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Lüscher TF, Wenzl FA, D'Ascenzo F, Friedman PA, Antoniades C. Artificial intelligence in cardiovascular medicine: clinical applications. Eur Heart J 2024:ehae465. [PMID: 39158472 DOI: 10.1093/eurheartj/ehae465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 06/07/2024] [Accepted: 07/03/2024] [Indexed: 08/20/2024] Open
Abstract
Clinical medicine requires the integration of various forms of patient data including demographics, symptom characteristics, electrocardiogram findings, laboratory values, biomarker levels, and imaging studies. Decision-making on the optimal management should be based on a high probability that the envisaged treatment is appropriate, provides benefit, and bears no or little potential harm. To that end, personalized risk-benefit considerations should guide the management of individual patients to achieve optimal results. These basic clinical tasks have become more and more challenging with the massively growing data now available; artificial intelligence and machine learning (AI/ML) can provide assistance for clinicians by obtaining and comprehensively preparing the history of patients, analysing face and voice and other clinical features, by integrating laboratory results, biomarkers, and imaging. Furthermore, AI/ML can provide a comprehensive risk assessment as a basis of optimal acute and chronic care. The clinical usefulness of AI/ML algorithms should be carefully assessed, validated with confirmation datasets before clinical use, and repeatedly re-evaluated as patient phenotypes change. This review provides an overview of the current data revolution that has changed and will continue to change the face of clinical medicine radically, if properly used, to the benefit of physicians and patients alike.
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Affiliation(s)
- Thomas F Lüscher
- Royal Brompton and Harefield Hospitals, London, UK
- National Heart and Lung Institute, Imperial College London, UK
- Cardiovascular Academic Group, King's College, London, UK
- Center for Molecular Cardiology, University of Zurich, Wagistrasse 12, 8952 Schlieren - Zurich, Switzerland
| | - Florian A Wenzl
- Center for Molecular Cardiology, University of Zurich, Wagistrasse 12, 8952 Schlieren - Zurich, Switzerland
- National Disease Registration and Analysis Service, NHS, London, UK
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- Department of Clinical Sciences, Karolinska Institutet, Stockholm, Sweden
| | - Fabrizio D'Ascenzo
- Division of Cardiology, Cardiovascular and Thoracic Department, Città della Salute e della Scienza Hospital, Turin, Italy
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic and Mayo Foundation, Rochester, MN, USA
| | - Charalambos Antoniades
- Acute Multidisciplinary Imaging and Interventional Centre, RDM Division of Cardiovascular Medicine, University of Oxford, Headley Way, Headington, Oxford OX39DU, UK
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Khera R, Oikonomou EK, Nadkarni GN, Morley JR, Wiens J, Butte AJ, Topol EJ. Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice: JACC State-of-the-Art Review. J Am Coll Cardiol 2024; 84:97-114. [PMID: 38925729 DOI: 10.1016/j.jacc.2024.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 05/03/2024] [Accepted: 05/07/2024] [Indexed: 06/28/2024]
Abstract
Artificial intelligence (AI) has the potential to transform every facet of cardiovascular practice and research. The exponential rise in technology powered by AI is defining new frontiers in cardiovascular care, with innovations that span novel diagnostic modalities, new digital native biomarkers of disease, and high-performing tools evaluating care quality and prognosticating clinical outcomes. These digital innovations promise expanded access to cardiovascular screening and monitoring, especially among those without access to high-quality, specialized care historically. Moreover, AI is propelling biological and clinical discoveries that will make future cardiovascular care more personalized, precise, and effective. The review brings together these diverse AI innovations, highlighting developments in multimodal cardiovascular AI across clinical practice and biomedical discovery, and envisioning this new future backed by contemporary science and emerging discoveries. Finally, we define the critical path and the safeguards essential to realizing this AI-enabled future that helps achieve optimal cardiovascular health and outcomes for all.
