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Marfisi D, Giannelli M, Marzi C, Del Meglio J, Barucci A, Masturzo L, Vignali C, Mascalchi M, Traino A, Casolo G, Diciotti S, Tessa C. Test-retest repeatability of myocardial radiomic features from quantitative cardiac magnetic resonance T1 and T2 mapping. Magn Reson Imaging 2024; 113:110217. [PMID: 39067653 DOI: 10.1016/j.mri.2024.110217] [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: 03/23/2024] [Revised: 06/14/2024] [Accepted: 07/23/2024] [Indexed: 07/30/2024]
Abstract
Radiomics of cardiac magnetic resonance (MR) imaging has proved to be potentially useful in the study of various myocardial diseases. Therefore, assessing the repeatability degree in radiomic features measurement is of fundamental importance. The aim of this study was to assess test-retest repeatability of myocardial radiomic features extracted from quantitative T1 and T2 maps. A representative group of 24 subjects (mean age 54 ± 18 years) referred for clinical cardiac MR imaging were enrolled in the study. For each subject, T1 and T2 mapping through MOLLI and T2-prepared TrueFISP acquisition sequences, respectively, were performed at 1.5 T. Then, 98 radiomic features of different classes (shape, first-order, second-order) were extracted from a region of interest encompassing the whole left ventricle myocardium in a short axis slice. The repeatability was assessed performing different and complementary analyses: intraclass correlation coefficient (ICC) and limits of agreement (LOA) (i.e., the interval within which 95% of the percentage differences between two repeated measures are expected to lie). Radiomic features were characterized by a relatively wide range of repeatability degree in terms of both ICC and LOA. Overall, 44.9% and 38.8% of radiomic features showed ICC values > 0.75 for T1 and T2 maps, respectively, while 25.5% and 23.4% of radiomic features showed LOA between ±10%. A subset of radiomic features for T1 (Mean, Median, 10Percentile, 90Percentile, RootMeanSquared, Imc2, RunLengthNonUniformityNormalized, RunPercentage and ShortRunEmphasis) and T2 (MaximumDiameter, RunLengthNonUniformityNormalized, RunPercentage, ShortRunEmphasis) maps presented both ICC > 0.75 and LOA between ±5%. Overall, radiomic features extracted from T1 maps showed better repeatability performance than those extracted from T2 maps, with shape features characterized by better repeatability than first-order and textural features. Moreover, only a limited subset of 9 and 4 radiomic features for T1 and T2 maps, respectively, showed high repeatability degree in terms of both ICC and LOA. These results confirm the importance of assessing test-retest repeatability degree in radiomic feature estimation and might be useful for a more effective/reliable use of myocardial T1 and T2 mapping radiomics in clinical or research studies.
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Affiliation(s)
- Daniela Marfisi
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", 56126 Pisa, Italy
| | - Marco Giannelli
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", 56126 Pisa, Italy.
| | - Chiara Marzi
- Department of Statistics, Computer Science, Applications "Giuseppe Parenti", University of Florence, 50134 Florence, Italy
| | - Jacopo Del Meglio
- Unit of Cardiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041 Lido di Camaiore, Italy
| | - Andrea Barucci
- Institute of Applied Physics "Nello Carrara" (IFAC), Council of National Research (CNR), 50019 Sesto Fiorentino, Italy
| | - Luigi Masturzo
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", 56126 Pisa, Italy
| | - Claudio Vignali
- Unit of Radiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041 Lido di Camaiore, Italy
| | - Mario Mascalchi
- Department of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, 50121 Florence, Italy; Clinical Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50139 Florence, Italy
| | - Antonio Traino
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", 56126 Pisa, Italy
| | - Giancarlo Casolo
- Unit of Cardiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041 Lido di Camaiore, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, 47522 Cesena, Italy
| | - Carlo Tessa
- Unit of Radiology, Azienda USL Toscana Nord Ovest, Apuane Hospital, 54100 Massa, Italy
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Aromiwura AA, Cavalcante JL, Kwong RY, Ghazipour A, Amini A, Bax J, Raman S, Pontone G, Kalra DK. The role of artificial intelligence in cardiovascular magnetic resonance imaging. Prog Cardiovasc Dis 2024:S0033-0620(24)00092-6. [PMID: 38925255 DOI: 10.1016/j.pcad.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2024] [Accepted: 06/23/2024] [Indexed: 06/28/2024]
Abstract
Cardiovascular magnetic resonance (CMR) imaging is the gold standard test for myocardial tissue characterization and chamber volumetric and functional evaluation. However, manual CMR analysis can be time-consuming and is subject to intra- and inter-observer variability. Artificial intelligence (AI) is a field that permits automated task performance through the identification of high-level and complex data relationships. In this review, we review the rapidly growing role of AI in CMR, including image acquisition, sequence prescription, artifact detection, reconstruction, segmentation, and data reporting and analysis including quantification of volumes, function, myocardial infarction (MI) and scar detection, and prediction of outcomes. We conclude with a discussion of the emerging challenges to widespread adoption and solutions that will allow for successful, broader uptake of this powerful technology.
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Affiliation(s)
| | | | - Raymond Y Kwong
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Aryan Ghazipour
- Medical Imaging Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA
| | - Amir Amini
- Medical Imaging Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA
| | - Jeroen Bax
- Department of Cardiology, Leiden University, Leiden, the Netherlands
| | - Subha Raman
- Division of Cardiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Gianluca Pontone
- Department of Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, University of Milan, Milan, Italy
| | - Dinesh K Kalra
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA; Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA.
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Baeßler B, Engelhardt S, Hekalo A, Hennemuth A, Hüllebrand M, Laube A, Scherer C, Tölle M, Wech T. Perfect Match: Radiomics and Artificial Intelligence in Cardiac Imaging. Circ Cardiovasc Imaging 2024; 17:e015490. [PMID: 38889216 DOI: 10.1161/circimaging.123.015490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
Abstract
Cardiovascular diseases remain a significant health burden, with imaging modalities like echocardiography, cardiac computed tomography, and cardiac magnetic resonance imaging playing a crucial role in diagnosis and prognosis. However, the inherent heterogeneity of these diseases poses challenges, necessitating advanced analytical methods like radiomics and artificial intelligence. Radiomics extracts quantitative features from medical images, capturing intricate patterns and subtle variations that may elude visual inspection. Artificial intelligence techniques, including deep learning, can analyze these features to generate knowledge, define novel imaging biomarkers, and support diagnostic decision-making and outcome prediction. Radiomics and artificial intelligence thus hold promise for significantly enhancing diagnostic and prognostic capabilities in cardiac imaging, paving the way for more personalized and effective patient care. This review explores the synergies between radiomics and artificial intelligence in cardiac imaging, following the radiomics workflow and introducing concepts from both domains. Potential clinical applications, challenges, and limitations are discussed, along with solutions to overcome them.
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Affiliation(s)
- Bettina Baeßler
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Germany (B.B., A. Hekalo, T.W.)
| | - Sandy Engelhardt
- Department of Internal Medicine III, Heidelberg University Hospital, Germany (S.E., M.T.)
- DZHK (German Centre for Cardiovascular Research), partner site Heidelberg/Mannheim (S.E., M.T.)
| | - Amar Hekalo
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Germany (B.B., A. Hekalo, T.W.)
| | - Anja Hennemuth
- Deutsches Herzzentrum der Charité, Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany (A. Hennemuth, M.H., A.L.)
- Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt Universität zu Berlin, Germany (A. Hennemuth, M.H., A.L.)
- Fraunhofer Institute for Digital Medicine MEVIS, Berlin, Germany (A. Hennemuth, M.H.)
- DZHK (German Centre for Cardiovascular Research), partner site Berlin (A. Hennemuth, M.H., A.L.)
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Germany (A. Hennemuth)
| | - Markus Hüllebrand
- Deutsches Herzzentrum der Charité, Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany (A. Hennemuth, M.H., A.L.)
- Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt Universität zu Berlin, Germany (A. Hennemuth, M.H., A.L.)
- Fraunhofer Institute for Digital Medicine MEVIS, Berlin, Germany (A. Hennemuth, M.H.)
- DZHK (German Centre for Cardiovascular Research), partner site Berlin (A. Hennemuth, M.H., A.L.)
| | - Ann Laube
- Deutsches Herzzentrum der Charité, Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany (A. Hennemuth, M.H., A.L.)
- Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt Universität zu Berlin, Germany (A. Hennemuth, M.H., A.L.)
- DZHK (German Centre for Cardiovascular Research), partner site Berlin (A. Hennemuth, M.H., A.L.)
| | - Clemens Scherer
- Department of Medicine I, LMU University Hospital, LMU Munich, Germany (C.S.)
- Munich Heart Alliance, German Center for Cardiovascular Research (DZHK), Germany (C.S.)
| | - Malte Tölle
- Department of Internal Medicine III, Heidelberg University Hospital, Germany (S.E., M.T.)
- DZHK (German Centre for Cardiovascular Research), partner site Heidelberg/Mannheim (S.E., M.T.)
| | - Tobias Wech
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Germany (B.B., A. Hekalo, T.W.)
- Comprehensive Heart Failure Center (CHFC), University Hospital Würzburg, Germany (T.W.)
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Xie H, Song C, Jian L, Guo Y, Li M, Luo J, Li Q, Tan T. A deep learning-based radiomics model for predicting lymph node status from lung adenocarcinoma. BMC Med Imaging 2024; 24:121. [PMID: 38789936 PMCID: PMC11127329 DOI: 10.1186/s12880-024-01300-w] [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: 03/06/2023] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
OBJECTIVES At present, there are many limitations in the evaluation of lymph node metastasis of lung adenocarcinoma. Currently, there is a demand for a safe and accurate method to predict lymph node metastasis of lung cancer. In this study, radiomics was used to accurately predict the lymph node status of lung adenocarcinoma patients based on contrast-enhanced CT. METHODS A total of 503 cases that fulfilled the analysis requirements were gathered from two distinct hospitals. Among these, 287 patients exhibited lymph node metastasis (LNM +) while 216 patients were confirmed to be without lymph node metastasis (LNM-). Using both traditional and deep learning methods, 22,318 features were extracted from the segmented images of each patient's enhanced CT. Then, the spearman test and the least absolute shrinkage and selection operator were used to effectively reduce the dimension of the feature data, enabling us to focus on the most pertinent features and enhance the overall analysis. Finally, the classification model of lung adenocarcinoma lymph node metastasis was constructed by machine learning algorithm. The Accuracy, AUC, Specificity, Precision, Recall and F1 were used to evaluate the efficiency of the model. RESULTS By incorporating a comprehensively selected set of features, the extreme gradient boosting method (XGBoost) effectively distinguished the status of lymph nodes in patients with lung adenocarcinoma. The Accuracy, AUC, Specificity, Precision, Recall and F1 of the prediction model performance on the external test set were 0.765, 0.845, 0.705, 0.784, 0.811 and 0.797, respectively. Moreover, the decision curve analysis, calibration curve and confusion matrix of the model on the external test set all indicated the stability and accuracy of the model. CONCLUSIONS Leveraging enhanced CT images, our study introduces a noninvasive classification prediction model based on the extreme gradient boosting method. This approach exhibits remarkable precision in identifying the lymph node status of lung adenocarcinoma patients, offering a safe and accurate alternative to invasive procedures. By providing clinicians with a reliable tool for diagnosing and assessing disease progression, our method holds the potential to significantly improve patient outcomes and enhance the overall quality of clinical practice.
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Affiliation(s)
- Hui Xie
- Department of Radiation Oncology, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, People's Republic of China
| | - Chaoling Song
- School of Medical Imaging, Laboratory Science and Rehabilitation, Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China
| | - Lei Jian
- School of Medical Imaging, Laboratory Science and Rehabilitation, Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China
| | - Yeang Guo
- School of Medical Imaging, Laboratory Science and Rehabilitation, Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China
| | - Mei Li
- School of Medical Imaging, Laboratory Science and Rehabilitation, Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China
| | - Jiang Luo
- School of Medical Imaging, Laboratory Science and Rehabilitation, Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China
| | - Qing Li
- Department of Radiation Oncology, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China
| | - Tao Tan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, People's Republic of China.
- Department of Radiology and Nuclear Medicine, Radboud University Medical Centre, Nijmegen, Netherlands.
