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      Identification of patients with unstable angina based on coronary CT angiography: the application of pericoronary adipose tissue radiomics

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          Abstract

          Objective

          To explore whether radiomics analysis of pericoronary adipose tissue (PCAT) captured by coronary computed tomography angiography (CCTA) could discriminate unstable angina (UA) from stable angina (SA).

          Methods

          In this single-center retrospective case-control study, coronary CT images and clinical data from 240 angina patients were collected and analyzed. Patients with unstable angina ( n = 120) were well-matched with those having stable angina ( n = 120). All patients were randomly divided into training (70%) and testing (30%) datasets. Automatic segmentation was performed on the pericoronary adipose tissue surrounding the proximal segments of the left anterior descending artery (LAD), left circumflex coronary artery (LCX), and right coronary artery (RCA). Corresponding radiomic features were extracted and selected, and the fat attenuation index (FAI) for these three vessels was quantified. Machine learning techniques were employed to construct the FAI and radiomic models. Multivariate logistic regression analysis was used to identify the most relevant clinical features, which were then combined with radiomic features to create clinical and integrated models. The performance of different models was compared in terms of area under the curve (AUC), calibration, clinical utility, and sensitivity.

          Results

          In both training and validation cohorts, the integrated model (AUC = 0.87, 0.74) demonstrated superior discriminatory ability compared to the FAI model (AUC = 0.68, 0.51), clinical feature model (AUC = 0.84, 0.67), and radiomic model (AUC = 0.85, 0.73). The nomogram derived from the combined radiomic and clinical features exhibited excellent performance in diagnosing and predicting unstable angina. Calibration curves showed good fit for all four machine learning models. Decision curve analysis indicated that the integrated model provided better clinical benefit than the other three models.

          Conclusions

          CCTA-based radiomics signature of PCAT is better than the FAI model in identifying unstable angina and stable angina. The integrated model constructed by combining radiomics and clinical features could further improve the diagnosis and differentiation ability of unstable angina.

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          Most cited references40

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          2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes

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            Heart Disease and Stroke Statistics—2023 Update: A Report From the American Heart Association

            BACKGROUND: The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS: The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year’s worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year’s edition includes additional COVID-19 (coronavirus disease 2019) publications, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains. RESULTS: Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS: The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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              Atherosclerosis: current pathogenesis and therapeutic options.

              Coronary artery disease (CAD) arising from atherosclerosis is a leading cause of death and morbidity worldwide. The underlying pathogenesis involves an imbalanced lipid metabolism and a maladaptive immune response entailing a chronic inflammation of the arterial wall. The disturbed equilibrium of lipid accumulation, immune responses and their clearance is shaped by leukocyte trafficking and homeostasis governed by chemokines and their receptors. New pro- and anti-inflammatory pathways linking lipid and inflammation biology have been discovered, and genetic profiling studies have unveiled variations involved in human CAD. The growing understanding of the inflammatory processes and mediators has uncovered an intriguing diversity of targetable mechanisms that can be exploited to complement lipid-lowering therapies. Here we aim to systematically survey recently identified molecular mechanisms, translational developments and clinical strategies for targeting lipid-related inflammation in atherosclerosis and CAD.

                Author and article information

                Contributors
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                URI : https://loop.frontiersin.org/people/2710273/overviewRole:
                Journal
                Front Cardiovasc Med
                Front Cardiovasc Med
                Front. Cardiovasc. Med.
                Frontiers in Cardiovascular Medicine
                Frontiers Media S.A.
                2297-055X
                12 December 2024
                2024
                : 11
                : 1462566
                Affiliations
                [ 1 ]Cardiovascular Medicine Department, Affiliated Hospital of North Sichuan Medical College , Nanchong, China
                [ 2 ]Digestive System Department, Affiliated Hospital of North Sichuan Medical College , Nanchong, China
                [ 3 ]Thoracic Surgery Department, Nan Chong Center Hospital , Nanchong, China
                [ 4 ]Dermatological Department, Nan Chong Center Hospital , Nanchong, China
                Author notes

                Edited by: Nazario Carrabba, Careggi Hospital, Italy

                Reviewed by: Valeria Pergola, University Hospital of Padua, Italy

                Giulia Benedetti, Guy's and St Thomas' NHS Foundation Trust, United Kingdom

                [* ] Correspondence: Ying Yang 18582547175@ 123456163.com
                Article
                10.3389/fcvm.2024.1462566
                11669672
                39726948
                9544c881-9b6a-4ccd-92b2-d2c20d837123
                © 2024 Zhan, Li, Luo, He, Long, Xu and Yang.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 26 July 2024
                : 25 November 2024
                Page count
                Figures: 5, Tables: 3, Equations: 0, References: 40, Pages: 12, Words: 0
                Funding
                Funded by: National Natural Science Youth Foundation
                Award ID: 81700417
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The National Natural Science Youth Foundation project (81700417).
                Categories
                Cardiovascular Medicine
                Original Research
                Custom metadata
                Cardiovascular Imaging

                pericoronary adipose tissue,radiomics,coronary computed tomography angiography,coronary heart disease,machine learning

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