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Guo W, Yi X. Advancements and future prospects in the study of panvascular disease. Clin Hemorheol Microcirc 2025:13860291241302593. [PMID: 39973436 DOI: 10.1177/13860291241302593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2025]
Abstract
Panvascular disease is characterized by the involvement of blood vessels across multiple regions of the body, and is associated with high morbidity, disability, and mortality rates. Its pathogenesis is multifaceted, necessitating risk assessment and treatment approaches that differ from those applied to single-organ diseases. Given that panvascular disease affects multiple vital organs, an integrated, multi-system management strategy offers significant advantages over conventional, organ-specific approaches. This article provides a comprehensive review of the epidemiological features, traditional and emerging risk factors, pathophysiological mechanisms, screening and risk assessment methods, as well as new strategies for the prevention and management of panvascular disease. The objective is to offer a theoretical foundation and technical support for enhancing prevention and control measures for this condition.
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Affiliation(s)
- Wei Guo
- Department of Geriatrics, Jining No.1 People's Hospital, Jining, Shandong, China
| | - Xin Yi
- Department of Medical Laboratory, Jining No.1 People's Hospital, Jining, Shandong, China
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2
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Ayx I, Bauer R, Schönberg SO, Hertel A. Cardiac Radiomics Analyses in Times of Photon-counting Computed Tomography for Personalized Risk Stratification in the Present and in the Future. ROFO-FORTSCHR RONTG 2025. [PMID: 39848255 DOI: 10.1055/a-2499-3122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2025]
Abstract
The need for effective early detection and optimal therapy monitoring of cardiovascular diseases as the leading cause of death has led to an adaptation of the guidelines with a focus on cardiac computed tomography (CCTA) in patients with a low to intermediate risk of coronary heart disease (CHD). In particular, the introduction of photon-counting computed tomography (PCCT) in CT diagnostics promises significant advances through higher temporal and spatial resolution, and also enables advanced texture analysis, known as radiomics analysis. Originally developed in oncological imaging, radiomics analysis is increasingly being used in cardiac imaging and research. The aim is to generate imaging biomarkers that improve the early detection of cardiovascular diseases and therapy monitoring.The present study summarizes the current developments in cardiac CT texture analysis with a particular focus on evaluations of PCCT data sets in different regions, including the myocardium, coronary plaques, and pericoronary/epicardial fat tissue.These developments could revolutionize the diagnosis and treatment of cardiovascular diseases and significantly improve patient prognoses worldwide. The aim of this review article is to shed light on the current state of radiomics research in cardiovascular imaging and to identify opportunities for establishing it in clinical routine in the future. · Radiomics: Enables deeper, objective analysis of cardiovascular structures via feature quantification.. · PCCT: Provides a higher quality image, improving stability and reproducibility in cardiac CT.. · Early detection: PCCT and radiomics enhance cardiovascular disease detection and management.. · Challenges: Technical and standardization issues hinder widespread clinical application.. · Future: Advancing PCCT technologies could soon integrate radiomics in routine practice.. · Ayx I, Bauer R, Schönberg SO et al. Cardiac Radiomics Analyses in Times of Photon-counting Computed Tomography for Personalized Risk Stratification in the Present and in the Future. Rofo 2025; DOI 10.1055/a-2499-3122.
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Affiliation(s)
- Isabelle Ayx
- Department of Radiology and Nuclear Medicine, Heidelberg University Medical Faculty Mannheim, Mannheim, Germany
| | - Rouven Bauer
- Department of Radiology and Nuclear Medicine, Heidelberg University Medical Faculty Mannheim, Mannheim, Germany
| | - Stefan O Schönberg
- Department of Radiology and Nuclear Medicine, Heidelberg University Medical Faculty Mannheim, Mannheim, Germany
| | - Alexander Hertel
- Department of Radiology and Nuclear Medicine, Heidelberg University Medical Faculty Mannheim, Mannheim, Germany
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3
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Corti A, Lo Iacono F, Ronchetti F, Mushtaq S, Pontone G, Colombo GI, Corino VDA. Enhancing cardiovascular risk stratification: Radiomics of coronary plaque and perivascular adipose tissue - Current insights and future perspectives. Trends Cardiovasc Med 2025; 35:47-59. [PMID: 38960074 DOI: 10.1016/j.tcm.2024.06.003] [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: 05/10/2024] [Revised: 06/25/2024] [Accepted: 06/28/2024] [Indexed: 07/05/2024]
Abstract
Radiomics, the quantitative extraction and mining of features from radiological images, has recently emerged as a promising source of non-invasive image-based cardiovascular biomarkers, potentially revolutionizing diagnostics and risk assessment. This review explores its application within coronary plaques and pericoronary adipose tissue, particularly focusing on plaque characterization and cardiac events prediction. By shedding light on the current state-of-the-art, achievements, and prospective avenues, this review contributes to a deeper understanding of the evolving landscape of radiomics in the context of coronary arteries. Finally, open challenges and existing gaps are emphasized to underscore the need for future efforts aimed at ensuring the robustness and reliability of radiomics studies, facilitating their clinical translation.
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Affiliation(s)
- Anna Corti
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, Milan 20133, Italy.
| | - Francesca Lo Iacono
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, Milan 20133, Italy
| | - Francesca Ronchetti
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Saima Mushtaq
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Gianluca Pontone
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy; Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
| | - Gualtiero I Colombo
- Unit of Immunology and Functional Genomics, Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Valentina D A Corino
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, Milan 20133, Italy; Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
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4
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Ionita C, Canty JM. Editorial commentary: Coronary plaque characterization and cardiovascular risk using radiomics and artificial intelligence. Trends Cardiovasc Med 2025; 35:60-61. [PMID: 39151744 DOI: 10.1016/j.tcm.2024.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2024] [Revised: 07/31/2024] [Accepted: 07/31/2024] [Indexed: 08/19/2024]
Affiliation(s)
- Ciprian Ionita
- VA WNY Health Care System and the Department of Medicine and Biomedical Engineering of the University at Buffalo, Buffalo, NY, USA
| | - John M Canty
- VA WNY Health Care System and the Department of Medicine and Biomedical Engineering of the University at Buffalo, Buffalo, NY, USA.
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Zheng YL, Cai PY, Li J, Huang DH, Wang WD, Li MM, Du JR, Wang YG, Cai YL, Zhang RC, Wu CC, Lin S, Lin HL. A novel radiomics-based technique for identifying vulnerable coronary plaques: a follow-up study. Coron Artery Dis 2025; 36:1-8. [PMID: 38767051 DOI: 10.1097/mca.0000000000001389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
BACKGROUND Previous reports have suggested that coronary computed tomography angiography (CCTA)-based radiomics analysis is a potentially helpful tool for assessing vulnerable plaques. We aimed to investigate whether coronary radiomic analysis of CCTA images could identify vulnerable plaques in patients with stable angina pectoris. METHODS This retrospective study included patients initially diagnosed with stable angina pectoris. Patients were randomly divided into either the training or test dataset at an 8 : 2 ratio. Radiomics features were extracted from CCTA images. Radiomics models for predicting vulnerable plaques were developed using the support vector machine (SVM) algorithm. The model performance was assessed using the area under the curve (AUC); the accuracy, sensitivity, and specificity were calculated to compare the diagnostic performance using the two cohorts. RESULTS A total of 158 patients were included in the analysis. The SVM radiomics model performed well in predicting vulnerable plaques, with AUC values of 0.977 and 0.875 for the training and test cohorts, respectively. With optimal cutoff values, the radiomics model showed accuracies of 0.91 and 0.882 in the training and test cohorts, respectively. CONCLUSION Although further larger population studies are necessary, this novel CCTA radiomics model may identify vulnerable plaques in patients with stable angina pectoris.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Shu Lin
- Centre of Neurological and Metabolic Research, the Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
- Diabetes and Metabolism Division, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
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6
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Zhu J, Zhu X, Lv S, Guo D, Li H, Zhao Z. Incremental Value of Pericoronary Adipose Tissue Radiomics Models in Identifying Vulnerable Plaques. J Comput Assist Tomogr 2024:00004728-990000000-00402. [PMID: 39724572 DOI: 10.1097/rct.0000000000001704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2024]
Abstract
OBJECTIVE Inflammatory characteristics in pericoronary adipose tissue (PCAT) may enhance the diagnostic capability of radiomics techniques for identifying vulnerable plaques. This study aimed to evaluate the incremental value of PCAT radiomics scores in identifying vulnerable plaques defined by intravascular ultrasound imaging (IVUS). METHODS In this retrospective study, a PCAT radiomics model was established and validated using IVUS as the reference standard. The dataset consisted of patients with coronary artery disease who underwent both coronary computed tomography angiography and IVUS examinations at a tertiary hospital between March 2023 and January 2024. The dataset was randomly assigned to the training and validation sets in a 7:3 ratio. The diagnostic performance of various models was evaluated on both sets using the area under the curve (AUC). RESULTS From 88 lesions in 79 patients, we selected 9 radiomics features (5 texture features, 1 shape feature, 1 gray matrix feature, and 2 first-order features) from the training cohort (n = 61) to build the PCAT model. The PCAT radiomics model demonstrated moderate to high AUCs (0.847 and 0.819) in both the training and test cohorts. Furthermore, the AUC of the PCAT radiomics model was significantly higher than that of the fat attenuation index model (0.847 vs 0.659, P < 0.05). The combined model had a higher AUC than the clinical model (0.925 vs 0.714, P < 0.01). CONCLUSIONS The PCAT radiomics signature of coronary CT angiography enabled the detection of vulnerable plaques defined by IVUS.
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Affiliation(s)
- Jinke Zhu
- From the School of Medicine, Shaoxing University, Shaoxing, Zhejiang, Shaoxing, Zhejiang, China
| | - Xiucong Zhu
- From the School of Medicine, Shaoxing University, Shaoxing, Zhejiang, Shaoxing, Zhejiang, China
| | - Sangying Lv
- Department of radiology, Shaoxing People's Hospital (Zhejiang University Shaoxing Hospital), Shaoxing, Zhejiang, China
| | - Danling Guo
- Department of radiology, Shaoxing People's Hospital (Zhejiang University Shaoxing Hospital), Shaoxing, Zhejiang, China
| | - Huaifeng Li
- Department of radiology, Shaoxing People's Hospital (Zhejiang University Shaoxing Hospital), Shaoxing, Zhejiang, China
| | - Zhenhua Zhao
- Department of radiology, Shaoxing People's Hospital (Zhejiang University Shaoxing Hospital), Shaoxing, Zhejiang, China
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Chen R, Li X, Jia H, Feng C, Dong S, Liu W, Lin S, Zhu X, Xu Y, Zhu Y. Radiomics Analysis of Pericoronary Adipose Tissue From Baseline Coronary Computed Tomography Angiography Enables Prediction of Coronary Plaque Progression. J Thorac Imaging 2024; 39:359-366. [PMID: 38704662 DOI: 10.1097/rti.0000000000000790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2024]
Abstract
PURPOSE The relationship between plaque progression and pericoronary adipose tissue (PCAT) radiomics has not been comprehensively evaluated. We aim to predict plaque progression with PCAT radiomics features and evaluate their incremental value over quantitative plaque characteristics. PATIENTS AND METHODS Between January 2009 and December 2020, 500 patients with suspected or known coronary artery disease who underwent serial coronary computed tomography angiography (CCTA) ≥2 years apart were retrospectively analyzed and randomly stratified into a training and testing data set with a ratio of 7:3. Plaque progression was defined with annual change in plaque burden exceeding the median value in the entire cohort. Quantitative plaque characteristics and PCAT radiomics features were extracted from baseline CCTA. Then we built 3 models including quantitative plaque characteristics (model 1), PCAT radiomics features (model 2), and the combined model (model 3) to compare the prediction performance evaluated by area under the curve. RESULTS The quantitative plaque characteristics of the training set showed the values of noncalcified plaque volume (NCPV), fibrous plaque volume, lesion length, and PCAT attenuation were larger in the plaque progression group than in the nonprogression group ( P < 0.05 for all). In multivariable logistic analysis, NCPV and PCAT attenuation were independent predictors of coronary plaque progression. PCAT radiomics exhibited significantly superior prediction over quantitative plaque characteristics both in the training (area under the curve: 0.814 vs 0.615, P < 0.001) and testing (0.736 vs 0.594, P = 0.007) data sets. CONCLUSIONS NCPV and PCAT attenuation were independent predictors of coronary plaque progression. PCAT radiomics derived from baseline CCTA achieved significantly better prediction than quantitative plaque characteristics.
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Affiliation(s)
- Rui Chen
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu
| | - Xiaohu Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui
| | - Han Jia
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu
| | - Changjing Feng
- Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Chaoyang, Beijing
| | - Siting Dong
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu
| | - Wangyan Liu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu
| | - Shushen Lin
- CT Collaboration, Siemens Healthineers, Shanghai
| | - Xiaomei Zhu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu
| | - Yi Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu
| | - Yinsu Zhu
- Department of Radiology, The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Nanjing, Jiangsu, China
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
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Huang Z, Lam S, Lin Z, Zhou L, Pei L, Song A, Wang T, Zhang Y, Qi R, Huang S. Predicting major adverse cardiac events using radiomics nomogram of pericoronary adipose tissue based on CCTA: A multi-center study. Med Phys 2024; 51:8348-8361. [PMID: 39042398 DOI: 10.1002/mp.17324] [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: 09/26/2023] [Revised: 06/19/2024] [Accepted: 07/06/2024] [Indexed: 07/24/2024] Open
Abstract
BACKGROUND The evolution of coronary atherosclerotic heart disease (CAD) is intricately linked to alterations in the pericoronary adipose tissue (PCAT). In recent epochs, characteristics of the PCAT have progressively ascended as focal points of research in CAD risk stratification and individualized clinical decision-making. Harnessing radiomic methodologies allows for the meticulous extraction of imaging features from these adipose deposits. Coupled with machine learning paradigms, we endeavor to establish predictive models for the onset of major adverse cardiovascular events (MACE). PURPOSE To appraise the predictive utility of radiomic features of PCAT derived from coronary computed tomography angiography (CCTA) in forecasting MACE. METHODS We retrospectively incorporated data from 314 suspected or confirmed CAD patients admitted to our institution from June 2019 to December 2022. An additional cohort of 242 patients from two external institutions was encompassed for external validation. The endpoint under consideration was the occurrence of MACE after a 1-year follow-up. MACE was delineated as cardiovascular mortality, newly diagnosed myocardial infarction, hospitalization (or re-hospitalization) for heart failure, and coronary target vessel revascularization occurring more than 30 days post-CCTA examination. All enrolled patients underwent CCTA scanning. Radiomic features were meticulously extracted from the optimal diastolic phase axial slices of CCTA images. Feature reduction was achieved through a composite feature selection algorithm, laying the groundwork for the radiomic signature model. Both univariate and multivariate analyses were employed to assess clinical variables. A multifaceted logistic regression analysis facilitated the crafting of a clinical-radiological-radiomic combined model (or nomogram). Receiver operating characteristic (ROC) curves, calibration, and decision curve analyses (DCA) were delineated, with the area under the ROC curve (AUCs) computed to gauge the predictive prowess of the clinical model, radiomic model, and the synthesized ensemble. RESULTS A total of 12 radiomic features closely associated with MACE were identified to establish the radiomic model. Multivariate logistic regression results demonstrated that smoking, age, hypertension, and dyslipidemia were significantly correlated with MACE. In the integrated nomogram, which amalgamated clinical, imaging, and radiomic parameters, the diagnostic performance was as follows: 0.970 AUC, 0.949 accuracy (ACC), 0.833 sensitivity (SEN), 0.981 specificity (SPE), 0.926 positive predictive value (PPV), and 0.955 negative predictive value (NPV). The calibration curve indicated a commendable concordance of the nomogram, and the decision curve analysis underscored its superior clinical utility. CONCLUSIONS The integration of radiomic signatures from PCAT based on CCTA, clinical indices, and imaging parameters into a nomogram stands as a promising instrument for prognosticating MACE events.
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Affiliation(s)
- Zhaoheng Huang
- Department of Radiology, The Second Affiliated Hospital of Nantong University, Nantong, China
| | - Saikit Lam
- Department of Biomedical Engineering, The Hong Kong Polytechnical University, Hong Kong, China
- Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong, China
| | - Zihe Lin
- Department of Computing, The Hong Kong Polytechnical University, Hong Kong, China
| | - Linjia Zhou
- Department of Medical Informatics, Nantong University, Nantong, China
| | - Liangchen Pei
- School of Automation, Southeast University, Nanjing, China
| | - Anyi Song
- Department of Radiology, The Second Affiliated Hospital of Nantong University, Nantong, China
| | - Tianle Wang
- Department of Radiology, The Second Affiliated Hospital of Nantong University, Nantong, China
| | - Yuanpeng Zhang
- Department of Medical Informatics, Nantong University, Nantong, China
| | - Rongxing Qi
- Department of Radiology, The Second Affiliated Hospital of Nantong University, Nantong, China
| | - Sheng Huang
- Department of Radiology, The Second Affiliated Hospital of Nantong University, Nantong, China
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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; 398:117580. [PMID: 38852022 PMCID: PMC11579307 DOI: 10.1016/j.atherosclerosis.2024.117580] [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] [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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10
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Kolossváry M, Lin A, Kwiecinski J, Cadet S, Slomka PJ, Newby DE, Dweck MR, Williams MC, Dey D. Coronary Plaque Radiomic Phenotypes Predict Fatal or Nonfatal Myocardial Infarction: Analysis of the SCOT-HEART Trial. JACC Cardiovasc Imaging 2024:S1936-878X(24)00376-0. [PMID: 39480364 DOI: 10.1016/j.jcmg.2024.08.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 08/06/2024] [Accepted: 08/07/2024] [Indexed: 11/07/2024]
Abstract
BACKGROUND Coronary computed tomography (CT) angiography-derived attenuation-based plaque burden assessments can identify patients at risk of myocardial infarction. OBJECTIVES This study sought to assess whether more detailed plaque morphology assessment using patient-based radiomic characterization could further enhance the identification of patients at risk of myocardial infarction during long-term follow-up. METHODS Post hoc analysis of coronary CT angiography was performed within the SCOT-HEART (Scottish Computed Tomography of the HEART) clinical trial. Coronary plaque segmentations were used to calculate plaque burdens and eigen radiomic features that described plaque morphology. Univariable and multivariable Cox proportional hazard models were used to evaluate the association between clinical and image-based features and fatal or nonfatal myocardial infarction, whereas Harrell's C-statistic and cumulative/dynamic area under the curve (AUC) values with cross-validation were used to evaluate prognostic performance. RESULTS Scans from 1,750 patients (aged 58 ± 9 years; 56% male) were analyzed. Over a median of 8.6 years of follow-up, 82 patients had a fatal or nonfatal myocardial infarction. Among the eigen radiomic features, 15 were associated with myocardial infarction in univariable analysis, and 8 features retained their association following adjustment for cardiovascular risk score and plaque burden metrics. Adding plaque burden metrics to a clinical model incorporating cardiovascular risk score, Agatston score and presence of obstructive coronary artery disease had similar prediction performance (C-statistic 0.70 vs 0.70), whereas further addition of eigen radiomic features improved model performance (C-statistic 0.74). In temporal analysis, the model including eigen radiomic features had higher cumulative/dynamic AUC values following the fifth year of follow-up. CONCLUSIONS Radiomics-based precision phenotyping of coronary plaque morphology provided improvements to long-term prediction of myocardial infarction by CT angiography over and above clinical factors and plaque burden. (Scottish Computed Tomography of the HEART [SCOT-HEART]; NCT01149590).