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Huang X, Wang X, Liu Y, Wang Z, Li S, Kuang P. Contrast-enhanced CT-based radiomics differentiate anterior mediastinum lymphoma from thymoma without myasthenia gravis and calcification. Clin Radiol 2024; 79:e500-e510. [PMID: 38242804 DOI: 10.1016/j.crad.2023.12.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 10/30/2023] [Accepted: 12/19/2023] [Indexed: 01/21/2024]
Abstract
AIM To explore the value of a radiomics model based on enhanced computed tomography (CT) in differentiating anterior mediastinal lymphoma (AML) and thymoma without myasthenia gravis (MG) and calcification. MATERIALS AND METHODS The present study analysed patients who were diagnosed histologically with AML and thymoma in three independent institutions. All pre-treatment patients underwent enhanced CT. In the training group of patients from institutions 1 (the First Affiliated Hospital of Kunming Medical University) and 3 (the Yunnan Cancer Hospital), two radiologists independently analysed the enhanced CT images and performed manual segmentation of each tumour. Radiomics features were screened using interobserver interclass coefficient (ICC) analysis, feature correlation analysis, and L1 regularisation. The discriminative efficacy of the logistic regression model was evaluated using receiver operating characteristic (ROC) analysis. Validation group of patients from institution 2 (the Second Affiliated Hospital of Zhejiang University School of Medicine) was used to validate the proposed models. RESULTS A total of 114 patients were enrolled in this study and 1,743 radiomics features were extracted from the enhanced CT images. After feature screening, the remaining 37 robust radiomics features were used to construct the model. In the training group, the AUC of the model was 0.987 (95% confidence interval [CI]: 0.976-0.999), the sensitivity, specificity, and accuracy were 0.912, 0.946, and 0.924, respectively. In the validation group, the AUC of the model was 0.798 (95% CI: 0.683-0.913), the sensitivity, specificity, and accuracy were 0.760, 0.700, and 0.743, respectively. CONCLUSION The radiomics model created provided effective information to assist in the selection of clinical strategies, thus reducing unnecessary procedures in patients with AML and guiding direct surgery in patients with thymoma to avoid biopsy.
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Affiliation(s)
- X Huang
- Department of Radiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - X Wang
- Department of Thoracic Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Y Liu
- Department of Radiology, Yunnan Cancer Hospital, Kunming, China
| | - Z Wang
- Department of Radiology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - S Li
- School of Basic Medical Sciences, Kunming Medical University, Kunming, Yunnan, China
| | - P Kuang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
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Hesse K, Khanji MY, Aung N, Dabbagh GS, Petersen SE, Chahal CAA. Assessing heterogeneity on cardiovascular magnetic resonance imaging: a novel approach to diagnosis and risk stratification in cardiac diseases. Eur Heart J Cardiovasc Imaging 2024; 25:437-445. [PMID: 37982176 DOI: 10.1093/ehjci/jead285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 10/13/2023] [Accepted: 10/16/2023] [Indexed: 11/21/2023] Open
Abstract
Cardiac disease affects the heart non-uniformly. Examples include focal septal or apical hypertrophy with reduced strain in hypertrophic cardiomyopathy, replacement fibrosis with akinesia in an infarct-related coronary artery territory, and a pattern of scarring in dilated cardiomyopathy. The detail and versatility of cardiovascular magnetic resonance (CMR) imaging mean it contains a wealth of information imperceptible to the naked eye and not captured by standard global measures. CMR-derived heterogeneity biomarkers could facilitate early diagnosis, better risk stratification, and a more comprehensive prediction of treatment response. Small cohort and case-control studies demonstrate the feasibility of proof-of-concept structural and functional heterogeneity measures. Detailed radiomic analyses of different CMR sequences using open-source software delineate unique voxel patterns as hallmarks of histopathological changes. Meanwhile, measures of dispersion applied to emerging CMR strain sequences describe variable longitudinal, circumferential, and radial function across the myocardium. Two of the most promising heterogeneity measures are the mean absolute deviation of regional standard deviations on native T1 and T2 and the standard deviation of time to maximum regional radial wall motion, termed the tissue synchronization index in a 16-segment left ventricle model. Real-world limitations include the non-standardization of CMR imaging protocols across different centres and the testing of large numbers of radiomic features in small, inadequately powered patient samples. We, therefore, propose a three-step roadmap to benchmark novel heterogeneity biomarkers, including defining normal reference ranges, statistical modelling against diagnosis and outcomes in large epidemiological studies, and finally, comprehensive internal and external validations.
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Affiliation(s)
- Kerrick Hesse
- Cardiology Department, James Cook University Hospital, Marton Road, Middlesbrough TS4 3BW, UK
- Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK
| | - Mohammed Y Khanji
- Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK
- Newham University Hospital, Barts Health NHS Trust, Geln Road, Plaistow, London E13 8SL, UK
- Barts Heart Centre, Barts Health NHS Trust, St Bartholomew's Hospital, West Smithfield, London EC1A 7BE, UK
| | - Nay Aung
- Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, Barts Health NHS Trust, St Bartholomew's Hospital, West Smithfield, London EC1A 7BE, UK
| | - Ghaith Sharaf Dabbagh
- Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA
- Center for Inherited Cardiovascular Diseases, WellSpan Health, 30 Monument Road, York, PA 17403, USA
| | - Steffen E Petersen
- Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, Barts Health NHS Trust, St Bartholomew's Hospital, West Smithfield, London EC1A 7BE, UK
- Health Data Research UK, Gibbs Building, 215 Euston Road, London NW1 2BE, UK
- Alan Turing Institute, 96 Euston Road, London NW1 2DB, UK
| | - C Anwar A Chahal
- Barts Heart Centre, Barts Health NHS Trust, St Bartholomew's Hospital, West Smithfield, London EC1A 7BE, UK
- Center for Inherited Cardiovascular Diseases, WellSpan Health, 30 Monument Road, York, PA 17403, USA
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Str, SW Rochester, MN 55905, USA
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Cau R, Pisu F, Pintus A, Palmisano V, Montisci R, Suri JS, Salgado R, Saba L. Cine-cardiac magnetic resonance to distinguish between ischemic and non-ischemic cardiomyopathies: a machine learning approach. Eur Radiol 2024:10.1007/s00330-024-10640-8. [PMID: 38451322 DOI: 10.1007/s00330-024-10640-8] [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: 08/26/2023] [Revised: 12/11/2023] [Accepted: 01/15/2024] [Indexed: 03/08/2024]
Abstract
OBJECTIVE This work aimed to derive a machine learning (ML) model for the differentiation between ischemic cardiomyopathy (ICM) and non-ischemic cardiomyopathy (NICM) on non-contrast cardiovascular magnetic resonance (CMR). METHODS This retrospective study evaluated CMR scans of 107 consecutive patients (49 ICM, 58 NICM), including atrial and ventricular strain parameters. We used these data to compare an explainable tree-based gradient boosting additive model with four traditional ML models for the differentiation of ICM and NICM. The models were trained and internally validated with repeated cross-validation according to discrimination and calibration. Furthermore, we examined important variables for distinguishing between ICM and NICM. RESULTS A total of 107 patients and 38 variables were available for the analysis. Of those, 49 were ICM (34 males, mean age 60 ± 9 years) and 58 patients were NICM (38 males, mean age 56 ± 19 years). After 10 repetitions of the tenfold cross-validation, the proposed model achieved the highest area under curve (0.82, 95% CI [0.47-1.00]) and lowest Brier score (0.19, 95% CI [0.13-0.27]), showing competitive diagnostic accuracy and calibration. At the Youden's index, sensitivity was 0.72 (95% CI [0.68-0.76]), the highest of all. Analysis of predictions revealed that both atrial and ventricular strain CMR parameters were important for the identification of ICM patients. CONCLUSION The current study demonstrated that using a ML model, multi chamber myocardial strain, and function on non-contrast CMR parameters enables the discrimination between ICM and NICM with competitive diagnostic accuracy. CLINICAL RELEVANCE STATEMENT A machine learning model based on non-contrast cardiovascular magnetic resonance parameters may discriminate between ischemic and non-ischemic cardiomyopathy enabling wider access to cardiovascular magnetic resonance examinations with lower costs and faster imaging acquisition. KEY POINTS • The exponential growth in cardiovascular magnetic resonance examinations may require faster and more cost-effective protocols. • Artificial intelligence models can be utilized to distinguish between ischemic and non-ischemic etiologies. • Machine learning using non-contrast CMR parameters can effectively distinguish between ischemic and non-ischemic cardiomyopathies.
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Affiliation(s)
- Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato s.s. 554 Monserrato, 09045, Cagliari, Italy
| | - Francesco Pisu
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato s.s. 554 Monserrato, 09045, Cagliari, Italy
| | - Alessandra Pintus
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato s.s. 554 Monserrato, 09045, Cagliari, Italy
| | | | - Roberta Montisci
- Department of Cardiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato s.s. 554 Monserrato, 09045, Cagliari, Italy
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | | | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato s.s. 554 Monserrato, 09045, Cagliari, Italy.
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8
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Wang ZC, Fan ZZ, Liu XY, Zhu MJ, Jiang SS, Tian S, Chen BH, Wu LM. Deep Learning for Discrimination of Hypertrophic Cardiomyopathy and Hypertensive Heart Disease on MRI Native T1 Maps. J Magn Reson Imaging 2024; 59:837-848. [PMID: 37431848 DOI: 10.1002/jmri.28904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 06/05/2023] [Accepted: 06/06/2023] [Indexed: 07/12/2023] Open
Abstract
BACKGROUND Native T1 and radiomics were used for hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD) differentiation previously. The current problem is that global native T1 remains modest discrimination performance and radiomics requires feature extraction beforehand. Deep learning (DL) is a promising technique in differential diagnosis. However, its feasibility for discriminating HCM and HHD has not been investigated. PURPOSE To examine the feasibility of DL in differentiating HCM and HHD based on T1 images and compare its diagnostic performance with other methods. STUDY TYPE Retrospective. POPULATION 128 HCM patients (men, 75; age, 50 years ± 16) and 59 HHD patients (men, 40; age, 45 years ± 17). FIELD STRENGTH/SEQUENCE 3.0T; Balanced steady-state free precession, phase-sensitive inversion recovery (PSIR) and multislice native T1 mapping. ASSESSMENT Compare HCM and HHD patients baseline data. Myocardial T1 values were extracted from native T1 images. Radiomics was implemented through feature extraction and Extra Trees Classifier. The DL network is ResNet32. Different input including myocardial ring (DL-myo), myocardial ring bounding box (DL-box) and the surrounding tissue without myocardial ring (DL-nomyo) were tested. We evaluate diagnostic performance through AUC of ROC curve. STATISTICAL TESTS Accuracy, sensitivity, specificity, ROC, and AUC were calculated. Independent t test, Mann-Whitney U-test and Chi-square test were adopted for HCM and HHD comparison. P < 0.05 was considered statistically significant. RESULTS DL-myo, DL-box, and DL-nomyo models showed an AUC (95% confidential interval) of 0.830 (0.702-0.959), 0.766 (0.617-0.915), 0.795 (0.654-0.936) in the testing set. AUC of native T1 and radiomics were 0.545 (0.352-0.738) and 0.800 (0.655-0.944) in the testing set. DATA CONCLUSION The DL method based on T1 mapping seems capable of discriminating HCM and HHD. Considering diagnostic performance, the DL network outperformed the native T1 method. Compared with radiomics, DL won an advantage for its high specificity and automated working mode. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Zi-Chen Wang
- Ottawa-Shanghai Joint School of Medicine, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Zhang-Zhengyi Fan
- Ottawa-Shanghai Joint School of Medicine, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xi-Yuan Liu
- Ottawa-Shanghai Joint School of Medicine, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Ming-Jie Zhu
- Ottawa-Shanghai Joint School of Medicine, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | | | | | - Bing-Hua Chen
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Lian-Ming Wu
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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Munoz C. Editorial for "Left Ventricular Vertical Run-Length Nonuniformity MRI Adds Prognostic Value to MACE in Patients with End-Stage Renal Disease". J Magn Reson Imaging 2024; 59:533-534. [PMID: 37436174 DOI: 10.1002/jmri.28888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/15/2023] [Accepted: 06/16/2023] [Indexed: 07/13/2023] Open
Abstract
Level of Evidence2Technical Efficacy Stage2
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Affiliation(s)
- Camila Munoz
- National Heart and Lung Institute, Imperial College London, London, UK
