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Zhou XY, Tang CX, Guo YK, Chen WC, Guo JZ, Ren GS, Li X, Li JH, Lu GM, Huang XH, Wang YN, Zhang LJ, Yang GF. Late gadolinium enhanced cardiac MR derived radiomics approach for predicting all-cause mortality in cardiac amyloidosis: a multicenter study. Eur Radiol 2024; 34:402-410. [PMID: 37552255 DOI: 10.1007/s00330-023-09999-x] [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: 11/02/2022] [Revised: 05/03/2023] [Accepted: 06/05/2023] [Indexed: 08/09/2023]
Abstract
OBJECTIVES To evaluate the prognostic value of radiomics features based on late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) images in patients with cardiac amyloidosis (CA). METHODS This retrospective study included 120 CA patients undergoing CMR at three institutions. Radiomics features were extracted from global and three different segments (base, mid-ventricular, and apex) of left ventricular (LV) on short-axis LGE images. Primary endpoint was all-cause mortality. The predictive performance of the radiomics features and semi-quantitative and quantitative LGE parameters were compared by ROC. The AUC was used to observe whether Rad-score had an incremental value for clinical stage. The Kaplan-Meier curve was used to further stratify the risk of CA patients. RESULTS During a median follow-up of 12.9 months, 30% (40/120) patients died. There was no significant difference in the predictive performance of the radiomics model in different LV sections in the validation set (AUCs of the global, basal, middle, and apical radiomics model were 0.75, 0.77, 0.76, and 0.77, respectively; all p > 0.05). The predictive performance of the Rad-score of the base-LV was better than that of the LGE total enhancement mass (AUC:0.77 vs. 0.54, p < 0.001) and LGE extent (AUC: 0.77 vs. 0.53, p = 0.004). Rad-score combined with Mayo stage had better predictive performance than Mayo stage alone (AUC: 0.86 vs. 0.81, p = 0.03). Rad-score (≥ 0.66) contributed to the risk stratification of all-cause mortality in CA. CONCLUSIONS Compared to quantitative LGE parameters, radiomics can better predict all-cause mortality in CA, while the combination of radiomics and Mayo stage could provide higher predictive accuracy. CLINICAL RELEVANCE STATEMENT Radiomics analysis provides incremental value and improved risk stratification for all-cause mortality in patients with cardiac amyloidosis. KEY POINTS • Radiomics in LV-base was superior to LGE semi-quantitative and quantitative parameters for predicting all-cause mortality in CA. • Rad-score combined with Mayo stage had better predictive performance than Mayo stage alone or radiomics alone. • Rad-score ≥ 0.66 was associated with a significantly increased risk of all-cause mortality in CA patients.
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Affiliation(s)
- Xi Yang Zhou
- Department of Nuclear Medicine, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China
- Department of Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China
| | - Chun Xiang Tang
- Department of Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China
| | - Ying Kun Guo
- Department of Radiology, West China Second University Hospital, Sichuan University, 20# South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Wen Cui Chen
- National Clinical Research Center of Kidney Disease, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, Jiangsu, China
| | - Jin Zhou Guo
- National Clinical Research Center of Kidney Disease, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, Jiangsu, China
| | - Gui Sheng Ren
- National Clinical Research Center of Kidney Disease, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, Jiangsu, China
| | - Xiao Li
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Jun Hao Li
- Department of Nuclear Medicine, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China
- Department of Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China
| | - Guang Ming Lu
- Department of Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China
| | - Xiang Hua Huang
- National Clinical Research Center of Kidney Disease, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, Jiangsu, China
| | - Yi Ning Wang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Long Jiang Zhang
- Department of Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
| | - Gui Fen Yang
- Department of Nuclear Medicine, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
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A X, Liu M, Chen T, Chen F, Qian G, Zhang Y, Chen Y. Non-Contrast Cine Cardiac Magnetic Resonance Derived-Radiomics for the Prediction of Left Ventricular Adverse Remodeling in Patients With ST-Segment Elevation Myocardial Infarction. Korean J Radiol 2023; 24:827-837. [PMID: 37634638 PMCID: PMC10462896 DOI: 10.3348/kjr.2023.0061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 05/26/2023] [Accepted: 06/15/2023] [Indexed: 08/29/2023] Open
Abstract
OBJECTIVE To investigate the predictive value of radiomics features based on cardiac magnetic resonance (CMR) cine images for left ventricular adverse remodeling (LVAR) after acute ST-segment elevation myocardial infarction (STEMI). MATERIALS AND METHODS We conducted a retrospective, single-center, cohort study involving 244 patients (random-split into 170 and 74 for training and testing, respectively) having an acute STEMI (88.5% males, 57.0 ± 10.3 years of age) who underwent CMR examination at one week and six months after percutaneous coronary intervention. LVAR was defined as a 20% increase in left ventricular end-diastolic volume 6 months after acute STEMI. Radiomics features were extracted from the one-week CMR cine images using the least absolute shrinkage and selection operator regression (LASSO) analysis. The predictive performance of the selected features was evaluated using receiver operating characteristic curve analysis and the area under the curve (AUC). RESULTS Nine radiomics features with non-zero coefficients were included in the LASSO regression of the radiomics score (RAD score). Infarct size (odds ratio [OR]: 1.04 (1.00-1.07); P = 0.031) and RAD score (OR: 3.43 (2.34-5.28); P < 0.001) were independent predictors of LVAR. The RAD score predicted LVAR, with an AUC (95% confidence interval [CI]) of 0.82 (0.75-0.89) in the training set and 0.75 (0.62-0.89) in the testing set. Combining the RAD score with infarct size yielded favorable performance in predicting LVAR, with an AUC of 0.84 (0.72-0.95). Moreover, the addition of the RAD score to the left ventricular ejection fraction (LVEF) significantly increased the AUC from 0.68 (0.52-0.84) to 0.82 (0.70-0.93) (P = 0.018), which was also comparable to the prediction provided by the combined microvascular obstruction, infarct size, and LVEF with an AUC of 0.79 (0.65-0.94) (P = 0.727). CONCLUSION Radiomics analysis using non-contrast cine CMR can predict LVAR after STEMI independently and incrementally to LVEF and may provide an alternative to traditional CMR parameters.
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Affiliation(s)
- Xin A
- Chinese People's Liberation Army General Hospital, Chinese People's Liberation Army Medical School, Beijing, China
- The Senior Department of Cardiology, the Sixth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Mingliang Liu
- Nankai University, School of Medicine, Tianjin, Nankai, China
| | - Tong Chen
- Chinese People's Liberation Army General Hospital, Chinese People's Liberation Army Medical School, Beijing, China
- The Senior Department of Cardiology, the Sixth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Feng Chen
- Department of Computer Science, the University of Adelaide, Adelaide, Australia
| | - Geng Qian
- Chinese People's Liberation Army General Hospital, Chinese People's Liberation Army Medical School, Beijing, China
- The Senior Department of Cardiology, the Sixth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Ying Zhang
- The Senior Department of Cardiology, the Sixth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Yundai Chen
- The Senior Department of Cardiology, the Sixth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China.
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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: 1] [Impact Index Per Article: 1.0] [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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Lin A, Pieszko K, Park C, Ignor K, Williams MC, Slomka P, Dey D. Artificial intelligence in cardiovascular imaging: enhancing image analysis and risk stratification. BJR Open 2023; 5:20220021. [PMID: 37396483 PMCID: PMC10311632 DOI: 10.1259/bjro.20220021] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 03/14/2023] [Accepted: 04/03/2023] [Indexed: 07/04/2023] Open
Abstract
In this review, we summarize state-of-the-art artificial intelligence applications for non-invasive cardiovascular imaging modalities including CT, MRI, echocardiography, and nuclear myocardial perfusion imaging.
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Affiliation(s)
| | | | - Caroline Park
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Katarzyna Ignor
- Department of Interventional Cardiology, Collegium Medicum, University of Zielona Góra, Zielona Góra, Poland
| | - Michelle C Williams
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Piotr Slomka
- Division of Artificial Intelligence, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
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Wang C, Ma Y, Liu Y, Li L, Cui C, Qin H, Zhao Z, Li C, Ju W, Chen M, Li D, Zhou W. Texture analysis of SPECT myocardial perfusion provides prognostic value for dilated cardiomyopathy. J Nucl Cardiol 2023; 30:504-515. [PMID: 35676551 DOI: 10.1007/s12350-022-03006-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 05/03/2022] [Indexed: 11/27/2022]
Abstract
BACKGROUND Texture analysis (TA) has demonstrated clinical values in extracting information, quantifying inhomogeneity, evaluating treatment outcomes, and predicting long-term prognosis for cardiac diseases. The aim of this study was to explore whether TA of SPECT myocardial perfusion could contribute to improving the prognosis of dilated cardiomyopathy (DCM) patients. METHODS Eighty-eight patients were recruited in our study between 2009 and 2020 who were diagnosed with DCM and underwent single-photon emission tomography myocardial perfusion imaging (SPECT MPI). Forty TA features were obtained from quantitative analysis of SPECT imaging in subjects with myocardial perfusion at rest. All patients were divided into two groups: the all-cause death group and the survival group. The prognostic value of texture parameters was assessed by Cox regression and Kaplan-Meier analysis. RESULTS Twenty-five all-cause deaths (28.4%) were observed during the follow-up (39.2±28.7 months). Compared with the survival group, NT-proBNP and total perfusion deficit (TPD) were higher and left ventricular ejection fraction (LVEF) was lower in the all-cause death group. In addition, 26 out of 40 texture parameters were significantly different between the two groups. Univariate Cox regression analysis revealed that NT-proBNP, LVEF, and 25 texture parameters were significantly associated with all-cause death. The multivariate Cox regression analysis showed that low gray-level emphasis (LGLE) (P = 0.010, HR = 4.698, 95% CI 1.457-15.145) and long-run low gray-level emphasis (LRLGE) (P =0.002, HR = 6.085, 95% CI 1.906-19.422) were independent predictors of the survival outcome. When added to clinical parameters, LVEF, TPD, and TA parameters, including LGLE and LRLGE, were incrementally associated with all-cause death (global chi-square statistic of 26.246 vs. 33.521; P = 0.028, global chi-square statistic of 26.246 vs. 34.711; P = 0.004). CONCLUSION TA based on gated SPECT MPI could discover independent prognostic predictors of all-cause death in medically treated patients with DCM. Moreover, TA parameters, including LGLE and LRLGE, independent of the total perfusion deficit of the cardiac myocardium, appeared to provide incremental prognostic value for DCM patients.