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Affiliation(s)
- Márton Kolossváry
- Gottsegen National Cardiovascular Center, Budapest, Hungary; Physiological Controls Research Center, University Research and Innovation Center, Óbuda University, Budapest, Hungary
| | - Andrew Lin
- Monash Victorian Heart Institute and Monash Health Heart, Victorian Heart Hospital, Monash University, Victoria, Australia
| | - Jacek Kwiecinski
- Gottsegen National Cardiovascular Center, Budapest, Hungary; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA; Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Interventional Cardiology and Angiology, Institute of Cardiology, Warsaw, Poland
| | - Sebastien Cadet
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA; Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Piotr J Slomka
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA; Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - David E Newby
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Marc R Dweck
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Michelle C Williams
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Damini Dey
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA; Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA.
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11
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Duo Y, Han L, Yang Y, Wang Z, Wang L, Chen J, Xiang Z, Yoon J, Luo G, Tang BZ. Aggregation-Induced Emission Luminogen: Role in Biopsy for Precision Medicine. Chem Rev 2024; 124:11242-11347. [PMID: 39380213 PMCID: PMC11503637 DOI: 10.1021/acs.chemrev.4c00244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 09/11/2024] [Accepted: 09/17/2024] [Indexed: 10/10/2024]
Abstract
Biopsy, including tissue and liquid biopsy, offers comprehensive and real-time physiological and pathological information for disease detection, diagnosis, and monitoring. Fluorescent probes are frequently selected to obtain adequate information on pathological processes in a rapid and minimally invasive manner based on their advantages for biopsy. However, conventional fluorescent probes have been found to show aggregation-caused quenching (ACQ) properties, impeding greater progresses in this area. Since the discovery of aggregation-induced emission luminogen (AIEgen) have promoted rapid advancements in molecular bionanomaterials owing to their unique properties, including high quantum yield (QY) and signal-to-noise ratio (SNR), etc. This review seeks to present the latest advances in AIEgen-based biofluorescent probes for biopsy in real or artificial samples, and also the key properties of these AIE probes. This review is divided into: (i) tissue biopsy based on smart AIEgens, (ii) blood sample biopsy based on smart AIEgens, (iii) urine sample biopsy based on smart AIEgens, (iv) saliva sample biopsy based on smart AIEgens, (v) biopsy of other liquid samples based on smart AIEgens, and (vi) perspectives and conclusion. This review could provide additional guidance to motivate interest and bolster more innovative ideas for further exploring the applications of various smart AIEgens in precision medicine.
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Affiliation(s)
- Yanhong Duo
- Department
of Radiation Oncology, Shenzhen People’s Hospital, The Second
Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen 518020, Guangdong China
- Wyss
Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts 02138, United States
| | - Lei Han
- College of
Chemistry and Pharmaceutical Sciences, Qingdao
Agricultural University, 700 Changcheng Road, Qingdao 266109, Shandong China
| | - Yaoqiang Yang
- Department
of Radiation Oncology, Shenzhen People’s Hospital, The Second
Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen 518020, Guangdong China
| | - Zhifeng Wang
- Department
of Urology, Henan Provincial People’s Hospital, Zhengzhou University
People’s Hospital, Henan University
People’s Hospital, Zhengzhou, 450003, China
| | - Lirong Wang
- State
Key Laboratory of Luminescent Materials and Devices, South China University of Technology, Guangzhou 510640, China
| | - Jingyi Chen
- Wyss
Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts 02138, United States
| | - Zhongyuan Xiang
- Department
of Laboratory Medicine, The Second Xiangya Hospital, Central South University, Changsha 410000, Hunan, China
| | - Juyoung Yoon
- Department
of Chemistry and Nanoscience, Ewha Womans
University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul 03760, Korea
| | - Guanghong Luo
- Department
of Radiation Oncology, Shenzhen People’s Hospital, The Second
Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen 518020, Guangdong China
| | - Ben Zhong Tang
- School
of Science and Engineering, Shenzhen Institute of Aggregate Science
and Technology, The Chinese University of
Hong Kong, Shenzhen 518172, Guangdong China
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Lee J, Gharaibeh Y, Zimin VN, Kim JN, Hassani NS, Dallan LAP, Pereira GTR, Makhlouf MHE, Hoori A, Wilson DL. Plaque Characteristics Derived from Intravascular Optical Coherence Tomography That Predict Cardiovascular Death. Bioengineering (Basel) 2024; 11:843. [PMID: 39199801 PMCID: PMC11351967 DOI: 10.3390/bioengineering11080843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2024] [Revised: 08/14/2024] [Accepted: 08/16/2024] [Indexed: 09/01/2024] Open
Abstract
This study aimed to investigate whether plaque characteristics derived from intravascular optical coherence tomography (IVOCT) could predict a long-term cardiovascular (CV) death. This study was a single-center, retrospective study on 104 patients who had undergone IVOCT-guided percutaneous coronary intervention. Plaque characterization was performed using Optical Coherence TOmography PlaqUe and Stent (OCTOPUS) software developed by our group. A total of 31 plaque features, including lesion length, lumen, calcium, fibrous cap (FC), and vulnerable plaque features (e.g., microchannel), were computed from the baseline IVOCT images. The discriminatory power for predicting CV death was determined using univariate/multivariate logistic regressions. Of 104 patients, CV death was identified in 24 patients (23.1%). Univariate logistic regression revealed that lesion length, calcium angle, calcium thickness, FC angle, FC area, and FC surface area were significantly associated with CV death (p < 0.05). In the multivariate logistic analysis, only the FC surface area (OR 2.38, CI 0.98-5.83, p < 0.05) was identified as a significant determinant for CV death, highlighting the importance of the 3D lesion analysis. The AUC of FC surface area for predicting CV death was 0.851 (95% CI 0.800-0.927, p < 0.05). Patients with CV death had distinct plaque characteristics (i.e., large FC surface area) in IVOCT. Studies such as this one might someday lead to recommendations for pharmaceutical and interventional approaches.
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Affiliation(s)
- Juhwan Lee
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA; (J.L.); (J.N.K.); (A.H.)
| | - Yazan Gharaibeh
- Department of Biomedical Engineering, Faculty of Engineering, The Hashemite University, Zarqa 13133, Jordan;
| | - Vladislav N. Zimin
- Brookdale University Hospital Medical Center, 1 Brookdale Plaza, Brooklyn, NY 11212, USA;
| | - Justin N. Kim
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA; (J.L.); (J.N.K.); (A.H.)
| | - Neda S. Hassani
- Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA; (N.S.H.); (L.A.P.D.); (G.T.R.P.); (M.H.E.M.)
| | - Luis A. P. Dallan
- Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA; (N.S.H.); (L.A.P.D.); (G.T.R.P.); (M.H.E.M.)
| | - Gabriel T. R. Pereira
- Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA; (N.S.H.); (L.A.P.D.); (G.T.R.P.); (M.H.E.M.)
| | - Mohamed H. E. Makhlouf
- Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA; (N.S.H.); (L.A.P.D.); (G.T.R.P.); (M.H.E.M.)
| | - Ammar Hoori
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA; (J.L.); (J.N.K.); (A.H.)
| | - David L. Wilson
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA; (J.L.); (J.N.K.); (A.H.)
- Department of Radiology, Case Western Reserve University, Cleveland, OH 44106, USA
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13
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Hosadurg N, Watts K, Wang S, Wingerter KE, Taylor AM, Villines TC, Patel AR, Bourque JM, Lindner JR, Kramer CM, Sharma G, Rodriguez Lozano PF. Emerging Pathway to a Precision Medicine Approach for Angina With Nonobstructive Coronary Arteries in Women. JACC. ADVANCES 2024; 3:101074. [PMID: 39055270 PMCID: PMC11269914 DOI: 10.1016/j.jacadv.2024.101074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 04/24/2024] [Indexed: 07/27/2024]
Abstract
Women are disproportionately affected by symptoms of angina with nonobstructive coronary arteries (ANOCA) which is associated with significant mortality and economic impact. Although distinct endotypes of ANOCA have been defined, it is underdiagnosed and is often incompletely characterized when identified. Patients are often unresponsive to traditional therapeutic options, which are typically antianginal, and the current ability to guide treatment modification by specific pathways is limited. Studies have associated specific genetic loci, transcriptomic features, and biomarkers with ANOCA. Such panomic data, in combination with known imaging and invasive diagnostic techniques, should be utilized to define more precise pathophysiologic subtypes of ANOCA in women, which will in turn help to identify targeted, effective therapies. A precision medicine-based approach to managing ANOCA incorporating these techniques in women has the potential to significantly improve their clinical care.
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Affiliation(s)
- Nisha Hosadurg
- Cardiovascular Division, Department of Medicine, University of Virginia Health, Charlottesville, Virginia, USA
| | - Kelsey Watts
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Shuo Wang
- Cardiovascular Division, Department of Medicine, University of Virginia Health, Charlottesville, Virginia, USA
| | - Kelly E. Wingerter
- Cardiovascular Division, Department of Medicine, University of Virginia Health, Charlottesville, Virginia, USA
| | - Angela M. Taylor
- Cardiovascular Division, Department of Medicine, University of Virginia Health, Charlottesville, Virginia, USA
| | - Todd C. Villines
- Cardiovascular Division, Department of Medicine, University of Virginia Health, Charlottesville, Virginia, USA
| | - Amit R. Patel
- Cardiovascular Division, Department of Medicine, University of Virginia Health, Charlottesville, Virginia, USA
| | - Jamieson M. Bourque
- Cardiovascular Division, Department of Medicine, University of Virginia Health, Charlottesville, Virginia, USA
| | - Jonathan R. Lindner
- Cardiovascular Division, Department of Medicine, University of Virginia Health, Charlottesville, Virginia, USA
| | - Christopher M. Kramer
- Cardiovascular Division, Department of Medicine, University of Virginia Health, Charlottesville, Virginia, USA
- Department of Radiology and Medical Imaging, University of Virginia Health, Charlottesville, Virginia, USA
| | - Garima Sharma
- INOVA Heart and Vascular Institute, Fairfax, Virginia, USA
| | - Patricia F. Rodriguez Lozano
- Cardiovascular Division, Department of Medicine, University of Virginia Health, Charlottesville, Virginia, USA
- Department of Radiology and Medical Imaging, University of Virginia Health, Charlottesville, Virginia, USA
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14
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Cheng K, Lin A, Psaltis PJ, Rajwani A, Baumann A, Brett N, Kangaharan N, Otton J, Nicholls SJ, Dey D, Wong DTL. Protocol and rationale of the Australian multicentre registry for serial cardiac computed tomography angiography (ARISTOCRAT): a prospective observational study of the natural history of pericoronary adipose tissue attenuation and radiomics. Cardiovasc Diagn Ther 2024; 14:447-458. [PMID: 38975008 PMCID: PMC11223934 DOI: 10.21037/cdt-23-392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 05/11/2024] [Indexed: 07/09/2024]
Abstract
Background Vascular inflammation plays a crucial role in the development of atherosclerosis and atherosclerotic plaque rupture resulting in acute coronary syndrome (ACS). Pericoronary adipose tissue (PCAT) attenuation quantified from routine coronary computed tomography angiography (CCTA) has emerged as a promising non-invasive imaging biomarker of coronary inflammation. However, a detailed understanding of the natural history of PCAT attenuation is required before it can be used as a surrogate endpoint in trials of novel therapies targeting coronary inflammation. This article aims to explore the natural history of PCAT attenuation and its association with changes in plaque characteristics. Methods The Australian natuRal hISTOry of periCoronary adipose tissue attenuation, RAdiomics and plaque by computed Tomographic angiography (ARISTOCRAT) registry is a multi-centre observational registry enrolling patients undergoing clinically indicated serial CCTA in 9 centres across Australia. CCTA scan parameters will be matched across serial scans. Quantitative analysis of plaque and PCAT will be performed using semiautomated software. Discussion The primary endpoint is to explore temporal changes in patient-level and lesion-level PCAT attenuation by CCTA and their associations with changes in plaque characteristics. Secondary endpoints include evaluating: (I) impact of statin therapy on PCAT attenuation and plaque characteristics; and (II) changes in PCAT attenuation and plaque characteristics in specific subgroups according to sex and risk factors. ARISTOCRAT will further our understanding of the natural history of PCAT attenuation and its association with changes in plaque characteristics. Trial Registration This study has been prospectively registered with the Australia and New Zealand Clinical Trials Registry (ACTRN12621001018808).
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Affiliation(s)
- Kevin Cheng
- Monash Cardiovascular Research Centre, Victorian Heart Institute, Monash Health, Monash University, Clayton, VIC, Australia
| | - Andrew Lin
- Monash Cardiovascular Research Centre, Victorian Heart Institute, Monash Health, Monash University, Clayton, VIC, Australia
- Cedars-Sinai Medical Center, Biomedical Imaging Research Institute, Los Angeles, CA, USA
| | - Peter J. Psaltis
- Vascular Research Centre, Heart and Vascular Program, Lifelong Health Theme, SAHMRI, Adelaide, SA, Australia
- Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
- Department of Cardiology, Royal Adelaide Hospital, Central Adelaide Local Health Network, Adelaide, SA, Australia
| | - Adil Rajwani
- Department of Cardiology, Royal Perth Hospital, Perth, WA, Australia
| | - Angus Baumann
- Alice Springs Hospital, Alice Springs, NT, Australia
| | - Nicholas Brett
- Department of Radiology, Royal Hobart Hospital, Hobart, TAS, Australia
| | | | - James Otton
- Department of Cardiology, Liverpool Hospital, Liverpool, NSW, Australia
| | - Stephen J. Nicholls
- Monash Cardiovascular Research Centre, Victorian Heart Institute, Monash Health, Monash University, Clayton, VIC, Australia
| | - Damini Dey
- Cedars-Sinai Medical Center, Biomedical Imaging Research Institute, Los Angeles, CA, USA
| | - Dennis T. L. Wong
- Monash Cardiovascular Research Centre, Victorian Heart Institute, Monash Health, Monash University, Clayton, VIC, Australia
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15
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Gao Y, Pan Y, Jia C. Influencing factors and improvement methods of coronary artery plaque evaluation in CT. Front Cardiovasc Med 2024; 11:1395350. [PMID: 38984352 PMCID: PMC11232181 DOI: 10.3389/fcvm.2024.1395350] [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: 03/03/2024] [Accepted: 05/14/2024] [Indexed: 07/11/2024] Open
Abstract
Accurate evaluation of the nature and composition of coronary plaque involves clinical follow-up and prognosis. Coronary CT angiography is the most commonly non-invasive method for plaque evaluation, however, the qualitative and quantitative evaluation of plaque based on CT value is inaccurate, due to the influence of luminal attenuation, tube voltage, parameter setting and the subjectivity.
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Affiliation(s)
- Yaqi Gao
- Department of Cardiovascular Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yao Pan
- Department of Cardiovascular Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Chongfu Jia
- Department of Cardiovascular Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
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Baeßler B, Engelhardt S, Hekalo A, Hennemuth A, Hüllebrand M, Laube A, Scherer C, Tölle M, Wech T. Perfect Match: Radiomics and Artificial Intelligence in Cardiac Imaging. Circ Cardiovasc Imaging 2024; 17:e015490. [PMID: 38889216 DOI: 10.1161/circimaging.123.015490] [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] [Indexed: 06/20/2024]
Abstract
Cardiovascular diseases remain a significant health burden, with imaging modalities like echocardiography, cardiac computed tomography, and cardiac magnetic resonance imaging playing a crucial role in diagnosis and prognosis. However, the inherent heterogeneity of these diseases poses challenges, necessitating advanced analytical methods like radiomics and artificial intelligence. Radiomics extracts quantitative features from medical images, capturing intricate patterns and subtle variations that may elude visual inspection. Artificial intelligence techniques, including deep learning, can analyze these features to generate knowledge, define novel imaging biomarkers, and support diagnostic decision-making and outcome prediction. Radiomics and artificial intelligence thus hold promise for significantly enhancing diagnostic and prognostic capabilities in cardiac imaging, paving the way for more personalized and effective patient care. This review explores the synergies between radiomics and artificial intelligence in cardiac imaging, following the radiomics workflow and introducing concepts from both domains. Potential clinical applications, challenges, and limitations are discussed, along with solutions to overcome them.
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Affiliation(s)
- Bettina Baeßler
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Germany (B.B., A. Hekalo, T.W.)
| | - Sandy Engelhardt
- Department of Internal Medicine III, Heidelberg University Hospital, Germany (S.E., M.T.)