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10
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Zhang TY, An DA, Zhou H, Chen B, Lu R, Fang W, Wang Q, Huang J, Jin H, Shen J, Zhou Y, Hu J, Bautista M, Ouchi T, Wu LM, Mou S. Left Ventricular Vertical Run-Length Nonuniformity MRI Adds Prognostic Value to MACE in Patients with End-Stage Renal Disease. J Magn Reson Imaging 2024; 59:522-532. [PMID: 37203257 DOI: 10.1002/jmri.28792] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 05/02/2023] [Accepted: 05/02/2023] [Indexed: 05/20/2023] Open
Abstract
BACKGROUND Vertical run-length nonuniformity (VRLN) is a texture feature representing heterogeneity within native T1 images and reflects the extent of cardiac fibrosis. In uremic cardiomyopathy, interstitial fibrosis was the major histological alteration. The prognostic value of VRLN in patients with end-stage renal disease (ESRD) remains unclear. PURPOSE To evaluate the prognostic value of VRLN MRI in patients with ESRD. STUDY TYPE Prospective. POPULATION A total of 127 ESRD patients (30 participants in the major adverse cardiac events, MACE group). FIELD STRENGTH/SEQUENCE 3.0 T/steady-state free precession sequence, modified Look-Locker imaging. ASSESSMENT MRI image qualities were assessed by three independent radiologists. VRLN values were measured in the myocardium on the mid-ventricular short-axis slice of T1 mapping. Left ventricular (LV) mass, LV end-diastolic and end-systolic volume, as well as LV global strain cardiac parameters were measured. STATISTICAL TESTS The primary endpoint was the incident of MACE from enrollment time to January 2023. MACE is a composite endpoint consisting of all-cause mortality, acute myocardial infarction, stroke, heart failure hospitalization, and life-threatening arrhythmia. Cox proportional-hazards regression was performed to test whether VRLN independently correlated with MACE. The intraclass correlation coefficients of VRLN were calculated to evaluate intraobserver and interobserver reproducibility. The C-index was computed to examine the prognostic value of VRLN. P-value <0.05 were considered statistically significant. RESULTS Participants were followed for a median of 26 months. VRLN, age, LV end-systolic volume index, and global longitudinal strain remained significantly associated with MACE in the multivariable model. Adding VRLN to a baseline model containing clinical and conventional cardiac MRI parameters significantly improved the accuracy of the predictive model (C-index of the baseline model: 0.781 vs. the model added VRLN: 0.814). DATA CONCLUSION VRLN is a novel marker for risk stratification toward MACE in patients with ESRD, superior to native T1 mapping and LV ejection fraction. EVIDENCE LEVEL 2 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Tian-Yi Zhang
- Department of Nephrology, Molecular Cell Lab for Kidney Disease, Shanghai Peritoneal Dialysis Research Center, Ren Ji Hospital, Uremia Diagnosis and Treatment Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Dong-Aolei An
- Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Hang Zhou
- Department of Nephrology, Molecular Cell Lab for Kidney Disease, Shanghai Peritoneal Dialysis Research Center, Ren Ji Hospital, Uremia Diagnosis and Treatment Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Binghua Chen
- Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Renhua Lu
- Department of Nephrology, Molecular Cell Lab for Kidney Disease, Shanghai Peritoneal Dialysis Research Center, Ren Ji Hospital, Uremia Diagnosis and Treatment Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Wei Fang
- Department of Nephrology, Molecular Cell Lab for Kidney Disease, Shanghai Peritoneal Dialysis Research Center, Ren Ji Hospital, Uremia Diagnosis and Treatment Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Qin Wang
- Department of Nephrology, Molecular Cell Lab for Kidney Disease, Shanghai Peritoneal Dialysis Research Center, Ren Ji Hospital, Uremia Diagnosis and Treatment Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Jiaying Huang
- Department of Nephrology, Molecular Cell Lab for Kidney Disease, Shanghai Peritoneal Dialysis Research Center, Ren Ji Hospital, Uremia Diagnosis and Treatment Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Haijiao Jin
- Department of Nephrology, Molecular Cell Lab for Kidney Disease, Shanghai Peritoneal Dialysis Research Center, Ren Ji Hospital, Uremia Diagnosis and Treatment Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Jianxiao Shen
- Department of Nephrology, Molecular Cell Lab for Kidney Disease, Shanghai Peritoneal Dialysis Research Center, Ren Ji Hospital, Uremia Diagnosis and Treatment Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Yin Zhou
- Department of Nephrology, Molecular Cell Lab for Kidney Disease, Shanghai Peritoneal Dialysis Research Center, Ren Ji Hospital, Uremia Diagnosis and Treatment Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Jiani Hu
- Department of Radiology, Wayne State University, Detroit, Michigan, 48201, USA
| | - Matthew Bautista
- Department of Radiology, Wayne State University, Detroit, Michigan, 48201, USA
| | - Takahiro Ouchi
- Department of Radiology, Wayne State University, Detroit, Michigan, 48201, USA
| | - Lian-Ming Wu
- Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Shan Mou
- Department of Nephrology, Molecular Cell Lab for Kidney Disease, Shanghai Peritoneal Dialysis Research Center, Ren Ji Hospital, Uremia Diagnosis and Treatment Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
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Cau R, Pisu F, Suri JS, Montisci R, Gatti M, Mannelli L, Gong X, Saba L. Artificial Intelligence in the Differential Diagnosis of Cardiomyopathy Phenotypes. Diagnostics (Basel) 2024; 14:156. [PMID: 38248033 PMCID: PMC11154548 DOI: 10.3390/diagnostics14020156] [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: 12/12/2023] [Revised: 01/03/2024] [Accepted: 01/08/2024] [Indexed: 01/23/2024] Open
Abstract
Artificial intelligence (AI) is rapidly being applied to the medical field, especially in the cardiovascular domain. AI approaches have demonstrated their applicability in the detection, diagnosis, and management of several cardiovascular diseases, enhancing disease stratification and typing. Cardiomyopathies are a leading cause of heart failure and life-threatening ventricular arrhythmias. Identifying the etiologies is fundamental for the management and diagnostic pathway of these heart muscle diseases, requiring the integration of various data, including personal and family history, clinical examination, electrocardiography, and laboratory investigations, as well as multimodality imaging, making the clinical diagnosis challenging. In this scenario, AI has demonstrated its capability to capture subtle connections from a multitude of multiparametric datasets, enabling the discovery of hidden relationships in data and handling more complex tasks than traditional methods. This review aims to present a comprehensive overview of the main concepts related to AI and its subset. Additionally, we review the existing literature on AI-based models in the differential diagnosis of cardiomyopathy phenotypes, and we finally examine the advantages and limitations of these AI approaches.
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Affiliation(s)
- Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy; (R.C.); (F.P.)
| | - Francesco Pisu
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy; (R.C.); (F.P.)
| | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoin™, Roseville, CA 95661, USA;
| | - Roberta Montisci
- Department of Cardiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy;
| | - Marco Gatti
- Department of Radiology, Università degli Studi di Torino, 10129 Turin, Italy;
| | | | - Xiangyang Gong
- Radiology Department, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou 310014, China;
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy; (R.C.); (F.P.)
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12
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Taleie H, Hajianfar G, Sabouri M, Parsaee M, Houshmand G, Bitarafan-Rajabi A, Zaidi H, Shiri I. Left Ventricular Myocardial Dysfunction Evaluation in Thalassemia Patients Using Echocardiographic Radiomic Features and Machine Learning Algorithms. J Digit Imaging 2023; 36:2494-2506. [PMID: 37735309 PMCID: PMC10584796 DOI: 10.1007/s10278-023-00891-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 07/23/2023] [Accepted: 07/25/2023] [Indexed: 09/23/2023] Open
Abstract
Heart failure caused by iron deposits in the myocardium is the primary cause of mortality in beta-thalassemia major patients. Cardiac magnetic resonance imaging (CMRI) T2* is the primary screening technique used to detect myocardial iron overload, but inherently bears some limitations. In this study, we aimed to differentiate beta-thalassemia major patients with myocardial iron overload from those without myocardial iron overload (detected by T2*CMRI) based on radiomic features extracted from echocardiography images and machine learning (ML) in patients with normal left ventricular ejection fraction (LVEF > 55%) in echocardiography. Out of 91 cases, 44 patients with thalassemia major with normal LVEF (> 55%) and T2* ≤ 20 ms and 47 people with LVEF > 55% and T2* > 20 ms as the control group were included in the study. Radiomic features were extracted for each end-systolic (ES) and end-diastolic (ED) image. Then, three feature selection (FS) methods and six different classifiers were used. The models were evaluated using various metrics, including the area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). Maximum relevance-minimum redundancy-eXtreme gradient boosting (MRMR-XGB) (AUC = 0.73, ACC = 0.73, SPE = 0.73, SEN = 0.73), ANOVA-MLP (AUC = 0.69, ACC = 0.69, SPE = 0.56, SEN = 0.83), and recursive feature elimination-K-nearest neighbors (RFE-KNN) (AUC = 0.65, ACC = 0.65, SPE = 0.64, SEN = 0.65) were the best models in ED, ES, and ED&ES datasets. Using radiomic features extracted from echocardiographic images and ML, it is feasible to predict cardiac problems caused by iron overload.
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Affiliation(s)
- Haniyeh Taleie
- Department of Medical Physics, Iran University of Medical Sciences, Tehran, Iran
| | - Ghasem Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH‑1211, Geneva 4, Switzerland
| | - Maziar Sabouri
- Department of Medical Physics, Iran University of Medical Sciences, Tehran, Iran
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Mozhgan Parsaee
- Echocardiography Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Golnaz Houshmand
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Ahmad Bitarafan-Rajabi
- Echocardiography Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.
- Cardiovascular Interventional Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH‑1211, Geneva 4, Switzerland.
- Geneva University Neurocenter, University of Geneva, Geneva, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH‑1211, Geneva 4, Switzerland.
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
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13
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Hou J, Zheng G, Han L, Shu Z, Wang H, Yuan Z, Peng J, Gong X. Coronary computed tomography angiography imaging features combined with computed tomography-fractional flow reserve, pericoronary fat attenuation index, and radiomics for the prediction of myocardial ischemia. J Nucl Cardiol 2023; 30:1838-1850. [PMID: 36859595 DOI: 10.1007/s12350-023-03221-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 01/19/2023] [Indexed: 03/03/2023]
Abstract
BACKGROUND This study aimed to predict myocardial ischemia (MIS) by constructing models with imaging features, CT-fractional flow reserve (CT-FFR), pericoronary fat attenuation index (pFAI), and radiomics based on coronary computed tomography angiography (CCTA). METHODS AND RESULTS This study included 96 patients who underwent CCTA and single photon emission computed tomography-myocardial perfusion imaging (SPECT-MPI). According to SPECT-MPI results, there were 72 vessels with MIS in corresponding supply area and 105 vessels with no-MIS. The conventional model [lesion length (LL), MDS (maximum stenosis diameter × 100% / reference vessel diameter), MAS (maximum stenosis area × 100% / reference vessel area) and CT value], radiomics model (radiomics features), and multi-faceted model (all features) were constructed using support vector machine. Conventional and radiomics models showed similar predictive efficacy [AUC: 0.76, CI 0.62-0.90 vs. 0.74, CI 0.61-0.88; p > 0.05]. Adding pFAI to the conventional model showed better predictive efficacy than adding CT-FFR (AUC: 0.88, CI 0.79-0.97 vs. 0.80, CI 0.68-0.92; p < 0.05). Compared with conventional and radiomics model, the multi-faceted model showed the highest predictive efficacy (AUC: 0.92, CI 0.82-0.98, p < 0.05). CONCLUSION pFAI is more effective for predicting MIS than CT-FFR. A multi-faceted model combining imaging features, CT-FFR, pFAI, and radiomics is a potential diagnostic tool for MIS.
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Affiliation(s)
- Jie Hou
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
- Heart Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Address: No. 158 Shangtang Road, Hanghzou City, 310014, Zhejiang Province, China
| | - Guangying Zheng
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Lu Han
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Zhenyu Shu
- Heart Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Address: No. 158 Shangtang Road, Hanghzou City, 310014, Zhejiang Province, China
| | - Haochu Wang
- Heart Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Address: No. 158 Shangtang Road, Hanghzou City, 310014, Zhejiang Province, China
| | - Zhongyu Yuan
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Jiaxuan Peng
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Xiangyang Gong
- Heart Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Address: No. 158 Shangtang Road, Hanghzou City, 310014, Zhejiang Province, China.