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Affiliation(s)
- Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA; Center for Outcomes Research and Evaluation (CORE), New Haven, Connecticut, USA; Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut, USA; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.
| | - Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Girish N Nadkarni
- The Samuel Bronfman Department of Medicine, Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jessica R Morley
- Digital Ethics Center, Yale University, New Haven, Connecticut, USA
| | - Jenna Wiens
- Electrical Engineering and Computer Science, Computer Science and Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Atul J Butte
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, USA; Center for Data-Driven Insights and Innovation, University of California Health, Oakland, California, USA
| | - Eric J Topol
- Molecular Medicine, Scripps Research Translational Institute, Scripps Research, La Jolla, California, USA
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Gandhi RS, Raman B. The complexity of cardiovascular long COVID: where we are. Cardiovasc Res 2024; 120:e30-e32. [PMID: 38757616 PMCID: PMC11218687 DOI: 10.1093/cvr/cvae090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 02/13/2024] [Indexed: 05/18/2024] Open
Affiliation(s)
- Rahul S Gandhi
- Wellington Cardiovascular Research Group, Wellington Hospital, Wellington, New Zealand
| | - Betty Raman
- Radcliffe Department of Medicine, University of Oxford and Oxford University Hospital Foundation NHS Trusts, John Radcliffe Hospital, Headley Way, Oxfordshire OX3 9DU
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Klüner LV, Chan K, Antoniades C. Using artificial intelligence to study atherosclerosis from computed tomography imaging: A state-of-the-art review of the current literature. Atherosclerosis 2024:117580. [PMID: 38852022 DOI: 10.1016/j.atherosclerosis.2024.117580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 05/03/2024] [Accepted: 05/14/2024] [Indexed: 06/10/2024]
Abstract
With the enormous progress in the field of cardiovascular imaging in recent years, computed tomography (CT) has become readily available to phenotype atherosclerotic coronary artery disease. New analytical methods using artificial intelligence (AI) enable the analysis of complex phenotypic information of atherosclerotic plaques. In particular, deep learning-based approaches using convolutional neural networks (CNNs) facilitate tasks such as lesion detection, segmentation, and classification. New radiotranscriptomic techniques even capture underlying bio-histochemical processes through higher-order structural analysis of voxels on CT images. In the near future, the international large-scale Oxford Risk Factors And Non-invasive Imaging (ORFAN) study will provide a powerful platform for testing and validating prognostic AI-based models. The goal is the transition of these new approaches from research settings into a clinical workflow. In this review, we present an overview of existing AI-based techniques with focus on imaging biomarkers to determine the degree of coronary inflammation, coronary plaques, and the associated risk. Further, current limitations using AI-based approaches as well as the priorities to address these challenges will be discussed. This will pave the way for an AI-enabled risk assessment tool to detect vulnerable atherosclerotic plaques and to guide treatment strategies for patients.
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Affiliation(s)
- Laura Valentina Klüner
- Acute Multidisciplinary Imaging and Interventional Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford NIHR Biomedical Research Centre, University of Oxford, United Kingdom
| | - Kenneth Chan
- Acute Multidisciplinary Imaging and Interventional Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford NIHR Biomedical Research Centre, University of Oxford, United Kingdom
| | - Charalambos Antoniades
- Acute Multidisciplinary Imaging and Interventional Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford NIHR Biomedical Research Centre, University of Oxford, United Kingdom.
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West HW, Dangas K, Antoniades C. Advances in Clinical Imaging of Vascular Inflammation: A State-of-the-Art Review. JACC Basic Transl Sci 2024; 9:710-732. [PMID: 38984055 PMCID: PMC11228120 DOI: 10.1016/j.jacbts.2023.10.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 10/12/2023] [Accepted: 10/12/2023] [Indexed: 07/11/2024]
Abstract
Vascular inflammation is a major contributor to cardiovascular disease, particularly atherosclerotic disease, and early detection of vascular inflammation may be key to the ultimate reduction of residual cardiovascular morbidity and mortality. This review paper discusses the progress toward the clinical utility of noninvasive imaging techniques for assessing vascular inflammation, with a focus on coronary atherosclerosis. A discussion of multiple modalities is included: computed tomography (CT) imaging (the major focus of the review), cardiac magnetic resonance, ultrasound, and positron emission tomography imaging. The review covers recent progress in new technologies such as the novel CT biomarkers of coronary inflammation (eg, the perivascular fat attenuation index), new inflammation-specific tracers for positron emission tomography-CT imaging, and others. The strengths and limitations of each modality are explored, highlighting the potential for multi-modality imaging and the use of artificial intelligence image interpretation to improve both diagnostic and prognostic potential for common conditions such as coronary artery disease.