- DZHK (German Centre for Cardiovascular Research), partner site Heidelberg/Mannheim (S.E., M.T.)
| | - Amar Hekalo
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Germany (B.B., A. Hekalo, T.W.)
| | - Anja Hennemuth
- Deutsches Herzzentrum der Charité, Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany (A. Hennemuth, M.H., A.L.)
- Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt Universität zu Berlin, Germany (A. Hennemuth, M.H., A.L.)
- Fraunhofer Institute for Digital Medicine MEVIS, Berlin, Germany (A. Hennemuth, M.H.)
- DZHK (German Centre for Cardiovascular Research), partner site Berlin (A. Hennemuth, M.H., A.L.)
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Germany (A. Hennemuth)
| | - Markus Hüllebrand
- Deutsches Herzzentrum der Charité, Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany (A. Hennemuth, M.H., A.L.)
- Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt Universität zu Berlin, Germany (A. Hennemuth, M.H., A.L.)
- Fraunhofer Institute for Digital Medicine MEVIS, Berlin, Germany (A. Hennemuth, M.H.)
- DZHK (German Centre for Cardiovascular Research), partner site Berlin (A. Hennemuth, M.H., A.L.)
| | - Ann Laube
- Deutsches Herzzentrum der Charité, Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany (A. Hennemuth, M.H., A.L.)
- Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt Universität zu Berlin, Germany (A. Hennemuth, M.H., A.L.)
- DZHK (German Centre for Cardiovascular Research), partner site Berlin (A. Hennemuth, M.H., A.L.)
| | - Clemens Scherer
- Department of Medicine I, LMU University Hospital, LMU Munich, Germany (C.S.)
- Munich Heart Alliance, German Center for Cardiovascular Research (DZHK), Germany (C.S.)
| | - Malte Tölle
- Department of Internal Medicine III, Heidelberg University Hospital, Germany (S.E., M.T.)
- DZHK (German Centre for Cardiovascular Research), partner site Heidelberg/Mannheim (S.E., M.T.)
| | - Tobias Wech
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Germany (B.B., A. Hekalo, T.W.)
- Comprehensive Heart Failure Center (CHFC), University Hospital Würzburg, Germany (T.W.)
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17
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Lee SE, Hong Y, Hong J, Jung J, Sung JM, Andreini D, Al-Mallah MH, Budoff MJ, Cademartiri F, Chinnaiyan K, Choi JH, Chun EJ, Conte E, Gottlieb I, Hadamitzky M, Kim YJ, Lee BK, Leipsic JA, Maffei E, Marques H, Gonçalves PDA, Pontone G, Shin S, Stone PH, Samady H, Virmani R, Narula J, Shaw LJ, Bax JJ, Lin FY, Min JK, Chang HJ. Prediction of the development of new coronary atherosclerotic plaques with radiomics. J Cardiovasc Comput Tomogr 2024; 18:274-280. [PMID: 38378314 DOI: 10.1016/j.jcct.2024.02.003] [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: 11/20/2023] [Revised: 02/01/2024] [Accepted: 02/12/2024] [Indexed: 02/22/2024]
Abstract
BACKGROUND Radiomics is expected to identify imaging features beyond the human eye. We investigated whether radiomics can identify coronary segments that will develop new atherosclerotic plaques on coronary computed tomography angiography (CCTA). METHODS From a prospective multinational registry of patients with serial CCTA studies at ≥ 2-year intervals, segments without identifiable coronary plaque at baseline were selected and radiomic features were extracted. Cox models using clinical risk factors (Model 1), radiomic features (Model 2) and both clinical risk factors and radiomic features (Model 3) were constructed to predict the development of a coronary plaque, defined as total PV ≥ 1 mm3, at follow-up CCTA in each segment. RESULTS In total, 9583 normal coronary segments were identified from 1162 patients (60.3 ± 9.2 years, 55.7% male) and divided 8:2 into training and test sets. At follow-up CCTA, 9.8% of the segments developed new coronary plaque. The predictive power of Models 1 and 2 was not different in both the training and test sets (C-index [95% confidence interval (CI)] of Model 1 vs. Model 2: 0.701 [0.690-0.712] vs. 0.699 [0.0.688-0.710] and 0.696 [0.671-0.725] vs. 0.0.691 [0.667-0.715], respectively, all p > 0.05). The addition of radiomic features to clinical risk factors improved the predictive power of the Cox model in both the training and test sets (C-index [95% CI] of Model 3: 0.772 [0.762-0.781] and 0.767 [0.751-0.787], respectively, all p < 00.0001 compared to Models 1 and 2). CONCLUSION Radiomic features can improve the identification of segments that would develop new coronary atherosclerotic plaque. CLINICAL TRIAL REGISTRATION ClinicalTrials.gov NCT0280341.
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Affiliation(s)
- Sang-Eun Lee
- Division of Cardiology, Department of Internal Medicine, College of Medicine, Ewha Womans University, Seoul, South Korea; CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Youngtaek Hong
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Jongsoo Hong
- Division of Biostatistics, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea
| | - Juyeong Jung
- Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, South Korea
| | - Ji Min Sung
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Daniele Andreini
- IRCCS Ospedale Galeazzi Sant'Ambrogio, Milan, Italy; Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - Mouaz H Al-Mallah
- Houston Methodist DeBakey Heart & Vascular Center, Houston Methodist Hospital, Houston, TX, USA
| | - Matthew J Budoff
- Department of Medicine, Lundquist Institute at Harbor-UCLA, Torrance, CA, USA
| | | | | | | | - Eun Ju Chun
- Seoul National University Bundang Hospital, Seongnam, South Korea
| | | | - Ilan Gottlieb
- Department of Radiology, Casa de Saude São Jose, Rio de Janeiro, Brazil
| | - Martin Hadamitzky
- Department of Radiology and Nuclear Medicine, German Heart Center Munich, Munich, Germany
| | - Yong Jin Kim
- Department of Internal Medicine, Seoul National University College of Medicine, Cardiovascular Center, Seoul National University Hospital, Seoul, South Korea
| | - Byoung Kwon Lee
- Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Jonathon A Leipsic
- Department of Medicine and Radiology, University of British Columbia, Vancouver, BC, Canada
| | | | - Hugo Marques
- UNICA, Unit of Cardiovascular Imaging, Hospital da Luz, Lisbon, Portugal
| | | | - Gianluca Pontone
- Centro Cardiologico Monzino IRCCS, Milan, Italy; Department of Biomedical, Dental and Surgical Sciences, University of Milan, Milan, Italy
| | - Sanghoon Shin
- Division of Cardiology, Department of Internal Medicine, College of Medicine, Ewha Womans University, Seoul, South Korea
| | - Peter H Stone
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Habib Samady
- Georgia Heart Institute, Northeast Georgia Health System, Gainesville, GA, USA
| | - Renu Virmani
- Department of Pathology, CVPath Institute, Gaithersburg, MD, USA
| | - Jagat Narula
- University of Texas Health Houston, Houston, TX, USA
| | - Leslee J Shaw
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jeroen J Bax
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Fay Y Lin
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Hyuk-Jae Chang
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea; Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea.
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18
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Cundari G, Marchitelli L, Pambianchi G, Catapano F, Conia L, Stancanelli G, Catalano C, Galea N. Imaging biomarkers in cardiac CT: moving beyond simple coronary anatomical assessment. LA RADIOLOGIA MEDICA 2024; 129:380-400. [PMID: 38319493 PMCID: PMC10942914 DOI: 10.1007/s11547-024-01771-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 01/03/2024] [Indexed: 02/07/2024]
Abstract
Cardiac computed tomography angiography (CCTA) is considered the standard non-invasive tool to rule-out obstructive coronary artery disease (CAD). Moreover, several imaging biomarkers have been developed on cardiac-CT imaging to assess global CAD severity and atherosclerotic burden, including coronary calcium scoring, the segment involvement score, segment stenosis score and the Leaman-score. Myocardial perfusion imaging enables the diagnosis of myocardial ischemia and microvascular damage, and the CT-based fractional flow reserve quantification allows to evaluate non-invasively hemodynamic impact of the coronary stenosis. The texture and density of the epicardial and perivascular adipose tissue, the hypodense plaque burden, the radiomic phenotyping of coronary plaques or the fat radiomic profile are novel CT imaging features emerging as biomarkers of inflammation and plaque instability, which may implement the risk stratification strategies. The ability to perform myocardial tissue characterization by extracellular volume fraction and radiomic features appears promising in predicting arrhythmogenic risk and cardiovascular events. New imaging biomarkers are expanding the potential of cardiac CT for phenotyping the individual profile of CAD involvement and opening new frontiers for the practice of more personalized medicine.
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Affiliation(s)
- Giulia Cundari
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Livia Marchitelli
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Giacomo Pambianchi
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Federica Catapano
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini, 4, Pieve Emanuele, 20090, Milano, Italy
- Humanitas Research Hospital IRCCS, Via Alessandro Manzoni, 56, Rozzano, 20089, Milano, Italy
| | - Luca Conia
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Giuseppe Stancanelli
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Carlo Catalano
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Nicola Galea
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy.
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19
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Xia J, Bachour K, Suleiman ARM, Roberts JS, Sayed S, Cho GW. Enhancing coronary artery plaque analysis via artificial intelligence-driven cardiovascular computed tomography. Ther Adv Cardiovasc Dis 2024; 18:17539447241303399. [PMID: 39625215 PMCID: PMC11615974 DOI: 10.1177/17539447241303399] [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: 02/26/2024] [Accepted: 11/12/2024] [Indexed: 12/06/2024] Open
Abstract
Coronary computed tomography angiography (CCTA) is a noninvasive imaging modality of cardiac structures and vasculature considered comparable to invasive coronary angiography for the evaluation of coronary artery disease (CAD) in several major cardiovascular guidelines. Conventional image acquisition, processing, and analysis of CCTA imaging have progressed significantly in the past decade through advances in technology, computation, and engineering. However, the advent of artificial intelligence (AI)-driven analysis of CCTA further drives past the limitations of conventional CCTA, allowing for greater achievements in speed, consistency, accuracy, and safety. AI-driven CCTA (AI-CCTA) has achieved a significant reduction in radiation exposure for patients, allowing for high-quality scans with sub-millisievert radiation doses. AI-CCTA has demonstrated comparable accuracy and consistency in manual coronary artery calcium scoring against expert human readers. An advantage over invasive coronary angiography, which provides luminal information only, CCTA allows for plaque characterization, providing detailed information on the quality of plaque and offering further prognosticative value for the management of CAD. Combined with AI, many recent studies demonstrate the efficacy, accuracy, efficiency, and precision of AI-driven analysis of CCTA imaging for the evaluation of CAD, including assessing degree stenosis, adverse plaque characteristics, and CT fractional flow reserve. The limitations of AI-CCTA include its early phase in investigation, the need for further improvements in AI modeling, possible medicolegal implications, and the need for further large-scale validation studies. Despite these limitations, AI-CCTA represents an important opportunity for improving cardiovascular care in an increasingly advanced and data-driven world of modern medicine.
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Affiliation(s)
- Jeffrey Xia
- David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Kinan Bachour
- David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | | | | | - Sammy Sayed
- David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Geoffrey W. Cho
- David Geffen School of Medicine at UCLA, 100 Medical Plaza, Suite 545, Los Angeles, CA 90024, USA
- Cardiovascular Research Foundation of Southern California, Beverly Hills, CA, USA
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20
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Wang Z, Xu L, Sun L, Jiang X, Wang J. The role of computed tomography angiography in assessing the correlation between properties of coronary atherosclerotic plaque and blood lipids. Technol Health Care 2024; 32:2265-2275. [PMID: 38393936 DOI: 10.3233/thc-231036] [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] [Indexed: 02/25/2024]
Abstract
BACKGROUND Coronary atherosclerotic heart disease (CAHD) is the leading cause of death in developed countries. OBJECTIVE This study aimed to explore the correlation between the properties of coronary atherosclerotic plaque and blood lipids using computed tomography angiography (CTA). METHODS A total of 83 patients with coronary heart disease were included in this study (males: 50; females: 33; average age: [59 ± 8] years old). They were classified into the stable angina group and unstable angina group. Atherosclerotic plaques were classified as fatty plaques (soft plaques), fibrous plaques, and calcified plaques based on the computed tomography (CT) values. SPSS 17.0 statistical software was used to analyze the correlation between the properties of angina and the CT values of atherosclerotic plaques, blood lipids, and plaque properties, and then compared between the stable and unstable angina groups. RESULTS There were statistically significant differences in plaque properties between the stable and unstable angina groups (P< 0.001). During CTA examination, we found statistically significant differences in the CT density values of atherosclerotic plaques between the stable and unstable angina groups (P< 0.001). There were statistically significant differences between the properties of angina and the level of blood lipids (P< 0.05). CONCLUSION Anginal properties negatively correlated with calcified plaques and positively correlated with non-calcified plaques. Calcified plaques negatively correlated with total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG), and positively correlated with high-density lipoprotein cholesterol (HDL-C). Non-calcified plaques negatively correlated with HDL-C and positively correlated with TC, LDL-C, and TG.
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Affiliation(s)
- Zhi Wang
- Department of Cardiovascular Medicine, Affiliated Hospital of Beihua University, Jilin, China
| | - Lei Xu
- Department of Cardiovascular Medicine, Affiliated Hospital of Beihua University, Jilin, China
| | - Lin Sun
- Department of Cardiovascular Medicine, Affiliated Hospital of Beihua University, Jilin, China
| | - Xin Jiang
- Department of Cardiovascular Medicine, Affiliated Hospital of Beihua University, Jilin, China
| | - Juan Wang
- Department of Gynecology, Jilin City Central Hospital, Jilin, China
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Chen Q, Zhou F, Xie G, Tang CX, Gao X, Zhang Y, Yin X, Xu H, Zhang LJ. Advances in Artificial Intelligence-Assisted Coronary Computed Tomographic Angiography for Atherosclerotic Plaque Characterization. Rev Cardiovasc Med 2024; 25:27. [PMID: 39077649 PMCID: PMC11262402 DOI: 10.31083/j.rcm2501027] [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: 07/08/2023] [Revised: 09/01/2023] [Accepted: 09/13/2023] [Indexed: 07/31/2024] Open
Abstract
Coronary artery disease is a leading cause of death worldwide. Major adverse cardiac events are associated not only with coronary luminal stenosis but also with atherosclerotic plaque components. Coronary computed tomography angiography (CCTA) enables non-invasive evaluation of atherosclerotic plaque along the entire coronary tree. However, precise and efficient assessment of plaque features on CCTA is still a challenge for physicians in daily practice. Artificial intelligence (AI) refers to algorithms that can simulate intelligent human behavior to improve clinical work efficiency. Recently, cardiovascular imaging has seen remarkable advancements with the use of AI. AI-assisted CCTA has the potential to facilitate the clinical workflow, offer objective and repeatable quantitative results, accelerate the interpretation of reports, and guide subsequent treatment. Several AI algorithms have been developed to provide a comprehensive assessment of atherosclerotic plaques. This review serves to highlight the cutting-edge applications of AI-assisted CCTA in atherosclerosis plaque characterization, including detecting obstructive plaques, assessing plaque volumes and vulnerability, monitoring plaque progression, and providing risk assessment. Finally, this paper discusses the current problems and future directions for implementing AI in real-world clinical settings.
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Affiliation(s)
- Qian Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, 210006 Nanjing, Jiangsu, China
- Department of Radiology, Jinling Hospital, Medical School of Nanjing University, 210002 Nanjing, Jiangsu, China
| | - Fan Zhou
- Department of Radiology, Jinling Hospital, Medical School of Nanjing University, 210002 Nanjing, Jiangsu, China
| | - Guanghui Xie
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, 210006 Nanjing, Jiangsu, China
| | - Chun Xiang Tang
- Department of Radiology, Jinling Hospital, Medical School of Nanjing University, 210002 Nanjing, Jiangsu, China
| | - Xiaofei Gao
- Department of Cardiology, Nanjing First Hospital, Nanjing Medical University, 210006 Nanjing, Jiangsu, China
| | - Yamei Zhang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, 210006 Nanjing, Jiangsu, China
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, 210006 Nanjing, Jiangsu, China
| | - Hui Xu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, 210006 Nanjing, Jiangsu, China
| | - Long Jiang Zhang
- Department of Radiology, Jinling Hospital, Medical School of Nanjing University, 210002 Nanjing, Jiangsu, China
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Feng C, Chen R, Dong S, Deng W, Lin S, Zhu X, Liu W, Xu Y, Li X, Zhu Y. Predicting coronary plaque progression with conventional plaque parameters and radiomics features derived from coronary CT angiography. Eur Radiol 2023; 33:8513-8520. [PMID: 37460800 DOI: 10.1007/s00330-023-09809-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 03/16/2023] [Accepted: 03/26/2023] [Indexed: 11/26/2023]
Abstract
OBJECTIVES To determine the value of combining conventional plaque parameters and radiomics features derived from coronary computed tomography angiography (CCTA) for predicting coronary plaque progression. MATERIALS AND METHODS Clinical data and CCTA images of 400 patients who underwent at least two CCTA examinations between January 2009 and August 2020 were analyzed retrospectively. Diameter stenosis, total plaque volume and burden, calcified plaque volume and burden, noncalcified plaque volume and burden (NCPB), pericoronary fat attenuation index (FAI), and other conventional plaque parameters were recorded. The patients were assigned to a training cohort (n = 280) and a validation cohort (n = 120) in a 7:3 ratio using a stratified random splitting method. The area under the receiver operating characteristics curve (AUC) was used to evaluate the predictive abilities of conventional parameters (model 1), radiomics features (model 2), and their combination (model 3). RESULTS FAI and NCPB were identified as independent risk factors for coronary plaque progression in the training cohort. Both model 2 (training cohort AUC: 0.814, p < 0.001; validation cohort AUC: 0.729, p = 0.288) and model 3 (training cohort AUC: 0.824, p < 0.001; validation cohort AUC: 0.758, p = 0.042) had better diagnostic performances in predicting plaque progression than model 1 (training cohort AUC: 0.646; validation cohort AUC: 0.654). Moreover, model 3 was slightly higher than model 2, although not statistically significant. CONCLUSIONS The combination of conventional coronary plaque parameters and CCTA-derived radiomics features had a better ability to predict plaque progression than conventional parameters alone. CLINICAL RELEVANCE STATEMENT The conventional coronary plaque characteristics such as noncalcified plaque burden, pericoronary fat attenuation index, and radiomics features derived from CCTA can identify plaques prone to progression, which is helpful for further clinical decision-making of coronary artery disease. KEY POINTS • FAI and NCPB were identified as independent risk factors for predicting plaque progression. • Coronary plaque radiomics features were more advantageous than conventional parameters in predicting plaque progression. • The combination of conventional coronary plaque parameters and radiomics features could significantly improve the predictive ability of plaque progression over conventional parameters alone.
