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Deng J, Zhou L, Li Y, Yu Y, Zhang J, Liao B, Luo G, Tian J, Zhou H, Tang H. Integration of Cine-cardiac Magnetic Resonance Radiomics and Machine Learning for Differentiating Ischemic and Dilated Cardiomyopathy. Acad Radiol 2024; 31:2704-2714. [PMID: 38704286 DOI: 10.1016/j.acra.2024.03.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 03/23/2024] [Accepted: 03/24/2024] [Indexed: 05/06/2024]
Abstract
RATIONALE AND OBJECTIVES This study aims to evaluate the capability of machine learning algorithms in utilizing radiomic features extracted from cine-cardiac magnetic resonance (CMR) sequences for differentiating between ischemic cardiomyopathy (ICM) and dilated cardiomyopathy (DCM). MATERIALS AND METHODS This retrospective study included 115 cardiomyopathy patients subdivided into ICM (n = 64) and DCM cohorts (n = 51). We collected invasive clinical (IC), noninvasive clinical (NIC), and combined clinical (CC) feature subsets. Radiomic features were extracted from regions of interest (ROIs) in the left ventricle (LV), LV cavity (LVC), and myocardium (MYO). We tested 10 classical machine learning classifiers and validated them through fivefold cross-validation. We compared the efficacy of clinical feature-based models and radiomics-based models to identify the superior diagnostic approach. RESULTS In the validation set, the Gaussian naive Bayes (GNB) model outperformed the other models in all categories, with areas under the curve (AUCs) of 0.879 for IC_GNB, 0.906 for NIC_GNB, and 0.906 for CC_GNB. Among the radiomics models, the MYO_LASSOCV_MLP model demonstrated the highest AUC (0.919). In the test set, the MYO_RFECV_GNB radiomics model achieved the highest AUC (0.857), surpassing the performance of the three clinical feature models (IC_GNB: 0.732; NIC_GNB: 0.75; CC_GNB: 0.786). CONCLUSION Radiomics models leveraging MYO images from cine-CMR exhibit promising potential for differentiating ICM from DCM, indicating the significant clinical application scope of such models. CLINICAL RELEVANCE STATEMENT The integration of radiomics models and machine learning methods utilizing cine-CMR sequences enhances the diagnostic capability to distinguish between ICM and DCM, minimizes examination risks for patients, and potentially reduces the duration of medical imaging procedures.
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Affiliation(s)
- Jia Deng
- The First Affiliated Hospital, Department of Radiology, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China (J.D., B.L., G.L., H.Z.); The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China (J.D., Y.L., Y.Y., J.Z., H.T.)
| | - Langtao Zhou
- School of Cyberspace Security, Guangzhou University, Guangzhou 510006, China (L.Z.)
| | - Yueyan Li
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China (J.D., Y.L., Y.Y., J.Z., H.T.)
| | - Ying Yu
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China (J.D., Y.L., Y.Y., J.Z., H.T.)
| | - Jingjing Zhang
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China (J.D., Y.L., Y.Y., J.Z., H.T.)
| | - Bihong Liao
- The First Affiliated Hospital, Department of Radiology, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China (J.D., B.L., G.L., H.Z.)
| | - Guanghua Luo
- The First Affiliated Hospital, Department of Radiology, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China (J.D., B.L., G.L., H.Z.)
| | - Jinwei Tian
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China (J.T.)
| | - Hong Zhou
- The First Affiliated Hospital, Department of Radiology, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China (J.D., B.L., G.L., H.Z.).
| | - Huifang Tang
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China (J.D., Y.L., Y.Y., J.Z., H.T.); The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China (H.T.); Clinical Research Center for Myocardial Injury in Hunan Province, Hengyang, Hunan 421001, China (H.T.); Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Huna 421001, China (H.T.)