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14
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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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Cau R, Pisu F, Suri JS, Mannelli L, Scaglione M, Masala S, Saba L. Artificial Intelligence Applications in Cardiovascular Magnetic Resonance Imaging: Are We on the Path to Avoiding the Administration of Contrast Media? Diagnostics (Basel) 2023; 13:2061. [PMID: 37370956 PMCID: PMC10297403 DOI: 10.3390/diagnostics13122061] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/10/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
In recent years, cardiovascular imaging examinations have experienced exponential growth due to technological innovation, and this trend is consistent with the most recent chest pain guidelines. Contrast media have a crucial role in cardiovascular magnetic resonance (CMR) imaging, allowing for more precise characterization of different cardiovascular diseases. However, contrast media have contraindications and side effects that limit their clinical application in determinant patients. The application of artificial intelligence (AI)-based techniques to CMR imaging has led to the development of non-contrast models. These AI models utilize non-contrast imaging data, either independently or in combination with clinical and demographic data, as input to generate diagnostic or prognostic algorithms. In this review, we provide an overview of the main concepts pertaining to AI, review the existing literature on non-contrast AI models in CMR, and finally, discuss the strengths and limitations of these AI models and their possible future development.
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Affiliation(s)
- Riccardo Cau
- Department of Radiology, University Hospital of Cagliari, 09042 Monserrato, Italy; (R.C.); (F.P.)
| | - Francesco Pisu
- Department of Radiology, University Hospital of Cagliari, 09042 Monserrato, Italy; (R.C.); (F.P.)
| | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA;
| | | | - Mariano Scaglione
- Department of Radiology, University Hospital of Sassari, 07100 Sassari, Italy; (M.S.); (S.M.)
| | - Salvatore Masala
- Department of Radiology, University Hospital of Sassari, 07100 Sassari, Italy; (M.S.); (S.M.)
| | - Luca Saba
- Department of Radiology, University Hospital of Cagliari, 09042 Monserrato, Italy; (R.C.); (F.P.)
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Pujadas ER, Raisi-Estabragh Z, Szabo L, McCracken C, Morcillo CI, Campello VM, Martín-Isla C, Atehortua AM, Vago H, Merkely B, Maurovich-Horvat P, Harvey NC, Neubauer S, Petersen SE, Lekadir K. Prediction of incident cardiovascular events using machine learning and CMR radiomics. Eur Radiol 2023; 33:3488-3500. [PMID: 36512045 PMCID: PMC10121487 DOI: 10.1007/s00330-022-09323-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 09/28/2022] [Accepted: 11/28/2022] [Indexed: 12/15/2022]
Abstract
OBJECTIVES Evaluation of the feasibility of using cardiovascular magnetic resonance (CMR) radiomics in the prediction of incident atrial fibrillation (AF), heart failure (HF), myocardial infarction (MI), and stroke using machine learning techniques. METHODS We identified participants from the UK Biobank who experienced incident AF, HF, MI, or stroke during the continuous longitudinal follow-up. The CMR indices and the vascular risk factors (VRFs) as well as the CMR images were obtained for each participant. Three-segmented regions of interest (ROIs) were computed: right ventricle cavity, left ventricle (LV) cavity, and LV myocardium in end-systole and end-diastole phases. Radiomics features were extracted from the 3D volumes of the ROIs. Seven integrative models were built for each incident cardiovascular disease (CVD) as an outcome. Each model was built with VRF, CMR indices, and radiomics features and a combination of them. Support vector machine was used for classification. To assess the model performance, the accuracy, sensitivity, specificity, and AUC were reported. RESULTS AF prediction model using the VRF+CMR+Rad model (accuracy: 0.71, AUC 0.76) obtained the best result. However, the AUC was similar to the VRF+Rad model. HF showed the most significant improvement with the inclusion of CMR metrics (VRF+CMR+Rad: 0.79, AUC 0.84). Moreover, adding only the radiomics features to the VRF reached an almost similarly good performance (VRF+Rad: accuracy 0.77, AUC 0.83). Prediction models looking into incident MI and stroke reached slightly smaller improvement. CONCLUSIONS Radiomics features may provide incremental predictive value over VRF and CMR indices in the prediction of incident CVDs. KEY POINTS • Prediction of incident atrial fibrillation, heart failure, stroke, and myocardial infarction using machine learning techniques. • CMR radiomics, vascular risk factors, and standard CMR indices will be considered in the machine learning models. • The experiments show that radiomics features can provide incremental predictive value over VRF and CMR indices in the prediction of incident cardiovascular diseases.
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Affiliation(s)
- Esmeralda Ruiz Pujadas
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain.
| | - Zahra Raisi-Estabragh
- 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, London, EC1A 7BE, UK
| | - Liliana Szabo
- 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, London, EC1A 7BE, UK
- Semmelweis University Heart and Vascular Center, Budapest, Hungary
| | - Celeste McCracken
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Cristian Izquierdo Morcillo
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Víctor M Campello
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Carlos Martín-Isla
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Angelica M Atehortua
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Hajnalka Vago
- Semmelweis University Heart and Vascular Center, Budapest, Hungary
| | - Bela Merkely
- Semmelweis University Heart and Vascular Center, Budapest, Hungary
| | | | - Nicholas C Harvey
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Steffen E Petersen
- 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, London, EC1A 7BE, UK
- Health Data Research UK, London, UK
- Alan Turing Institute, London, UK
| | - Karim Lekadir
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain.
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18
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Cau R, Pisu F, Porcu M, Cademartiri F, Montisci R, Bassareo P, Muscogiuri G, Amadu A, Sironi S, Esposito A, Suri JS, Saba L. Machine learning approach in diagnosing Takotsubo cardiomyopathy: The role of the combined evaluation of atrial and ventricular strain, and parametric mapping. Int J Cardiol 2023; 373:124-133. [PMID: 36410545 DOI: 10.1016/j.ijcard.2022.11.021] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/23/2022] [Accepted: 11/15/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND Cardiac magnetic resonance (CMR) with late gadolinium enhancement (LGE) is a key diagnostic tool in the differential diagnosis between non-ischemic cause of cardiac chest pain. Some patients are not eligible for a gadolinium contrast-enhanced CMR; in this scenario, the diagnosis remains challenging without invasive examination. Our purpose was to derive a machine learning model integrating some non-contrast CMR parameters and demographic factors to identify Takotsubo cardiomyopathy (TTC) in subjects with cardiac chest pain. MATERIAL AND METHODS Three groups of patients were retrospectively studied: TTC, acute myocarditis, and healthy controls. Global and regional left ventricular longitudinal, circumferential, and radial strain (RS) analysis included were assessed. Reservoir, conduit, and booster bi-atrial functions were evaluated by tissue-tracking. Parametric mapping values were also assessed in all the patients. Five different tree-based ensemble learning algorithms were tested concerning their ability in recognizing TTC in a fully cross-validated framework. RESULTS The CMR-based machine learning (ML) ensemble model, by using the Extremely Randomized Trees algorithm with Elastic Net feature selection, showed a sensitivity of 92% (95% CI 78-100), specificity of 86% (95% CI 80-92) and area under the ROC of 0.94 (95% CI 0.90-0.99) in diagnosing TTC. Among non-contrast CMR parameters, the Shapley additive explanations analysis revealed that left atrial (LA) strain and strain rate were the top imaging markers in identifying TTC patients. CONCLUSIONS Our study demonstrated that using a tree-based ensemble learning algorithm on non-contrast CMR parameters and demographic factors enables the identification of subjects with TTC with good diagnostic accuracy. TRANSLATIONAL OUTLOOK Our results suggest that non-contrast CMR features can be implemented in a ML model to accurately identify TTC subjects. This model could be a valuable tool for aiding in the diagnosis of subjects with a contraindication to the contrast media. Furthermore, the left atrial conduit strain and strain rate were imaging markers that had a strong impact on TTC identification. Further prospective and longitudinal studies are needed to validate these findings and assess predictive performance in different cohorts, such as those with different ethnicities, and social backgrounds and undergoing different treatments.
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Affiliation(s)
- Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria, Monserrato (Cagliari), Italy
| | - Francesco Pisu
- Department of Radiology, Azienda Ospedaliero Universitaria, Monserrato (Cagliari), Italy
| | - Michele Porcu
- Department of Radiology, Azienda Ospedaliero Universitaria, Monserrato (Cagliari), Italy
| | | | - Roberta Montisci
- Department of Cardiology, Azienda Ospedaliero Universitaria, Monserrato (Cagliari), Italy
| | - Pierpaolo Bassareo
- University College of Dublin, Mater Misericordiae University Hospital and Our Lady's Children's Hospital, Crumlin, Dublin, Ireland
| | - Giuseppe Muscogiuri
- Department of Radiology, IRCCS Istituto Auxologico Italiano, San Luca Hospital, Milan, Italy; University Milano Bicocca, Italy
| | | | - Sandro Sironi
- Department of Radiology, University of Milan-Bicocca, Milan, Italy
| | | | - Jasjit S Suri
- Stroke Monitoring and Diagnosis Division, AtheroPoint(tm) Roseville, CA, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, Monserrato (Cagliari), Italy.
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19
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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: 25] [Impact Index Per Article: 25.0] [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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20
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Yang YC, Dou Y, Wang ZW, Yin RH, Pan CJ, Duan SF, Tang XQ. Prediction of myocardial ischemia in coronary heart disease patients using a CCTA-Based radiomic nomogram. Front Cardiovasc Med 2023; 10:1024773. [PMID: 36742075 PMCID: PMC9893015 DOI: 10.3389/fcvm.2023.1024773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 01/03/2023] [Indexed: 01/20/2023] Open
Abstract
Objective The present study aimed to predict myocardial ischemia in coronary heart disease (CHD) patients based on the radiologic features of coronary computed tomography angiography (CCTA) combined with clinical factors. Methods The imaging and clinical data of 110 patients who underwent CCTA scan before DSA or FFR examination in Changzhou Second People's Hospital, Nanjing Medical University (90 patients), and The First Affiliated Hospital of Soochow University (20 patients) from March 2018 to January 2022 were retrospectively analyzed. According to the digital subtraction angiography (DSA) and fractional flow reserve (FFR) results, all patients were assigned to myocardial ischemia (n = 58) and normal myocardial blood supply (n = 52) groups. All patients were further categorized into training (n = 64) and internal validation (n = 26) sets at a ratio of 7:3, and the patients from second site were used as external validation. Clinical indicators of patients were collected, the left ventricular myocardium were segmented from CCTA images using CQK software, and the radiomics features were extracted using pyradiomics software. Independent prediction models and combined prediction models were established. The predictive performance of the model was assessed by calibration curve analysis, receiver operating characteristic (ROC) curve and decision curve analysis. Results The combined model consisted of one important clinical factor and eight selected radiomic features. The area under the ROC curve (AUC) of radiomic model was 0.826 in training set, and 0.744 in the internal validation set. For the combined model, the AUCs were 0.873, 0.810, 0.800 in the training, internal validation, and external validation sets, respectively. The calibration curves demonstrated that the probability of myocardial ischemia predicted by the combined model was in good agreement with the observed values in both training and validation sets. The decision curve was within the threshold range of 0.1-1, and the clinical value of nomogram was higher than that of clinical model. Conclusion The radiomic characteristics of CCTA combined with clinical factors have a good clinical value in predicting myocardial ischemia in CHD patients.
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Affiliation(s)
- You-Chang Yang
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Shandong, China
| | - Yang Dou
- Graduate School of Dalian Medical University, Dalian Medical University, Dalian, China
| | - Zhi-Wei Wang
- Graduate School of Dalian Medical University, Dalian Medical University, Dalian, China
| | - Ruo-Han Yin
- Department of Radiology, The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, China
| | - Chang-Jie Pan
- Department of Radiology, The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, China
| | - Shao-Feng Duan
- GE Healthcare, Precision Health Institution, Shanghai, China
| | - Xiao-Qiang Tang
- Department of Radiology, The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, China,*Correspondence: Xiao-Qiang Tang,
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21
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Szabo L, Raisi-Estabragh Z, Salih A, McCracken C, Ruiz Pujadas E, Gkontra P, Kiss M, Maurovich-Horvath P, Vago H, Merkely B, Lee AM, Lekadir K, Petersen SE. Clinician's guide to trustworthy and responsible artificial intelligence in cardiovascular imaging. Front Cardiovasc Med 2022; 9:1016032. [PMID: 36426221 PMCID: PMC9681217 DOI: 10.3389/fcvm.2022.1016032] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 10/11/2022] [Indexed: 12/01/2023] Open
Abstract
A growing number of artificial intelligence (AI)-based systems are being proposed and developed in cardiology, driven by the increasing need to deal with the vast amount of clinical and imaging data with the ultimate aim of advancing patient care, diagnosis and prognostication. However, there is a critical gap between the development and clinical deployment of AI tools. A key consideration for implementing AI tools into real-life clinical practice is their "trustworthiness" by end-users. Namely, we must ensure that AI systems can be trusted and adopted by all parties involved, including clinicians and patients. Here we provide a summary of the concepts involved in developing a "trustworthy AI system." We describe the main risks of AI applications and potential mitigation techniques for the wider application of these promising techniques in the context of cardiovascular imaging. Finally, we show why trustworthy AI concepts are important governing forces of AI development.