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Affiliation(s)
- Henry W West
- Acute Multidisciplinary Imaging and Interventional Centre, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
- Central Clinical School, Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia
| | - Katerina Dangas
- Acute Multidisciplinary Imaging and Interventional Centre, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Charalambos Antoniades
- Acute Multidisciplinary Imaging and Interventional Centre, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
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Lui KO, Ma Z, Dimmeler S. SARS-CoV-2 induced vascular endothelial dysfunction: direct or indirect effects? Cardiovasc Res 2024; 120:34-43. [PMID: 38159046 DOI: 10.1093/cvr/cvad191] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 10/18/2023] [Accepted: 11/03/2023] [Indexed: 01/03/2024] Open
Abstract
Clinical evidence reveals that manifestations of endothelial dysfunction are widely observed in COVID-19 and long-COVID patients. However, whether these detrimental effects are caused by direct infection of the endothelium or are indirectly mediated by systemic inflammation has been a matter of debate. It has been well acknowledged that endothelial cells (ECs) of the cardiovascular system ubiquitously express the SARS-CoV-2 entry receptor angiotensin-converting enzyme 2 (ACE2), yet accumulating evidence suggests that it is more predominantly expressed by pericytes and vascular smooth muscle cells of the mammalian blood vessel. Besides, replicative infection of ECs by SARS-CoV-2 has yet to be demonstrated both in vitro and in vivo. In this study, we review latest research on endothelial ACE2 expression in different vascular beds, and the heterogeneity in various EC subsets with differential ACE2 expression in response to SARS-CoV-2. We also discuss ACE2-independent alternative mechanisms underlying endothelial activation in COVID-19, and the clinical manifestations of SARS-CoV-2-induced endothelial dysfunction. Altogether, understanding ACE2-dependent and ACE2-independent mechanisms driving SARS-CoV-2-induced vascular dysfunction would shed light on strategies of more effective therapies targeting cardiovascular complications associated with COVID-19.
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Affiliation(s)
- Kathy O Lui
- Department of Chemical Pathology, and Li Ka Shing Institute of Health Science, Prince of Wales Hospital, The Chinese University of Hong Kong, 30-32 Ngan Shing Street, Sha Tin, New Territories, 999077 Hong Kong, China
| | - Zhangjing Ma
- Department of Chemical Pathology, and Li Ka Shing Institute of Health Science, Prince of Wales Hospital, The Chinese University of Hong Kong, 30-32 Ngan Shing Street, Sha Tin, New Territories, 999077 Hong Kong, China
| | - Stefanie Dimmeler
- Institute for Cardiovascular Regeneration, and Faculty of Biological Sciences, Goethe University Frankfurt, Theodor Stern Kai 7, 60590 Frankfurt, Germany
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Nie JY, Chen WX, Zhu Z, Zhang MY, Zheng YJ, Wu QD. Initial experience with radiomics of carotid perivascular adipose tissue in identifying symptomatic plaque. Front Neurol 2024; 15:1340202. [PMID: 38434202 PMCID: PMC10907991 DOI: 10.3389/fneur.2024.1340202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 01/29/2024] [Indexed: 03/05/2024] Open
Abstract
Background Carotid atherosclerotic ischemic stroke threatens human health and life. The aim of this study is to establish a radiomics model of perivascular adipose tissue (PVAT) around carotid plaque for evaluation of the association between Peri-carotid Adipose Tissue structural changes with stroke and transient ischemic attack. Methods A total of 203 patients underwent head and neck computed tomography angiography examination in our hospital. All patients were divided into a symptomatic group (71 cases) and an asymptomatic group (132 cases) according to whether they had acute/subacute stroke or transient ischemic attack. The radiomic signature (RS) of carotid plaque PVAT was extracted, and the minimum redundancy maximum correlation, recursive feature elimination, and linear discriminant analysis algorithms were used for feature screening and dimensionality reduction. Results It was found that the RS model achieved the best diagnostic performance in the Bagging Decision Tree algorithm, and the training set (AUC, 0.837; 95%CI: 0.775, 0.899), testing set (AUC, 0.834; 95%CI: 0.685, 0.982). Compared with the traditional feature model, the RS model significantly improved the diagnostic efficacy for identifying symptomatic plaques in the testing set (AUC: 0.834 vs. 0.593; Z = 2.114, p = 0.0345). Conclusion The RS model of PVAT of carotid plaque can be used as an objective indicator to evaluate the risk of plaque and provide a basis for risk stratification of carotid atherosclerotic disease.