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Affiliation(s)
- Changjing Feng
- Department of Radiology, Chaoyang Hospital, Capital Medical University, Beijing, 100020, China
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, Jiangsu, China
| | - Rui Chen
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, Jiangsu, China
| | - Siting Dong
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, Jiangsu, China
| | - Wei Deng
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No.218 Jixi Road, Hefei, 230022, Anhui, China
| | - Shushen Lin
- Siemens Healthineers, Shanghai, 201318, China
| | - Xiaomei Zhu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, Jiangsu, China
| | - Wangyan Liu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, Jiangsu, China
| | - Yi Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, Jiangsu, China.
| | - Xiaohu Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No.218 Jixi Road, Hefei, 230022, Anhui, China.
| | - Yinsu Zhu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, Jiangsu, China.
- Department of Radiology, The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, 42 Baiziting, Nanjing, 210009, Jiangsu, China.
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Shi J, Sun Y, Hou J, Li X, Fan J, Zhang L, Zhang R, You H, Wang Z, Zhang A, Zhang J, Jin Q, Zhao L, Yang B. Radiomics Signatures of Carotid Plaque on Computed Tomography Angiography : An Approach to Identify Symptomatic Plaques. Clin Neuroradiol 2023; 33:931-941. [PMID: 37195452 PMCID: PMC10654187 DOI: 10.1007/s00062-023-01289-9] [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: 11/18/2022] [Accepted: 03/23/2023] [Indexed: 05/18/2023]
Abstract
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.
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Affiliation(s)
- Jinglong Shi
- Jinzhou Medical University General Hospital of Northern Theater, Command Postgraduate Training Base, Shenyang, China
| | - Yu Sun
- Department of Radiology, General Hospital of Northern Theater Command, 83 Wenhua Road, 110016, Shenyang, Liaoning Province, China
- Key Laboratory of Cardiovascular Imaging and Research of Liaoning Province, Shenyang, China
| | - Jie Hou
- Department of Radiology, General Hospital of Northern Theater Command, 83 Wenhua Road, 110016, Shenyang, Liaoning Province, China
- Key Laboratory of Cardiovascular Imaging and Research of Liaoning Province, Shenyang, China
| | - Xiaogang Li
- Department of Radiology, General Hospital of Northern Theater Command, 83 Wenhua Road, 110016, Shenyang, Liaoning Province, China
- Key Laboratory of Cardiovascular Imaging and Research of Liaoning Province, Shenyang, China
| | - Jitao Fan
- Beijing Deepwise & League of PHD Technology Co. Ltd, Beijing, China
| | - Libo Zhang
- Department of Radiology, General Hospital of Northern Theater Command, 83 Wenhua Road, 110016, Shenyang, Liaoning Province, China
- Key Laboratory of Cardiovascular Imaging and Research of Liaoning Province, Shenyang, China
| | - Rongrong Zhang
- Department of Radiology, General Hospital of Northern Theater Command, 83 Wenhua Road, 110016, Shenyang, Liaoning Province, China
- Key Laboratory of Cardiovascular Imaging and Research of Liaoning Province, Shenyang, China
| | - Hongrui You
- Department of Radiology, General Hospital of Northern Theater Command, 83 Wenhua Road, 110016, Shenyang, Liaoning Province, China
- Key Laboratory of Cardiovascular Imaging and Research of Liaoning Province, Shenyang, China
| | - Zhenguo Wang
- Department of Radiology, General Hospital of Northern Theater Command, 83 Wenhua Road, 110016, Shenyang, Liaoning Province, China
- Key Laboratory of Cardiovascular Imaging and Research of Liaoning Province, Shenyang, China
| | - Anxiaonan Zhang
- Department of Radiology, General Hospital of Northern Theater Command, 83 Wenhua Road, 110016, Shenyang, Liaoning Province, China
- Key Laboratory of Cardiovascular Imaging and Research of Liaoning Province, Shenyang, China
| | - Jianhua Zhang
- Department of Radiology, General Hospital of Northern Theater Command, 83 Wenhua Road, 110016, Shenyang, Liaoning Province, China
- Key Laboratory of Cardiovascular Imaging and Research of Liaoning Province, Shenyang, China
| | - Qiuyue Jin
- Department of Radiology, General Hospital of Northern Theater Command, 83 Wenhua Road, 110016, Shenyang, Liaoning Province, China
- Key Laboratory of Cardiovascular Imaging and Research of Liaoning Province, Shenyang, China
| | - Lianlian Zhao
- Department of Radiology, General Hospital of Northern Theater Command, 83 Wenhua Road, 110016, Shenyang, Liaoning Province, China
- Key Laboratory of Cardiovascular Imaging and Research of Liaoning Province, Shenyang, China
| | - Benqiang Yang
- Department of Radiology, General Hospital of Northern Theater Command, 83 Wenhua Road, 110016, Shenyang, Liaoning Province, China.
- Key Laboratory of Cardiovascular Imaging and Research of Liaoning Province, Shenyang, China.
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24
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Chen Q, Xie G, Tang CX, Yang L, Xu P, Gao X, Lu M, Fu Y, Huo Y, Zheng S, Tao X, Xu H, Yin X, Zhang LJ. Development and Validation of CCTA-based Radiomics Signature for Predicting Coronary Plaques With Rapid Progression. Circ Cardiovasc Imaging 2023; 16:e015340. [PMID: 37725670 DOI: 10.1161/circimaging.123.015340] [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: 02/07/2023] [Accepted: 08/18/2023] [Indexed: 09/21/2023]
Abstract
BACKGROUND Rapid plaque progression (RPP) is associated with a higher risk of acute coronary syndromes compared with gradual plaque progression. We aimed to develop and validate a coronary computed tomography angiography (CCTA)-based radiomics signature (RS) of plaques for predicting RPP. METHODS A total of 214 patients who underwent serial CCTA examinations from 2 tertiary hospitals (development group, 137 patients with 164 lesions; validation group, 77 patients with 101 lesions) were retrospectively enrolled. Conventional CCTA-defined morphological parameters (eg, high-risk plaque characteristics and plaque burden) and radiomics features of plaques were analyzed. RPP was defined as an annual progression of plaque burden ≥1.0% on lesion-level at follow-up CCTA. RS was built to predict RPP using XGBoost method. RESULTS RS significantly outperformed morphological parameters for predicting RPP in both the development group (area under the receiver operating characteristic curve, 0.82 versus 0.74; P=0.04) and validation group (area under the receiver operating characteristic curve, 0.81 versus 0.69; P=0.04). Multivariable analysis identified RS (odds ratio, 2.35 [95% CI, 1.32-4.46]; P=0.005) as an independent predictor of subsequent RPP in the validation group after adjustment of morphological confounders. Unlike unchanged RS in the non-RPP group, RS increased significantly in the RPP group at follow-up in the whole dataset (P<0.001). CONCLUSIONS The proposed CCTA-based RS had a better discriminative value to identify plaques at risk of rapid progression compared with conventional morphological plaque parameters. These data suggest the promising utility of radiomics for predicting RPP in a low-risk group on CCTA.
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Affiliation(s)
- Qian Chen
- Department of Radiology, Affiliated Jinling Hospital (Q.C., C.X.T., L.Y., P.X., L.J.Z.), Nanjing Medical University, China
- Department of Radiology, Nanjing First Hospital (Q.C., G.X., Y.F., Y.H., S.Z., H.X., X.Y.), Nanjing Medical University, China
| | - Guanghui Xie
- Department of Radiology, Nanjing First Hospital (Q.C., G.X., Y.F., Y.H., S.Z., H.X., X.Y.), Nanjing Medical University, China
| | - Chun Xiang Tang
- Department of Radiology, Affiliated Jinling Hospital (Q.C., C.X.T., L.Y., P.X., L.J.Z.), Nanjing Medical University, China
| | - Liu Yang
- Department of Radiology, Affiliated Jinling Hospital (Q.C., C.X.T., L.Y., P.X., L.J.Z.), Nanjing Medical University, China
| | - Pengpeng Xu
- Department of Radiology, Affiliated Jinling Hospital (Q.C., C.X.T., L.Y., P.X., L.J.Z.), Nanjing Medical University, China
| | - Xiaofei Gao
- Department of Cardiology, Nanjing First Hospital (X.G.), Nanjing Medical University, China
| | - Mengjie Lu
- School of Public Health, Shanghai JiaoTong University School of Medicine, China (M.L.)
| | - Yunlei Fu
- Department of Radiology, Nanjing First Hospital (Q.C., G.X., Y.F., Y.H., S.Z., H.X., X.Y.), Nanjing Medical University, China
| | - Yingsong Huo
- Department of Radiology, Nanjing First Hospital (Q.C., G.X., Y.F., Y.H., S.Z., H.X., X.Y.), Nanjing Medical University, China
| | - Shaoqing Zheng
- Department of Radiology, Nanjing First Hospital (Q.C., G.X., Y.F., Y.H., S.Z., H.X., X.Y.), Nanjing Medical University, China
| | - Xinwei Tao
- Bayer Healthcare, Shanghai, China (X.T.)
| | - Hui Xu
- Department of Radiology, Nanjing First Hospital (Q.C., G.X., Y.F., Y.H., S.Z., H.X., X.Y.), Nanjing Medical University, China
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital (Q.C., G.X., Y.F., Y.H., S.Z., H.X., X.Y.), Nanjing Medical University, China
| | - Long Jiang Zhang
- Department of Radiology, Affiliated Jinling Hospital (Q.C., C.X.T., L.Y., P.X., L.J.Z.), Nanjing Medical University, China
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Degtiarova G, Garefa C, Boehm R, Ciancone D, Sepulcri D, Gebhard C, Giannopoulos AA, Pazhenkottil AP, Kaufmann PA, Buechel RR. Radiomics for the detection of diffusely impaired myocardial perfusion: A proof-of-concept study using 13N-ammonia positron emission tomography. J Nucl Cardiol 2023; 30:1474-1483. [PMID: 36600174 PMCID: PMC10371953 DOI: 10.1007/s12350-022-03179-y] [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: 04/18/2022] [Accepted: 11/28/2022] [Indexed: 01/06/2023]
Abstract
AIM The current proof-of-concept study investigates the value of radiomic features from normal 13N-ammonia positron emission tomography (PET) myocardial retention images to identify patients with reduced global myocardial flow reserve (MFR). METHODS Data from 100 patients with normal retention 13N-ammonia PET scans were divided into two groups, according to global MFR (i.e., < 2 and ≥ 2), as derived from quantitative PET analysis. We extracted radiomic features from retention images at each of five different gray-level (GL) discretization (8, 16, 32, 64, and 128 bins). Outcome independent and dependent feature selection and subsequent univariate and multivariate analyses was performed to identify image features predicting reduced global MFR. RESULTS A total of 475 radiomic features were extracted per patient. Outcome independent and dependent feature selection resulted in a remainder of 35 features. Discretization at 16 bins (GL16) yielded the highest number of significant predictors of reduced MFR and was chosen for the final analysis. GLRLM_GLNU was the most robust parameter and at a cut-off of 948 yielded an accuracy, sensitivity, specificity, negative and positive predictive value of 67%, 74%, 58%, 64%, and 69%, respectively, to detect diffusely impaired myocardial perfusion. CONCLUSION A single radiomic feature (GLRLM_GLNU) extracted from visually normal 13N-ammonia PET retention images independently predicts reduced global MFR with moderate accuracy. This concept could potentially be applied to other myocardial perfusion imaging modalities based purely on relative distribution patterns to allow for better detection of diffuse disease.
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Affiliation(s)
- Ganna Degtiarova
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, 8091 Zurich, Switzerland
| | - Chrysoula Garefa
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, 8091 Zurich, Switzerland
| | - Reto Boehm
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, 8091 Zurich, Switzerland
| | - Domenico Ciancone
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, 8091 Zurich, Switzerland
| | - Daniel Sepulcri
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, 8091 Zurich, Switzerland
| | - Catherine Gebhard
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, 8091 Zurich, Switzerland
| | - Andreas A. Giannopoulos
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, 8091 Zurich, Switzerland
| | - Aju P. Pazhenkottil
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, 8091 Zurich, Switzerland
| | - Philipp A. Kaufmann
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, 8091 Zurich, Switzerland
| | - Ronny R. Buechel
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, 8091 Zurich, Switzerland
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Varga-Szemes A, Maurovich-Horvat P, Schoepf UJ, Zsarnoczay E, Pelberg R, Stone GW, Budoff MJ. Computed Tomography Assessment of Coronary Atherosclerosis: From Threshold-Based Evaluation to Histologically Validated Plaque Quantification. J Thorac Imaging 2023; 38:226-234. [PMID: 37115957 PMCID: PMC10287054 DOI: 10.1097/rti.0000000000000711] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
Arterial plaque rupture and thrombosis is the primary cause of major cardiovascular and neurovascular events. The identification of atherosclerosis, especially high-risk plaques, is therefore crucial to identify high-risk patients and to implement preventive therapies. Computed tomography angiography has the ability to visualize and characterize vascular plaques. The standard methods for plaque evaluation rely on the assessment of plaque burden, stenosis severity, the presence of positive remodeling, napkin ring sign, and spotty calcification, as well as Hounsfield Unit (HU)-based thresholding for plaque quantification; the latter with multiple shortcomings. Semiautomated threshold-based segmentation techniques with predefined HU ranges identify and quantify limited plaque characteristics, such as low attenuation, non-calcified, and calcified plaque components. Contrary to HU-based thresholds, histologically validated plaque characterization, and quantification, an emerging Artificial intelligence-based approach has the ability to differentiate specific tissue types based on a biological correlate, such as lipid-rich necrotic core and intraplaque hemorrhage that determine plaque vulnerability. In this article, we review the relevance of plaque characterization and quantification and discuss the benefits and limitations of the currently available plaque assessment and classification techniques.
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Affiliation(s)
- Akos Varga-Szemes
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC
| | - Pal Maurovich-Horvat
- MTA-SE Cardiovascular Imaging Research Group, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - U. Joseph Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC
| | - Emese Zsarnoczay
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC
- MTA-SE Cardiovascular Imaging Research Group, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Robert Pelberg
- Heart and Vascular Institute at The Christ Hospital Health Network, Cincinnati, OH
| | - Gregg W. Stone
- Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Matthew J. Budoff
- Department of Medicine, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA
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Kwiecinski J, Kolossváry M, Tzolos E, Meah MN, Adamson PD, Joshi NV, Williams MC, van Beek EJR, Berman DS, Maurovich-Horvat P, Newby DE, Dweck MR, Dey D, Slomka PJ. Latent Coronary Plaque Morphology From Computed Tomography Angiography, Molecular Disease Activity on Positron Emission Tomography, and Clinical Outcomes. Arterioscler Thromb Vasc Biol 2023; 43:e279-e290. [PMID: 37165878 PMCID: PMC11006237 DOI: 10.1161/atvbaha.123.319332] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 04/27/2023] [Indexed: 05/12/2023]
Abstract
BACKGROUND Assessments of coronary disease activity with 18F-sodium fluoride positron emission tomography and radiomics-based precision coronary plaque phenotyping derived from coronary computed tomography angiography may enhance risk stratification in patients with coronary artery disease. We sought to investigate whether the prognostic information provided by these 2 approaches is complementary in the prediction of myocardial infarction. METHODS Patients with known coronary artery disease underwent coronary 18F-sodium fluoride positron emission tomography and coronary computed tomography angiography on a hybrid positron emission tomography/computed tomography scanner. Coronary 18F-NaF uptake was determined by the coronary microcalcification activity. We performed quantitative plaque analysis of coronary computed tomography angiography datasets and extracted 1103 radiomic features for each plaque. Using weighted correlation network analysis, we derived latent morphological features of coronary lesions which were aggregated to patient-level radiomics nomograms to predict myocardial infarction. RESULTS Among 260 patients with established coronary artery disease (age, 65±9 years; 83% men), 179 (69%) participants showed increased coronary 18F-NaF activity (coronary microcalcification activity>0). Over 53 (40-59) months of follow-up, 18 patients had a myocardial infarction. Using weighted correlation network analysis, we derived 15 distinct eigen radiomic features representing latent morphological coronary plaque patterns in an unsupervised fashion. Following adjustments for calcified, noncalcified, and low-density noncalcified plaque volumes and 18F-NaF coronary microcalcification activity, 4 radiomic features remained independent predictors of myocardial infarction (hazard ratio, 1.46 [95% CI, 1.03-2.08]; P=0.03; hazard ratio, 1.62 [95% CI, 1.04-2.54]; P=0.02; hazard ratio, 1.49 [95% CI, 1.07-2.06]; P=0.01; and hazard ratio, 1.50 (95% CI, 1.05-2.13); P=0.02). CONCLUSIONS In patients with established coronary artery disease, latent coronary plaque morphological features, quantitative plaque volumes, and disease activity on 18F-sodium fluoride positron emission tomography are additive predictors of myocardial infarction.
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Affiliation(s)
- Jacek Kwiecinski
- Departments of Medicine (Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA (J.K., E.T., D.S.B., D.D., P.J.S.)
- Department of Interventional Cardiology and Angiology, Institute of Cardiology, Warsaw, Poland (J.K.)
| | - Márton Kolossváry
- Gottsegen National Cardiovascular Center, Budapest, Hungary (M.K.)
- Physiological Controls Research Center, University Research and Innovation Center, Óbuda University, Budapest, Hungary (M.K.)
| | - Evangelos Tzolos
- Departments of Medicine (Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA (J.K., E.T., D.S.B., D.D., P.J.S.)