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Affiliation(s)
- Liliana Szabo
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
- Semmelweis University Heart and Vascular Center, Budapest, Hungary
| | - Zahra Raisi-Estabragh
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Ahmed Salih
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Celeste McCracken
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, University of Oxford, Oxford, United Kingdom
| | - Esmeralda Ruiz Pujadas
- Departament de Matemàtiques i Informàtica, Artificial Intelligence in Medicine Lab (BCN-AIM), Universitat de Barcelona, Barcelona, Spain
| | - Polyxeni Gkontra
- Departament de Matemàtiques i Informàtica, Artificial Intelligence in Medicine Lab (BCN-AIM), Universitat de Barcelona, Barcelona, Spain
| | - Mate Kiss
- Siemens Healthcare Hungary, Budapest, Hungary
| | - Pal Maurovich-Horvath
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Hajnalka Vago
- Semmelweis University Heart and Vascular Center, Budapest, Hungary
| | - Bela Merkely
- Semmelweis University Heart and Vascular Center, Budapest, Hungary
| | - Aaron M. Lee
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Karim Lekadir
- Departament de Matemàtiques i Informàtica, Artificial Intelligence in Medicine Lab (BCN-AIM), Universitat de Barcelona, Barcelona, Spain
| | - Steffen E. Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
- Health Data Research UK, London, United Kingdom
- Alan Turing Institute, London, United Kingdom
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22
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Jiang S, Zhang L, Wang J, Li X, Hu S, Fu Y, Wang X, Hao S, Hu C. Differentiating between cardiac amyloidosis and hypertrophic cardiomyopathy on non-contrast cine-magnetic resonance images using machine learning-based radiomics. Front Cardiovasc Med 2022; 9:1001269. [PMID: 36386316 PMCID: PMC9643770 DOI: 10.3389/fcvm.2022.1001269] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 10/10/2022] [Indexed: 11/23/2022] Open
Abstract
Objectives This study aimed to determine whether texture analysis (TA) and machine learning-based classifications can be applied in differential diagnosis of cardiac amyloidosis (CA) and hypertrophic cardiomyopathy (HCM) using non-contrast cine cardiac magnetic resonance (CMR) images. Methods In this institutional review board-approved study, we consecutively enrolled 167 patients with CA (n = 85), HCM (n = 82), and 84 patients with normal CMR served as controls. All cases were randomized into training [119 patients (70%)] and validation [48 patients (30%)] groups. A total of 275 texture features were extracted from cine images. Based on regression analysis with the least absolute shrinkage and selection operator (LASSO), nine machine learning models were established and their diagnostic performance determined. Results Nineteen radiomics texture features derived from cine images were used to differentiate CA and HCM. In the validation cohort, the support vector machine (SVM), which had an accuracy of 0.85, showed the best performance (MCC = 0.637). Gray level non-uniformity (GLevNonU) was the single most effective feature. The combined model of radiomics texture features and conventional MR metrics had superior discriminatory performance (AUC = 0.89) over conventional MR metrics model (AUC = 0.79). Moreover, results showed that GLevNonU levels in HCM patients were significantly higher compared with levels in CA patients and control groups (P < 0.001). A cut-off of GLevNonU ≥ 25 was shown to differentiate between CA and HCM patients, with an area under the curve (AUC) of 0.86 (CI:0.804–0.920). Multiple comparisons tests showed that GLevNonU was significantly greater in LGE+, relative to LGE-patient groups (CA+ vs. CA- and HCM+ vs. HCM-, P = 0.01, 0.001, respectively). Conclusion Machine learning-based classifiers can accurately differentiate between CA and HCM on non-contrast cine images. The radiomics-MR combined model can be used to improve the discriminatory performance. TA may be used to assess myocardial microstructure changes that occur during different stages of cardiomyopathies.
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Affiliation(s)
- Shu Jiang
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
- Department of Radiology, The Yancheng Clinical College of Xuzhou Medical University and The First People’s Hospital of Yancheng, Yancheng, China
| | - Lianlian Zhang
- Department of Ultrasound, The Yancheng Clinical College of Xuzhou Medical University and The First People’s Hospital of Yancheng, Yancheng, China
| | - Jia Wang
- Department of Radiology, Northern Jiangsu People’s Hospital, Yangzhou, China
| | - Xia Li
- Department of General Medicine, The Sixth Affiliated Hospital of Nantong University, Yancheng Third People’s Hospital, Yancheng, China
| | - Su Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yigang Fu
- Department of Radiology, The Yancheng Clinical College of Xuzhou Medical University and The First People’s Hospital of Yancheng, Yancheng, China
| | - Xin Wang
- Department of Clinical Nutrition, The Sixth Affiliated Hospital of Nantong University, Yancheng Third People’s Hospital, Yancheng, China
| | - Shaowei Hao
- Siemens Healthineers Digital Technology (Shanghai) Co., Ltd., Shanghai, China
| | - Chunhong Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
- *Correspondence: Chunhong Hu,
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23
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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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24
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Arian F, Amini M, Mostafaei S, Rezaei Kalantari K, Haddadi Avval A, Shahbazi Z, Kasani K, Bitarafan Rajabi A, Chatterjee S, Oveisi M, Shiri I, Zaidi H. Myocardial Function Prediction After Coronary Artery Bypass Grafting Using MRI Radiomic Features and Machine Learning Algorithms. J Digit Imaging 2022; 35:1708-1718. [PMID: 35995896 DOI: 10.1007/s10278-022-00681-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 06/21/2022] [Accepted: 07/12/2022] [Indexed: 01/02/2023] Open
Abstract
The main aim of the present study was to predict myocardial function improvement in cardiac MR (LGE-CMR) images in patients after coronary artery bypass grafting (CABG) using radiomics and machine learning algorithms. Altogether, 43 patients who had visible scars on short-axis LGE-CMR images and were candidates for CABG surgery were selected and enrolled in this study. MR imaging was performed preoperatively using a 1.5-T MRI scanner. All images were segmented by two expert radiologists (in consensus). Prior to extraction of radiomics features, all MR images were resampled to an isotropic voxel size of 1.8 × 1.8 × 1.8 mm3. Subsequently, intensities were quantized to 64 discretized gray levels and a total of 93 features were extracted. The applied algorithms included a smoothly clipped absolute deviation (SCAD)-penalized support vector machine (SVM) and the recursive partitioning (RP) algorithm as a robust classifier for binary classification in this high-dimensional and non-sparse data. All models were validated with repeated fivefold cross-validation and 10,000 bootstrapping resamples. Ten and seven features were selected with SCAD-penalized SVM and RP algorithm, respectively, for CABG responder/non-responder classification. Considering univariate analysis, the GLSZM gray-level non-uniformity-normalized feature achieved the best performance (AUC: 0.62, 95% CI: 0.53-0.76) with SCAD-penalized SVM. Regarding multivariable modeling, SCAD-penalized SVM obtained an AUC of 0.784 (95% CI: 0.64-0.92), whereas the RP algorithm achieved an AUC of 0.654 (95% CI: 0.50-0.82). In conclusion, different radiomics texture features alone or combined in multivariate analysis using machine learning algorithms provide prognostic information regarding myocardial function in patients after CABG.
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Affiliation(s)
- Fatemeh Arian
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, CH-1211, Switzerland
| | - Shayan Mostafaei
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Kiara Rezaei Kalantari
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran.,Cardio-Oncology Research Center, Rajaei Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | | | - Zahra Shahbazi
- Department of Biostatistics, School of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Kianosh Kasani
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Ahmad Bitarafan Rajabi
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran. .,Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran. .,Echocardiography Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran. .,Cardiovascular interventional research center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.
| | - Saikat Chatterjee
- School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Brinellvägen 8, Stockholm, Sweden
| | - Mehrdad Oveisi
- Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine, Kings College London, London, UK.,Department of Computer Science, University of British Columbia, Vancouver BC, Canada
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, CH-1211, Switzerland.
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, CH-1211, Switzerland. .,Geneva University Neurocenter, Geneva University, Geneva, Switzerland. .,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands. .,Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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25
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Abdulkareem M, Kenawy AA, Rauseo E, Lee AM, Sojoudi A, Amir-Khalili A, Lekadir K, Young AA, Barnes MR, Barckow P, Khanji MY, Aung N, Petersen SE. Predicting post-contrast information from contrast agent free cardiac MRI using machine learning: Challenges and methods. Front Cardiovasc Med 2022; 9:894503. [PMID: 36051279 PMCID: PMC9426684 DOI: 10.3389/fcvm.2022.894503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 06/27/2022] [Indexed: 11/29/2022] Open
Abstract
Objectives Currently, administering contrast agents is necessary for accurately visualizing and quantifying presence, location, and extent of myocardial infarction (MI) with cardiac magnetic resonance (CMR). In this study, our objective is to investigate and analyze pre- and post-contrast CMR images with the goal of predicting post-contrast information using pre-contrast information only. We propose methods and identify challenges. Methods The study population consists of 272 retrospectively selected CMR studies with diagnoses of MI (n = 108) and healthy controls (n = 164). We describe a pipeline for pre-processing this dataset for analysis. After data feature engineering, 722 cine short-axis (SAX) images and segmentation mask pairs were used for experimentation. This constitutes 506, 108, and 108 pairs for the training, validation, and testing sets, respectively. We use deep learning (DL) segmentation (UNet) and classification (ResNet50) models to discover the extent and location of the scar and classify between the ischemic cases and healthy cases (i.e., cases with no regional myocardial scar) from the pre-contrast cine SAX image frames, respectively. We then capture complex data patterns that represent subtle signal and functional changes in the cine SAX images due to MI using optical flow, rate of change of myocardial area, and radiomics data. We apply this dataset to explore two supervised learning methods, namely, the support vector machines (SVM) and the decision tree (DT) methods, to develop predictive models for classifying pre-contrast cine SAX images as being a case of MI or healthy. Results Overall, for the UNet segmentation model, the performance based on the mean Dice score for the test set (n = 108) is 0.75 (±0.20) for the endocardium, 0.51 (±0.21) for the epicardium and 0.20 (±0.17) for the scar. For the classification task, the accuracy, F1 and precision scores of 0.68, 0.69, and 0.64, respectively, were achieved with the SVM model, and of 0.62, 0.63, and 0.72, respectively, with the DT model. Conclusion We have presented some promising approaches involving DL, SVM, and DT methods in an attempt to accurately predict contrast information from non-contrast images. While our initial results are modest for this challenging task, this area of research still poses several open problems.
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Affiliation(s)
- Musa Abdulkareem
- Barts Heart Centre, Barts Health National Health Service (NHS) Trust, London, United Kingdom
- National Institute for Health Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- Health Data Research UK, London, United Kingdom
| | - Asmaa A. Kenawy
- Barts Heart Centre, Barts Health National Health Service (NHS) Trust, London, United Kingdom
- National Institute for Health Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Elisa Rauseo
- Barts Heart Centre, Barts Health National Health Service (NHS) Trust, London, United Kingdom
- National Institute for Health Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Aaron M. Lee
- Barts Heart Centre, Barts Health National Health Service (NHS) Trust, London, United Kingdom
- National Institute for Health Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | | | | | - Karim Lekadir
- Artificial Intelligence in Medicine Lab (BCN-AIM), Faculty of Mathematics and Computer Science, University of Barcelona, Barcelona, Spain
| | - Alistair A. Young
- Department of Biomedical Engineering, King’s College London, London, United Kingdom
| | - Michael R. Barnes
- Centre for Translational Bioinformatics, William Harvey Research Institute, Faculty of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | | | - Mohammed Y. Khanji
- Barts Heart Centre, Barts Health National Health Service (NHS) Trust, London, United Kingdom
- National Institute for Health Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- Newham University Hospital, Barts Health National Health Service (NHS) Trust, London, United Kingdom
| | - Nay Aung
- Barts Heart Centre, Barts Health National Health Service (NHS) Trust, London, United Kingdom
- National Institute for Health Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Steffen E. Petersen
- Barts Heart Centre, Barts Health National Health Service (NHS) Trust, London, United Kingdom
- National Institute for Health Research (NIHR) 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
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26
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Marfisi D, Tessa C, Marzi C, Del Meglio J, Linsalata S, Borgheresi R, Lilli A, Lazzarini R, Salvatori L, Vignali C, Barucci A, Mascalchi M, Casolo G, Diciotti S, Traino AC, Giannelli M. Image resampling and discretization effect on the estimate of myocardial radiomic features from T1 and T2 mapping in hypertrophic cardiomyopathy. Sci Rep 2022; 12:10186. [PMID: 35715531 PMCID: PMC9205876 DOI: 10.1038/s41598-022-13937-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 03/21/2022] [Indexed: 12/24/2022] Open
Abstract
Radiomics is emerging as a promising and useful tool in cardiac magnetic resonance (CMR) imaging applications. Accordingly, the purpose of this study was to investigate, for the first time, the effect of image resampling/discretization and filtering on radiomic features estimation from quantitative CMR T1 and T2 mapping. Specifically, T1 and T2 maps of 26 patients with hypertrophic cardiomyopathy (HCM) were used to estimate 98 radiomic features for 7 different resampling voxel sizes (at fixed bin width), 9 different bin widths (at fixed resampling voxel size), and 7 different spatial filters (at fixed resampling voxel size/bin width). While we found a remarkable dependence of myocardial radiomic features from T1 and T2 mapping on image filters, many radiomic features showed a limited sensitivity to resampling voxel size/bin width, in terms of intraclass correlation coefficient (> 0.75) and coefficient of variation (< 30%). The estimate of most textural radiomic features showed a linear significant (p < 0.05) correlation with resampling voxel size/bin width. Overall, radiomic features from T2 maps have proven to be less sensitive to image preprocessing than those from T1 maps, especially when varying bin width. Our results might corroborate the potential of radiomics from T1/T2 mapping in HCM and hopefully in other myocardial diseases.