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Affiliation(s)
- Ji-Yan Nie
- Department of Radiology, Shunde Hospital of Guangzhou University of Traditional Chinese Medicine, Shunde, China
- Graduate School, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Wen-Xi Chen
- Department of Radiology, Shunde Hospital of Guangzhou University of Traditional Chinese Medicine, Shunde, China
- Graduate School, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Zhi Zhu
- Department of Radiology, Shunde Hospital of Guangzhou University of Traditional Chinese Medicine, Shunde, China
- Graduate School, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Ming-Yu Zhang
- Department of Radiology, Shunde Hospital of Guangzhou University of Traditional Chinese Medicine, Shunde, China
- Graduate School, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yu-Jin Zheng
- Department of Radiology, Shunde Hospital of Guangzhou University of Traditional Chinese Medicine, Shunde, China
| | - Qing-De Wu
- Department of Radiology, Shunde Hospital of Guangzhou University of Traditional Chinese Medicine, Shunde, China
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Polkinghorne MD, West HW, Antoniades C. Adipose Tissue in Cardiovascular Disease: From Basic Science to Clinical Translation. Annu Rev Physiol 2024; 86:175-198. [PMID: 37931169 DOI: 10.1146/annurev-physiol-042222-021346] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2023]
Abstract
The perception of adipose tissue as a metabolically quiescent tissue, primarily responsible for lipid storage and energy balance (with some endocrine, thermogenic, and insulation functions), has changed. It is now accepted that adipose tissue is a crucial regulator of metabolic health, maintaining bidirectional communication with other organs including the cardiovascular system. Additionally, adipose tissue depots are functionally and morphologically heterogeneous, acting not only as sources of bioactive molecules that regulate the physiological functioning of the vasculature and myocardium but also as biosensors of the paracrine and endocrine signals arising from these tissues. In this way, adipose tissue undergoes phenotypic switching in response to vascular and/or myocardial signals (proinflammatory, profibrotic, prolipolytic), a process that novel imaging technologies are able to visualize and quantify with implications for clinical prognosis. Furthermore, a range of therapeutic modalities have emerged targeting adipose tissue metabolism and altering its secretome, potentially benefiting those at risk of cardiovascular disease.
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Affiliation(s)
- Murray D Polkinghorne
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom;
- Acute Multidisciplinary Imaging and Interventional Centre, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Henry W West
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom;
- Acute Multidisciplinary Imaging and Interventional Centre, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
- Central Clinical School, Sydney Medical School, The University of Sydney, Sydney, New South Wales, Australia
| | - Charalambos Antoniades
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom;
- Acute Multidisciplinary Imaging and Interventional Centre, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
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Mátyás BB, Benedek I, Raț N, Blîndu E, Parajkó Z, Mihăilă T, Benedek T. Assessing the Impact of Long-Term High-Dose Statin Treatment on Pericoronary Inflammation and Plaque Distribution-A Comprehensive Coronary CTA Follow-Up Study. Int J Mol Sci 2024; 25:1700. [PMID: 38338972 PMCID: PMC10855947 DOI: 10.3390/ijms25031700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 01/23/2024] [Accepted: 01/28/2024] [Indexed: 02/12/2024] Open
Abstract
Computed tomography angiography (CTA) has validated the use of pericoronary adipose tissue (PCAT) attenuation as a credible indicator of coronary inflammation, playing a crucial role in coronary artery disease (CAD). This study aimed to evaluate the long-term effects of high-dose statins on PCAT attenuation at coronary lesion sites and changes in plaque distribution. Our prospective observational study included 52 patients (mean age 60.43) with chest pain, a low-to-intermediate likelihood of CAD, who had documented atheromatous plaque through CTA, performed approximately 1 year and 3 years after inclusion. We utilized the advanced features of the CaRi-Heart® and syngo.via Frontier® systems to assess coronary plaques and changes in PCAT attenuation. The investigation of changes in plaque morphology revealed significant alterations. Notably, in mixed plaques, calcified portions increased (p < 0.0001), while non-calcified plaque volume (NCPV) decreased (p = 0.0209). PCAT attenuation generally decreased after one year and remained low, indicating reduced inflammation in the following arteries: left anterior descending artery (LAD) (p = 0.0142), left circumflex artery (LCX) (p = 0.0513), and right coronary artery (RCA) (p = 0.1249). The CaRi-Heart® risk also decreased significantly (p = 0.0041). Linear regression analysis demonstrated a correlation between increased PCAT attenuation and higher volumes of NCPV (p < 0.0001, r = 0.3032) and lipid-rich plaque volume (p < 0.0001, r = 0.3281). Our study provides evidence that high-dose statin therapy significantly reduces CAD risk factors, inflammation, and plaque vulnerability, as evidenced by the notable decrease in PCAT attenuation, a critical indicator of plaque progression.