- BHF Centre for Cardiovascular Science (E.T., M.N.M., M.C.W., E.J.R.v.B., D.E.N., M.R.B.), University of Edinburgh, United Kingdom
| | - Mohammed N Meah
- BHF Centre for Cardiovascular Science (E.T., M.N.M., M.C.W., E.J.R.v.B., D.E.N., M.R.B.), University of Edinburgh, United Kingdom
| | - Philip D Adamson
- Christchurch Heart Institute, University of Otago, Christchurch, New Zealand (P.D.A.)
| | - Nikhil V Joshi
- Bristol Heart Institute, University of Bristol, United Kingdom (N.V.J.)
| | - Michelle C Williams
- BHF Centre for Cardiovascular Science (E.T., M.N.M., M.C.W., E.J.R.v.B., D.E.N., M.R.B.), University of Edinburgh, United Kingdom
| | - Edwin J R van Beek
- BHF Centre for Cardiovascular Science (E.T., M.N.M., M.C.W., E.J.R.v.B., D.E.N., M.R.B.), University of Edinburgh, United Kingdom
- Edinburgh Imaging, Queens Medical Research Institute (E.J.R.v.B.), University of Edinburgh, United Kingdom
| | - Daniel S Berman
- Departments of Medicine (Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA (J.K., E.T., D.S.B., D.D., P.J.S.)
| | - Pál Maurovich-Horvat
- MTA-SE Cardiovascular Imaging Research Group, Department of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary (P.M.-H.)
| | - David E Newby
- BHF Centre for Cardiovascular Science (E.T., M.N.M., M.C.W., E.J.R.v.B., D.E.N., M.R.B.), University of Edinburgh, United Kingdom
| | - Marc R Dweck
- BHF Centre for Cardiovascular Science (E.T., M.N.M., M.C.W., E.J.R.v.B., D.E.N., M.R.B.), University of Edinburgh, United Kingdom
| | - Damini Dey
- Departments of Medicine (Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA (J.K., E.T., D.S.B., D.D., P.J.S.)
| | - Piotr J Slomka
- Departments of Medicine (Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA (J.K., E.T., D.S.B., D.D., P.J.S.)
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Polidori T, De Santis D, Rucci C, Tremamunno G, Piccinni G, Pugliese L, Zerunian M, Guido G, Pucciarelli F, Bracci B, Polici M, Laghi A, Caruso D. Radiomics applications in cardiac imaging: a comprehensive review. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01658-x. [PMID: 37326780 DOI: 10.1007/s11547-023-01658-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 05/26/2023] [Indexed: 06/17/2023]
Abstract
Radiomics is a new emerging field that includes extraction of metrics and quantification of so-called radiomic features from medical images. The growing importance of radiomics applied to oncology in improving diagnosis, cancer staging and grading, and improved personalized treatment, has been well established; yet, this new analysis technique has still few applications in cardiovascular imaging. Several studies have shown promising results describing how radiomics principles could improve the diagnostic accuracy of coronary computed tomography angiography (CCTA) and magnetic resonance imaging (MRI) in diagnosis, risk stratification, and follow-up of patients with coronary heart disease (CAD), ischemic heart disease (IHD), hypertrophic cardiomyopathy (HCM), hypertensive heart disease (HHD), and many other cardiovascular diseases. Such quantitative approach could be useful to overcome the main limitations of CCTA and MRI in the evaluation of cardiovascular diseases, such as readers' subjectiveness and lack of repeatability. Moreover, this new discipline could potentially overcome some technical problems, namely the need of contrast administration or invasive examinations. Despite such advantages, radiomics is still not applied in clinical routine, due to lack of standardized parameters acquisition, inconsistent radiomic methods, lack of external validation, and different knowledge and experience among the readers. The purpose of this manuscript is to provide a recent update on the status of radiomics clinical applications in cardiovascular imaging.
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Affiliation(s)
- Tiziano Polidori
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Domenico De Santis
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Carlotta Rucci
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Giuseppe Tremamunno
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Giulia Piccinni
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Luca Pugliese
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Marta Zerunian
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Gisella Guido
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Francesco Pucciarelli
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Benedetta Bracci
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Michela Polici
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Andrea Laghi
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy.
| | - Damiano Caruso
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
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29
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Kumar A, Stillman AE, Chatzizisis YS. Coronary plaque phenotyping with cardiac CTA: Separating the signal from the noise. Atherosclerosis 2023; 373:66-68. [PMID: 37147221 DOI: 10.1016/j.atherosclerosis.2023.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 04/05/2023] [Indexed: 05/07/2023]
Affiliation(s)
- Arnav Kumar
- Division of Cardiology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Arthur E Stillman
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA
| | - Yiannis S Chatzizisis
- Division of Cardiovascular Medicine, Miller School of Medicine, University of Miami, Miami, FL, USA
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30
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Kim JN, Gomez-Perez L, Zimin VN, Makhlouf MHE, Al-Kindi S, Wilson DL, Lee J. Pericoronary Adipose Tissue Radiomics from Coronary Computed Tomography Angiography Identifies Vulnerable Plaques. Bioengineering (Basel) 2023; 10:360. [PMID: 36978751 PMCID: PMC10045206 DOI: 10.3390/bioengineering10030360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/07/2023] [Accepted: 03/12/2023] [Indexed: 03/17/2023] Open
Abstract
Pericoronary adipose tissue (PCAT) features on Computed Tomography (CT) have been shown to reflect local inflammation and increased cardiovascular risk. Our goal was to determine whether PCAT radiomics extracted from coronary CT angiography (CCTA) images are associated with intravascular optical coherence tomography (IVOCT)-identified vulnerable-plaque characteristics (e.g., microchannels (MC) and thin-cap fibroatheroma (TCFA)). The CCTA and IVOCT images of 30 lesions from 25 patients were registered. The vessels with vulnerable plaques were identified from the registered IVOCT images. The PCAT-radiomics features were extracted from the CCTA images for the lesion region of interest (PCAT-LOI) and the entire vessel (PCAT-Vessel). We extracted 1356 radiomic features, including intensity (first-order), shape, and texture features. The features were reduced using standard approaches (e.g., high feature correlation). Using stratified three-fold cross-validation with 1000 repeats, we determined the ability of PCAT-radiomics features from CCTA to predict IVOCT vulnerable-plaque characteristics. In the identification of TCFA lesions, the PCAT-LOI and PCAT-Vessel radiomics models performed comparably (Area Under the Curve (AUC) ± standard deviation 0.78 ± 0.13, 0.77 ± 0.14). For the identification of MC lesions, the PCAT-Vessel radiomics model (0.89 ± 0.09) was moderately better associated than the PCAT-LOI model (0.83 ± 0.12). In addition, both the PCAT-LOI and the PCAT-Vessel radiomics model identified coronary vessels thought to be highly vulnerable to a similar standard (i.e., both TCFA and MC; 0.88 ± 0.10, 0.91 ± 0.09). The most favorable radiomic features tended to be those describing the texture and size of the PCAT. The application of PCAT radiomics can identify coronary vessels with TCFA or MC, consistent with IVOCT. Furthermore, the use of CCTA radiomics may improve risk stratification by noninvasively detecting vulnerable-plaque characteristics that are only visible with IVOCT.
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Affiliation(s)
- Justin N. Kim
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Lia Gomez-Perez
- Department of Biomedical Engineering, The Ohio State University, Columbus, OH 43210, USA
| | - Vladislav N. Zimin
- Cardiovascular Imaging Core Laboratory, Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
| | - Mohamed H. E. Makhlouf
- Cardiovascular Imaging Core Laboratory, Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
| | - Sadeer Al-Kindi
- Cardiovascular Imaging Core Laboratory, Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
| | - David L. Wilson
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
- Department of Radiology, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Juhwan Lee
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
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31
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Du G, Cao M, Hou Z, Cai Z, Yu T, Zheng H, Dai Z, Yang Z, Shen J, Lin D. The value of quantitative plaque analysis based on coronary computed tomography angiography in predicting the percutaneous coronary intervention outcome of chronic total occlusion lesions. Quant Imaging Med Surg 2023; 13:1563-1576. [PMID: 36915301 PMCID: PMC10006140 DOI: 10.21037/qims-22-428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 12/08/2022] [Indexed: 02/12/2023]
Abstract
Background Due to the uncertainty of the success of percutaneous coronary intervention (PCI) and the complexity of selecting suitable treatment cases, the interventional outcome of coronary chronic total occlusion (CTO) remains challenging. The purpose of this study was to evaluate the role of quantitative plaque analysis based on coronary computed tomography angiography (CCTA) in predicting the CTO-PCI outcome. Methods We retrospectively included 78 patients with CTO (80 lesions) confirmed by invasive coronary angiography from July 2016 to December 2018. All patients underwent PCI treatment according to standard practice. A total of 47 lesions in 47 patients were successfully treated with PCI. PCI failed in the remaining 33 lesions in 31 patients. The following conventional CCTA morphologic parameters were evaluated and compared between the PCI-success and PCI-failure groups: stump morphology; occlusion length, tortuous course; CTO lesion calcium; bridging collateral vessel; retrograde collateral vessel; the appearance of the occluded distal segment; and quantitative CTO plaque characteristics, including total plaque volume, calcified plaque (CP) volume, noncalcified plaque (NCP) volume, low-density noncalcified plaque (LDNCP) volume, and plaque length. Univariate and multivariate logistic regression analyses were performed to determine independent parameters predictive of CTO-PCI outcomes. The predictive performances were assessed using receiver operating characteristic curve analysis. Results The blunt stump was the only independent CCTA morphologic parameter to predict the outcome of CTO-PCI [odds ratio (OR): 10.807; P<0.001]. NCP volume (OR: 1.018; P<0.001), CP volume (OR: 1.026; P=0.049), and plaque length (OR: 1.058; P=0.037) were independent quantitative CTO plaque characteristics predictive of CTO-PCI outcomes. The plaque-based model combining NCP volume with CP volume and plaque length had a higher area under the curve (AUC =0.96) than did the morphology-based model that included blunt stump (AUC 0.68) in predicting the outcomes of CTO-PCI (P<0.001). Conclusions The CCTA-based plaque characteristics, including NCP volume, CP volume, and plaque length, outperformed morphologic parameters in predicting the CTO-PCI outcomes.
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Affiliation(s)
- Guangzhou Du
- Department of Radiology, Shantou Central Hospital, Shantou, China
| | - Minghui Cao
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhihui Hou
- Department of Radiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhaoxi Cai
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Taihui Yu
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Haisheng Zheng
- Department of Cardiology, Shantou Central Hospital, Shantou, China
| | - Zhuozhi Dai
- Department of Radiology, Shantou Central Hospital, Shantou, China.,Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zehong Yang
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jun Shen
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Daiying Lin
- Department of Radiology, Shantou Central Hospital, Shantou, China
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32
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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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33
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Chen Q, Pan T, Wang YN, Schoepf UJ, Bidwell SL, Qiao H, Feng Y, Xu C, Xu H, Xie G, Gao X, Tao XW, Lu M, Xu PP, Zhong J, Wei Y, Yin X, Zhang J, Zhang LJ. A Coronary CT Angiography Radiomics Model to Identify Vulnerable Plaque and Predict Cardiovascular Events. Radiology 2023; 307:e221693. [PMID: 36786701 DOI: 10.1148/radiol.221693] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2023]
Abstract
Background A noninvasive coronary CT angiography (CCTA)-based radiomics technique may facilitate the identification of vulnerable plaques and patients at risk for future adverse events. Purpose To assess whether a CCTA-based radiomic signature (RS) of vulnerable plaques defined with intravascular US was associated with increased risk for future major adverse cardiac events (MACE). Materials and Methods In a retrospective study, an RS of vulnerable plaques was developed and validated using intravascular US as the reference standard. The RS development data set included patients first undergoing CCTA and then intravascular US within 3 months between June 2013 and December 2020 at one tertiary hospital. The development set was randomly assigned to training and validation sets at a 7:3 ratio. Diagnostic performance was assessed internally and externally from three tertiary hospitals using the area under the curve (AUC). The prognostic value of the RS for predicting MACE was evaluated in a prospective cohort with suspected coronary artery disease between April 2018 and March 2019. Multivariable Cox regression analysis was used to evaluate the RS and conventional anatomic plaque features (eg, segment involvement score) for predicting MACE. Results The RS development data set included 419 lesions from 225 patients (mean age, 64 years ± 10 [SD]; 68 men), while the prognostic cohort included 1020 lesions from 708 patients (mean age, 62 years ± 11; 498 men). Sixteen radiomic features, including two shape features and 14 textural features, were selected to build the RS. The RS yielded a moderate to good AUC in the training, validation, internal, and external test sets (AUC = 0.81, 0.75, 0.80, and 0.77, respectively). A high RS (≥1.07) was independently associated with MACE over a median 3-year follow-up (hazard ratio, 2.01; P = .005). Conclusion A coronary CT angiography-derived radiomic signature of coronary plaque enabled the detection of vulnerable plaques that were associated with increased risk for future adverse cardiac outcomes. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by De Cecco and van Assen in this issue.
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Affiliation(s)
- Qian Chen
- From the Departments of Radiology (Q.C., H.X., G.X., X.Y.) and Cardiology (T.P., X.G., J. Zhang), Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing 210002, China (Q.C., U.J.S., P.P.X., J. Zhong, L.J.Z.); Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (Y.N.W., C.X.); Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (U.J.S., S.L.B.); Department of Medical Imaging, Affiliated Hospital of Jiangnan University, Wuxi, China (H.Q.); Department of Medical Imaging, Medical Imaging Center, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, China (Y.F.); Bayer Healthcare, Shanghai, China (X.W.T.); School of Public Health, Shanghai JiaoTong University School of Medicine, Shanghai, China (M.L.); and Department of Biostatistics, School of Public Health, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China (Y.W.)
| | - Tao Pan
- From the Departments of Radiology (Q.C., H.X., G.X., X.Y.) and Cardiology (T.P., X.G., J. Zhang), Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing 210002, China (Q.C., U.J.S., P.P.X., J. Zhong, L.J.Z.); Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (Y.N.W., C.X.); Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (U.J.S., S.L.B.); Department of Medical Imaging, Affiliated Hospital of Jiangnan University, Wuxi, China (H.Q.); Department of Medical Imaging, Medical Imaging Center, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, China (Y.F.); Bayer Healthcare, Shanghai, China (X.W.T.); School of Public Health, Shanghai JiaoTong University School of Medicine, Shanghai, China (M.L.); and Department of Biostatistics, School of Public Health, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China (Y.W.)
| | - Yi Ning Wang
- From the Departments of Radiology (Q.C., H.X., G.X., X.Y.) and Cardiology (T.P., X.G., J. Zhang), Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing 210002, China (Q.C., U.J.S., P.P.X., J. Zhong, L.J.Z.); Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (Y.N.W., C.X.); Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (U.J.S., S.L.B.); Department of Medical Imaging, Affiliated Hospital of Jiangnan University, Wuxi, China (H.Q.); Department of Medical Imaging, Medical Imaging Center, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, China (Y.F.); Bayer Healthcare, Shanghai, China (X.W.T.); School of Public Health, Shanghai JiaoTong University School of Medicine, Shanghai, China (M.L.); and Department of Biostatistics, School of Public Health, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China (Y.W.)
| | - U Joseph Schoepf
- From the Departments of Radiology (Q.C., H.X., G.X., X.Y.) and Cardiology (T.P., X.G., J. Zhang), Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing 210002, China (Q.C., U.J.S., P.P.X., J. Zhong, L.J.Z.); Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (Y.N.W., C.X.); Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (U.J.S., S.L.B.); Department of Medical Imaging, Affiliated Hospital of Jiangnan University, Wuxi, China (H.Q.); Department of Medical Imaging, Medical Imaging Center, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, China (Y.F.); Bayer Healthcare, Shanghai, China (X.W.T.); School of Public Health, Shanghai JiaoTong University School of Medicine, Shanghai, China (M.L.); and Department of Biostatistics, School of Public Health, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China (Y.W.)
| | - Samuel L Bidwell
- From the Departments of Radiology (Q.C., H.X., G.X., X.Y.) and Cardiology (T.P., X.G., J. Zhang), Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing 210002, China (Q.C., U.J.S., P.P.X., J. Zhong, L.J.Z.); Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (Y.N.W., C.X.); Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (U.J.S., S.L.B.); Department of Medical Imaging, Affiliated Hospital of Jiangnan University, Wuxi, China (H.Q.); Department of Medical Imaging, Medical Imaging Center, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, China (Y.F.); Bayer Healthcare, Shanghai, China (X.W.T.); School of Public Health, Shanghai JiaoTong University School of Medicine, Shanghai, China (M.L.); and Department of Biostatistics, School of Public Health, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China (Y.W.)
| | - Hongyan Qiao
- From the Departments of Radiology (Q.C., H.X., G.X., X.Y.) and Cardiology (T.P., X.G., J. Zhang), Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing 210002, China (Q.C., U.J.S., P.P.X., J. Zhong, L.J.Z.); Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (Y.N.W., C.X.); Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (U.J.S., S.L.B.); Department of Medical Imaging, Affiliated Hospital of Jiangnan University, Wuxi, China (H.Q.); Department of Medical Imaging, Medical Imaging Center, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, China (Y.F.); Bayer Healthcare, Shanghai, China (X.W.T.); School of Public Health, Shanghai JiaoTong University School of Medicine, Shanghai, China (M.L.); and Department of Biostatistics, School of Public Health, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China (Y.W.)
| | - Yun Feng
- From the Departments of Radiology (Q.C., H.X., G.X., X.Y.) and Cardiology (T.P., X.G., J. Zhang), Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing 210002, China (Q.C., U.J.S., P.P.X., J. Zhong, L.J.Z.); Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (Y.N.W., C.X.); Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (U.J.S., S.L.B.); Department of Medical Imaging, Affiliated Hospital of Jiangnan University, Wuxi, China (H.Q.); Department of Medical Imaging, Medical Imaging Center, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, China (Y.F.); Bayer Healthcare, Shanghai, China (X.W.T.); School of Public Health, Shanghai JiaoTong University School of Medicine, Shanghai, China (M.L.); and Department of Biostatistics, School of Public Health, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China (Y.W.)