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Affiliation(s)
- Daniela Marfisi
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Via Roma 67, 56126, Pisa, Italy
| | - Carlo Tessa
- Unit of Radiology, Azienda USL Toscana Nord Ovest, Apuane Hospital, 54100, Massa, Italy
| | - Chiara Marzi
- Institute of Applied Physics "Nello Carrara", Italian National Research Council, 50019, Sesto Fiorentino, Italy
| | - Jacopo Del Meglio
- Unit of Cardiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041, Lido di Camaiore, Italy
| | - Stefania Linsalata
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Via Roma 67, 56126, Pisa, Italy
| | - Rita Borgheresi
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Via Roma 67, 56126, Pisa, Italy
| | - Alessio Lilli
- Unit of Cardiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041, Lido di Camaiore, Italy
| | - Riccardo Lazzarini
- Unit of Radiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041, Lido di Camaiore, Italy
| | - Luca Salvatori
- Unit of Radiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041, Lido di Camaiore, Italy
| | - Claudio Vignali
- Unit of Radiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041, Lido di Camaiore, Italy
| | - Andrea Barucci
- Institute of Applied Physics "Nello Carrara", Italian National Research Council, 50019, Sesto Fiorentino, Italy
| | - Mario Mascalchi
- Department of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, 50121, Florence, Italy
| | - Giancarlo Casolo
- Unit of Cardiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041, Lido di Camaiore, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, 47522, Cesena, Italy
| | - Antonio Claudio Traino
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Via Roma 67, 56126, Pisa, Italy
| | - Marco Giannelli
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Via Roma 67, 56126, Pisa, Italy.
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Diffusion-Weighted Imaging Prior to Percutaneous Sclerotherapy of Venous Malformations—Proof of Concept Study for Prediction of Clinical Outcome. Diagnostics (Basel) 2022; 12:diagnostics12061430. [PMID: 35741240 PMCID: PMC9222207 DOI: 10.3390/diagnostics12061430] [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] [Received: 05/18/2022] [Revised: 06/07/2022] [Accepted: 06/07/2022] [Indexed: 12/18/2022] Open
Abstract
Prediction of response to percutaneous sclerotherapy in patients with venous malformations (VM) is currently not possible with baseline clinical or imaging characteristics. This prospective single-center study aimed to predict treatment outcome of percutaneous sclerotherapy as measured by quality of life (QoL) by using radiomic analysis of diffusion-weighted (dw) magnetic resonance imaging (MRI) before and after first percutaneous sclerotherapy. In all patients (n = 16) pre-interventional (PRE-) and delta (DELTA-) radiomic features (RF) were extracted from dw-MRI before and after first percutaneous sclerotherapy with ethanol gel or polidocanol foam, while QoL was assessed using the Toronto Extremity Salvage Score (TESS) and the 36-Item Short Form Survey (SF-36) health questionnaire. For selecting features that allow differentiation of clinical response, a stepwise dimension reduction was performed. Logistic regression models were fitted and selected PRE-/DELTA-RF were tested for their predictive value. QoL improved significantly after percutaneous sclerotherapy. While no common baseline patient characteristics were able to predict response to percutaneous sclerotherapy, the radiomics signature of VMs (independent PRE/DELTA-RF) revealed high potential for the prediction of clinical response after percutaneous sclerotherapy. This proof-of-concept study provides first evidence on the potential predictive value of (delta) radiomic analysis from diffusion-weighted MRI for Quality-of-Life outcome after percutaneous sclerotherapy in patients with venous malformations.
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Zhang L, Lyu Q, Ding Y, Hu C, Hui P. Texture Analysis Based on Vascular Ultrasound to Identify the Vulnerable Carotid Plaques. Front Neurosci 2022; 16:885209. [PMID: 35720730 PMCID: PMC9204477 DOI: 10.3389/fnins.2022.885209] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 05/09/2022] [Indexed: 11/13/2022] Open
Abstract
Vulnerable carotid plaques are closely related to the occurrence of ischemic stroke. Therefore, accurate and rapid identification of the nature of carotid plaques is essential. This study aimed to determine whether texture analysis based on a vascular ultrasound can be applied to identify vulnerable plaques. Data from a total of 150 patients diagnosed with atherosclerotic plaque (AP) by carotid ultrasound (CDU) and high-resolution magnetic resonance imaging (HRMRI) were collected. HRMRI is the in vivo reference to assess the nature of AP. MaZda software was used to delineate the region of interest and extract 303 texture features from ultrasonic images of plaques. Following regression analysis using the least absolute shrinkage and selection operator (LASSO) algorithm, the overall cohort was randomized 7:3 into the training (n = 105) and testing (n = 45) sets. In the training set, the conventional ultrasound model, the texture feature model, and the conventional ultrasound-texture feature combined model were constructed. The testing set was used to validate the model’s effectiveness by calculating the area under the curve (AUC), accuracy, sensitivity, and specificity. Based on the combined model, a nomogram risk prediction model was established, and the consistency index (C-index) and the calibration curve were obtained. In the training and testing sets, the AUC of the prediction performance of the conventional ultrasonic-texture feature combined model was higher than that of the conventional ultrasonic model and the texture feature model. In the training set, the AUC of the combined model was 0.88, while in the testing set, AUC was 0.87. In addition, the C-index results were also favorable (0.89 in the training set and 0.84 in the testing set). Furthermore, the calibration curve was close to the ideal curve, indicating the accuracy of the nomogram. This study proves the performance of vascular ultrasound-based texture analysis in identifying the vulnerable carotid plaques. Texture feature extraction combined with CDU sonogram features can accurately predict the vulnerability of AP.
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Lee JW, Park CH, Im DJ, Lee KH, Kim TH, Han K, Hur J. CT-based radiomics signature for differentiation between cardiac tumors and a thrombi: a retrospective, multicenter study. Sci Rep 2022; 12:8173. [PMID: 35581366 PMCID: PMC9114026 DOI: 10.1038/s41598-022-12229-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Accepted: 05/06/2022] [Indexed: 12/20/2022] Open
Abstract
The study aimed to develop and validate whether the computed tomography (CT) radiomics analysis is effective in differentiating cardiac tumors and thrombi. For this retrospective study, a radiomics model was developed on the basis of a training dataset of 192 patients (61.9 ± 13.3 years, 90 men) with cardiac masses detected in cardiac CT from January 2010 to September 2019. We constructed three models for discriminating between a cardiac tumor and a thrombus: a radiomics model, a clinical model, which included clinical and conventional CT variables, and a model that combined clinical and radiomics models. In the training dataset, the radiomics model and the combined model yielded significantly higher differentiation performance between cardiac tumors and cardiac thrombi than the clinical model (AUC 0.973 vs 0.870, p < 0.001 and AUC 0.983 vs 0.870, p < 0.001, respectively). In the external validation dataset with 63 patients (59.8 ± 13.2 years, 26 men), the combined model yielded a larger AUC compared to the clinical model (AUC 0.911 vs 0.802, p = 0.037). CT radiomics analysis is effective in differentiating cardiac tumors and thrombi. In conclusion, the combination of clinical, conventional CT, and radiomics features demonstrated an additional benefit in differentiating between cardiac tumor and thrombi compared to clinical data and conventional CT features alone.
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Affiliation(s)
- Ji Won Lee
- Department of Radiology, Pusan National University Hospital, Pusan National University School of Medicine and Medical Research Institute, Busan, South Korea
| | - Chul Hwan Park
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Dong Jin Im
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Kye Ho Lee
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Tae Hoon Kim
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.
| | - Jin Hur
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.
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Radiomics Feature Analysis Using Native T1 Mapping for Discriminating Between Cardiac Tumors and Thrombi. Acad Radiol 2022; 29 Suppl 4:S1-S8. [PMID: 33419643 DOI: 10.1016/j.acra.2020.12.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 12/06/2020] [Accepted: 12/17/2020] [Indexed: 11/22/2022]
Abstract
RATIONALE AND OBJECTIVES Accurate differential diagnosis is essential because cardiac tumors and thrombi have different prognoses and therapeutic approaches. Native T1 map provides an objective T1 time quantifications of cardiac mass without the need for a contrast agent. We examined the diagnostic performance of radiomics features for differentiating cardiac tumors from thrombi using cardiac magnetic resonance imaging T1 mapping technique compared to that of late gadolinium enhancement (LGE) imaging. MATERIALS AND METHODS This retrospective study included 22 cardiac tumors and 21 thrombi of 41 patients who underwent cardiac magnetic resonance imaging from December 2013 to May 2018. Fifty-six radiomics features were extracted from native T1 images. The least absolute shrinkage and selection operator method was used for feature selection and rad score extraction. The diagnostic performance of the rad score was compared to that of the native T1 value (mean T1) and LGE ratio. RESULTS The area under the receiver operating characteristic curve of the rad score was higher than that of the mean T1 and LGE ratio (0.98 vs. 0.86 vs. 0.82, p = 0.001). With the optimal cut-off value, the rad score showed sensitivity, specificity, and accuracy of 95.4%, 95.2%, and 95.2%, respectively. Combination of the rad score and mean T1 showed a significantly higher diagnostic performance than mean T1 (p = 0.019) or LGE ratio (p = 0.022). CONCLUSION The rad score derived from native T1 maps can differentiate thrombi from tumors better than the mean T1 or LGE ratio. This is valuable for determining a treatment strategy for cardiac lesions in patients who cannot tolerate contrast agents.
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31
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Zhou XY, Tang CX, Guo YK, Tao XW, Chen WC, Guo JZ, Ren GS, Li X, Luo S, Li JH, Huang WW, Lu GM, Zhang LJ, Huang XH, Wang YN, Yang GF. Diagnosis of Cardiac Amyloidosis Using a Radiomics Approach Applied to Late Gadolinium-Enhanced Cardiac Magnetic Resonance Images: A Retrospective, Multicohort, Diagnostic Study. Front Cardiovasc Med 2022; 9:818957. [PMID: 35433852 PMCID: PMC9005767 DOI: 10.3389/fcvm.2022.818957] [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] [Received: 11/20/2021] [Accepted: 02/28/2022] [Indexed: 11/24/2022] Open
Abstract
Objectives To assess the potential of a radiomics approach of late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) in the diagnosis of cardiac amyloidosis (CA). Materials and Methods This retrospective study included 200 patients with biopsy-proven light-chain (AL) amyloidosis. CA was diagnosed on the basis of systemic amyloidosis confirmed with evidence of cardiac involvement by imaging and clinical biomarkers. A total of 139 patients [54 ± 8 years, 75 (54%) men] in our institution were divided into training cohort [n = 97, mean age of 53 ± 8 years, 54 (56%) men] and internal validation cohort [n = 42, mean age: 56 ± 8 years, 21 (50%) men] with a ratio of 7:3, while 61 patients [mean age: 60 ± 9 years, 42 (69%) men] from the other two institutions were enrolled for external validation. Radiomics features were extracted from global (all short-axis images from base-to-apex) left ventricular (LV) myocardium and three different segments (basal, midventricular, and apex) on short-axis LGE images using the phase-sensitive reconstruction (PSIR) sequence. The Boruta algorithm was used to select the radiomics features. This model was built using the XGBoost algorithm. The two readers performed qualitative and semiquantitative assessment of the LGE images based on the visual LGE patterns, while the quantitative assessment was measured using a dedicated semi-automatic CMR software. The diagnostic performance of the radiomics and other qualitative and quantitative parameters were compared by a receiver operating characteristic (ROC) curve analysis. A correlation between radiomics and the degree of myocardial involvement by amyloidosis was tested. Results A total of 1,906 radiomics features were extracted for each LV section. No statistical significance was indicated between any two slices for diagnosing CA, and the highest area under the curve (AUC) was found in basal section {0.92 [95% confidence interval (CI), 0.86–0.97] in the LGE images in the training set, 0.89 (95% CI, 0.79–1.00) in the internal validation set, and 0.92 (95% CI, 0.85–0.99) in the external validation set}, which was superior to the visual assessment and quantitative LGE parameters. Moderate correlations between global or basal radiomics scores (Rad-scores) and Mayo stage in all patients were reported (Spearman’s Rho = 0.61, 0.62; all p < 0.01). Conclusion A radiomics analysis of the LGE images provides incremental information compared with the visual assessment and quantitative parameters on CMR to diagnose CA. Radiomics was moderately correlated with the severity of CA. Further studies are needed to assess the prognostic significance of radiomics in patients with CA.