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Affiliation(s)
- Botond Barna Mátyás
- Clinic of Cardiology, Mureș County Emergency Clinical Hospital, 540136 Târgu Mureș, Romania; (B.B.M.); (I.B.); (E.B.); (Z.P.); (T.M.); (T.B.)
- Doctoral School of Medicine and Pharmacy, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, 540139 Târgu Mureș, Romania
| | - Imre Benedek
- Clinic of Cardiology, Mureș County Emergency Clinical Hospital, 540136 Târgu Mureș, Romania; (B.B.M.); (I.B.); (E.B.); (Z.P.); (T.M.); (T.B.)
- Department of Cardiology, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, 540139 Târgu Mureș, Romania
| | - Nóra Raț
- Clinic of Cardiology, Mureș County Emergency Clinical Hospital, 540136 Târgu Mureș, Romania; (B.B.M.); (I.B.); (E.B.); (Z.P.); (T.M.); (T.B.)
- Department of Cardiology, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, 540139 Târgu Mureș, Romania
| | - Emanuel Blîndu
- Clinic of Cardiology, Mureș County Emergency Clinical Hospital, 540136 Târgu Mureș, Romania; (B.B.M.); (I.B.); (E.B.); (Z.P.); (T.M.); (T.B.)
- Doctoral School of Medicine and Pharmacy, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, 540139 Târgu Mureș, Romania
| | - Zsolt Parajkó
- Clinic of Cardiology, Mureș County Emergency Clinical Hospital, 540136 Târgu Mureș, Romania; (B.B.M.); (I.B.); (E.B.); (Z.P.); (T.M.); (T.B.)
- Doctoral School of Medicine and Pharmacy, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, 540139 Târgu Mureș, Romania
| | - Theofana Mihăilă
- Clinic of Cardiology, Mureș County Emergency Clinical Hospital, 540136 Târgu Mureș, Romania; (B.B.M.); (I.B.); (E.B.); (Z.P.); (T.M.); (T.B.)
- Doctoral School of Medicine and Pharmacy, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, 540139 Târgu Mureș, Romania
| | - Theodora Benedek
- Clinic of Cardiology, Mureș County Emergency Clinical Hospital, 540136 Târgu Mureș, Romania; (B.B.M.); (I.B.); (E.B.); (Z.P.); (T.M.); (T.B.)