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Affiliation(s)
- Cheng Wang
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - Ying Ma
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - Yanyun Liu
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Shaanxi, 710126, China
| | - Longxi Li
- School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, China
| | - Chang Cui
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - Huiyuan Qin
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - Zhongqiang Zhao
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - Chunxiang Li
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - Weizhu Ju
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - Minglong Chen
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - Dianfu Li
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China.
| | - Weihua Zhou
- Department of Applied Computing, Michigan Technological University, 1400 Townsend Dr, Houghton, MI, 49931, USA.
- Center for Biocomputing and Digital Health, Institute of Computing and Cybersystems, and Health Research Institute, Michigan Technological University, Houghton, USA.
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Zaidi HA, Jones RE, Hammersley DJ, Hatipoglu S, Balaban G, Mach L, Halliday BP, Lamata P, Prasad SK, Bishop MJ. Machine learning analysis of complex late gadolinium enhancement patterns to improve risk prediction of major arrhythmic events. Front Cardiovasc Med 2023; 10:1082778. [PMID: 36824460 PMCID: PMC9941157 DOI: 10.3389/fcvm.2023.1082778] [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: 10/28/2022] [Accepted: 01/13/2023] [Indexed: 02/10/2023] Open
Abstract
Background Machine learning analysis of complex myocardial scar patterns affords the potential to enhance risk prediction of life-threatening arrhythmia in stable coronary artery disease (CAD). Objective To assess the utility of computational image analysis, alongside a machine learning (ML) approach, to identify scar microstructure features on late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) that predict major arrhythmic events in patients with CAD. Methods Patients with stable CAD were prospectively recruited into a CMR registry. Shape-based scar microstructure features characterizing heterogeneous ('peri-infarct') and homogeneous ('core') fibrosis were extracted. An ensemble of machine learning approaches were used for risk stratification, in addition to conventional analysis using Cox modeling. Results Of 397 patients (mean LVEF 45.4 ± 16.0) followed for a median of 6 years, 55 patients (14%) experienced a major arrhythmic event. When applied within an ML model for binary classification, peri-infarct zone (PIZ) entropy, peri-infarct components and core interface area outperformed a model representative of the current standard of care (LVEF<35% and NYHA>Class I): AUROC (95%CI) 0.81 (0.81-0.82) vs. 0.64 (0.63-0.65), p = 0.002. In multivariate cox regression analysis, these features again remained significant after adjusting for LVEF<35% and NYHA>Class I: PIZ entropy hazard ratio (HR) 1.88, 95% confidence interval (CI) 1.38-2.56, p < 0.001; number of PIZ components HR 1.34, 95% CI 1.08-1.67, p = 0.009; core interface area HR 1.6, 95% CI 1.29-1.99, p = <0.001. Conclusion Machine learning models using LGE-CMR scar microstructure improved arrhythmic risk stratification as compared to guideline-based clinical parameters; highlighting a potential novel approach to identifying candidates for implantable cardioverter defibrillators in stable CAD.
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Affiliation(s)
- Hassan A. Zaidi
- Department of Biomedical Engineering, School of Biomedical and Imaging Sciences, King’s College London, London, United Kingdom
| | - Richard E. Jones
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Daniel J. Hammersley
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Suzan Hatipoglu
- Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Gabriel Balaban
- Department of Biomedical Engineering, School of Biomedical and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Computational Physiology, Simula Research Laboratory, Oslo, Norway
| | - Lukas Mach
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Brian P. Halliday
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Pablo Lamata
- Department of Biomedical Engineering, School of Biomedical and Imaging Sciences, King’s College London, London, United Kingdom
| | - Sanjay K. Prasad
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Martin J. Bishop
- Department of Biomedical Engineering, School of Biomedical and Imaging Sciences, King’s College London, London, United Kingdom
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McCague C, Ramlee S, Reinius M, Selby I, Hulse D, Piyatissa P, Bura V, Crispin-Ortuzar M, Sala E, Woitek R. Introduction to radiomics for a clinical audience. Clin Radiol 2023; 78:83-98. [PMID: 36639175 DOI: 10.1016/j.crad.2022.08.149] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 08/31/2022] [Indexed: 01/12/2023]
Abstract
Radiomics is a rapidly developing field of research focused on the extraction of quantitative features from medical images, thus converting these digital images into minable, high-dimensional data, which offer unique biological information that can enhance our understanding of disease processes and provide clinical decision support. To date, most radiomics research has been focused on oncological applications; however, it is increasingly being used in a raft of other diseases. This review gives an overview of radiomics for a clinical audience, including the radiomics pipeline and the common pitfalls associated with each stage. Key studies in oncology are presented with a focus on both those that use radiomics analysis alone and those that integrate its use with other multimodal data streams. Importantly, clinical applications outside oncology are also presented. Finally, we conclude by offering a vision for radiomics research in the future, including how it might impact our practice as radiologists.
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Affiliation(s)
- C McCague
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
| | - S Ramlee
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - M Reinius
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - I Selby
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - D Hulse
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - P Piyatissa
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - V Bura
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Department of Radiology and Medical Imaging, County Clinical Emergency Hospital, Cluj-Napoca, Romania
| | - M Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Department of Oncology, University of Cambridge, Cambridge, UK
| | - E Sala
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - R Woitek
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Research Centre for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Faculty of Medicine and Dentistry, Danube Private University, Krems, Austria
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Radiomics-based machine learning models in STEMI: a promising tool for the prediction of major adverse cardiac events. Eur Radiol 2023:10.1007/s00330-023-09394-6. [PMID: 36633675 DOI: 10.1007/s00330-023-09394-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 12/12/2022] [Accepted: 12/23/2022] [Indexed: 01/13/2023]
Abstract
OBJECTIVE To evaluate the potential value of the machine learning (ML) models using radiomic features of late gadolinium enhancement (LGE) and cine images on magnetic resonance imaging (MRI) along with relevant clinical information and conventional MRI parameters for the prediction of major adverse cardiac events (MACE) in ST-segment elevation myocardial infarction (STEMI) patients. METHODS This retrospective study included 60 patients with the first STEMI. MACE consisted of new-onset congestive heart failure, ventricular arrhythmia, and cardiac death. Radiomic features were extracted from cine and LGE images. Inter-class correlation coefficients (ICCs) were calculated to assess inter-observer reproducibility. LASSO (least absolute shrinkage and selection operator) method was used for radiomic feature selection. Seven separate models using a different combination of the available information were investigated. Classifications with repeat random sampling were done using adaptive boosting, k-nearest neighbor, naive Bayes, neural network, random forest, stochastic gradient descent, and support vector machine algorithms. RESULTS Of the 1748 extracted radiomic features, 1393 showed good inter-observer agreement. With LASSO, 25 features were selected. Among the ML algorithms, the neural network showed the highest predictive performance on average (area under the curve (AUC) 0.822 ± 0.181). Of the best-calculated model, the one using clinical parameters, CMRI parameters, and selected radiomic features (model 7), the diagnostic performance was as follows: 0.965 AUC, 0.894 classification accuracy, 0.906 sensitivity, 0.883 specificity, 0.875 positive predictive value (PPV), and 0.912 negative predictive value (NPV). CONCLUSION The radiomics-based ML models incorporating clinical and conventional MRI parameters are promising for predicting MACE occurrence in STEMI patients in the follow-up period. KEY POINTS • Acute coronary occlusion results in variable changes at the cellular level ranging from myocyte swelling to myonecrosis depending on the duration of the ischemia and the metabolic state of the heart, which causes subtle heterogeneous signal changes that are imperceptible to the human eye with cardiac MRI. • Radiomics-based machine learning analysis of cardiac MR images is promising for risk prediction. • Combining MRI-derived parameters and clinical variables increases the accuracy of predictive models.
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9
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Machine Learning in Cardiovascular Imaging: A Scoping Review of Published Literature. CURRENT RADIOLOGY REPORTS 2023; 11:34-45. [PMID: 36531124 PMCID: PMC9742664 DOI: 10.1007/s40134-022-00407-8] [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] [Accepted: 11/17/2022] [Indexed: 12/14/2022]
Abstract
Purpose of Review In this study, we planned and carried out a scoping review of the literature to learn how machine learning (ML) has been investigated in cardiovascular imaging (CVI). Recent Findings During our search, we found numerous studies that developed or utilized existing ML models for segmentation, classification, object detection, generation, and regression applications involving cardiovascular imaging data. We first quantitatively investigated the different aspects of study characteristics, data handling, model development, and performance evaluation in all studies that were included in our review. We then supplemented these findings with a qualitative synthesis to highlight the common themes in the studied literature and provided recommendations to pave the way for upcoming research. Summary ML is a subfield of artificial intelligence (AI) that enables computers to learn human-like decision-making from data. Due to its novel applications, ML is gaining more and more attention from researchers in the healthcare industry. Cardiovascular imaging is an active area of research in medical imaging with lots of room for incorporating new technologies, like ML. Supplementary Information The online version contains supplementary material available at 10.1007/s40134-022-00407-8.
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10
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Velasco C, Fletcher TJ, Botnar RM, Prieto C. Artificial intelligence in cardiac magnetic resonance fingerprinting. Front Cardiovasc Med 2022; 9:1009131. [PMID: 36204566 PMCID: PMC9530662 DOI: 10.3389/fcvm.2022.1009131] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 08/30/2022] [Indexed: 11/13/2022] Open
Abstract
Magnetic resonance fingerprinting (MRF) is a fast MRI-based technique that allows for multiparametric quantitative characterization of the tissues of interest in a single acquisition. In particular, it has gained attention in the field of cardiac imaging due to its ability to provide simultaneous and co-registered myocardial T1 and T2 mapping in a single breath-held cardiac MRF scan, in addition to other parameters. Initial results in small healthy subject groups and clinical studies have demonstrated the feasibility and potential of MRF imaging. Ongoing research is being conducted to improve the accuracy, efficiency, and robustness of cardiac MRF. However, these improvements usually increase the complexity of image reconstruction and dictionary generation and introduce the need for sequence optimization. Each of these steps increase the computational demand and processing time of MRF. The latest advances in artificial intelligence (AI), including progress in deep learning and the development of neural networks for MRI, now present an opportunity to efficiently address these issues. Artificial intelligence can be used to optimize candidate sequences and reduce the memory demand and computational time required for reconstruction and post-processing. Recently, proposed machine learning-based approaches have been shown to reduce dictionary generation and reconstruction times by several orders of magnitude. Such applications of AI should help to remove these bottlenecks and speed up cardiac MRF, improving its practical utility and allowing for its potential inclusion in clinical routine. This review aims to summarize the latest developments in artificial intelligence applied to cardiac MRF. Particularly, we focus on the application of machine learning at different steps of the MRF process, such as sequence optimization, dictionary generation and image reconstruction.