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Affiliation(s)
- Xi Yang Zhou
- Department of Nuclear Medicine, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Chun Xiang Tang
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Ying Kun Guo
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, China
| | | | - Wen Cui Chen
- National Clinical Research Center of Kidney Disease, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Jin Zhou Guo
- National Clinical Research Center of Kidney Disease, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Gui Sheng Ren
- National Clinical Research Center of Kidney Disease, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Xiao Li
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Song Luo
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Jun Hao Li
- Department of Nuclear Medicine, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Wei Wei Huang
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Guang Ming Lu
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Long Jiang Zhang
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Xiang Hua Huang
- Bayer Healthcare, Shanghai, China
- *Correspondence: Xiang Hua Huang,
| | - Yi Ning Wang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Yi Ning Wang,
| | - Gui Fen Yang
- Department of Nuclear Medicine, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
- Gui Fen Yang,
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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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Kagiyama N, Tokodi M, Sengupta PP. Machine Learning in Cardiovascular Imaging. Heart Fail Clin 2022; 18:245-258. [DOI: 10.1016/j.hfc.2021.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Demircioğlu A. Evaluation of the dependence of radiomic features on the machine learning model. Insights Imaging 2022; 13:28. [PMID: 35201534 PMCID: PMC8873309 DOI: 10.1186/s13244-022-01170-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 02/03/2022] [Indexed: 12/03/2022] Open
Abstract
Background In radiomic studies, several models are often trained with different combinations of feature selection methods and classifiers. The features of the best model are usually considered relevant to the problem, and they represent potential biomarkers. Features selected from statistically similarly performing models are generally not studied. To understand the degree to which the selected features of these statistically similar models differ, 14 publicly available datasets, 8 feature selection methods, and 8 classifiers were used in this retrospective study. For each combination of feature selection and classifier, a model was trained, and its performance was measured with AUC-ROC. The best-performing model was compared to other models using a DeLong test. Models that were statistically similar were compared in terms of their selected features. Results Approximately 57% of all models analyzed were statistically similar to the best-performing model. Feature selection methods were, in general, relatively unstable (0.58; range 0.35–0.84). The features selected by different models varied largely (0.19; range 0.02–0.42), although the selected features themselves were highly correlated (0.71; range 0.4–0.92). Conclusions Feature relevance in radiomics strongly depends on the model used, and statistically similar models will generally identify different features as relevant. Considering features selected by a single model is misleading, and it is often not possible to directly determine whether such features are candidate biomarkers. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-022-01170-2.
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Affiliation(s)
- Aydin Demircioğlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45157, Essen, Germany.
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Shu ZY, Cui SJ, Zhang YQ, Xu YY, Hung SC, Fu LP, Pang PP, Gong XY, Jin QY. Predicting Chronic Myocardial Ischemia Using CCTA-Based Radiomics Machine Learning Nomogram. J Nucl Cardiol 2022; 29:262-274. [PMID: 32557238 DOI: 10.1007/s12350-020-02204-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 05/05/2020] [Indexed: 12/13/2022]
Abstract
BACKGROUND Coronary computed tomography angiography (CCTA) is a well-established non-invasive diagnostic test for the assessment of coronary artery diseases (CAD). CCTA not only provides information on luminal stenosis but also permits non-invasive assessment and quantitative measurement of stenosis based on radiomics. PURPOSE This study is aimed to develop and validate a CT-based radiomics machine learning for predicting chronic myocardial ischemia (MIS). METHODS CCTA and SPECT-myocardial perfusion imaging (MPI) of 154 patients with CAD were retrospectively analyzed and 94 patients were diagnosed with MIS. The patients were randomly divided into two sets: training (n = 107) and test (n = 47). Features were extracted for each CCTA cross-sectional image to identify myocardial segments. Multivariate logistic regression was used to establish a radiomics signature after feature dimension reduction. Finally, the radiomics nomogram was built based on a predictive model of MIS which in turn was constructed by machine learning combined with the clinically related factors. We then validated the model using data from 49 CAD patients and included 18 MIS patients from another medical center. The receiver operating characteristic curve evaluated the diagnostic accuracy of the nomogram based on the training set and was validated by the test and validation set. Decision curve analysis (DCA) was used to validate the clinical practicability of the nomogram. RESULTS The accuracy of the nomogram for the prediction of MIS in the training, test and validation sets was 0.839, 0.832, and 0.816, respectively. The diagnosis accuracy of the nomogram, signature, and vascular stenosis were 0.824, 0.736 and 0.708, respectively. A significant difference in the number of patients with MIS between the high and low-risk groups was identified based on the nomogram (P < .05). The DCA curve demonstrated that the nomogram was clinically feasible. CONCLUSION The radiomics nomogram constructed based on the image of CCTA act as a non-invasive tool for predicting MIS that helps to identify high-risk patients with coronary artery disease.
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Affiliation(s)
- Zhen-Yu Shu
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, Zhejiang, China
| | - Si-Jia Cui
- Second Clinical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yue-Qiao Zhang
- Department of Radiology, Shao-Yifu Hospital Affiliated to Zhejiang University, Hangzhou, China
| | - Yu-Yun Xu
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, Zhejiang, China
| | - Shng-Che Hung
- Division of Neuroradiology, Department of Radiology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Li-Ping Fu
- Department of Nuclear Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, China
| | | | - Xiang-Yang Gong
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, Zhejiang, China.
- Institute of Artificial Intelligence and Remote Imaging, Hangzhou Medical College, Hangzhou, China.
| | - Qin-Yang Jin
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, Zhejiang, China.
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Radiomics in Cardiovascular Disease Imaging: from Pixels to the Heart of the Problem. CURRENT CARDIOVASCULAR IMAGING REPORTS 2022. [DOI: 10.1007/s12410-022-09563-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Abstract
Purpose of Review
This review of the literature aims to present potential applications of radiomics in cardiovascular radiology and, in particular, in cardiac imaging.
Recent Findings
Radiomics and machine learning represent a technological innovation which may be used to extract and analyze quantitative features from medical images. They aid in detecting hidden pattern in medical data, possibly leading to new insights in pathophysiology of different medical conditions. In the recent literature, radiomics and machine learning have been investigated for numerous potential applications in cardiovascular imaging. They have been proposed to improve image acquisition and reconstruction, for anatomical structure automated segmentation or automated characterization of cardiologic diseases.
Summary
The number of applications for radiomics and machine learning is continuing to rise, even though methodological and implementation issues still limit their use in daily practice. In the long term, they may have a positive impact in patient management.
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Sharifrazi D, Alizadehsani R, Joloudari JH, Band SS, Hussain S, Sani ZA, Hasanzadeh F, Shoeibi A, Dehzangi A, Sookhak M, Alinejad-Rokny H. CNN-KCL: Automatic myocarditis diagnosis using convolutional neural network combined with k-means clustering. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:2381-2402. [PMID: 35240789 DOI: 10.3934/mbe.2022110] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Myocarditis is the form of an inflammation of the middle layer of the heart wall which is caused by a viral infection and can affect the heart muscle and its electrical system. It has remained one of the most challenging diagnoses in cardiology. Myocardial is the prime cause of unexpected death in approximately 20% of adults less than 40 years of age. Cardiac MRI (CMR) has been considered a noninvasive and golden standard diagnostic tool for suspected myocarditis and plays an indispensable role in diagnosing various cardiac diseases. However, the performance of CMR depends heavily on the clinical presentation and features such as chest pain, arrhythmia, and heart failure. Besides, other imaging factors like artifacts, technical errors, pulse sequence, acquisition parameters, contrast agent dose, and more importantly qualitatively visual interpretation can affect the result of the diagnosis. This paper introduces a new deep learning-based model called Convolutional Neural Network-Clustering (CNN-KCL) to diagnose Myocarditis. In this study, we used 47 subjects with a total number of 98,898 images to diagnose myocarditis disease. Our results demonstrate that the proposed method achieves an accuracy of 97.41% based on 10 fold-cross validation technique with 4 clusters for diagnosis of Myocarditis. To the best of our knowledge, this research is the first to use deep learning algorithms for the diagnosis of myocarditis.
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Affiliation(s)
- Danial Sharifrazi
- Department of Computer Engineering, School of Technical and Engineering, Shiraz Branch, Islamic Azad University, Shiraz, IR
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC 3216, AU
| | | | - Shahab S Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology 123 University Road, Section 3, Douliou, Yunlin 64002, TW
| | - Sadiq Hussain
- System Administrator, Dibrugarh University, Assam 786004, IN
| | - Zahra Alizadeh Sani
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
- Omid hospital, Iran University of Medical Sciences, Tehran, IR
| | | | - Afshin Shoeibi
- FPGA Laboratory, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, IR
| | - Abdollah Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ 08102, USA
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA
| | - Mehdi Sookhak
- Department of Computer Science, Texas A & M University at Corpus Christi, Corpus Christi, TX 78412, USA
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, AU
- Health Data Analytics Program, AI-enabled Processes (AIP) Research Centre, Macquarie University, Sydney 2109, AU
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de Azevedo-Marques PM, Ferreira JR. Medical Image Analyst: A Radiology Career Focused on Comprehensive Quantitative Imaging Analytics to Improve Healthcare. Acad Radiol 2022; 29:170. [PMID: 33731285 DOI: 10.1016/j.acra.2020.11.028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 11/06/2020] [Accepted: 11/07/2020] [Indexed: 01/12/2023]
Affiliation(s)
| | - José Raniery Ferreira
- Ribeirão Preto Medical School, University of São Paulo, Av. dos Bandeirantes 3900, 14049-900, Ribeirão Preto, SP, Brazil.
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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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Raisi-Estabragh Z, Jaggi A, Gkontra P, McCracken C, Aung N, Munroe PB, Neubauer S, Harvey NC, Lekadir K, Petersen SE. Cardiac Magnetic Resonance Radiomics Reveal Differential Impact of Sex, Age, and Vascular Risk Factors on Cardiac Structure and Myocardial Tissue. Front Cardiovasc Med 2021; 8:763361. [PMID: 35004880 PMCID: PMC8727756 DOI: 10.3389/fcvm.2021.763361] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 11/30/2021] [Indexed: 11/30/2022] Open
Abstract
Background: Cardiovascular magnetic resonance (CMR) radiomics analysis provides multiple quantifiers of ventricular shape and myocardial texture, which may be used for detailed cardiovascular phenotyping. Objectives: We studied variation in CMR radiomics phenotypes by age and sex in healthy UK Biobank participants. Then, we examined independent associations of classical vascular risk factors (VRFs: smoking, diabetes, hypertension, high cholesterol) with CMR radiomics features, considering potential sex and age differential relationships. Design: Image acquisition was with 1.5 Tesla scanners (MAGNETOM Aera, Siemens). Three regions of interest were segmented from short axis stack images using an automated pipeline: right ventricle, left ventricle, myocardium. We extracted 237 radiomics features from each study using Pyradiomics. In a healthy subset of participants (n = 14,902) without cardiovascular disease or VRFs, we estimated independent associations of age and sex with each radiomics feature using linear regression models adjusted for body size. We then created a sample comprising individuals with at least one VRF matched to an equal number of healthy participants (n = 27,400). We linearly modelled each radiomics feature against age, sex, body size, and all the VRFs. Bonferroni adjustment for multiple testing was applied to all p-values. To aid interpretation, we organised the results into six feature clusters. Results: Amongst the healthy subset, men had larger ventricles with dimmer and less texturally complex myocardium than women. Increasing age was associated with smaller ventricles and greater variation in myocardial intensities. Broadly, all the VRFs were associated with dimmer, less varied signal intensities, greater uniformity of local intensity levels, and greater relative presence of low signal intensity areas within the myocardium. Diabetes and high cholesterol were also associated with smaller ventricular size, this association was of greater magnitude in men than women. The pattern of alteration of radiomics features with the VRFs was broadly consistent in men and women. However, the associations between intensity based radiomics features with both diabetes and hypertension were more prominent in women than men. Conclusions: We demonstrate novel independent associations of sex, age, and major VRFs with CMR radiomics phenotypes. Further studies into the nature and clinical significance of these phenotypes are needed.