- Department of Cardiology, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, 540139 Târgu Mureș, Romania
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Yu H, Yang Z, Wei Y, Shi W, Zhu M, Liu L, Wang M, Wang Y, Zhu Q, Liang Z, Zhao W, Chen LA. Computed tomography-based radiomics improves non-invasive diagnosis of Pneumocystis jirovecii pneumonia in non-HIV patients: a retrospective study. BMC Pulm Med 2024; 24:11. [PMID: 38167022 PMCID: PMC10762815 DOI: 10.1186/s12890-023-02827-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 12/21/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Pneumocystis jirovecii pneumonia (PCP) could be fatal to patients without human immunodeficiency virus (HIV) infection. Current diagnostic methods are either invasive or inaccurate. We aimed to establish an accurate and non-invasive radiomics-based way to identify the risk of PCP infection in non-HIV patients with computed tomography (CT) manifestation of pneumonia. METHODS This is a retrospective study including non-HIV patients hospitalized for suspected PCP from January 2010 to December 2022 in one hospital. The patients were randomized in a 7:3 ratio into training and validation cohorts. Computed tomography (CT)-based radiomics features were extracted automatically and used to construct a radiomics model. A diagnostic model with traditional clinical and CT features was also built. The area under the curve (AUC) were calculated and used to evaluate the diagnostic performance of the models. The combination of the radiomics features and serum β-D-glucan levels was also evaluated for PCP diagnosis. RESULTS A total of 140 patients (PCP: N = 61, non-PCP: N = 79) were randomized into training (N = 97) and validation (N = 43) cohorts. The radiomics model consisting of nine radiomic features performed significantly better (AUC = 0.954; 95% CI: 0.898-1.000) than the traditional model consisting of serum β-D-glucan levels (AUC = 0.752; 95% CI: 0.597-0.908) in identifying PCP (P = 0.002). The combination of radiomics features and serum β-D-glucan levels showed an accuracy of 95.8% for identifying PCP infection (positive predictive value: 95.7%, negative predictive value: 95.8%). CONCLUSIONS Radiomics showed good diagnostic performance in differentiating PCP from other types of pneumonia in non-HIV patients. A combined diagnostic method including radiomics and serum β-D-glucan has the potential to provide an accurate and non-invasive way to identify the risk of PCP infection in non-HIV patients with CT manifestation of pneumonia. TRIAL REGISTRATION ClinicalTrials.gov (NCT05701631).
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Affiliation(s)
- Hang Yu
- Department of Respiratory and Critical Care Medicine, Medical School of Chinese People's Liberation Army, Beijing, China
| | - Zhen Yang
- Department of Respiratory and Critical Care Medicine, the Eighth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Yuanhui Wei
- Department of Respiratory and Critical Care Medicine, Medical School of Chinese People's Liberation Army, Beijing, China
| | - Wenjia Shi
- Department of Respiratory and Critical Care Medicine, Medical School of Chinese People's Liberation Army, Beijing, China
| | - Minghui Zhu
- Department of Pulmonary and Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Lu Liu
- Department of Nutrition, the First Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Miaoyu Wang
- Department of Respiratory and Critical Care Medicine, Medical School of Chinese People's Liberation Army, Beijing, China
| | - Yueming Wang
- Department of Respiratory and Critical Care Medicine, Medical School of Chinese People's Liberation Army, Beijing, China
| | - Qiang Zhu
- Department of Respiratory and Critical Care Medicine, the Eighth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Zhixin Liang
- Department of Respiratory and Critical Care Medicine, the Eighth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Wei Zhao
- Department of Respiratory and Critical Care Medicine, the Eighth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Liang-An Chen
- Department of Respiratory and Critical Care Medicine, the Eighth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China.
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Antonopoulos AS, Simantiris S. Preventative Imaging with Coronary Computed Tomography Angiography. Curr Cardiol Rep 2023; 25:1623-1632. [PMID: 37897677 DOI: 10.1007/s11886-023-01982-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/04/2023] [Indexed: 10/30/2023]
Abstract
PURPOSE OF REVIEW Coronary computed tomography angiography (CCTA) is the diagnostic modality of choice for patients with stable chest pain. In this review, we scrutinize the evidence on the use of CCTA for the screening of asymptomatic patients. RECENT FINDINGS Clinical evidence suggests that CCTA imaging enhances cardiovascular risk stratification and prompts the timely initiation of preventive treatment leading to reduced risk of major adverse coronary events. Visualization of coronary plaques by CCTA also helps patients to comply with preventive medications. The presence of non-obstructive plaques and total plaque burden are prognostic for cardiovascular events. High-risk plaque features and pericoronary fat attenuation index, enrich the prognostic output of CCTA on top of anatomical information by capturing information on plaque vulnerability and coronary inflammatory burden. Timely detection of atherosclerotic disease or coronary inflammation by CCTA can assist in the deployment of targeted preventive strategies and novel therapeutics to prevent cardiovascular disease.
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Affiliation(s)
- Alexios S Antonopoulos
- Biomedical Research Foundation of the Academy of Athens (BRFAA), 4 Soranou Efesiou Street, Athens, Greece.