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Affiliation(s)
- Carlos Velasco
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- *Correspondence: Carlos Velasco
| | - Thomas J. Fletcher
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - René M. Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
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11
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Chong JH, Abdulkareem M, Petersen SE, Khanji MY. Artificial intelligence and cardiovascular magnetic resonance imaging in myocardial infarction patients. Curr Probl Cardiol 2022; 47:101330. [PMID: 35870544 DOI: 10.1016/j.cpcardiol.2022.101330] [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: 07/09/2022] [Accepted: 07/17/2022] [Indexed: 11/03/2022]
Abstract
Cardiovascular magnetic resonance (CMR) is an important cardiac imaging tool for assessing the prognostic extent of myocardial injury after myocardial infarction (MI). Within the context of clinical trials, CMR is also useful for assessing the efficacy of potential cardioprotective therapies in reducing MI size and preventing adverse left ventricular (LV) remodelling in reperfused MI. However, manual contouring and analysis can be time-consuming with interobserver and intraobserver variability, which can in turn lead to reduction in accuracy and precision of analysis. There is thus a need to automate CMR scan analysis in MI patients to save time, increase accuracy, increase reproducibility and increase precision. In this regard, automated imaging analysis techniques based on artificial intelligence (AI) that are developed with machine learning (ML), and more specifically deep learning (DL) strategies, can enable efficient, robust, accurate and clinician-friendly tools to be built so as to try and improve both clinician productivity and quality of patient care. In this review, we discuss basic concepts of ML in CMR, important prognostic CMR imaging biomarkers in MI and the utility of current ML applications in their analysis as assessed in research studies. We highlight potential barriers to the mainstream implementation of these automated strategies and discuss related governance and quality control issues. Lastly, we discuss the future role of ML applications in clinical trials and the need for global collaboration in growing this field.
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Affiliation(s)
- Jun Hua Chong
- National Heart Centre Singapore, Singapore; Cardiovascular Sciences Academic Clinical Programme, Duke-National University of Singapore Medical School, Singapore.
| | - Musa Abdulkareem
- Barts Heart Centre, Barts Health National Health Service Trust, London, United Kingdom; National Institute for Health Research Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom; Health Data Research UK, London, United Kingdom
| | - Steffen E Petersen
- Barts Heart Centre, Barts Health National Health Service Trust, London, United Kingdom; National Institute for Health Research Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom; Health Data Research UK, London, United Kingdom; The Alan Turing Institute, London, United Kingdom
| | - Mohammed Y Khanji
- Barts Heart Centre, Barts Health National Health Service Trust, London, United Kingdom; National Institute for Health Research Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom; Department of Cardiology, Newham University Hospital, Barts Health NHS Trust, Glen Road, London E13 8SL, UK
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12
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Brown G, Conway S, Ahmad M, Adegbie D, Patel N, Myneni V, Alradhawi M, Kumar N, Obaid DR, Pimenta D, Bray JJH. Role of artificial intelligence in defibrillators: a narrative review. Open Heart 2022; 9:openhrt-2022-001976. [PMID: 35790317 PMCID: PMC9258481 DOI: 10.1136/openhrt-2022-001976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 05/17/2022] [Indexed: 02/01/2023] Open
Abstract
Automated external defibrillators (AEDs) and implantable cardioverter defibrillators (ICDs) are used to treat life-threatening arrhythmias. AEDs and ICDs use shock advice algorithms to classify ECG tracings as shockable or non-shockable rhythms in clinical practice. Machine learning algorithms have recently been assessed for shock decision classification with increasing accuracy. Outside of rhythm classification alone, they have been evaluated in diagnosis of causes of cardiac arrest, prediction of success of defibrillation and rhythm classification without the need to interrupt cardiopulmonary resuscitation. This review explores the many applications of machine learning in AEDs and ICDs. While these technologies are exciting areas of research, there remain limitations to their widespread use including high processing power, cost and the ‘black-box’ phenomenon.
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Affiliation(s)
- Grace Brown
- Cardiology Department, Royal Free Hospital, London, UK
| | - Samuel Conway
- Cardiology Department, Royal Free Hospital, London, UK
| | - Mahmood Ahmad
- Medical Sciences, University College London, London, UK
| | - Divine Adegbie
- Cardiology Department, East and North Hertfordshire NHS Trust, Stevenage, Hertfordshire, UK
| | - Nishil Patel
- Cardiology Department, North Middlesex University Hospital, London, UK
| | | | | | - Niraj Kumar
- Institute of Cardiovascular Science, University College London, London, UK.,Cardiology Department, Barts Health NHS Trust, London, UK
| | - Daniel R Obaid
- Institute of Life Sciences, Swansea University, Swansea, UK
| | - Dominic Pimenta
- Cardiology Department, Richmond Research Institute, London, UK
| | - Jonathan J H Bray
- Cardiff University College of Biomedical and Life Sciences, Cardiff, UK
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13
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Argentiero A, Muscogiuri G, Rabbat MG, Martini C, Soldato N, Basile P, Baggiano A, Mushtaq S, Fusini L, Mancini ME, Gaibazzi N, Santobuono VE, Sironi S, Pontone G, Guaricci AI. The Applications of Artificial Intelligence in Cardiovascular Magnetic Resonance-A Comprehensive Review. J Clin Med 2022; 11:jcm11102866. [PMID: 35628992 PMCID: PMC9147423 DOI: 10.3390/jcm11102866] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/13/2022] [Accepted: 05/16/2022] [Indexed: 12/11/2022] Open
Abstract
Cardiovascular disease remains an integral field on which new research in both the biomedical and technological fields is based, as it remains the leading cause of mortality and morbidity worldwide. However, despite the progress of cardiac imaging techniques, the heart remains a challenging organ to study. Artificial intelligence (AI) has emerged as one of the major innovations in the field of diagnostic imaging, with a dramatic impact on cardiovascular magnetic resonance imaging (CMR). AI will be increasingly present in the medical world, with strong potential for greater diagnostic efficiency and accuracy. Regarding the use of AI in image acquisition and reconstruction, the main role was to reduce the time of image acquisition and analysis, one of the biggest challenges concerning magnetic resonance; moreover, it has been seen to play a role in the automatic correction of artifacts. The use of these techniques in image segmentation has allowed automatic and accurate quantification of the volumes and masses of the left and right ventricles, with occasional need for manual correction. Furthermore, AI can be a useful tool to directly help the clinician in the diagnosis and derivation of prognostic information of cardiovascular diseases. This review addresses the applications and future prospects of AI in CMR imaging, from image acquisition and reconstruction to image segmentation, tissue characterization, diagnostic evaluation, and prognostication.
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Affiliation(s)
- Adriana Argentiero
- University Cardiology Unit, Cardio-Thoracic Department, Policlinic University Hospital, 70121 Bari, Italy; (A.A.); (N.S.); (P.B.); (V.E.S.)
| | - Giuseppe Muscogiuri
- School of Medicine and Surgery, University of Milano-Bicocca, 20126 Milan, Italy; (G.M.); (S.S.)
- Department of Radiology, IRCCS Istituto Auxologico Italiano, San Luca Hospital, 20149 Milan, Italy
| | - Mark G. Rabbat
- Division of Cardiology, Loyola University of Chicago, Chicago, IL 60660, USA;
| | - Chiara Martini
- Radiologic Sciences, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy;
| | - Nicolò Soldato
- University Cardiology Unit, Cardio-Thoracic Department, Policlinic University Hospital, 70121 Bari, Italy; (A.A.); (N.S.); (P.B.); (V.E.S.)
| | - Paolo Basile
- University Cardiology Unit, Cardio-Thoracic Department, Policlinic University Hospital, 70121 Bari, Italy; (A.A.); (N.S.); (P.B.); (V.E.S.)
| | - Andrea Baggiano
- Perioperative and Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (A.B.); (S.M.); (L.F.); (M.E.M.); (G.P.)
| | - Saima Mushtaq
- Perioperative and Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (A.B.); (S.M.); (L.F.); (M.E.M.); (G.P.)
| | - Laura Fusini
- Perioperative and Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (A.B.); (S.M.); (L.F.); (M.E.M.); (G.P.)
| | - Maria Elisabetta Mancini
- Perioperative and Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (A.B.); (S.M.); (L.F.); (M.E.M.); (G.P.)
| | - Nicola Gaibazzi
- Department of Cardiology, Azienda Ospedaliero-Universitaria, 43126 Parma, Italy;
| | - Vincenzo Ezio Santobuono
- University Cardiology Unit, Cardio-Thoracic Department, Policlinic University Hospital, 70121 Bari, Italy; (A.A.); (N.S.); (P.B.); (V.E.S.)
| | - Sandro Sironi
- School of Medicine and Surgery, University of Milano-Bicocca, 20126 Milan, Italy; (G.M.); (S.S.)
- Department of Radiology, ASST Papa Giovanni XXIII Hospital, 24127 Bergamo, Italy
| | - Gianluca Pontone
- Perioperative and Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (A.B.); (S.M.); (L.F.); (M.E.M.); (G.P.)
| | - Andrea Igoren Guaricci
- University Cardiology Unit, Cardio-Thoracic Department, Policlinic University Hospital, 70121 Bari, Italy; (A.A.); (N.S.); (P.B.); (V.E.S.)