| | - Cheng Xu
- From the Departments of Radiology (Q.C., H.X., G.X., X.Y.) and Cardiology (T.P., X.G., J. Zhang), Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing 210002, China (Q.C., U.J.S., P.P.X., J. Zhong, L.J.Z.); Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (Y.N.W., C.X.); Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (U.J.S., S.L.B.); Department of Medical Imaging, Affiliated Hospital of Jiangnan University, Wuxi, China (H.Q.); Department of Medical Imaging, Medical Imaging Center, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, China (Y.F.); Bayer Healthcare, Shanghai, China (X.W.T.); School of Public Health, Shanghai JiaoTong University School of Medicine, Shanghai, China (M.L.); and Department of Biostatistics, School of Public Health, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China (Y.W.)
| | - Hui Xu
- From the Departments of Radiology (Q.C., H.X., G.X., X.Y.) and Cardiology (T.P., X.G., J. Zhang), Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing 210002, China (Q.C., U.J.S., P.P.X., J. Zhong, L.J.Z.); Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (Y.N.W., C.X.); Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (U.J.S., S.L.B.); Department of Medical Imaging, Affiliated Hospital of Jiangnan University, Wuxi, China (H.Q.); Department of Medical Imaging, Medical Imaging Center, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, China (Y.F.); Bayer Healthcare, Shanghai, China (X.W.T.); School of Public Health, Shanghai JiaoTong University School of Medicine, Shanghai, China (M.L.); and Department of Biostatistics, School of Public Health, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China (Y.W.)
| | - Guanghui Xie
- From the Departments of Radiology (Q.C., H.X., G.X., X.Y.) and Cardiology (T.P., X.G., J. Zhang), Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing 210002, China (Q.C., U.J.S., P.P.X., J. Zhong, L.J.Z.); Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (Y.N.W., C.X.); Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (U.J.S., S.L.B.); Department of Medical Imaging, Affiliated Hospital of Jiangnan University, Wuxi, China (H.Q.); Department of Medical Imaging, Medical Imaging Center, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, China (Y.F.); Bayer Healthcare, Shanghai, China (X.W.T.); School of Public Health, Shanghai JiaoTong University School of Medicine, Shanghai, China (M.L.); and Department of Biostatistics, School of Public Health, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China (Y.W.)
| | - Xiaofei Gao
- From the Departments of Radiology (Q.C., H.X., G.X., X.Y.) and Cardiology (T.P., X.G., J. Zhang), Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing 210002, China (Q.C., U.J.S., P.P.X., J. Zhong, L.J.Z.); Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (Y.N.W., C.X.); Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (U.J.S., S.L.B.); Department of Medical Imaging, Affiliated Hospital of Jiangnan University, Wuxi, China (H.Q.); Department of Medical Imaging, Medical Imaging Center, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, China (Y.F.); Bayer Healthcare, Shanghai, China (X.W.T.); School of Public Health, Shanghai JiaoTong University School of Medicine, Shanghai, China (M.L.); and Department of Biostatistics, School of Public Health, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China (Y.W.)
| | - Xin-Wei Tao
- From the Departments of Radiology (Q.C., H.X., G.X., X.Y.) and Cardiology (T.P., X.G., J. Zhang), Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing 210002, China (Q.C., U.J.S., P.P.X., J. Zhong, L.J.Z.); Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (Y.N.W., C.X.); Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (U.J.S., S.L.B.); Department of Medical Imaging, Affiliated Hospital of Jiangnan University, Wuxi, China (H.Q.); Department of Medical Imaging, Medical Imaging Center, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, China (Y.F.); Bayer Healthcare, Shanghai, China (X.W.T.); School of Public Health, Shanghai JiaoTong University School of Medicine, Shanghai, China (M.L.); and Department of Biostatistics, School of Public Health, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China (Y.W.)
| | - Mengjie Lu
- From the Departments of Radiology (Q.C., H.X., G.X., X.Y.) and Cardiology (T.P., X.G., J. Zhang), Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing 210002, China (Q.C., U.J.S., P.P.X., J. Zhong, L.J.Z.); Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (Y.N.W., C.X.); Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (U.J.S., S.L.B.); Department of Medical Imaging, Affiliated Hospital of Jiangnan University, Wuxi, China (H.Q.); Department of Medical Imaging, Medical Imaging Center, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, China (Y.F.); Bayer Healthcare, Shanghai, China (X.W.T.); School of Public Health, Shanghai JiaoTong University School of Medicine, Shanghai, China (M.L.); and Department of Biostatistics, School of Public Health, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China (Y.W.)
| | - Peng Peng Xu
- From the Departments of Radiology (Q.C., H.X., G.X., X.Y.) and Cardiology (T.P., X.G., J. Zhang), Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing 210002, China (Q.C., U.J.S., P.P.X., J. Zhong, L.J.Z.); Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (Y.N.W., C.X.); Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (U.J.S., S.L.B.); Department of Medical Imaging, Affiliated Hospital of Jiangnan University, Wuxi, China (H.Q.); Department of Medical Imaging, Medical Imaging Center, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, China (Y.F.); Bayer Healthcare, Shanghai, China (X.W.T.); School of Public Health, Shanghai JiaoTong University School of Medicine, Shanghai, China (M.L.); and Department of Biostatistics, School of Public Health, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China (Y.W.)
| | - Jian Zhong
- From the Departments of Radiology (Q.C., H.X., G.X., X.Y.) and Cardiology (T.P., X.G., J. Zhang), Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing 210002, China (Q.C., U.J.S., P.P.X., J. Zhong, L.J.Z.); Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (Y.N.W., C.X.); Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (U.J.S., S.L.B.); Department of Medical Imaging, Affiliated Hospital of Jiangnan University, Wuxi, China (H.Q.); Department of Medical Imaging, Medical Imaging Center, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, China (Y.F.); Bayer Healthcare, Shanghai, China (X.W.T.); School of Public Health, Shanghai JiaoTong University School of Medicine, Shanghai, China (M.L.); and Department of Biostatistics, School of Public Health, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China (Y.W.)
| | - Yongyue Wei
- From the Departments of Radiology (Q.C., H.X., G.X., X.Y.) and Cardiology (T.P., X.G., J. Zhang), Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing 210002, China (Q.C., U.J.S., P.P.X., J. Zhong, L.J.Z.); Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (Y.N.W., C.X.); Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (U.J.S., S.L.B.); Department of Medical Imaging, Affiliated Hospital of Jiangnan University, Wuxi, China (H.Q.); Department of Medical Imaging, Medical Imaging Center, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, China (Y.F.); Bayer Healthcare, Shanghai, China (X.W.T.); School of Public Health, Shanghai JiaoTong University School of Medicine, Shanghai, China (M.L.); and Department of Biostatistics, School of Public Health, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China (Y.W.)
| | - Xindao Yin
- From the Departments of Radiology (Q.C., H.X., G.X., X.Y.) and Cardiology (T.P., X.G., J. Zhang), Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing 210002, China (Q.C., U.J.S., P.P.X., J. Zhong, L.J.Z.); Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (Y.N.W., C.X.); Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (U.J.S., S.L.B.); Department of Medical Imaging, Affiliated Hospital of Jiangnan University, Wuxi, China (H.Q.); Department of Medical Imaging, Medical Imaging Center, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, China (Y.F.); Bayer Healthcare, Shanghai, China (X.W.T.); School of Public Health, Shanghai JiaoTong University School of Medicine, Shanghai, China (M.L.); and Department of Biostatistics, School of Public Health, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China (Y.W.)
| | - Junjie Zhang
- From the Departments of Radiology (Q.C., H.X., G.X., X.Y.) and Cardiology (T.P., X.G., J. Zhang), Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing 210002, China (Q.C., U.J.S., P.P.X., J. Zhong, L.J.Z.); Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (Y.N.W., C.X.); Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (U.J.S., S.L.B.); Department of Medical Imaging, Affiliated Hospital of Jiangnan University, Wuxi, China (H.Q.); Department of Medical Imaging, Medical Imaging Center, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, China (Y.F.); Bayer Healthcare, Shanghai, China (X.W.T.); School of Public Health, Shanghai JiaoTong University School of Medicine, Shanghai, China (M.L.); and Department of Biostatistics, School of Public Health, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China (Y.W.)
| | - Long Jiang Zhang
- From the Departments of Radiology (Q.C., H.X., G.X., X.Y.) and Cardiology (T.P., X.G., J. Zhang), Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing 210002, China (Q.C., U.J.S., P.P.X., J. Zhong, L.J.Z.); Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (Y.N.W., C.X.); Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (U.J.S., S.L.B.); Department of Medical Imaging, Affiliated Hospital of Jiangnan University, Wuxi, China (H.Q.); Department of Medical Imaging, Medical Imaging Center, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, China (Y.F.); Bayer Healthcare, Shanghai, China (X.W.T.); School of Public Health, Shanghai JiaoTong University School of Medicine, Shanghai, China (M.L.); and Department of Biostatistics, School of Public Health, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China (Y.W.)
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De Cecco CN, van Assen M. Can Radiomics Help in the Identification of Vulnerable Coronary Plaque? Radiology 2023; 307:e223342. [PMID: 36786708 DOI: 10.1148/radiol.223342] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2023]
Affiliation(s)
- Carlo N De Cecco
- From the Division of Cardiothoracic Imaging and Medical Informatics, Department of Radiology and Imaging Sciences, Emory University Hospital, Emory Healthcare, 1365 Clifton Rd NE, Suite AT503, Atlanta, GA 30322 (C.N.D.C.); and Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Emory University, Atlanta, Ga (M.v.A.)
| | - Marly van Assen
- From the Division of Cardiothoracic Imaging and Medical Informatics, Department of Radiology and Imaging Sciences, Emory University Hospital, Emory Healthcare, 1365 Clifton Rd NE, Suite AT503, Atlanta, GA 30322 (C.N.D.C.); and Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Emory University, Atlanta, Ga (M.v.A.)
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Ding Y, Zhang C, Wu W, Pu J, Zhao X, Zhang H, Zhao L, Schoenhagen P, Liu S, Ma X. A radiomics model based on aortic computed tomography angiography: the impact on predicting the prognosis of patients with aortic intramural hematoma (IMH). Quant Imaging Med Surg 2023; 13:598-609. [PMID: 36819258 PMCID: PMC9929381 DOI: 10.21037/qims-22-480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 11/16/2022] [Indexed: 12/13/2022]
Abstract
Background The prognosis of aortic intramural hematoma (IMH) is unpredictable, but computed tomography angiography (CTA) plays an important role of high diagnostic performance in the initial diagnosis and during follow-up of patients. In this study, we investigated the value of a radiomics model based on aortic CTA for predicting the prognosis of patients with medically treated IMH. Method A total of 120 patients with IMH were enrolled in this study. The follow-up duration ranged from 32 to 1,346 days (median 232 days). Progression of these patients was classified as follows: destabilization, which refers to deterioration in the aortic condition, including significant increases in the thickness of the IMH, the progression of IMH to a penetrating aortic ulcer (PAU), aortic dissection (AD), or rupture; or stabilization, which refers to an unchanged appearance or a decrease in the size or disappearance of the IMH. The patients were divided into a training cohort (n=84) and a validation cohort (n=36). Six different machine learning classifiers were applied: random forest (RF), K-nearest neighbor (KNN), Gaussian Naive Bayes, decision tree, logistic regression, and support vector machine (SVM). The clinical-radiomics combined nomogram model was established by multivariate logistic regression. The area under the receiver operating characteristic (ROC) curve (AUC) was implemented to evaluate the discrimination performance of the models. The calibration curves and Hosmer-Lemeshow test were used for evaluating model calibration. DeLong's test was performed to compare the AUC performance of models. Results Among all of the patients, 60 patients showed destabilization and 60 patients remained stable. A total of 12 radiomic features were retained after application of the least absolute shrinkage and selection operator (LASSO). These features were used for the machine learning model construction. The SVM-radial basis function (SVM-RBF) model obtained the best performance with an AUC of 0.765 (95% CI, 0.593-0.906). In the validation cohort, the combined clinical-radiomics model [AUC =0.787; 95% confidence interval (CI), 0.619-0.923] showed a significantly higher performance than did the clinical model (AUC =0.596; 95% CI, 0.413-0.796; P=0.021) and had a similar performance to the radiomics model (AUC =0.765; 95% CI, 0.589-0.906; P=0.672). Conclusions A quantitative nomogram based on radiomic features of CTA images can be used to predict disease progression in patients with IMH and may help improve clinical decision-making.
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Affiliation(s)
- Yan Ding
- Department of Interventional Diagnosis and Treatment, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Chen Zhang
- Department of Interventional Diagnosis and Treatment, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Wenhui Wu
- Interventional Center of Valvular Heart Disease, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Junzhou Pu
- Interventional Center of Valvular Heart Disease, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Xinghan Zhao
- Department of Interventional Diagnosis and Treatment, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Hongbo Zhang
- Department of Interventional Diagnosis and Treatment, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Lei Zhao
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Paul Schoenhagen
- Cardiovascular Imaging, Miller Pavilion Desk J1-4, Cleveland Clinic, Cleveland, OH, USA
| | | | - Xiaohai Ma
- Department of Interventional Diagnosis and Treatment, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
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Ling R, Chen X, Yu Y, Yu L, Yang W, Xu Z, Li Y, Zhang J. Computed Tomography Radiomics Model Predicts Procedure Success of Coronary Chronic Total Occlusions. Circ Cardiovasc Imaging 2023; 16:e014826. [PMID: 36802447 DOI: 10.1161/circimaging.122.014826] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
BACKGROUND Coronary computed tomography (CT) angiography imaging is useful for the preprocedural evaluation of chronic total occlusion (CTO). However, the predictive value of CT radiomics model for successful percutaneous coronary intervention (PCI) has not been studied. We aimed to develop and validate a CT radiomics model for predicting PCI success of CTOs. METHODS In this retrospective study, a radiomics-based model for predicting PCI success was developed on the training and internal validation sets of 202 and 98 patients with CTO, collected from 1 tertiary hospital. The proposed model was validated on an external test set of 75 CTO patients enrolled from another tertiary hospital. CT radiomics features of each CTO lesion were manually labeled and extracted. Other anatomical parameters, including occlusion length, entry morphology, tortuosity, and calcification burden, were also measured. Fifteen radiomics features, 2 quantitative plaque features, and CT-derived Multicenter CTO Registry of Japan score were used to train different models. The predictive values of each model were evaluated for predicting revascularization success. RESULTS In the external test set, 75 patients (60 men; 65 years [58.5, 71.5]) with 83 CTO lesions were assessed. Occlusion length was shorter (13.00 mm versus 29.30 mm, P=0.007) in PCI success group whereas the presence of tortuous course was more commonly presented in PCI failure group (1.49% versus 25.00%, P=0.004). The radiomics score was significantly smaller in PCI success group (0.10 versus 0.55, P<0.001). The area under the curve of CT radiomics-based model was significantly higher than that of CT-derived Multicenter CTO Registry of Japan score for predicting PCI success (area under the curve=0.920 versus 0.752, P=0.008). The proposed radiomics model accurately identified 89.16% (74/83) CTO lesions with procedure success. CONCLUSIONS CT radiomics-based model outperformed CT-derived Multicenter CTO Registry of Japan score for predicting PCI success. The proposed model is more accurate than the conventional anatomical parameters to identify CTO lesions with PCI success.
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Affiliation(s)
- Runjianya Ling
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, China (R.L., Y.L.)
| | - Xiuyu Chen
- Department of Magnetic Resonance Imaging, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (X.C.)
| | - Yarong Yu
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, China (Y.Y., L.Y., J.Z.)
| | - Lihua Yu
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, China (Y.Y., L.Y., J.Z.)
| | - Wenyi Yang
- Department of Cardiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, China (W.Y.)
| | - Zhihan Xu
- Siemen Healthineers, CT collaboration, Shanghai, China (Z.X.)
| | - Yuehua Li
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, China (R.L., Y.L.)
| | - Jiayin Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, China (Y.Y., L.Y., J.Z.)
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Zhang X, Hua Z, Chen R, Jiao Z, Shan J, Li C, Li Z. Identifying vulnerable plaques: A 3D carotid plaque radiomics model based on HRMRI. Front Neurol 2023; 14:1050899. [PMID: 36779063 PMCID: PMC9908750 DOI: 10.3389/fneur.2023.1050899] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 01/10/2023] [Indexed: 01/27/2023] Open
Abstract
Background Identification of vulnerable carotid plaque is important for the treatment and prevention of stroke. In previous studies, plaque vulnerability was assessed qualitatively. We aimed to develop a 3D carotid plaque radiomics model based on high-resolution magnetic resonance imaging (HRMRI) to quantitatively identify vulnerable plaques. Methods Ninety patients with carotid atherosclerosis who underwent HRMRI were randomized into training and test cohorts. Using the radiological characteristics of carotid plaques, a traditional model was constructed. A 3D carotid plaque radiomics model was constructed using the radiomics features of 3D T1-SPACE and its contrast-enhanced sequences. A combined model was constructed using radiological and radiomics characteristics. Nomogram was generated based on the combined models, and ROC curves were utilized to assess the performance of each model. Results 48 patients (53.33%) were symptomatic and 42 (46.67%) were asymptomatic. The traditional model was constructed using intraplaque hemorrhage, plaque enhancement, wall remodeling pattern, and lumen stenosis, and it provided an area under the curve (AUC) of 0.816 vs. 0.778 in the training and testing sets. In the two cohorts, the 3D carotid plaque radiomics model and the combined model had an AUC of 0.915 vs. 0.835 and 0.957 vs. 0.864, respectively. In the training set, both the radiomics model and the combination model outperformed the traditional model, but there was no significant difference between the radiomics model and the combined model. Conclusions HRMRI-based 3D carotid radiomics models can improve the precision of detecting vulnerable carotid plaques, consequently improving risk classification and clinical decision-making in patients with carotid stenosis.