- 1st Cardiology Department, Hippokration Hospital, National and Kapodistrian University of Athens, Athens, Greece.
| | - Spyridon Simantiris
- 1st Cardiology Department, Hippokration Hospital, National and Kapodistrian University of Athens, Athens, Greece
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Antoniades C, Tousoulis D, Vavlukis M, Fleming I, Duncker DJ, Eringa E, Manfrini O, Antonopoulos AS, Oikonomou E, Padró T, Trifunovic-Zamaklar D, De Luca G, Guzik T, Cenko E, Djordjevic-Dikic A, Crea F. Perivascular adipose tissue as a source of therapeutic targets and clinical biomarkers. Eur Heart J 2023; 44:3827-3844. [PMID: 37599464 PMCID: PMC10568001 DOI: 10.1093/eurheartj/ehad484] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 05/03/2023] [Accepted: 07/17/2023] [Indexed: 08/22/2023] Open
Abstract
Obesity is a modifiable cardiovascular risk factor, but adipose tissue (AT) depots in humans are anatomically, histologically, and functionally heterogeneous. For example, visceral AT is a pro-atherogenic secretory AT depot, while subcutaneous AT represents a more classical energy storage depot. Perivascular adipose tissue (PVAT) regulates vascular biology via paracrine cross-talk signals. In this position paper, the state-of-the-art knowledge of various AT depots is reviewed providing a consensus definition of PVAT around the coronary arteries, as the AT surrounding the artery up to a distance from its outer wall equal to the luminal diameter of the artery. Special focus is given to the interactions between PVAT and the vascular wall that render PVAT a potential therapeutic target in cardiovascular diseases. This Clinical Consensus Statement also discusses the role of PVAT as a clinically relevant source of diagnostic and prognostic biomarkers of vascular function, which may guide precision medicine in atherosclerosis, hypertension, heart failure, and other cardiovascular diseases. In this article, its role as a 'biosensor' of vascular inflammation is highlighted with description of recent imaging technologies that visualize PVAT in clinical practice, allowing non-invasive quantification of coronary inflammation and the related residual cardiovascular inflammatory risk, guiding deployment of therapeutic interventions. Finally, the current and future clinical applicability of artificial intelligence and machine learning technologies is reviewed that integrate PVAT information into prognostic models to provide clinically meaningful information in primary and secondary prevention.
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Affiliation(s)
- Charalambos Antoniades
- Acute Multidisciplinary Imaging and Interventional Centre, RDM Division of Cardiovascular Medicine, University of Oxford, Headley Way, Headington, Oxford OX39DU, UK
| | - Dimitris Tousoulis
- 1st Cardiology Department, National and Kapodistrian University of Athens, Greece
| | - Marija Vavlukis
- Medical Faculty, University Clinic for Cardiology, University Ss’ Cyril and Methodius in Skopje, Skopje, North Macedonia
| | - Ingrid Fleming
- Institute for Vascular Signalling, Centre of Molecular Medicine, Goethe University, Frankfurt, Germany
| | - Dirk J Duncker
- Department of Cardiology, Thorax Center, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Etto Eringa
- Cardiovascular-Program ICCC, Research Institute—Hospital Santa Creu i Sant Pau, IIB-Sant Pau, Barcelona, Spain
| | - Olivia Manfrini
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - Alexios S Antonopoulos
- Acute Multidisciplinary Imaging and Interventional Centre, RDM Division of Cardiovascular Medicine, University of Oxford, Headley Way, Headington, Oxford OX39DU, UK
- 1st Cardiology Department, National and Kapodistrian University of Athens, Greece
| | - Evangelos Oikonomou
- 1st Cardiology Department, National and Kapodistrian University of Athens, Greece
| | - Teresa Padró
- Cardiovascular Program-ICCC, Institut d’Investigació Biomèdica Sant Pau (IIB SANT PAU), Barcelona, Spain
- CiberCV, Institute Carlos III, Madrid, Spain
| | | | - Giuseppe De Luca
- Division of Cardiology, AOU Policlinico G. Martino, Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
- Cardiologia Ospedaliera, Nuovo Galeazzi-Sant’Ambrogio, Milan, Italy
| | - Tomasz Guzik
- Cardiovascular Science, Queens Medical Research Institute, University of Edinburgh, UK
- Department of Medicine, Jagiellonian University, Collegium Medicum, Krakow, Poland
| | - Edina Cenko
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - Ana Djordjevic-Dikic
- Medical Faculty, Cardiology Clinic, University Clinical Center, University of Belgrade, Serbia
| | - Filippo Crea
- Department of Cardiology and Pulmonary Sciences, Catholic University of the Sacred Heart, Rome, Italy
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Logotheti S, Georgakilas AG. More than Meets the Eye: Integration of Radiomics with Transcriptomics for Reconstructing the Tumor Microenvironment and Predicting Response to Therapy. Cancers (Basel) 2023; 15:cancers15061634. [PMID: 36980519 PMCID: PMC10046885 DOI: 10.3390/cancers15061634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 02/28/2023] [Accepted: 03/03/2023] [Indexed: 03/09/2023] Open
Abstract
For over a decade, large cancer-related datasets (big data) have continuously been produced and made publicly available to the scientific community [...]