- Department of Emergency and Organ Transplantation, University of Bari, 70121 Bari, Italy
- Correspondence:
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14
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Chung CT, Bazoukis G, Lee S, Liu Y, Liu T, Letsas KP, Armoundas AA, Tse G. Machine learning techniques for arrhythmic risk stratification: a review of the literature. INTERNATIONAL JOURNAL OF ARRHYTHMIA 2022; 23. [PMID: 35449883 PMCID: PMC9020640 DOI: 10.1186/s42444-022-00062-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Ventricular arrhythmias (VAs) and sudden cardiac death (SCD) are significant adverse events that affect the morbidity and mortality of both the general population and patients with predisposing cardiovascular risk factors. Currently, conventional disease-specific scores are used for risk stratification purposes. However, these risk scores have several limitations, including variations among validation cohorts, the inclusion of a limited number of predictors while omitting important variables, as well as hidden relationships between predictors. Machine learning (ML) techniques are based on algorithms that describe intervariable relationships. Recent studies have implemented ML techniques to construct models for the prediction of fatal VAs. However, the application of ML study findings is limited by the absence of established frameworks for its implementation, in addition to clinicians’ unfamiliarity with ML techniques. This review, therefore, aims to provide an accessible and easy-to-understand summary of the existing evidence about the use of ML techniques in the prediction of VAs. Our findings suggest that ML algorithms improve arrhythmic prediction performance in different clinical settings. However, it should be emphasized that prospective studies comparing ML algorithms to conventional risk models are needed while a regulatory framework is required prior to their implementation in clinical practice.
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15
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Corianò M, Tona F. Strategies for Sudden Cardiac Death Prevention. Biomedicines 2022; 10:639. [PMID: 35327441 PMCID: PMC8944952 DOI: 10.3390/biomedicines10030639] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/06/2022] [Accepted: 03/06/2022] [Indexed: 12/12/2022] Open
Abstract
Sudden cardiac death (SCD) represents a major challenge in modern medicine. The prevention of SCD orbits on two levels, the general population level and individual level. Much research has been done with the aim to improve risk stratification of SCD, although no radical changes in evidence and in therapeutic strategy have been achieved. Artificial intelligence (AI), and in particular machine learning (ML) models, represent novel technologic tools that promise to improve predictive ability of fatal arrhythmic events. In this review, firstly, we analyzed the electrophysiological basis and the major clues of SCD prevention at population and individual level; secondly, we reviewed the main research where ML models were used for risk stratification in other field of cardiology, suggesting its potentiality in the field of SCD prevention.
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Affiliation(s)
| | - Francesco Tona
- Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, 35128 Padova, Italy;
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16
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Chang S, Han K, Suh YJ, Choi BW. Quality of science and reporting for radiomics in cardiac magnetic resonance imaging studies: a systematic review. Eur Radiol 2022; 32:4361-4373. [PMID: 35230519 DOI: 10.1007/s00330-022-08587-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 12/31/2021] [Accepted: 01/19/2022] [Indexed: 01/06/2023]
Abstract
OBJECTIVES To evaluate the quality of radiomics studies using cardiac magnetic resonance imaging (CMR) according to the radiomics quality score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines, and the standards defined by the Image Biomarker Standardization Initiative (IBSI) and identify areas needing improvement. MATERIALS AND METHODS PubMed and Embase were searched to identify radiomics studies using CMR until March 10, 2021. Of the 259 identified articles, 32 relevant original research articles were included. Studies were scored according to the RQS, TRIPOD guidelines, and IBSI standards by two cardiac radiologists. RESULTS The mean RQS was 14.3% of the maximum (5.16 out of 36). RQS were low for the demonstration of validation (-60.6%), calibration statistics (1.6%), potential clinical utility (3.1%), and open science (3.1%) items. No study conducted a phantom study or cost-effectiveness analysis. The adherence to TRIPOD guidelines was 55.9%. Studies were deficient in reporting title (3.1%), stating objective in abstract and introduction (6.3% and 9.4%), missing data (0%), discrimination/calibration (3.1%), and how to use the prediction model (3.1%). According to the IBSI standards, non-uniformity correction, image interpolation, grey-level discretization, and signal intensity normalization were performed in two (6.3%), four (12.5%), six (18.8%), and twelve (37.5%) studies, respectively. CONCLUSION The quality of radiomics studies using CMR is suboptimal. Improvements are needed in the areas of validation, calibration, clinical utility, and open science. Complete reporting of study objectives, missing data, discrimination/calibration, how to use the prediction model, and preprocessing steps are necessary. KEY POINTS • The quality of science in radiomics studies using CMR is currently inadequate. • RQS were low for validation, calibration, clinical utility, and open science; no study conducted a phantom study or cost-effectiveness analysis. • In stating the study objective, missing data, discrimination/calibration, how to use the prediction model, and preprocessing steps, improvements are needed.
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Affiliation(s)
- Suyon Chang
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Kyunghwa Han
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Young Joo Suh
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
| | - Byoung Wook Choi
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
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Wu LM, Shi RY, Wu CW, Jiang M, Guo Q, Zhu YS, Tang LL, Xu JR, Pu J, Zhou Y, Wu R. A Radiomic MRI based Nomogram for Prediction of Heart Failure with Preserved Ejection Fraction in Systemic Lupus Erythematosus Patients: Insights From a Three-Center Prospective Study. J Magn Reson Imaging 2022; 56:779-789. [PMID: 35049073 DOI: 10.1002/jmri.28070] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 12/26/2021] [Accepted: 12/29/2021] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND Myocardial T1 and extracellular volume (ECV) fraction values have important roles in the prognostication of heart failure with preserved ejection fraction (HFpEF). However, the traditional mean quantification of intensity levels is not sufficient. PURPOSE To evaluate a T1 map-based radiomic nomogram as a long-term prognosticator for HFpEF in systemic lupus erythematosus (SLE) patients. STUDY TYPE Prospective. POPULATION A total of 115 SLE patients and 50 age- and gender-matched controls. FIELD STRENGTH/SEQUENCE A 3.0 T scanner; cine imaging, precontrast and post-contrast T1 mapping and T2 mapping sequences. ASSESSMENT A radiomic nomogram was developed based on precontrast T1 mapping. Three independent readers assessed and compared the ECV value and the value of the radiomic nomogram for predicting HFpEF in SLE patients. STATISTICAL TEST Cox proportional hazard models, Youden index for determining cut-off values for high HFpEF risk vs. low HFpEF risk classification, Kaplan-Meier analysis, intraclass correlation (ICC), and Uno C statistic test. RESULTS During a median follow-up of 27 (interquartile range, 19-37) months, 31 SLE patients developed HFpEF. Patients with elevated ECV (≥31%) and a higher output (≥42.7) from the radiomic feature "S_33_sum average" of the precontrast T1 map had a significantly higher risk of developing HFpEF than those who had lower ECV (<31%) and an output <42.7. Patients with a higher "S_33_sum average" value on precontrast T1 map had a significantly increased risk for HFpEF (hazard ratio, 1.363, 95% CI, 1.130-1.645), after adjusting for covariates including ECV and LVEF. Finally, "S_33_sum average" from precontrast T1 mapping had modest but significantly incremental prognostic value over the mean ECV value (Uno C statistic comparing models, 0.860 vs. 0.835). DATA CONCLUSION The precontrast T1 map-based radiomic nomogram, as a measure of diffuse myocardial fibrosis was associated with HFpEF and provided modest prognostic value for predicting HFpEF in SLE patients. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Lian-Ming Wu
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Ruo-Yang Shi
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Chong-Wen Wu
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Meng Jiang
- Department of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Qiang Guo
- Department of Rheumatology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Yin-Su Zhu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nan Jing, Jiang Su, 210029, China
| | - Lang-Lang Tang
- Department of Radiology, Longyan First Hospital of Fujian Medical University, Long Yan, Fu Jian, 364031, China
| | - Jian-Rong Xu
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Jun Pu
- Department of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Yan Zhou
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Rui Wu
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
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Fotaki A, Puyol-Antón E, Chiribiri A, Botnar R, Pushparajah K, Prieto C. Artificial Intelligence in Cardiac MRI: Is Clinical Adoption Forthcoming? Front Cardiovasc Med 2022; 8:818765. [PMID: 35083303 PMCID: PMC8785419 DOI: 10.3389/fcvm.2021.818765] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 12/15/2021] [Indexed: 12/24/2022] Open
Abstract
Artificial intelligence (AI) refers to the area of knowledge that develops computerised models to perform tasks that typically require human intelligence. These algorithms are programmed to learn and identify patterns from "training data," that can be subsequently applied to new datasets, without being explicitly programmed to do so. AI is revolutionising the field of medical imaging and in particular of Cardiovascular Magnetic Resonance (CMR) by providing deep learning solutions for image acquisition, reconstruction and analysis, ultimately supporting the clinical decision making. Numerous methods have been developed over recent years to enhance and expedite CMR data acquisition, image reconstruction, post-processing and analysis; along with the development of promising AI-based biomarkers for a wide spectrum of cardiac conditions. The exponential rise in the availability and complexity of CMR data has fostered the development of different AI models. Integration in clinical routine in a meaningful way remains a challenge. Currently, innovations in this field are still mostly presented in proof-of-concept studies with emphasis on the engineering solutions; often recruiting small patient cohorts or relying on standardised databases such as Multi-ethnic Study on atherosclerosis (MESA), UK Biobank and others. The wider incorporation of clinically valid endpoints such as symptoms, survival, need and response to treatment remains to be seen. This review briefly summarises the current principles of AI employed in CMR and explores the relevant prospective observational studies in cardiology patient cohorts. It provides an overview of clinical studies employing undersampled reconstruction techniques to speed up the scan encompassing cine imaging, whole-heart imaging, multi-parametric mapping and magnetic resonance fingerprinting along with the clinical utility of AI applications in image post-processing, and analysis. Specific focus is given to studies that have incorporated CMR-derived prediction models for prognostication in cardiac disease. It also discusses current limitations and proposes potential developments to enable multi-disciplinary collaboration for improved evidence-based medicine. AI is an extremely promising field and the timely integration of clinician's input in the ingenious technical investigator's paradigm holds promise for a bright future in the medical field.