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Affiliation(s)
- Xun Zhang
- Department of Endovascular Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Zhaohui Hua
- Department of Endovascular Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Rui Chen
- Department of Magnetic Resonance Imaging, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Zhouyang Jiao
- Department of Endovascular Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Jintao Shan
- Department of Endovascular Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Chong Li
- Division of Vascular Surgery, New York University Medical Center, New York, NY, United States
| | - Zhen Li
- Department of Endovascular Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China,*Correspondence: Zhen Li ✉
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Radiomics in Cardiac Computed Tomography. Diagnostics (Basel) 2023; 13:diagnostics13020307. [PMID: 36673115 PMCID: PMC9857691 DOI: 10.3390/diagnostics13020307] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/09/2023] [Accepted: 01/12/2023] [Indexed: 01/18/2023] Open
Abstract
In recent years, there has been an increasing recognition of coronary computed tomographic angiography (CCTA) and gated non-contrast cardiac CT in the workup of coronary artery disease in patients with low and intermediate pretest probability, through the readjustment guidelines by medical societies. However, in routine clinical practice, these CT data sets are usually evaluated dominantly regarding relevant coronary artery stenosis and calcification. The implementation of radiomics analysis, which provides visually elusive quantitative information from digital images, has the potential to open a new era for cardiac CT that goes far beyond mere stenosis or calcification grade estimation. This review offers an overview of the results obtained from radiomics analyses in cardiac CT, including the evaluation of coronary plaques, pericoronary adipose tissue, and the myocardium itself. It also highlights the advantages and disadvantages of use in routine clinical practice.
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Ramasamy A, Hamid A Khan A, Cooper J, Simon J, Maurovich-Horvat P, Bajaj R, Kitslaar P, Amersey R, Jain A, Deaner A, Reiber JH, Moon JC, Dijkstra J, Serruys PW, Mathur A, Baumbach A, Torii R, Pugliese F, Bourantas CV. Implications of computed tomography reconstruction algorithms on coronary atheroma quantification: Comparison with intravascular ultrasound. J Cardiovasc Comput Tomogr 2023; 17:43-51. [PMID: 36270952 DOI: 10.1016/j.jcct.2022.09.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 09/03/2022] [Accepted: 09/17/2022] [Indexed: 10/14/2022]
Abstract
BACKGROUND Advances in coronary computed tomography angiography (CCTA) reconstruction algorithms are expected to enhance the accuracy of CCTA plaque quantification. We aim to evaluate different CCTA reconstruction approaches in assessing vessel characteristics in coronary atheroma using intravascular ultrasound (IVUS) as the reference standard. METHODS Matched cross-sections (n = 7241) from 50 vessels in 15 participants with chronic coronary syndrome who prospectively underwent CCTA and 3-vessel near-infrared spectroscopy-IVUS were included. Twelve CCTA datasets per patient were reconstructed using two different kernels, two slice thicknesses (0.75 mm and 0.50 mm) and three different strengths of advanced model-based iterative reconstruction (IR) algorithms. Lumen and vessel wall borders were manually annotated in every IVUS and CCTA cross-section which were co-registered using dedicated software. Image quality was sub-optimal in the reconstructions with a sharper kernel, so these were excluded. Intraclass correlation coefficient (ICC) and repeatability coefficient (RC) were used to compare the estimations of the 6 CT reconstruction approaches with those derived by IVUS. RESULTS Segment-level analysis showed good agreement between CCTA and IVUS for assessing atheroma volume with approach 0.50/5 (slice thickness 0.50 mm and highest strength 5 ADMIRE IR) being the best (total atheroma volume ICC: 0.91, RC: 0.67, p < 0.001 and percentage atheroma volume ICC: 0.64, RC: 14.06, p < 0.001). At lesion-level, there was no difference between the CCTA reconstructions for detecting plaques (accuracy range: 0.64-0.67; p = 0.23); however, approach 0.50/5 was superior in assessing IVUS-derived lesion characteristics associated with plaque vulnerability (minimum lumen area ICC: 0.64, RC: 1.31, p < 0.001 and plaque burden ICC: 0.45, RC: 32.0, p < 0.001). CONCLUSION CCTA reconstruction with thinner slice thickness, smooth kernel and highest strength advanced IR enabled more accurate quantification of the lumen and plaque at a segment-, and lesion-level analysis in coronary atheroma when validated against intravascular ultrasound. CLINICALTRIALS gov (NCT03556644).
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Affiliation(s)
- Anantharaman Ramasamy
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University London, UK
| | - Ameer Hamid A Khan
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK
| | - Jackie Cooper
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University London, UK
| | - Judit Simon
- MTA-SE Cardiovascular Imaging Research Group, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Pal Maurovich-Horvat
- MTA-SE Cardiovascular Imaging Research Group, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Retesh Bajaj
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University London, UK
| | - Pieter Kitslaar
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands; Medis Medical Imaging, Leiden, the Netherlands
| | - Rajiv Amersey
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK
| | - Ajay Jain
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK
| | - Andrew Deaner
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK
| | - Johan Hc Reiber
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands; Medis Medical Imaging, Leiden, the Netherlands
| | - James C Moon
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Institute of Cardiovascular Sciences, University College London, London, UK
| | - Jouke Dijkstra
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Patrick W Serruys
- Faculty of Medicine, National Heart & Lung Institute, Imperial College London, UK; Department of Cardiology, National University of Ireland, Galway, Ireland
| | - Anthony Mathur
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University London, UK
| | - Andreas Baumbach
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University London, UK
| | - Ryo Torii
- Department of Mechanical Engineering, University College London, London, UK
| | - Francesca Pugliese
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University London, UK
| | - Christos V Bourantas
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University London, UK; Institute of Cardiovascular Sciences, University College London, London, UK.
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Kolossváry M, Bluemke DA, Fishman EK, Gerstenblith G, Celentano D, Mandler RN, Khalsa J, Bhatia S, Chen S, Lai S, Lai H. Temporal assessment of lesion morphology on radiological images beyond lesion volumes-a proof-of-principle study. Eur Radiol 2022; 32:8748-8760. [PMID: 35648210 PMCID: PMC9712148 DOI: 10.1007/s00330-022-08894-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 04/05/2022] [Accepted: 05/16/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVES To develop a general framework to assess temporal changes in lesion morphology on radiological images beyond volumetric changes and to test whether cocaine abstinence changes coronary plaque structure on serial coronary CT angiography (CTA). METHODS Chronic cocaine users with human immunodeficiency virus (HIV) infection were prospectively enrolled to undergo cash-based contingency management to achieve cocaine abstinence. Participants underwent coronary CTA at baseline and 6 and 12 months following recruitment. We segmented all coronary plaques and extracted 1103 radiomic features. We implemented weighted correlation network analysis to derive consensus eigen radiomic features (named as different colors) and used linear mixed models and mediation analysis to assess whether cocaine abstinence affects plaque morphology correcting for clinical variables and plaque volumes and whether serum biomarkers causally mediate these changes. Furthermore, we used Bayesian hidden Markov network changepoint analysis to assess the potential rewiring of the radiomic network. RESULTS Sixty-nine PLWH (median age 55 IQR: 52-59 years, 19% female) completed the study, of whom 26 achieved total abstinence. Twenty consensus eigen radiomic features were derived. Cocaine abstinence significantly affected the pink and cyan eigen features (-0.04 CI: [-0.06; -0.02], p = 0.0009; 0.03 CI: [0.001; 0.04], p = 0.0017, respectively). These effects were mediated through changes in endothelin-1 levels. In abstinent individuals, we observed significant rewiring of the latent radiomic signature network. CONCLUSIONS Using our proposed framework, we found 1 year of cocaine abstinence to significantly change specific latent coronary plaque morphological features and rewire the latent morphologic network above and beyond changes in plaque volumes and clinical characteristics. KEY POINTS • We propose a general methodology to decompose the latent morphology of lesions on radiological images using a radiomics-based systems biology approach. • As a proof-of-principle, we show that 1 year of cocaine abstinence results in significant changes in specific latent coronary plaque morphologic features and rewiring of the latent morphologic network above and beyond changes in plaque volumes and clinical characteristics. • We found endothelin-1 levels to mediate these structural changes providing potential pathological pathways warranting further investigation.
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Affiliation(s)
- Márton Kolossváry
- Department of Pathology, Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD, 21287, USA
| | - David A Bluemke
- University of Wisconsin School of Medicine and Public Health, 750 Highland Ave, Madison, WI, 53726, USA
| | - Elliot K Fishman
- Department of Radiology, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD, 21205, USA
| | - Gary Gerstenblith
- Department of Medicine, Johns Hopkins University School of Medicine, 733 N Broadway, Baltimore, MD, 21205, USA
| | - David Celentano
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, 614 Wolfe N Wolfe St, Baltimore, MD, 21205, USA
| | - Raul N Mandler
- National Institute on Drug Abuse, National Institutes of Health, Bethesda, MD, 10 Center Dr, Bethesda, MD, 20814, USA
| | - Jag Khalsa
- Institute of Human Virology, University of Maryland School of Medicine, 725 W Lombard Street, Baltimore, MD, 21201, USA
| | - Sandeepan Bhatia
- Institute of Human Virology, University of Maryland School of Medicine, 725 W Lombard Street, Baltimore, MD, 21201, USA
| | - Shaoguang Chen
- Institute of Human Virology, University of Maryland School of Medicine, 725 W Lombard Street, Baltimore, MD, 21201, USA
| | - Shenghan Lai
- Department of Pathology, Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD, 21287, USA.
- Department of Radiology, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD, 21205, USA.
- Department of Medicine, Johns Hopkins University School of Medicine, 733 N Broadway, Baltimore, MD, 21205, USA.
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, 614 Wolfe N Wolfe St, Baltimore, MD, 21205, USA.
- Institute of Human Virology, University of Maryland School of Medicine, 725 W Lombard Street, Baltimore, MD, 21201, USA.
| | - Hong Lai
- Department of Radiology, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD, 21205, USA
- Institute of Human Virology, University of Maryland School of Medicine, 725 W Lombard Street, Baltimore, MD, 21201, USA
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Li H, Liu J, Dong Z, Chen X, Zhou C, Huang C, Li Y, Liu Q, Su X, Cheng X, Lu G. Identification of high-risk intracranial plaques with 3D high-resolution magnetic resonance imaging-based radiomics and machine learning. J Neurol 2022; 269:6494-6503. [PMID: 35951103 DOI: 10.1007/s00415-022-11315-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 07/27/2022] [Accepted: 07/28/2022] [Indexed: 10/15/2022]
Abstract
BACKGROUND Identifying high-risk intracranial plaques is significant for the treatment and prevention of stroke. OBJECTIVE To develop a high-risk plaque model using three-dimensional (3D) high-resolution magnetic resonance imaging (HRMRI) based radiomics features and machine learning. METHODS 136 patients with documented symptomatic intracranial artery stenosis and available HRMRI data were included. Among these patients, 136 and 92 plaques were identified as symptomatic and asymptomatic plaques, respectively. A conventional model was developed by recording and quantifying the radiological plaque characteristics. Radiomics features from T1-weighted images (T1WI) and contrast-enhanced T1WI (CE-T1WI) were used to construct a high-risk plaque model with linear support vector classification (linear SVC). The radiological and radiomics features were combined to build a combined model. Receiver operating characteristic (ROC) curves were used to evaluate these models. RESULTS Plaque length, burden, and enhancement were independently associated with clinical symptoms and were included in the conventional model, which had an AUC of 0.853 vs. 0.837 in the training and test sets. While the radiomics and the combined model showed an improved AUC: 0.923 vs. 0.925 for the training sets and 0.906 vs. 0.903 in the test sets. Both the radiomics model (p = 0.024, p = 0.018) and combined model (p = 0.042, p = 0.049) outperformed the conventional model in the two sets, whereas the performance of the combined model was not significantly different from that of the radiomics model in the two sets (p = 0.583 and p = 0.606). CONCLUSION The radiomics model based on 3D HRMRI can accurately differentiate symptomatic from asymptomatic intracranial arterial plaques and significantly outperforms the conventional model.
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Affiliation(s)
- Hongxia Li
- Department of Medical Imaging, The First School of Clinical Medicine, Jinling Hospital, Southern Medical University, Nanjing, 210002, Jiangsu, China
| | - Jia Liu
- Department of Medical Imaging, The First School of Clinical Medicine, Jinling Hospital, Southern Medical University, Nanjing, 210002, Jiangsu, China
| | - Zheng Dong
- Department of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Xingzhi Chen
- Department of Research Collaboration, R&D Center, Beijing Deepwise and League of PHD Technology Co., Ltd, Beijing, 100081, China
| | - Changsheng Zhou
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise and League of PHD Technology Co., Ltd, Beijing, 100081, China
| | - Yingle Li
- Department of Medical Imaging, The First School of Clinical Medicine, Jinling Hospital, Southern Medical University, Nanjing, 210002, Jiangsu, China
| | - Quanhui Liu
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China
| | - Xiaoqin Su
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China
| | - Xiaoqing Cheng
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
| | - Guangming Lu
- Department of Medical Imaging, The First School of Clinical Medicine, Jinling Hospital, Southern Medical University, Nanjing, 210002, Jiangsu, China.
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Theofilis P, Sagris M, Antonopoulos AS, Oikonomou E, Tsioufis K, Tousoulis D. Non-Invasive Modalities in the Assessment of Vulnerable Coronary Atherosclerotic Plaques. Tomography 2022; 8:1742-1758. [PMID: 35894012 PMCID: PMC9326642 DOI: 10.3390/tomography8040147] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/04/2022] [Accepted: 07/04/2022] [Indexed: 12/26/2022] Open
Abstract
Coronary atherosclerosis is a complex, multistep process that may lead to critical complications upon progression, revolving around plaque disruption through either rupture or erosion. Several high-risk features are associated with plaque vulnerability and may add incremental prognostic information. Although invasive imaging modalities such as optical coherence tomography or intravascular ultrasound are considered to be the gold standard in the assessment of vulnerable coronary atherosclerotic plaques (VCAPs), contemporary evidence suggests a potential role for non-invasive methods in this context. Biomarkers associated with deleterious pathophysiologic pathways, including inflammation and extracellular matrix degradation, have been correlated with VCAP characteristics and adverse prognosis. However, coronary computed tomography (CT) angiography has been the most extensively investigated technique, significantly correlating with invasive method-derived VCAP features. The estimation of perivascular fat attenuation as well as radiomic-based approaches represent additional concepts that may add incremental information. Cardiac magnetic resonance imaging (MRI) has also been evaluated in clinical studies, with promising results through the various image sequences that have been tested. As far as nuclear cardiology is concerned, the implementation of positron emission tomography in the VCAP assessment currently faces several limitations with the myocardial uptake of the radiotracer in cases of fluorodeoxyglucose use, as well as with motion correction. Moreover, the search for the ideal radiotracer and the most adequate combination (CT or MRI) is still ongoing. With a look to the future, the possible combination of imaging and circulating inflammatory and extracellular matrix degradation biomarkers in diagnostic and prognostic algorithms may represent the essential next step for the assessment of high-risk individuals.
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Affiliation(s)
- Panagiotis Theofilis
- 1st Cardiology Department, “Hippokration” General Hospital, Medical School, University of Athens, 11527 Athens, Greece; (M.S.); (A.S.A.); (E.O.); (K.T.); (D.T.)
- Correspondence:
| | - Marios Sagris
- 1st Cardiology Department, “Hippokration” General Hospital, Medical School, University of Athens, 11527 Athens, Greece; (M.S.); (A.S.A.); (E.O.); (K.T.); (D.T.)
| | - Alexios S. Antonopoulos
- 1st Cardiology Department, “Hippokration” General Hospital, Medical School, University of Athens, 11527 Athens, Greece; (M.S.); (A.S.A.); (E.O.); (K.T.); (D.T.)
| | - Evangelos Oikonomou
- 1st Cardiology Department, “Hippokration” General Hospital, Medical School, University of Athens, 11527 Athens, Greece; (M.S.); (A.S.A.); (E.O.); (K.T.); (D.T.)
- 3rd Cardiology Department, Thoracic Diseases Hospital “Sotiria”, University of Athens Medical School, 11527 Athens, Greece
| | - Konstantinos Tsioufis
- 1st Cardiology Department, “Hippokration” General Hospital, Medical School, University of Athens, 11527 Athens, Greece; (M.S.); (A.S.A.); (E.O.); (K.T.); (D.T.)
| | - Dimitris Tousoulis
- 1st Cardiology Department, “Hippokration” General Hospital, Medical School, University of Athens, 11527 Athens, Greece; (M.S.); (A.S.A.); (E.O.); (K.T.); (D.T.)
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Zhang L, Xu Z, Jiang B, Zhang Y, Wang L, de Bock GH, Vliegenthart R, Xie X. Machine-learning-based radiomics identifies atrial fibrillation on the epicardial fat in contrast-enhanced and non-enhanced chest CT. Br J Radiol 2022; 95:20211274. [PMID: 35357893 PMCID: PMC10996326 DOI: 10.1259/bjr.20211274] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 02/22/2022] [Accepted: 03/25/2022] [Indexed: 12/28/2022] Open
Abstract
OBJECTIVE The purpose is to establish and validate a machine-learning-derived radiomics approach to determine the existence of atrial fibrillation (AF) by analyzing epicardial adipose tissue (EAT) in CT images. METHODS Patients with AF based on electrocardiographic tracing who underwent contrast-enhanced (n = 200) or non-enhanced (n = 300) chest CT scans were analyzed retrospectively. After EAT segmentation and radiomics feature extraction, the segmented EAT yielded 1691 radiomics features. The most contributive features to AF were selected by the Boruta algorithm and machine-learning-based random forest algorithm, and combined to construct a radiomics signature (EAT-score). Multivariate logistic regression was used to build clinical factor and nested models. RESULTS In the test cohort of contrast-enhanced scanning (n = 60/200), the AUC of EAT-score for identifying patients with AF was 0.92 (95%CI: 0.84-1.00), higher than 0.71 (0.58-0.85) of the clinical factor model (total cholesterol and body mass index) (DeLong's p = 0.01), and higher than 0.73 (0.61-0.86) of the EAT volume model (p = 0.01). In the test cohort of non-enhanced scanning (n = 100/300), the AUC of EAT-score was 0.85 (0.77-0.92), higher than that of the CT attenuation model (p < 0.001). The two nested models (EAT-score+volume and EAT-score+volume+clinical factors) for contrast-enhanced scan and one (EAT-score+CT attenuation) for non-enhanced scan showed similar AUCs with that of EAT-score (all p > 0.05). CONCLUSION EAT-score generated by machine-learning-based radiomics achieved high performance in identifying patients with AF. ADVANCES IN KNOWLEDGE A radiomics analysis based on machine learning allows for the identification of AF on the EAT in contrast-enhanced and non-enhanced chest CT.