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Affiliation(s)
- Stella Logotheti
- Correspondence: (S.L.); (A.G.G.); Tel.: +30-210-7724453 (S.L. & A.G.G.)
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Baeßler B, Götz M, Antoniades C, Heidenreich JF, Leiner T, Beer M. Artificial intelligence in coronary computed tomography angiography: Demands and solutions from a clinical perspective. Front Cardiovasc Med 2023; 10:1120361. [PMID: 36873406 PMCID: PMC9978503 DOI: 10.3389/fcvm.2023.1120361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 01/25/2023] [Indexed: 02/18/2023] Open
Abstract
Coronary computed tomography angiography (CCTA) is increasingly the cornerstone in the management of patients with chronic coronary syndromes. This fact is reflected by current guidelines, which show a fundamental shift towards non-invasive imaging - especially CCTA. The guidelines for acute and stable coronary artery disease (CAD) of the European Society of Cardiology from 2019 and 2020 emphasize this shift. However, to fulfill this new role, a broader availability in adjunct with increased robustness of data acquisition and speed of data reporting of CCTA is needed. Artificial intelligence (AI) has made enormous progress for all imaging methodologies concerning (semi)-automatic tools for data acquisition and data post-processing, with outreach toward decision support systems. Besides onco- and neuroimaging, cardiac imaging is one of the main areas of application. Most current AI developments in the scenario of cardiac imaging are related to data postprocessing. However, AI applications (including radiomics) for CCTA also should enclose data acquisition (especially the fact of dose reduction) and data interpretation (presence and extent of CAD). The main effort will be to integrate these AI-driven processes into the clinical workflow, and to combine imaging data/results with further clinical data, thus - beyond the diagnosis of CAD- enabling prediction and forecast of morbidity and mortality. Furthermore, data fusing for therapy planning (e.g., invasive angiography/TAVI planning) will be warranted. The aim of this review is to present a holistic overview of AI applications in CCTA (including radiomics) under the umbrella of clinical workflows and clinical decision-making. The review first summarizes and analyzes applications for the main role of CCTA, i.e., to non-invasively rule out stable coronary artery disease. In the second step, AI applications for additional diagnostic purposes, i.e., to improve diagnostic power (CAC = coronary artery classifications), improve differential diagnosis (CT-FFR and CT perfusion), and finally improve prognosis (again CAC plus epi- and pericardial fat analysis) are reviewed.
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Affiliation(s)
- Bettina Baeßler
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Michael Götz
- Division of Experimental Radiology, Department for Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| | - Charalambos Antoniades
- British Heart Foundation Chair of Cardiovascular Medicine, Cardiovascular Medicine, University of Oxford, Oxford, United Kingdom
| | - Julius F. Heidenreich
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Tim Leiner
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Meinrad Beer
- Department for Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
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Antoniades C, Patel P, Antonopoulos AS. Using artificial intelligence to study atherosclerosis, predict risk and guide treatments in clinical practice. Eur Heart J 2023; 44:437-439. [PMID: 36592110 DOI: 10.1093/eurheartj/ehac751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Affiliation(s)
- Charalambos Antoniades
- Acute Multidisciplinary Imaging & Interventional Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, John Radcliffe Hospital, Level 6 West Wing, Headley Way, Oxford OX37TH, UK
| | - Parijat Patel
- Acute Multidisciplinary Imaging & Interventional Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, John Radcliffe Hospital, Level 6 West Wing, Headley Way, Oxford OX37TH, UK
| | - Alexios S Antonopoulos
- Acute Multidisciplinary Imaging & Interventional Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, John Radcliffe Hospital, Level 6 West Wing, Headley Way, Oxford OX37TH, UK
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