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Affiliation(s)
- Anastasia Fotaki
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
| | - Esther Puyol-Antón
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Amedeo Chiribiri
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
| | - René Botnar
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Kuberan Pushparajah
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
| | - Claudia Prieto
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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Wang J, Bravo L, Zhang J, Liu W, Wan K, Sun J, Zhu Y, Han Y, Gkoutos GV, Chen Y. Radiomics Analysis Derived From LGE-MRI Predict Sudden Cardiac Death in Participants With Hypertrophic Cardiomyopathy. Front Cardiovasc Med 2021; 8:766287. [PMID: 34957254 PMCID: PMC8702805 DOI: 10.3389/fcvm.2021.766287] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 11/10/2021] [Indexed: 02/05/2023] Open
Abstract
Objectives: To identify significant radiomics features derived from late gadolinium enhancement (LGE) images in participants with hypertrophic cardiomyopathy (HCM) and assess their prognostic value in predicting sudden cardiac death (SCD) endpoint. Method: The 157 radiomic features of 379 sequential participants with HCM who underwent cardiovascular magnetic resonance imaging (MRI) were extracted. CoxNet (Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net) and Random Forest models were applied to optimize feature selection for the SCD risk prediction and cross-validation was performed. Results: During a median follow-up of 29 months (interquartile range, 20–42 months), 27 participants with HCM experienced SCD events. Cox analysis revealed that two selected features, local binary patterns (LBP) (19) (hazard ratio (HR), 1.028, 95% CI: 1.032–1.134; P = 0.001) and Moment (1) (HR, 1.212, 95%CI: 1.032–1.423; P = 0.02) provided significant prognostic value to predict the SCD endpoints after adjustment for the clinical risk predictors and late gadolinium enhancement. Furthermore, the univariately significant risk predictor was improved by the addition of the selected radiomics features, LBP (19) and Moment (1), to predict SCD events (P < 0.05). Conclusion: The radiomics features of LBP (19) and Moment (1) extracted from LGE images, reflecting scar heterogeneity, have independent prognostic value in identifying high SCD risk patients with HCM.
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Affiliation(s)
- Jie Wang
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China.,College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Laura Bravo
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Jinquan Zhang
- West China School of Public Health, Sichuan University, Chengdu, China
| | - Wen Liu
- West China School of Public Health, Sichuan University, Chengdu, China
| | - Ke Wan
- Department of Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Jiayu Sun
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yanjie Zhu
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen, China
| | - Yuchi Han
- Department of Medicine (Cardiovascular Division), University of Pennsylvania, Philadelphia, PA, United States
| | - Georgios V Gkoutos
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom.,Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom.,Health Data Research UK (HDR), Midlands Site, United Kingdom
| | - Yucheng Chen
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China.,Center of Rare Diseases, West China Hospital, Sichuan University, Chengdu, China
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20
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Maragna R, Giacari CM, Guglielmo M, Baggiano A, Fusini L, Guaricci AI, Rossi A, Rabbat M, Pontone G. Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management. Front Cardiovasc Med 2021; 8:736223. [PMID: 34631834 PMCID: PMC8493089 DOI: 10.3389/fcvm.2021.736223] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 08/25/2021] [Indexed: 12/14/2022] Open
Abstract
Coronary artery disease (CAD) represents one of the most important causes of death around the world. Multimodality imaging plays a fundamental role in both diagnosis and risk stratification of acute and chronic CAD. For example, the role of Coronary Computed Tomography Angiography (CCTA) has become increasingly important to rule out CAD according to the latest guidelines. These changes and others will likely increase the request for appropriate imaging tests in the future. In this setting, artificial intelligence (AI) will play a pivotal role in echocardiography, CCTA, cardiac magnetic resonance and nuclear imaging, making multimodality imaging more efficient and reliable for clinicians, as well as more sustainable for healthcare systems. Furthermore, AI can assist clinicians in identifying early predictors of adverse outcome that human eyes cannot see in the fog of “big data.” AI algorithms applied to multimodality imaging will play a fundamental role in the management of patients with suspected or established CAD. This study aims to provide a comprehensive overview of current and future AI applications to the field of multimodality imaging of ischemic heart disease.
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Affiliation(s)
- Riccardo Maragna
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Carlo Maria Giacari
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Marco Guglielmo
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Andrea Baggiano
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy.,Department of Clinical Sciences and Community Health, Cardiovascular Section, University of Milan, Milan, Italy
| | - Laura Fusini
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Andrea Igoren Guaricci
- Department of Emergency and Organ Transplantation, Institute of Cardiovascular Disease, University Hospital Policlinico of Bari, Bari, Italy
| | - Alexia Rossi
- Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland.,Center for Molecular Cardiology, University Hospital Zurich, Zurich, Switzerland
| | - Mark Rabbat
- Department of Medicine and Radiology, Division of Cardiology, Loyola University of Chicago, Chicago, IL, United States.,Department of Medicine, Division of Cardiology, Edward Hines Jr. VA Hospital, Hines, IL, United States
| | - Gianluca Pontone
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
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21
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Lauzier PT, Avram R, Dey D, Slomka P, Afilalo J, Chow BJ. The evolving role of artificial intelligence in cardiac image analysis. Can J Cardiol 2021; 38:214-224. [PMID: 34619340 DOI: 10.1016/j.cjca.2021.09.030] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 09/28/2021] [Accepted: 09/28/2021] [Indexed: 12/13/2022] Open
Abstract
Research in artificial intelligence (AI) have progressed over the last decade. The field of cardiac imaging has seen significant developments using newly developed deep learning methods for automated image analysis and AI tools for disease detection and prognostication. This review article is aimed at those without special background in AI. We review AI concepts and we survey the growing contemporary applications of AI for image analysis in echocardiography, nuclear cardiology, cardiac computed tomography, cardiac magnetic resonance, and invasive angiography.
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Affiliation(s)
| | - Robert Avram
- University of Ottawa Heart Institute, Ottawa, ON, Canada; Montreal Heart Institute, Montreal, QC, Canada
| | - Damini Dey
- Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Piotr Slomka
- Cedars-Sinai Medical Center, Los Angeles, CA, USA
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22
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Sharma UC, Zhao K, Mentkowski K, Sonkawade SD, Karthikeyan B, Lang JK, Ying L. Modified GAN Augmentation Algorithms for the MRI-Classification of Myocardial Scar Tissue in Ischemic Cardiomyopathy. Front Cardiovasc Med 2021; 8:726943. [PMID: 34589528 PMCID: PMC8473636 DOI: 10.3389/fcvm.2021.726943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 08/18/2021] [Indexed: 01/07/2023] Open
Abstract
Contrast-enhanced cardiac magnetic resonance imaging (MRI) is routinely used to determine myocardial scar burden and make therapeutic decisions for coronary revascularization. Currently, there are no optimized deep-learning algorithms for the automated classification of scarred vs. normal myocardium. We report a modified Generative Adversarial Network (GAN) augmentation method to improve the binary classification of myocardial scar using both pre-clinical and clinical approaches. For the initial training of the MobileNetV2 platform, we used the images generated from a high-field (9.4T) cardiac MRI of a mouse model of acute myocardial infarction (MI). Once the system showed 100% accuracy for the classification of acute MI in mice, we tested the translational significance of this approach in 91 patients with an ischemic myocardial scar, and 31 control subjects without evidence of myocardial scarring. To obtain a comparable augmentation dataset, we rotated scar images 8-times and control images 72-times, generating a total of 6,684 scar images and 7,451 control images. In humans, the use of Progressive Growing GAN (PGGAN)-based augmentation showed 93% classification accuracy, which is far superior to conventional automated modules. The use of other attention modules in our CNN further improved the classification accuracy by up to 5%. These data are of high translational significance and warrant larger multicenter studies in the future to validate the clinical implications.
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Affiliation(s)
- Umesh C. Sharma
- Department of Medicine, Division of Cardiology, Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, United States
- Department of Pharmacology and Toxicology, University at Buffalo, Buffalo, NY, United States
| | - Kanhao Zhao
- Department of Biomedical Engineering, University at Buffalo, Buffalo, NY, United States
| | - Kyle Mentkowski
- Department of Medicine, Division of Cardiology, Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, United States
- Department of Biomedical Engineering, University at Buffalo, Buffalo, NY, United States
| | - Swati D. Sonkawade
- Department of Medicine, Division of Cardiology, Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, United States
| | - Badri Karthikeyan
- Department of Medicine, Division of Cardiology, Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, United States
| | - Jennifer K. Lang
- Department of Medicine, Division of Cardiology, Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, United States
- Department of Pharmacology and Toxicology, University at Buffalo, Buffalo, NY, United States
- Department of Biomedical Engineering, University at Buffalo, Buffalo, NY, United States
- Veterans Affairs Western New York Healthcare System, Buffalo, NY, United States
| | - Leslie Ying
- Department of Biomedical Engineering, University at Buffalo, Buffalo, NY, United States
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23
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Abstract
Rapid development of artificial intelligence (AI) is gaining grounds in medicine. Its huge impact and inevitable necessity are also reflected in cardiovascular imaging. Although AI would probably never replace doctors, it can significantly support and improve their productivity and diagnostic performance. Many algorithms have already proven useful at all stages of the cardiac imaging chain. Their crucial practical applications include classification, automatic quantification, notification, diagnosis, and risk prediction. Consequently, more reproducible and repeatable studies are obtained, and personalized reports may be available to any patient. Utilization of AI also increases patient safety and decreases healthcare costs. Furthermore, AI is particularly useful for beginners in the field of cardiac imaging as it provides anatomic guidance and interpretation of complex imaging results. In contrast, lack of interpretability and explainability in AI carries a risk of harmful recommendations. This review was aimed at summarizing AI principles, essential execution requirements, and challenges as well as its recent applications in cardiovascular imaging.
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24
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Raisi-Estabragh Z, Izquierdo C, Campello VM, Martin-Isla C, Jaggi A, Harvey NC, Lekadir K, Petersen SE. Cardiac magnetic resonance radiomics: basic principles and clinical perspectives. Eur Heart J Cardiovasc Imaging 2021; 21:349-356. [PMID: 32142107 PMCID: PMC7082724 DOI: 10.1093/ehjci/jeaa028] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 12/16/2019] [Accepted: 02/06/2020] [Indexed: 01/21/2023] Open
Abstract
Radiomics is a novel image analysis technique, whereby voxel-level information is extracted from digital images and used to derive multiple numerical quantifiers of shape and tissue character. Cardiac magnetic resonance (CMR) is the reference imaging modality for assessment of cardiac structure and function. Conventional analysis of CMR scans is mostly reliant on qualitative image analysis and basic geometric quantifiers. Small proof-of-concept studies have demonstrated the feasibility and superior diagnostic accuracy of CMR radiomics analysis over conventional reporting. CMR radiomics has the potential to transform our approach to defining image phenotypes and, through this, improve diagnostic accuracy, treatment selection, and prognostication. The purpose of this article is to provide an overview of radiomics concepts for clinicians, with particular consideration of application to CMR. We will also review existing literature on CMR radiomics, discuss challenges, and consider directions for future work.