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Affiliation(s)
- Lu Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai
Jiao Tong University School of Medicine,
Shanghai, China
| | - Zhihan Xu
- Siemens Healthineers Ltd., Zhouzhu Rd.278,
Shanghai, China
| | - Beibei Jiang
- Department of Radiology, Shanghai General Hospital, Shanghai
Jiao Tong University School of Medicine,
Shanghai, China
| | - Yaping Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai
Jiao Tong University School of Medicine,
Shanghai, China
| | - Lingyun Wang
- Department of Radiology, Shanghai General Hospital, Shanghai
Jiao Tong University School of Medicine,
Shanghai, China
| | - Geertruida H de Bock
- Department of Epidemiology, University of Groningen, University
Medical Center Groningen,
Groningen, The Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, University of Groningen, University
Medical Center Groningen,
Groningen, The Netherlands
| | - Xueqian Xie
- Department of Radiology, Shanghai General Hospital, Shanghai
Jiao Tong University School of Medicine,
Shanghai, China
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Shang J, Guo Y, Ma Y, Hou Y. Cardiac computed tomography radiomics: a narrative review of current status and future directions. Quant Imaging Med Surg 2022; 12:3436-3453. [PMID: 35655815 PMCID: PMC9131324 DOI: 10.21037/qims-21-1022] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 03/23/2022] [Indexed: 08/18/2023]
Abstract
BACKGROUND AND OBJECTIVE In an era of profound growth of medical data and rapid development of advanced imaging modalities, precision medicine increasingly requires further expansion of what can be interpreted from medical images. However, the current interpretation of cardiac computed tomography (CT) images mainly depends on subjective and qualitative analysis. Radiomics uses advanced image analysis to extract numerous quantitative features from digital images that are unrecognizable to the naked eye. Visualization of these features can reveal underlying connections between image phenotyping and biological characteristics and support clinical outcomes. Although research into radiomics on cardiovascular disease began only recently, several studies have indicated its potential clinical value in assessing future cardiac risk and guiding prevention and management strategies. Our review aimed to summarize the current applications of cardiac CT radiomics in the cardiovascular field and discuss its advantages, challenges, and future directions. METHODS We searched for English-language articles published between January 2010 and August 2021 in the databases of PubMed, Embase, and Google Scholar. The keywords used in the search included computed tomography or CT, radiomics, cardiovascular or cardiac. KEY CONTENT AND FINDINGS The current applications of radiomics in cardiac CT were found to mainly involve research into coronary plaques, perivascular adipose tissue (PVAT), myocardial tissue, and intracardiac lesions. Related findings on cardiac CT radiomics suggested the technique can assist the identification of vulnerable plaques or patients, improve cardiac risk prediction and stratification, discriminate myocardial pathology and etiologies behind intracardiac lesions, and offer new perspective and development prospects to personalized cardiovascular medicine. CONCLUSIONS Cardiac CT radiomics can gather additional disease-related information at a microstructural level and establish a link between imaging phenotyping and tissue pathology or biology alone. Therefore, cardiac CT radiomics has significant clinical implications, including a contribution to clinical decision-making. Along with advancements in cardiac CT imaging, cardiac CT radiomics is expected to provide more precise phenotyping of cardiovascular disease for patients and doctors, which can improve diagnostic, prognostic, and therapeutic decision making in the future.
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Affiliation(s)
- Jin Shang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yan Guo
- GE Healthcare, Beijing, China
| | - Yue Ma
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yang Hou
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
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Jiang XY, Shao ZQ, Chai YT, Liu YN, Li Y. Non-contrast CT-based radiomic signature of pericoronary adipose tissue for screening non-calcified plaque. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac69a7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 04/22/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Objective. To develop two combined clinical-radiomics models of pericoronary adipose tissue (PCAT) for the presence and characterization of non-calcified plaques on non-contrast CT scan. Approach. Altogether, 431 patients undergoing Coronary Computed Tomography Angiography from March 2019 to June 2021 who had complete data were enrolled, including 173 patients with non-calcified plaques of the right coronary artery(RCA) and 258 with no abnormality. PCAT was segmented around the proximal RCA on non-contrast CT scan (calcium score acquisition). Two best models were established by screening features and classifiers respectively using FeAture Explorer software. Model 1 distinguished normal coronary arteries from those with non-calcified plaques, and model 2 distinguished vulnerable plaques in non-calcified plaques. Performance was assessed by the area under the receiver operating characteristic curve (AUC-ROC). Main results. 4 and 9 features were selected for the establishment of the radiomics model respectively through Model 1 and 2. In the test group, the AUC values, sensitivity, specificity and accuracy were 0.833%, 78.3%, 80.8%, 76.6% and 0.7467%, 75.0%, 77.8%, 73.5%, respectively. The combined model including radiomics features and independent clinical factors yielded an AUC, sensitivity, specificity and accuracy of 0.896%, 81.4%, 86.5%, 77.9% for model 1 and 0.752%, 75.0%, 77.8%, 73.5% for model 2. Significance. The combined clinical-radiomics models based on non-contrast CT images of PCAT had good diagnostic efficacy for non-calcified and vulnerable plaques.
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O'Brien H, Williams MC, Rajani R, Niederer S. Radiomics and Machine Learning for Detecting Scar Tissue on CT Delayed Enhancement Imaging. Front Cardiovasc Med 2022; 9:847825. [PMID: 35647044 PMCID: PMC9133416 DOI: 10.3389/fcvm.2022.847825] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 02/21/2022] [Indexed: 11/24/2022] Open
Abstract
Background Delayed enhancement CT (CT-DE) has been evaluated as a tool for the detection of myocardial scar and compares well to the gold standard of MRI with late gadolinium enhancement (MRI-LGE). Prior work has established that high performance can be achieved with manual reading; however, few studies have looked at quantitative measures to differentiate scar and healthy myocardium on CT-DE or automated analysis. Methods Eighteen patients with clinically indicated MRI-LGE were recruited for CT-DE at multiple 80 and 100 kV post contrast imaging. Left ventricle segmentation was performed on both imaging modalities, along with scar segmentation on MRI-LGE. Segmentations were registered together and scar regions were estimated on CT-DE. 93 radiomic features were calculated and analysed for their ability to differentiate between scarred and non-scarred myocardium regions. Machine learning (ML) classifiers were trained using the strongest set of radiomic features to classify segments containing scar on CT-DE. Features and classifiers were compared across both tube voltages and combined-energy images. Results There were 59 and 51 statistically significant features in the 80 and 100 kV images respectively. Combined-energy imaging increased this to 63 with more features having area under the curve (AUC) above 0.9. The 10 highest AUC features for each image were used in the ML classifiers. The 100 kV images produced the best ML classifier, a support vector machine with an AUC of 0.88 (95% CI 0.87-0.90). Comparable performance was achieved with both the 80 kV and combined-energy images. Conclusions CT-DE can be quantitatively analyzed using radiomic feature calculations. These features may be suitable for ML classification techniques to prospectively identify AHA segments with performance comparable to previously reported manual reading. Future work on larger CT-DE datasets is warranted to establish optimum imaging parameters and features.
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Affiliation(s)
- Hugh O'Brien
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Michelle C. Williams
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Ronak Rajani
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Steven Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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Hu P, Chen L, Zhong Y, Lin Y, Yu X, Hu X, Tao X, Lin S, Niu T, Chen R, Wu X, Sun J. Effects of slice thickness on CT radiomics features and models for staging liver fibrosis caused by chronic liver disease. Jpn J Radiol 2022; 40:1061-1068. [PMID: 35523919 DOI: 10.1007/s11604-022-01284-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 04/12/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVES To investigate the effects of slice thickness on CT radiomics features and models for staging liver fibrosis. METHODS A total of 108 pathologically confirmed liver fibrosis patients from a single center were retrospectively collected and divided into different groups. Both thick (5- or 7-mm) and thin slices (1.3- or 2-mm) were analyzed. A fivefold cross-validation with 100 repeats was conducted. The minimum redundancy-maximum relevance algorithm was used to reduce the radiomics features, and the top 10 ranking features were included for further analysis for each loop. The random forest was used for model establishment. The models with median AUC were selected for the assessment of the discriminative performance for both datasets. Mutual features selected by the models with AUC > 0.8 were searched and considered as the most predictive ones. RESULTS A total of 162 and 643 radiomics features with excellent reliability were selected from thick- and thin-slice datasets, respectively. The overall discriminative performance of the 500 AUCs from the thin-slice dataset was better than the thick slice. The median AUC values of the thick-sliced datasets were significantly lower than those of the thin-sliced datasets (0.78 and 0.90 for differentiating F1 vs. F2-4, 0.72 and 0.85 for differentiating F1-2 vs. F3-4, both P = 0.03). For differentiating F1-3 vs. F4, no significant difference was found (0.85 vs 0.94, P = 0.15). Six mutual predictive features across all the datasets were found. CONCLUSIONS The radiomics features extracted from thin-slice images and their corresponding models were better and more stable for staging liver fibrosis.
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Affiliation(s)
- Peng Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 East Qingchun Road, Hangzhou, 310016, Zhejiang, China
| | - Liye Chen
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 East Qingchun Road, Hangzhou, 310016, Zhejiang, China
| | - Yaoying Zhong
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 East Qingchun Road, Hangzhou, 310016, Zhejiang, China
| | - Yudong Lin
- Zhejiang University School of Medicine, Hangzhou, 310011, China
| | - Xiaojing Yu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 East Qingchun Road, Hangzhou, 310016, Zhejiang, China
| | - Xi Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 East Qingchun Road, Hangzhou, 310016, Zhejiang, China
| | - Xinwei Tao
- Bayer HealthCare, No.399, West Haiyang Road, Shanghai, China
| | - Shushen Lin
- Siemens Healthineers China, No.399, West Haiyang Road, Shanghai, China
| | - Tianye Niu
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, Zhejiang, China.,Institute of Translational Medicine, Zhejiang University, Hangzhou, 310016, Zhejiang, China
| | - Ran Chen
- Department of Diagnostic Ultrasound and Echocardiography, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, Zhejiang, China
| | - Xia Wu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 East Qingchun Road, Hangzhou, 310016, Zhejiang, China
| | - Jihong Sun
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 East Qingchun Road, Hangzhou, 310016, Zhejiang, China. .,Cancer Center, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
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Motwani M. Are You a Robot?: Please Select the Images Containing Unstable Plaque. JACC Cardiovasc Imaging 2022; 15:872-874. [PMID: 35512958 DOI: 10.1016/j.jcmg.2021.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 12/16/2021] [Indexed: 10/19/2022]
Affiliation(s)
- Manish Motwani
- Manchester Heart Institute, Manchester University NHS Foundation Trust, Manchester, United Kingdom; Institute of Cardiovascular Science, University of Manchester, Manchester, United Kingdom.
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Lee S, Han K, Suh YJ. Quality assessment of radiomics research in cardiac CT: a systematic review. Eur Radiol 2022; 32:3458-3468. [PMID: 34981135 DOI: 10.1007/s00330-021-08429-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 09/13/2021] [Accepted: 10/22/2021] [Indexed: 01/01/2023]
Abstract
OBJECTIVES To assess the quality of current radiomics research on cardiac CT using radiomics quality score (RQS) and Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) systems. METHODS Systematic searches of PubMed and EMBASE were performed to identify all potentially relevant original research articles about cardiac CT radiomics. Fifteen original research articles were selected. Two cardiac radiologists assessed the quality of the methodology adopted in those studies according to the RQS and TRIPOD guidelines. Basic adherence rates for the following six key domains were evaluated: image protocol and reproducibility, feature reduction and validation, biologic/clinical utility, performance index, high level of evidence, and open science. RESULTS Among the 15 included articles, six (40%) were about coronary artery disease and six (40%) were about myocardial infarction. The mean RQS was 9.9 ± 7.3 (27.4% of the ideal score of 36), and the basic adherence rate was 44.6%. Fourteen (93.3%) and nine (60%) studies performed feature selection and validation, but only two (13.3%) of them performed external validation. Two studies (13.3%) were prospective, and only one study (6.7%) conducted calibration analysis and stated the potential clinical utility. None of the studies conducted phantom study and cost-effective analysis. The overall adherence rate for TRIPOD was 63%. CONCLUSION The quality of radiomics studies in cardiac CT is currently insufficient. A higher level of evidence is required, and analysis of clinical utility and calibration of model performance need to be improved. KEY POINTS • The quality of science of radiomics studies in cardiac CT is currently insufficient. • No study conducted a phantom study or cost-effective analysis, with further limitations being demonstrated in a high level of evidence for radiomics studies. • Analysis of clinical utility and calibration of model performance need to be improved, and a higher level of evidence is required.
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Affiliation(s)
- Suji Lee
- Department of Radiology, Center for Clinical Imaging Data Science, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Kyunghwa Han
- Department of Radiology, Center for Clinical Imaging Data Science, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Young Joo Suh
- Department of Radiology, Center for Clinical Imaging Data Science, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
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50
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Lin A, Kolossváry M, Cadet S, McElhinney P, Goeller M, Han D, Yuvaraj J, Nerlekar N, Slomka PJ, Marwan M, Nicholls SJ, Achenbach S, Maurovich-Horvat P, Wong DTL, Dey D. Radiomics-Based Precision Phenotyping Identifies Unstable Coronary Plaques From Computed Tomography Angiography. JACC Cardiovasc Imaging 2022; 15:859-871. [PMID: 35512957 PMCID: PMC9072980 DOI: 10.1016/j.jcmg.2021.11.016] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 11/11/2021] [Accepted: 11/16/2021] [Indexed: 12/14/2022]
Abstract
OBJECTIVES The aim of this study was to precisely phenotype culprit and nonculprit lesions in myocardial infarction (MI) and lesions in stable coronary artery disease (CAD) using coronary computed tomography angiography (CTA)-based radiomic analysis. BACKGROUND It remains debated whether any single coronary atherosclerotic plaque within the vulnerable patient exhibits unique morphology conferring an increased risk of clinical events. METHODS A total of 60 patients with acute MI prospectively underwent coronary CTA before invasive angiography and were matched to 60 patients with stable CAD. For all coronary lesions, high-risk plaque (HRP) characteristics were qualitatively assessed, followed by semiautomated plaque quantification and extraction of 1,103 radiomic features. Machine learning models were built to examine the additive value of radiomic features for discriminating culprit lesions over and above HRP and plaque volumes. RESULTS Culprit lesions had higher mean volumes of noncalcified plaque (NCP) and low-density noncalcified plaque (LDNCP) compared with the highest-grade stenosis nonculprits and highest-grade stenosis stable CAD lesions (NCP: 138.1 mm3 vs 110.7 mm3 vs 102.7 mm3; LDNCP: 14.2 mm3 vs 9.8 mm3 vs 8.4 mm3; both Ptrend < 0.01). In multivariable linear regression adjusted for NCP and LDNCP volumes, 14.9% (164 of 1,103) of radiomic features were associated with culprits and 9.7% (107 of 1,103) were associated with the highest-grade stenosis nonculprits (critical P < 0.0007) when compared with highest-grade stenosis stable CAD lesions as reference. Hierarchical clustering of significant radiomic features identified 9 unique data clusters (latent phenotypes): 5 contained radiomic features specific to culprits, 1 contained features specific to highest-grade stenosis nonculprits, and 3 contained features associated with either lesion type. Radiomic features provided incremental value for discriminating culprit lesions when added to a machine learning model containing HRP and plaque volumes (area under the receiver-operating characteristic curve 0.86 vs 0.76; P = 0.004). CONCLUSIONS Culprit lesions and highest-grade stenosis nonculprit lesions in MI have distinct radiomic signatures compared with lesions in stable CAD. Within the vulnerable patient may exist individual vulnerable plaques identifiable by coronary CTA-based precision phenotyping.
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Affiliation(s)
- Andrew Lin
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA; Monash Cardiovascular Research Centre, Victorian Heart Institute, Monash University and MonashHeart, Monash Health, Melbourne, Victoria, Australia
| | - Márton Kolossváry
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Sebastien Cadet
- Department of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Priscilla McElhinney
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Markus Goeller
- Department of Cardiology, Friedrich-Alexander-University Erlangen-Nürnberg, Faculty of Medicine, Erlangen, Germany
| | - Donghee Han
- Department of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Jeremy Yuvaraj
- Monash Cardiovascular Research Centre, Victorian Heart Institute, Monash University and MonashHeart, Monash Health, Melbourne, Victoria, Australia
| | - Nitesh Nerlekar
- Monash Cardiovascular Research Centre, Victorian Heart Institute, Monash University and MonashHeart, Monash Health, Melbourne, Victoria, Australia
| | - Piotr J Slomka
- Artificial Intelligence in Medicine Program, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Mohamed Marwan
- Department of Cardiology, Friedrich-Alexander-University Erlangen-Nürnberg, Faculty of Medicine, Erlangen, Germany
| | - Stephen J Nicholls
- Monash Cardiovascular Research Centre, Victorian Heart Institute, Monash University and MonashHeart, Monash Health, Melbourne, Victoria, Australia
| | - Stephan Achenbach
- Department of Cardiology, Friedrich-Alexander-University Erlangen-Nürnberg, Faculty of Medicine, Erlangen, Germany
| | - Pál Maurovich-Horvat
- Medical Imaging Centre, Semmelweis University, Budapest, Hungary; MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Dennis T L Wong
- Monash Cardiovascular Research Centre, Victorian Heart Institute, Monash University and MonashHeart, Monash Health, Melbourne, Victoria, Australia
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
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