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Crean AM. Scanning the Imaging Horizon for Hypertrophic Cardiomyopathy. Can J Cardiol 2024; 40:899-906. [PMID: 38467329 DOI: 10.1016/j.cjca.2024.02.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 02/27/2024] [Accepted: 02/27/2024] [Indexed: 03/13/2024] Open
Abstract
In this article some of the recent advances in the use of noninvasive imaging applied to patients with hypertrophic cardiomyopathy (HCM) are discussed. Echocardiography and cardiac computed tomography are briefly discussed with respect to their power to detect apical aneurysmal disease. Echocardiographic phenotype-genotype correlations and the use of echocardiography to characterize myocardial work are reviewed. Positron emission tomography is reviewed in the context of ischemia imaging and also in the context of the use of a new tracer that might allow for recognition of early activation of the fibrosis pathway. Next, the technical capabilities of cardiovascular magnetic resonance to measure myocardial perfusion, oxygenation, and disarray are discussed as they apply to HCM. The application of radiomics to improve prediction of sudden cardiac death is touched upon. Finally, a deep learning approach to the recognition of HCM vs phenocopies is presented as a potential future diagnostic aid in the not-too-distant future.
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Affiliation(s)
- Andrew M Crean
- Manchester Heart Center, University of Manchester, Manchester, United Kingdom; Division of Cardiology, Ottawa Heart Institute, Ottawa, Ontario, Canada.
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Diao K, Liang HQ, Yin HK, Yuan MJ, Gu M, Yu PX, He S, Sun J, Song B, Li K, He Y. Multi-channel deep learning model-based myocardial spatial-temporal morphology feature on cardiac MRI cine images diagnoses the cause of LVH. Insights Imaging 2023; 14:70. [PMID: 37093501 PMCID: PMC10126185 DOI: 10.1186/s13244-023-01401-0] [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: 11/23/2022] [Accepted: 03/08/2023] [Indexed: 04/25/2023] Open
Abstract
BACKGROUND To develop a fully automatic framework for the diagnosis of cause for left ventricular hypertrophy (LVH) via cardiac cine images. METHODS A total of 302 LVH patients with cine MRI images were recruited as the primary cohort. Another 53 LVH patients prospectively collected or from multi-centers were used as the external test dataset. Different models based on the cardiac regions (Model 1), segmented ventricle (Model 2) and ventricle mask (Model 3) were constructed. The diagnostic performance was accessed by the confusion matrix with respect to overall accuracy. The capability of the predictive models for binary classification of cardiac amyloidosis (CA), hypertrophic cardiomyopathy (HCM) or hypertensive heart disease (HHD) were also evaluated. Additionally, the diagnostic performance of best Model was compared with that of 7 radiologists/cardiologists. RESULTS Model 3 showed the best performance with an overall classification accuracy up to 77.4% in the external test datasets. On the subtasks for identifying CA, HCM or HHD only, Model 3 also achieved the best performance with AUCs yielding 0.895-0.980, 0.879-0.984 and 0.848-0.983 in the validation, internal test and external test datasets, respectively. The deep learning model showed non-inferior diagnostic capability to the cardiovascular imaging expert and outperformed other radiologists/cardiologists. CONCLUSION The combined model based on the mask of left ventricular segmented from multi-sequences cine MR images shows favorable and robust performance in diagnosing the cause of left ventricular hypertrophy, which could be served as a noninvasive tool and help clinical decision.
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Affiliation(s)
- Kaiyue Diao
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Hong-Qing Liang
- Department of Radiology, First Affiliated Hospital to Army Medical University (Third Military Medical University Southwest Hospital), Chongqing, China
| | - Hong-Kun Yin
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Ming-Jing Yuan
- Department of Radiology, Yongchuan Hospital, Chongqing Medical University, Chongqing, China
| | - Min Gu
- Department of Radiology, Chongqing General Hospital, University of Chinese Academy of Sciences, Chongqing, China
| | - Peng-Xin Yu
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Sen He
- Department of Cardiology, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China
| | - Jiayu Sun
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Bin Song
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Department of Radiology, Sanya Municipal People's Hospital, Sanya, Hainan, China
| | - Kang Li
- West China Biomedical Big Data Center, Med-X Center for Informatics, West China Hospital, Sichuan University, 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China.
- Med-X Center for Informatics, Sichuan University, Chengdu, China.
| | - Yong He
- Department of Cardiology, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China.