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Affiliation(s)
- Zahra Raisi-Estabragh
- Department of advanced cardiovascular imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK.,Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, EC1A 7BE London, UK
| | - Cristian Izquierdo
- Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Gran Via de les Corts Catalanes, 585, 08007 Barcelona, Spain
| | - Victor M Campello
- Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Gran Via de les Corts Catalanes, 585, 08007 Barcelona, Spain
| | - Carlos Martin-Isla
- Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Gran Via de les Corts Catalanes, 585, 08007 Barcelona, Spain
| | - Akshay Jaggi
- Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Gran Via de les Corts Catalanes, 585, 08007 Barcelona, Spain
| | - Nicholas C Harvey
- MRC Lifecourse Epidemiology Unit, University of Southampton, Tremona Road, Southampton SO16 6YD, UK.,NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Tremona Road, Southampton, SO16 6YD, UK
| | - Karim Lekadir
- Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Gran Via de les Corts Catalanes, 585, 08007 Barcelona, Spain
| | - Steffen E Petersen
- Department of advanced cardiovascular imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK.,Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, EC1A 7BE London, UK
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25
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Willemink MJ, Varga-Szemes A, Schoepf UJ, Codari M, Nieman K, Fleischmann D, Mastrodicasa D. Emerging methods for the characterization of ischemic heart disease: ultrafast Doppler angiography, micro-CT, photon-counting CT, novel MRI and PET techniques, and artificial intelligence. Eur Radiol Exp 2021; 5:12. [PMID: 33763754 PMCID: PMC7991013 DOI: 10.1186/s41747-021-00207-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 01/22/2021] [Indexed: 12/24/2022] Open
Abstract
After an ischemic event, disruptive changes in the healthy myocardium may gradually develop and may ultimately turn into fibrotic scar. While these structural changes have been described by conventional imaging modalities mostly on a macroscopic scale-i.e., late gadolinium enhancement at magnetic resonance imaging (MRI)-in recent years, novel imaging methods have shown the potential to unveil an even more detailed picture of the postischemic myocardial phenomena. These new methods may bring advances in the understanding of ischemic heart disease with potential major changes in the current clinical practice. In this review article, we provide an overview of the emerging methods for the non-invasive characterization of ischemic heart disease, including coronary ultrafast Doppler angiography, photon-counting computed tomography (CT), micro-CT (for preclinical studies), low-field and ultrahigh-field MRI, and 11C-methionine positron emission tomography. In addition, we discuss new opportunities brought by artificial intelligence, while addressing promising future scenarios and the challenges for the application of artificial intelligence in the field of cardiac imaging.
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Affiliation(s)
- Martin J. Willemink
- grid.168010.e0000000419368956Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94035 USA
| | - Akos Varga-Szemes
- grid.259828.c0000 0001 2189 3475Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC USA
| | - U. Joseph Schoepf
- grid.259828.c0000 0001 2189 3475Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC USA
| | - Marina Codari
- grid.168010.e0000000419368956Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94035 USA
| | - Koen Nieman
- grid.168010.e0000000419368956Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA USA ,Stanford Cardiovascular Institute, Stanford, CA 94305 USA
| | - Dominik Fleischmann
- grid.168010.e0000000419368956Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94035 USA ,Stanford Cardiovascular Institute, Stanford, CA 94305 USA
| | - Domenico Mastrodicasa
- Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94035, USA. .,Stanford Cardiovascular Institute, Stanford, CA, 94305, USA.
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26
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Di Renzi P, Coniglio A, Abella A, Belligotti E, Rossi P, Pasqualetti P, Simonelli I, Della Longa G. Volumetric histogram-based analysis of cardiac magnetic resonance T1 mapping: A tool to evaluate myocardial diffuse fibrosis. Phys Med 2021; 82:185-191. [PMID: 33662882 DOI: 10.1016/j.ejmp.2021.01.080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 12/09/2020] [Accepted: 01/29/2021] [Indexed: 01/19/2023] Open
Affiliation(s)
- P Di Renzi
- S. Giovanni Calibita Hospital, Fatebenefratelli Hospital, Isola Tiberina, Department of Radiology, Rome, Italy
| | - A Coniglio
- S. Giovanni Calibita, Fatebenefratelli Hospital, Isola Tiberina, Department of Medical Physics, Rome, Italy; ASL Roma 1, PO San Filippo Neri, Department of Medical Physics, Rome, Italy.
| | - A Abella
- S. Giovanni Calibita Hospital, Fatebenefratelli Hospital, Isola Tiberina, Department of Radiology, Rome, Italy
| | - E Belligotti
- Ospedali Riuniti Marche Nord, Department of Medical Physics and High Technologies, Pesaro, Italy
| | - P Rossi
- S. Giovanni Calibita Hospital, Fatebenefratelli Hospital, Isola Tiberina, Arrhythmology Unit, Rome, Italy
| | - P Pasqualetti
- Department of Public Health and Infectious Diseases, Section of Health Statistics and Biometry, Sapienza University of Rome, Italy
| | - I Simonelli
- Fatebenefratelli Foundation for Health Research and Education, AFaR Division, Rome, Italy
| | - G Della Longa
- S. Giovanni Calibita Hospital, Fatebenefratelli Hospital, Isola Tiberina, Department of Radiology, Rome, Italy
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27
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Raisi-Estabragh Z, Gkontra P, Jaggi A, Cooper J, Augusto J, Bhuva AN, Davies RH, Manisty CH, Moon JC, Munroe PB, Harvey NC, Lekadir K, Petersen SE. Repeatability of Cardiac Magnetic Resonance Radiomics: A Multi-Centre Multi-Vendor Test-Retest Study. Front Cardiovasc Med 2020; 7:586236. [PMID: 33344517 PMCID: PMC7738466 DOI: 10.3389/fcvm.2020.586236] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 11/02/2020] [Indexed: 12/31/2022] Open
Abstract
Aims: To evaluate the repeatability of cardiac magnetic resonance (CMR) radiomics features on test-retest scanning using a multi-centre multi-vendor dataset with a varied case-mix. Methods and Results: The sample included 54 test-retest studies from the VOLUMES resource (thevolumesresource.com). Images were segmented according to a pre-defined protocol to select three regions of interest (ROI) in end-diastole and end-systole: right ventricle, left ventricle (LV), and LV myocardium. We extracted radiomics shape features from all three ROIs and, additionally, first-order and texture features from the LV myocardium. Overall, 280 features were derived per study. For each feature, we calculated intra-class correlation coefficient (ICC), within-subject coefficient of variation, and mean relative difference. We ranked robustness of features according to mean ICC stratified by feature category, ROI, and cardiac phase, demonstrating a wide range of repeatability. There were features with good and excellent repeatability (ICC ≥ 0.75) within all feature categories and ROIs. A high proportion of first-order and texture features had excellent repeatability (ICC ≥ 0.90), however, these categories also contained features with the poorest repeatability (ICC < 0.50). Conclusion: CMR radiomic features have a wide range of repeatability. This paper is intended as a reference for future researchers to guide selection of the most robust features for clinical CMR radiomics models. Further work in larger and richer datasets is needed to further define the technical performance and clinical utility of CMR radiomics.
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Affiliation(s)
- Zahra Raisi-Estabragh
- NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom.,Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Polyxeni Gkontra
- Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Akshay Jaggi
- Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Jackie Cooper
- NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - João Augusto
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom.,Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Anish N Bhuva
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom.,Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Rhodri H Davies
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom.,Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Charlotte H Manisty
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom.,Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - James C Moon
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom.,Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Patricia B Munroe
- NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Nicholas C Harvey
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, United Kingdom.,NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom
| | - Karim Lekadir
- Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Steffen E Petersen
- NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom.,Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
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28
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Abstract
PURPOSE OF REVIEW The aim of this structured review is to summarize the current research applications and opportunities arising from artificial intelligence (AI) and texture analysis with regard to cardiac imaging. RECENT FINDINGS Current research findings suggest tremendous potential for AI in cardiac imaging, especially with regard to objective image analyses, overcoming the limitations of an observer-dependent subjective image interpretation. Researchers have used this technique across multiple imaging modalities, for instance to detect myocardial scars in cardiac MR imaging, to predict contrast enhancement in non-contrast studies, and to improve image acquisition and reconstruction. AI in medical imaging has the potential to provide novel, much-needed applications for improving patient care pertaining to the cardiovascular system. While several shortcomings are still present in the current methodology, AI may serve as a resourceful assistant to radiologists and clinicians alike.
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29
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Zhang J, Liu A, Gao M, Chen X, Zhang X, Chen X. ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network. Artif Intell Med 2020; 106:101856. [DOI: 10.1016/j.artmed.2020.101856] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 03/05/2020] [Accepted: 04/02/2020] [Indexed: 01/16/2023]
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30
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Lin A, Kolossváry M, Išgum I, Maurovich-Horvat P, Slomka PJ, Dey D. Artificial intelligence: improving the efficiency of cardiovascular imaging. Expert Rev Med Devices 2020; 17:565-577. [PMID: 32510252 PMCID: PMC7382901 DOI: 10.1080/17434440.2020.1777855] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 06/01/2020] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Artificial intelligence (AI) describes the use of computational techniques to mimic human intelligence. In healthcare, this typically involves large medical datasets being used to predict a diagnosis, identify new disease genotypes or phenotypes, or guide treatment strategies. Noninvasive imaging remains a cornerstone for the diagnosis, risk stratification, and management of patients with cardiovascular disease. AI can facilitate every stage of the imaging process, from acquisition and reconstruction, to segmentation, measurement, interpretation, and subsequent clinical pathways. AREAS COVERED In this paper, we review state-of-the-art AI techniques and their current applications in cardiac imaging, and discuss the future role of AI as a precision medicine tool. EXPERT OPINION Cardiovascular medicine is primed for scalable AI applications which can interpret vast amounts of clinical and imaging data in greater depth than ever before. AI-augmented medical systems have the potential to improve workflow and provide reproducible and objective quantitative results which can inform clinical decisions. In the foreseeable future, AI may work in the background of cardiac image analysis software and routine clinical reporting, automatically collecting data and enabling real-time diagnosis and risk stratification.
