CI, 1.002-1.025], SR (OR, 1.521; 95% CI, 1.054-2.195), and kink (OR, 1.823; 95% CI, 1.074-3.096) were independently associated with aneurysm rupture. Conclusion: Study results suggest that FA, SR, and ECA kink were independent risk factors associated with anterior circulation intracranial aneurysm rupture.

Automatic quantification of epicardial adipose tissue volume.

PURPOSE: Epicardial fat is the adipose tissue between the serosal pericardial wall layer and the visceral layer. It is distributed mainly around the atrioventricular groove, atrial septum, ventricular septum and coronary arteries. Studies have shown that the density, thickness, volume and other characteristics of epicardial adipose tissue (EAT) are independently correlated with a variety of cardiovascular diseases. Given this association, the accurate determination of EAT volume is an essential aim of future research. Therefore, the purpose of this study was to establish a framework for fully automatic EAT segmentation and quantification in coronary computed tomography angiography (CCTA) scans. METHODS: A set of 103 scans are randomly selected from our medical center. An automatic pipeline has been developed to segment and quantify the volume of EAT. First, a multi-slice deep neural network is used to simultaneously segment the pericardium in multiple adjacent slices. Then a deformable model is employed to reduce false positive and negative regions in the segmented binary pericardial images. Finally, the pericardium mask is used to define the region of interest (ROI) and the threshold method is utilized to extract the pixels ranging from -175 Hounsfield units (HU) to -15 HU for the segmentation of EAT. RESULTS: The Dice indices of the pericardial segmentation using the proposed method with respect to the manual delineation results of two radiology experts were 97.1%  ±  0.7% and 96.9%  ±  0.6%, respectively. The inter-observer variability was also assessed, resulting in a Dice index of 97.0%  ±  0.7%. For the EAT segmentation results, the Dice indices between the proposed method and the two radiology experts were 93.4%  ±  1.5% and 93.3%  ±  1.3%, respectively, and the same measurement between the experts themselves was 93.6%  ±  1.9%. The Pearson's correlation coefficients between the EAT volumes computed from the results of the proposed method and the manual delineation by the two experts were 1.00 and 0.99 and the same coefficients between the experts was 0.99. CONCLUSIONS: This work describes the development of a fully automatic EAT segmentation and quantification method from CCTA scans and the results compare favorably with the assessments of two independent experts. The proposed method is also packaged with a graphical user interface which can be found at https://github.com/MountainAndMorning/EATSeg.