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Zhang X, Cui C, Zhao S, Xie L, Tian Y. Cardiac magnetic resonance radiomics for disease classification. Eur Radiol 2023; 33:2312-2323. [PMID: 36378251 DOI: 10.1007/s00330-022-09236-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 10/07/2022] [Accepted: 10/12/2022] [Indexed: 11/17/2022]
Abstract
OBJECTIVES This study investigated the discriminability of quantitative radiomics features extracted from cardiac magnetic resonance (CMR) images for hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), and healthy (NOR) patients. METHODS The data of two hundred and eighty-three patients with HCM (n = 48) or DCM (n = 52) and NOR (n = 123) were extracted from two publicly available datasets. Ten feature selection methods were first performed on twenty-one different sets of radiomics features extracted from the left ventricle, right ventricle, and myocardium segmented from CMR images in the end-diastolic frame, end-systolic frame, and a combination of both; then, nine classical machine learning methods were trained with the selected radiomics features to distinguish HCM, DCM, and NOR. Ninety classification models were constructed based on combinations of the ten feature selection methods and nine classifiers. The classification models were evaluated, and the optimal model was selected. The diagnostic performance of the selected model was also compared to that of state-of-the-art methods. RESULTS The random forest minimum redundancy maximum relevance model with features based on LeastAxisLength, Maximum2DDiameterSlice, Median, MinorAxisLength, Sphericity, VoxelVolume, Kurtosis, Flatness, and Skewness was the highest performing model, achieving 91.2% classification accuracy. The cross-validated areas under the curve on the test dataset were 0.938, 0.966, and 0.936 for NOR, DCM, and HCM, respectively. Furthermore, compared with those of the state-of-the-art methods, the sensitivity and accuracy of this model were greatly improved. CONCLUSIONS A predictive model was proposed based on CMR radiomics features for classifying HCM, DCM, and NOR patients. The model had good discriminability. KEY POINTS • The first-order features and the features extracted from the LOG-filtered images have potential in distinguishing HCM patients from DCM patients. • The features extracted from the RV play little role in distinguishing DCM from HCM. • The VoxelVolume of the myocardium in the ED frame is important in the recognition of DCM.
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Affiliation(s)
- Xiaoxuan Zhang
- School of Artificial Intelligence, Beijing Normal University, Beijing, 100875, China
| | - Caixia Cui
- Department of Medical Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, China
| | - Shifeng Zhao
- School of Artificial Intelligence, Beijing Normal University, Beijing, 100875, China.
| | - Lizhi Xie
- MR Research China, GE Healthcare, Beijing, 100176, China
| | - Yun Tian
- School of Artificial Intelligence, Beijing Normal University, Beijing, 100875, China.
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Mehta C, Shah R, Yanamala N, Sengupta PP. Cardiovascular Imaging Databases: Building Machine Learning Algorithms for Regenerative Medicine. CURRENT STEM CELL REPORTS 2022. [DOI: 10.1007/s40778-022-00216-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Edalat-Javid M, Shiri I, Hajianfar G, Abdollahi H, Arabi H, Oveisi N, Javadian M, Shamsaei Zafarghandi M, Malek H, Bitarafan-Rajabi A, Oveisi M, Zaidi H. Cardiac SPECT radiomic features repeatability and reproducibility: A multi-scanner phantom study. J Nucl Cardiol 2021; 28:2730-2744. [PMID: 32333282 DOI: 10.1007/s12350-020-02109-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 03/12/2020] [Indexed: 11/30/2022]
Abstract
BACKGROUND The aim of this work was to assess the robustness of cardiac SPECT radiomic features against changes in imaging settings, including acquisition, and reconstruction parameters. METHODS Four commercial SPECT and SPECT/CT cameras were used to acquire images of a static cardiac phantom mimicking typical myorcardial perfusion imaging using 185 MBq of 99mTc. The effects of different image acquisition and reconstruction parameters, including number of views, view matrix size, attenuation correction, as well as image reconstruction related parameters (algorithm, number of iterations, number of subsets, type of post-reconstruction filter, and its associated parameters, including filter order and cut-off frequency) were studied. In total, 5,063 transverse views were reconstructed by varying the aforementioned factors. Eighty-seven radiomic features including first-, second-, and high-order textures were extracted from these images. To assess reproducibility