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Affiliation(s)
- Andrew Lin
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Márton Kolossváry
- MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, Amsterdam, Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, Amsterdam, Netherlands
| | - Pál Maurovich-Horvat
- MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
- Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Piotr J Slomka
- Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
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31
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Di Noto T, von Spiczak J, Mannil M, Gantert E, Soda P, Manka R, Alkadhi H. Radiomics for Distinguishing Myocardial Infarction from Myocarditis at Late Gadolinium Enhancement at MRI: Comparison with Subjective Visual Analysis. Radiol Cardiothorac Imaging 2019; 1:e180026. [PMID: 33778525 DOI: 10.1148/ryct.2019180026] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 08/26/2019] [Accepted: 09/05/2019] [Indexed: 12/21/2022]
Abstract
Purpose To evaluate whether radiomics features of late gadolinium enhancement (LGE) regions at cardiac MRI enable distinction between myocardial infarction (MI) and myocarditis and to compare radiomics with subjective visual analyses by readers with different experience levels. Materials and Methods In this retrospective, institutional review board-approved study, consecutive MRI examinations of 111 patients with MI and 62 patients with myocarditis showing LGE were included. By using open-source software, classification performances attained from two-dimensional (2D) and three-dimensional (3D) texture analysis, shape, and first-order descriptors were compared, applying five different machine learning algorithms. A nested, stratified 10-fold cross-validation was performed. Classification performances were compared through Wilcoxon signed-rank tests. Supervised and unsupervised feature selection techniques were tested; the effect of resampling MR images was analyzed. Subjective image analysis was performed on 2D and 3D image sets by two independent, blinded readers with different experience levels. Results When trained with recursive feature elimination (RFE), a support vector machine achieved the best results (accuracy: 88%) for 2D features, whereas linear discriminant analysis (LDA) showed the highest accuracy (85%) for 3D features (P <.05). When trained with principal component analysis (PCA), LDA attained the highest accuracy with both 2D (86%) and 3D (89%; P =.4) features. Results found for classifiers trained with spline resampling were less accurate than those achieved with one-dimensional (1D) nearest-neighbor interpolation (P <.05), whereas results for classifiers trained with 1D nearest-neighbor interpolation and without resampling were similar (P =.1). As compared with the radiomics approach, subjective visual analysis performance was lower for the less experienced and higher for the experienced reader for both 2D and 3D data. Conclusion Radiomics features of LGE permit the distinction between MI and myocarditis with high accuracy by using either 2D features and RFE or 3D features and PCA.© RSNA, 2019Supplemental material is available for this article.
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Affiliation(s)
- Tommaso Di Noto
- Institute of Diagnostic and Interventional Radiology (T.D.N., J.v.S., M.M., E.G., R.M., H.A.) and Department of Cardiology, University Heart Center (R.M.), University Hospital Zurich, University of Zurich, Raemistr 100, CH-8091, Zurich, Switzerland; Unit of Computer Systems and Bioinformatics, University Campus Bio-Medico of Rome, Rome, Italy (T.D.N., P.S.); and Institute for Biomedical Engineering, University and ETH Zurich (R.M.)
| | - Jochen von Spiczak
- Institute of Diagnostic and Interventional Radiology (T.D.N., J.v.S., M.M., E.G., R.M., H.A.) and Department of Cardiology, University Heart Center (R.M.), University Hospital Zurich, University of Zurich, Raemistr 100, CH-8091, Zurich, Switzerland; Unit of Computer Systems and Bioinformatics, University Campus Bio-Medico of Rome, Rome, Italy (T.D.N., P.S.); and Institute for Biomedical Engineering, University and ETH Zurich (R.M.)
| | - Manoj Mannil
- Institute of Diagnostic and Interventional Radiology (T.D.N., J.v.S., M.M., E.G., R.M., H.A.) and Department of Cardiology, University Heart Center (R.M.), University Hospital Zurich, University of Zurich, Raemistr 100, CH-8091, Zurich, Switzerland; Unit of Computer Systems and Bioinformatics, University Campus Bio-Medico of Rome, Rome, Italy (T.D.N., P.S.); and Institute for Biomedical Engineering, University and ETH Zurich (R.M.)
| | - Elena Gantert
- Institute of Diagnostic and Interventional Radiology (T.D.N., J.v.S., M.M., E.G., R.M., H.A.) and Department of Cardiology, University Heart Center (R.M.), University Hospital Zurich, University of Zurich, Raemistr 100, CH-8091, Zurich, Switzerland; Unit of Computer Systems and Bioinformatics, University Campus Bio-Medico of Rome, Rome, Italy (T.D.N., P.S.); and Institute for Biomedical Engineering, University and ETH Zurich (R.M.)
| | - Paolo Soda
- Institute of Diagnostic and Interventional Radiology (T.D.N., J.v.S., M.M., E.G., R.M., H.A.) and Department of Cardiology, University Heart Center (R.M.), University Hospital Zurich, University of Zurich, Raemistr 100, CH-8091, Zurich, Switzerland; Unit of Computer Systems and Bioinformatics, University Campus Bio-Medico of Rome, Rome, Italy (T.D.N., P.S.); and Institute for Biomedical Engineering, University and ETH Zurich (R.M.)
| | - Robert Manka
- Institute of Diagnostic and Interventional Radiology (T.D.N., J.v.S., M.M., E.G., R.M., H.A.) and Department of Cardiology, University Heart Center (R.M.), University Hospital Zurich, University of Zurich, Raemistr 100, CH-8091, Zurich, Switzerland; Unit of Computer Systems and Bioinformatics, University Campus Bio-Medico of Rome, Rome, Italy (T.D.N., P.S.); and Institute for Biomedical Engineering, University and ETH Zurich (R.M.)
| | - Hatem Alkadhi
- Institute of Diagnostic and Interventional Radiology (T.D.N., J.v.S., M.M., E.G., R.M., H.A.) and Department of Cardiology, University Heart Center (R.M.), University Hospital Zurich, University of Zurich, Raemistr 100, CH-8091, Zurich, Switzerland; Unit of Computer Systems and Bioinformatics, University Campus Bio-Medico of Rome, Rome, Italy (T.D.N., P.S.); and Institute for Biomedical Engineering, University and ETH Zurich (R.M.)
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Abstract
OBJECTIVE. The purpose of this article is to review the nascent field of radiomics in cardiac MRI. CONCLUSION. Cardiac MRI produces a large number of images in a fairly inefficient manner with sometimes limited clinical application. In the era of precision medicine, there is increasing need for imaging to account for a broader array of diseases in an efficient and objective manner. Radiomics, the extraction and analysis of quantitative imaging features from medical imaging, may offer potential solutions to this need.
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Leiner T, Rueckert D, Suinesiaputra A, Baeßler B, Nezafat R, Išgum I, Young AA. Machine learning in cardiovascular magnetic resonance: basic concepts and applications. J Cardiovasc Magn Reson 2019; 21:61. [PMID: 31590664 PMCID: PMC6778980 DOI: 10.1186/s12968-019-0575-y] [Citation(s) in RCA: 120] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 09/02/2019] [Indexed: 12/18/2022] Open
Abstract
Machine learning (ML) is making a dramatic impact on cardiovascular magnetic resonance (CMR) in many ways. This review seeks to highlight the major areas in CMR where ML, and deep learning in particular, can assist clinicians and engineers in improving imaging efficiency, quality, image analysis and interpretation, as well as patient evaluation. We discuss recent developments in the field of ML relevant to CMR in the areas of image acquisition & reconstruction, image analysis, diagnostic evaluation and derivation of prognostic information. To date, the main impact of ML in CMR has been to significantly reduce the time required for image segmentation and analysis. Accurate and reproducible fully automated quantification of left and right ventricular mass and volume is now available in commercial products. Active research areas include reduction of image acquisition and reconstruction time, improving spatial and temporal resolution, and analysis of perfusion and myocardial mapping. Although large cohort studies are providing valuable data sets for ML training, care must be taken in extending applications to specific patient groups. Since ML algorithms can fail in unpredictable ways, it is important to mitigate this by open source publication of computational processes and datasets. Furthermore, controlled trials are needed to evaluate methods across multiple centers and patient groups.
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Affiliation(s)
- Tim Leiner
- Department of Radiology | E.01.132, Utrecht University Medical Center, Heidelberglaan 100, 3584CX Utrecht, The Netherlands
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College, London, UK
| | - Avan Suinesiaputra
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Bettina Baeßler
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Centre Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Reza Nezafat
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Centre, Harvard Medical School, Boston, MA USA
| | - Ivana Išgum
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Alistair A. Young
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
- Department of Biomedical Engineering, King’s College London, London, UK
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Mavrogeni SI, Markousis-Mavrogenis G, Karapanagiotou O, Toutouzas K, Argyriou P, Velitsista S, Kanoupakis G, Apostolou D, Hautemann D, Sfikakis PP, Tektonidou MG. Silent Myocardial Perfusion Abnormalities Detected by Stress Cardiovascular Magnetic Resonance in Antiphospholipid Syndrome: A Case-Control Study. J Clin Med 2019; 8:E1084. [PMID: 31340567 PMCID: PMC6678220 DOI: 10.3390/jcm8071084] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Revised: 07/12/2019] [Accepted: 07/20/2019] [Indexed: 12/17/2022] Open
Abstract
Objective: To examine the prevalence of silent myocardial ischemia and fibrosis in antiphospholipid syndrome (APS), using stress cardiovascular magnetic resonance (CMR). Methods: Forty-four consecutive APS patients without prior cardiac disease (22 primary APS, 22 systemic lupus erythematosus (SLE)/APS, mean age 44 (12.9) years, 64% women) and 44 age/gender-matched controls were evaluated using CMR at 1.5 T. Steady-state free precession imaging for function assessment and adenosine stress-CMR for perfusion-fibrosis evaluation were employed. The myocardial perfusion reserve index (MPRI), and myocardial fibrosis expressed as late gadolinium enhancement (LGE), were evaluated. Coronary angiography was indicated in patients with LGE. Associations with APS characteristics, classic cardiovascular disease (CVD) risk factors, high-sensitivity CRP (hs-CRP) and high-sensitivity Troponin (hs-TnT) levels were tested. All patients were followed up for 12 months. Results: Median MPRI was significantly lower in APS patients versus controls [1.5 (0.9-1.9) vs. 2.7 (2.2-3.2), p < 0.001], independently of any LGE presence. LGE was detected in 16 (36.3%) patients versus none of controls (p < 0.001); 12/16 were subsequently examined with coronary angiography and only two of them had coronary artery lesions. In multivariable analysis, none of the APS-related and classic CVD risk factors, or hs-CRP and hs-TnT covariates, were significant predictors of abnormal MPRI or LGE. At the twelve month follow-up, three (6.8%) patients experienced coronary artery disease, notably those with the lowest MPRI values. Conclusions: Abnormal MPRI and LGE are common in asymptomatic APS patients, independently so of any APS-related and classic CVD risk factors, or coronary angiography findings in cases with LGE. Stress-CMR is a valuable tool to detect silent myocardial ischemia and fibrosis in APS.
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Affiliation(s)
| | | | | | - Konstantinos Toutouzas
- First Cardiology Department, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | | | | | | | | | - David Hautemann
- Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Petros P Sfikakis
- First Department of Propaedeutic Internal Medicine, Joint Rheumatology Program, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Maria G Tektonidou
- First Department of Propaedeutic Internal Medicine, Joint Rheumatology Program, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece.
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Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients. Comput Biol Chem 2019; 78:481-490. [DOI: 10.1016/j.compbiolchem.2018.11.017] [Citation(s) in RCA: 257] [Impact Index Per Article: 51.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2018] [Accepted: 11/18/2018] [Indexed: 11/18/2022]
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Schofield R, Ganeshan B, Fontana M, Nasis A, Castelletti S, Rosmini S, Treibel TA, Manisty C, Endozo R, Groves A, Moon JC. Texture analysis of cardiovascular magnetic resonance cine images differentiates aetiologies of left ventricular hypertrophy. Clin Radiol 2019; 74:140-149. [PMID: 30527518 DOI: 10.1016/j.crad.2018.09.016] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Accepted: 09/26/2018] [Indexed: 10/28/2022]
Abstract
AIM To investigate whether unenhanced cardiovascular magnetic resonance (CMR) balanced steady state free precession (bSSFP) cine images could be analysed using textural analysis (TA) software to differentiate different aetiologies of disease causing increased myocardial wall thickness (left ventricular hypertrophy [LVH]) and indicate the severity of myocardial tissue abnormality. MATERIALS AND METHODS A mid short axis unenhanced cine frame of 216 patients comprising 50 cases of hypertrophic cardiomyopathy (HCM; predominantly Left ventricular outflow tract obstruction [LVOTO] subtype), 52 cases of cardiac amyloid (CA; predominantly AL: light chain subtype), 68 cases of aortic stenosis (AS), 15 hypertensive patients with LVH (HTN+LVH), and 31 healthy volunteers (HV) underwent TA of the CMR cine images (CMRTA) using TexRAD (TexRAD Ltd, Cambridge, UK). Among the HV, 16/31 were scanned twice to form a test-retest reproducibility cohort. CMRTA comprised a filtration-histogram technique to extract and quantify features using six parameters. RESULTS Test-retest analysis in the HV showed a medium filter (3 mm) was the most reproducible (intra-class correlation of 0.9 for kurtosis and skewness and 0.8 for mean and SD). Disease cohorts were statistically different (p<0.001) to HV for all parameters. Pairwise comparisons of CMRTA parameters showed kurtosis and skewness was consistently significant in ranking the degree of difference from HV (greatest to least): CA, HCM, LVH+HTN, AS (p<0.001). Similarly, mean, standard deviation, entropy, and mean positive pixel (MPP) were consistent in ranking degree of difference from HV: HCM, CA, AS and HTN+LVH. CONCLUSION Radiomic features of bSSFP CMR data sets derived using TA show promise in discriminating between the aetiologies of LVH.
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Affiliation(s)
- R Schofield
- Bart's Heart Centre, London, UK; Institute of Cardiovascular Science, University College London, UK.
| | - B Ganeshan
- Institute of Nuclear Medicine, University College London, UK
| | - M Fontana
- National Amyloid Centre, Royal Free Hospital, London, UK
| | - A Nasis
- Monash Cardiovascular Research Centre, Monash University Department of Medicine (MMC), Melbourne, Australia
| | | | | | - T A Treibel
- Bart's Heart Centre, London, UK; Institute of Cardiovascular Science, University College London, UK
| | - C Manisty
- Bart's Heart Centre, London, UK; Institute of Cardiovascular Science, University College London, UK
| | - R Endozo
- Institute of Nuclear Medicine, University College London, UK
| | - A Groves
- Institute of Nuclear Medicine, University College London, UK
| | - J C Moon
- Bart's Heart Centre, London, UK; Institute of Cardiovascular Science, University College London, UK
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Radiomic Analysis of Myocardial Native T 1 Imaging Discriminates Between Hypertensive Heart Disease and Hypertrophic Cardiomyopathy. JACC Cardiovasc Imaging 2019; 12:1946-1954. [PMID: 30660549 DOI: 10.1016/j.jcmg.2018.11.024] [Citation(s) in RCA: 110] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 10/20/2018] [Accepted: 11/07/2018] [Indexed: 02/06/2023]
Abstract
OBJECTIVES This study sought to examine the diagnostic ability of radiomic texture analysis (TA) on quantitative cardiovascular magnetic resonance images to differentiate between hypertensive heart disease (HHD) and hypertrophic cardiomyopathy (HCM). BACKGROUND HHD and HCM are associated with increased left ventricular wall thickness (LVWT). Contemporary guidelines define HCM as LVWT ≥15 mm that is unexplained by other disease, which complicates diagnosis in cases of co-occurrences. Conventional global native T1 mapping involves calculation of mean T1 values as a surrogate for fibrosis. However, there may be differences in its spatial localization, such as diffuse and more focal fibrosis in HHD and HCM, respectively. METHODS This study identified 232 subjects (53 with HHD, 108 with HCM, and 71 control subjects) for TA who consecutively underwent free-breathing multislice native T1 mapping. Four sets of texture descriptors were applied to capture spatially dependent and independent pixel statistics. Six texture features were sequentially selected with the best discriminatory capacity between HHD and HCM and were tested using a support vector machine (SVM) classifier. Each disease group was randomly split 4:1 (feature selection/test validation), in which the reproducibility of the pattern was analyzed in the test validation dataset. RESULTS The selected texture features provided the maximum diagnostic accuracy of 86.2% (c-statistic: 0.820; 95% confidence interval [CI]: 0.769 to 0.903) using the SVM. For the test validation dataset, the accuracy of the pattern remained high at 80.0% (c-statistic: 0.89; 95% CI: 0.77 to 1.00). Global native T1, with an accuracy of 64%, separated HHD and HCM patients modestly (c-statistic: 0.549; 95% CI: 0.452 to 0.640). CONCLUSIONS Radiomics analysis of native T1 images discriminates between HHD and HCM patients and provides incremental value over global native T1 mapping.
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Tran TT, Pham VT, Lin C, Yang HW, Wang YH, Shyu KK, Tseng WYI, Su MYM, Lin LY, Lo MT. Empirical Mode Decomposition and Monogenic Signal-Based Approach for Quantification of Myocardial Infarction From MR Images. IEEE J Biomed Health Inform 2018; 23:731-743. [PMID: 29994104 DOI: 10.1109/jbhi.2018.2821675] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Quantification of myocardial infarction on late Gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) images into heterogeneous infarct periphery (or gray zone) and infarct core plays an important role in cardiac diagnosis, especially in identifying patients at high risk of cardiovascular mortality. However, quantification task is challenging due to noise corrupted in cardiac MR images, the contrast variation, and limited resolution of images. In this study, we propose a novel approach for automatic myocardial infarction quantification, termed DEMPOT, which consists of three key parts: Decomposition of image into intrinsic modes, monogenic phase performing on combined dominant modes, and multilevel Otsu thresholding on the phase. In particular, inspired by the Hilbert-Huang transform, we perform the multidimensional ensemble empirical mode decomposition and 2-D generalization of the Hilbert transform known as the Riesz transform on the MR image to obtain the monogenic phase that is robust to noise and contrast variation. Then, a two-stage algorithm using multilevel Otsu thresholding is accomplished on the monogenic phase to automatically quantify the myocardium into healthy, gray zone, and infarct core regions. Experiments on LGE-CMR images with myocardial infarction from 82 patients show the superior performance of the proposed approach in terms of reproducibility, robustness, and effectiveness.
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Kadi I, Idri A, Fernandez-Aleman JL. Systematic mapping study of data mining–based empirical studies in cardiology. Health Informatics J 2017; 25:741-770. [DOI: 10.1177/1460458217717636] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Data mining provides the methodology and technology to transform huge amount of data into useful information for decision making. It is a powerful process to extract knowledge and discover new patterns embedded in large data sets. Data mining has been increasingly used in medicine, particularly in cardiology. In fact, data mining applications can greatly benefits all parts involved in cardiology such as patients, cardiologists and nurses. This article aims to perform a systematic mapping study so as to analyze and synthesize empirical studies on the application of data mining techniques in cardiology. A total of 142 articles published between 2000 and 2015 were therefore selected, studied and analyzed according to the four following criteria: year and channel of publication, research type, medical task and empirical type. The results of this mapping study are discussed and a list of recommendations for researchers and cardiologists is provided.
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Affiliation(s)
| | - Ali Idri
- Mohammed V University in Rabat, Morocco
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40
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Kadi I, Idri A, Fernandez-Aleman J. Knowledge discovery in cardiology: A systematic literature review. Int J Med Inform 2017; 97:12-32. [DOI: 10.1016/j.ijmedinf.2016.09.005] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2016] [Revised: 09/01/2016] [Accepted: 09/11/2016] [Indexed: 11/24/2022]
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