and repeatability, the coefficient of variation (COV), as a widely adopted metric, was measured for each of the radiomic features over the different imaging settings. RESULTS The Inverse Difference Moment Normalized (IDMN) and Inverse Difference Normalized (IDN) features from the Gray Level Co-occurrence Matrix (GLCM), Run Percentage (RP) from the Gray Level Co-occurrence Matrix (GLRLM), Zone Entropy (ZE) from the Gray Level Size Zone Matrix (GLSZM), and Dependence Entropy (DE) from the Gray Level Dependence Matrix (GLDM) feature sets were the only features that exhibited high reproducibility (COV ≤ 5%) against changes in all imaging settings. In addition, Large Area Low Gray Level Emphasis (LALGLE), Small Area Low Gray Level Emphasis (SALGLE) and Low Gray Level Zone Emphasis (LGLZE) from GLSZM, and Small Dependence Low Gray Level Emphasis (SDLGLE) from GLDM feature sets turned out to be less reproducible (COV > 20%) against changes in imaging settings. The GLRLM (31.88%) and GLDM feature set (54.2%) had the highest (COV < 5%) and lowest (COV > 20%) number of the reproducible features, respectively. Matrix size had the largest impact on feature variability as most of the features were not repeatable when matrix size was modified with 82.8% of them having a COV > 20%. CONCLUSION The repeatability and reproducibility of SPECT/CT cardiac radiomic features under different imaging settings is feature-dependent. Different image acquisition and reconstruction protocols have variable effects on radiomic features. The radiomic features exhibiting low COV are potential candidates for future clinical studies.
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Affiliation(s)
- Mohammad Edalat-Javid
- Department of Energy Engineering and Physics, Amir Kabir University of Technology, Tehran, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland
| | - Ghasem Hajianfar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiologic Sciences and Medical Physics, Faculty of Allied Medicine, Kerman University, Kerman, Iran
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland
| | - Niki Oveisi
- School of Population and Public Health, The University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Mohammad Javadian
- Department of Computer Engineering, Faculty of Information Technology, Kermanshah University of Technology, Kermanshah, Iran
| | | | - Hadi Malek
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Ahmad Bitarafan-Rajabi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
- Cardiovascular Intervention Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
- Echocardiography Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Mehrdad Oveisi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland.
- Geneva University Neurocenter, Geneva University, 1205, Geneva, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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Seetharam K, Brito D, Farjo PD, Sengupta PP. The Role of Artificial Intelligence in Cardiovascular Imaging: State of the Art Review. Front Cardiovasc Med 2020; 7:618849. [PMID: 33426010 PMCID: PMC7786371 DOI: 10.3389/fcvm.2020.618849] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 12/08/2020] [Indexed: 12/15/2022] Open
Abstract
In this current digital landscape, artificial intelligence (AI) has established itself as a powerful tool in the commercial industry and is an evolving technology in healthcare. Cutting-edge imaging modalities outputting multi-dimensional data are becoming increasingly complex. In this era of data explosion, the field of cardiovascular imaging is undergoing a paradigm shift toward machine learning (ML) driven platforms. These diverse algorithms can seamlessly analyze information and automate a range of tasks. In this review article, we explore the role of ML in the field of cardiovascular imaging.
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Affiliation(s)
- Karthik Seetharam
- Department of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, United States
| | - Daniel Brito
- Department of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, United States
| | - Peter D Farjo
- Department of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, United States
| | - Partho P Sengupta
- Department of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, United States
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Artificial intelligence for plaque characterization: A scientific exercise looking for a clinical application. Atherosclerosis 2019; 288:158-159. [DOI: 10.1016/j.atherosclerosis.2019.06.914] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2019] [Revised: 06/20/2019] [Accepted: 06/27/2019] [Indexed: 01/09/2023]
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