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Affiliation(s)
- Zahra Raisi-Estabragh
- National Institute for Health Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- Barts Health National Health Service (NHS) Trust, Barts Heart Centre, St Bartholomew's Hospital, West Smithfield, United Kingdom
| | - Akshay Jaggi
- Departament de Matemàtiques and Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Polyxeni Gkontra
- Departament de Matemàtiques and Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Celeste McCracken
- National Institute for Health Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Nay Aung
- National Institute for Health Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- Barts Health National Health Service (NHS) Trust, Barts Heart Centre, St Bartholomew's Hospital, West Smithfield, United Kingdom
| | - Patricia B. Munroe
- National Institute for Health Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Nicholas C. Harvey
- Medical Research Council (MRC) Lifecourse Epidemiology Centre, 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 and Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Steffen E. Petersen
- National Institute for Health Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- Barts Health National Health Service (NHS) Trust, Barts Heart Centre, St Bartholomew's Hospital, West Smithfield, United Kingdom
- Health Data Research UK, London, United Kingdom
- Alan Turing Institute, London, United Kingdom
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Blüthgen C, Patella M, Euler A, Baessler B, Martini K, von Spiczak J, Schneiter D, Opitz I, Frauenfelder T. Computed tomography radiomics for the prediction of thymic epithelial tumor histology, TNM stage and myasthenia gravis. PLoS One 2021; 16:e0261401. [PMID: 34928978 PMCID: PMC8687592 DOI: 10.1371/journal.pone.0261401] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 12/01/2021] [Indexed: 12/21/2022] Open
Abstract
Objectives To evaluate CT-derived radiomics for machine learning-based classification of thymic epithelial tumor (TET) stage (TNM classification), histology (WHO classification) and the presence of myasthenia gravis (MG). Methods Patients with histologically confirmed TET in the years 2000–2018 were retrospectively included, excluding patients with incompatible imaging or other tumors. CT scans were reformatted uniformly, gray values were normalized and discretized. Tumors were segmented manually; 15 scans were re-segmented after 2 weeks by two readers. 1316 radiomic features were calculated (pyRadiomics). Features with low intra-/inter-reader agreement (ICC<0.75) were excluded. Repeated nested cross-validation was used for feature selection (Boruta algorithm), model training, and evaluation (out-of-fold predictions). Shapley additive explanation (SHAP) values were calculated to assess feature importance. Results 105 patients undergoing surgery for TET were identified. After applying exclusion criteria, 62 patients (28 female; mean age, 57±14 years; range, 22–82 years) with 34 low-risk TET (LRT; WHO types A/AB/B1), 28 high-risk TET (HRT; WHO B2/B3/C) in early stage (49, TNM stage I-II) or advanced stage (13, TNM III-IV) were included. 14(23%) of the patients had MG. 334(25%) features were excluded after intra-/inter-reader analysis. Discriminatory performance of the random forest classifiers was good for histology(AUC, 87.6%; 95% confidence interval, 76.3–94.3) and TNM stage(AUC, 83.8%; 95%CI, 66.9–93.4) but poor for the prediction of MG (AUC, 63.9%; 95%CI, 44.8–79.5). Conclusions CT-derived radiomic features may be a useful imaging biomarker for TET histology and TNM stage.
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Affiliation(s)
- Christian Blüthgen
- Institute of Diagnostic and Interventional Radiology, University Hospital of Zurich, Zurich, Switzerland
- * E-mail:
| | - Miriam Patella
- Department of Thoracic Surgery, University Hospital of Zurich, Zurich, Switzerland
| | - André Euler
- Institute of Diagnostic and Interventional Radiology, University Hospital of Zurich, Zurich, Switzerland
| | - Bettina Baessler
- Institute of Diagnostic and Interventional Radiology, University Hospital of Zurich, Zurich, Switzerland
| | - Katharina Martini
- Institute of Diagnostic and Interventional Radiology, University Hospital of Zurich, Zurich, Switzerland
| | - Jochen von Spiczak
- Institute of Diagnostic and Interventional Radiology, University Hospital of Zurich, Zurich, Switzerland
| | - Didier Schneiter
- Department of Thoracic Surgery, University Hospital of Zurich, Zurich, Switzerland
| | - Isabelle Opitz
- Department of Thoracic Surgery, University Hospital of Zurich, Zurich, Switzerland
| | - Thomas Frauenfelder
- Institute of Diagnostic and Interventional Radiology, University Hospital of Zurich, Zurich, Switzerland
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Corrias G, Micheletti G, Barberini L, Suri JS, Saba L. Texture analysis imaging "what a clinical radiologist needs to know". Eur J Radiol 2021; 146:110055. [PMID: 34902669 DOI: 10.1016/j.ejrad.2021.110055] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 04/09/2021] [Accepted: 11/15/2021] [Indexed: 02/07/2023]
Abstract
Texture analysis has arisen as a tool to explore the amount of data contained in images that cannot be explored by humans visually. Radiomics is a method that extracts a large number of features from radiographic medical images using data-characterisation algorithms. These features, termed radiomic features, have the potential to uncover disease characteristics. The goal of both radiomics and texture analysis is to go beyond size or human-eye based semantic descriptors, to enable the non-invasive extraction of quantitative radiological data to correlate them with clinical outcomes or pathological characteristics. In the latest years there has been a flourishing sub-field of radiology where texture analysis and radiomics have been used in many settings. It is difficult for the clinical radiologist to cope with such amount of data in all the different radiological sub-fields and to identify the most significant papers. The aim of this review is to provide a tool to better understand the basic principles underlining texture analysis and radiological data mining and a summary of the most significant papers of the latest years.
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Affiliation(s)
| | | | | | - Jasjit S Suri
- Stroke Diagnosis and Monitoring Division, AtheroPoint™, Roseville, CA, USA and Knowledge Engineering Center, Global Biomedical Technologies, Inc, Roseville, CA, USA
| | - Luca Saba
- Department of Radiology, University of Cagliari, Italy.
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Rauseo E, Izquierdo Morcillo C, Raisi-Estabragh Z, Gkontra P, Aung N, Lekadir K, Petersen SE. New Imaging Signatures of Cardiac Alterations in Ischaemic Heart Disease and Cerebrovascular Disease Using CMR Radiomics. Front Cardiovasc Med 2021; 8:716577. [PMID: 34631820 PMCID: PMC8494975 DOI: 10.3389/fcvm.2021.716577] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 09/02/2021] [Indexed: 11/17/2022] Open
Abstract
Background: Ischaemic heart disease (IHD) and cerebrovascular disease are two closely inter-related clinical entities. Cardiovascular magnetic resonance (CMR) radiomics may capture subtle cardiac changes associated with these two diseases providing new insights into the brain-heart interactions. Objective: To define the CMR radiomics signatures for IHD and cerebrovascular disease and study their incremental value for disease discrimination over conventional CMR indices. Methods: We analysed CMR images of UK Biobank's subjects with pre-existing IHD, ischaemic cerebrovascular disease, myocardial infarction (MI), and ischaemic stroke (IS) (n = 779, 267, 525, and 107, respectively). Each disease group was compared with an equal number of healthy controls. We extracted 446 shape, first-order, and texture radiomics features from three regions of interest (right ventricle, left ventricle, and left ventricular myocardium) in end-diastole and end-systole defined from segmentation of short-axis cine images. Systematic feature selection combined with machine learning (ML) algorithms (support vector machine and random forest) and 10-fold cross-validation tests were used to build the radiomics signature for each condition. We compared the discriminatory power achieved by the radiomics signature with conventional indices for each disease group, using the area under the curve (AUC), receiver operating characteristic (ROC) analysis, and paired t-test for statistical significance. A third model combining both radiomics and conventional indices was also evaluated. Results: In all the study groups, radiomics signatures provided a significantly better disease discrimination than conventional indices, as suggested by AUC (IHD:0.82 vs. 0.75; cerebrovascular disease: 0.79 vs. 0.77; MI: 0.87 vs. 0.79, and IS: 0.81 vs. 0.72). Similar results were observed with the combined models. In IHD and MI, LV shape radiomics were dominant. However, in IS and cerebrovascular disease, the combination of shape and intensity-based features improved the disease discrimination. A notable overlap of the radiomics signatures of IHD and cerebrovascular disease was also found. Conclusions: This study demonstrates the potential value of CMR radiomics over conventional indices in detecting subtle cardiac changes associated with chronic ischaemic processes involving the brain and heart, even in the presence of more heterogeneous clinical pictures. Radiomics analysis might also improve our understanding of the complex mechanisms behind the brain-heart interactions during ischaemia.
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Affiliation(s)
- Elisa Rauseo
- William Harvey Research Institute, National Institute for Health Research Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health National Health Service Trust, London, United Kingdom
| | - Cristian Izquierdo Morcillo
- Departament de Matematiques i Informatica, Universitat de Barcelona, Artificial Intelligence in Medicine Lab, Barcelona, Spain
| | - Zahra Raisi-Estabragh
- William Harvey Research Institute, National Institute for Health Research Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health National Health Service Trust, London, United Kingdom
| | - Polyxeni Gkontra
- Departament de Matematiques i Informatica, Universitat de Barcelona, Artificial Intelligence in Medicine Lab, Barcelona, Spain
| | - Nay Aung
- William Harvey Research Institute, National Institute for Health Research Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health National Health Service Trust, London, United Kingdom
| | - Karim Lekadir
- Departament de Matematiques i Informatica, Universitat de Barcelona, Artificial Intelligence in Medicine Lab, Barcelona, Spain
| | - Steffen E. Petersen
- William Harvey Research Institute, National Institute for Health Research Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health National Health Service Trust, London, United Kingdom
- Health Data Research UK, London, United Kingdom
- Alan Turing Institute, London, United Kingdom
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Automatic machine learning based on native T1 mapping can identify myocardial fibrosis in patients with hypertrophic cardiomyopathy. Eur Radiol 2021; 32:1044-1053. [PMID: 34477909 DOI: 10.1007/s00330-021-08228-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 07/07/2021] [Accepted: 07/23/2021] [Indexed: 02/05/2023]
Abstract
OBJECTIVES To investigate the feasibility of automatic machine learning (autoML) based on native T1 mapping to predict late gadolinium enhancement (LGE) status in hypertrophic cardiomyopathy (HCM). METHODS Ninety-one HCM patients and 44 healthy controls who underwent cardiovascular MRI were enrolled. The native T1 maps of HCM patients were classified as LGE ( +) or LGE (-) based on location-matched LGE images. An autoML pipeline was implemented using the tree-based pipeline optimization tool (TPOT) for 3 binary classifications: LGE ( +) and LGE (-), LGE (-) and control, and HCM and control. TPOT modeling was repeated 10 times to obtain the optimal model for each classification. The diagnostic performance of the best models by slice and by case was evaluated using sensitivity, specificity, accuracy, and microaveraged area under the curve (AUC). RESULTS Ten prediction models were generated by TPOT for each of the 3 binary classifications. The diagnostic accuracy obtained with the best pipeline in detecting LGE status in the testing cohort of HCM patients was 0.80 by slice and 0.79 by case. In addition, the TPOT model also showed discriminability between LGE (-) patients and control (accuracy: 0.77 by slice; 0.78 by case) and for all HCM patients and controls (accuracy: 0.88 for both). CONCLUSIONS Native T1 map analysis based on autoML correlates with LGE ( +) or (-) status. The TPOT machine learning algorithm could be a promising method for predicting myocardial fibrosis, as reflected by the presence of LGE in HCM patients without the need for late contrast-enhanced MRI sequences. KEY POINTS • The tree-based pipeline optimization tool (TPOT) is a machine learning algorithm that could help predict late gadolinium enhancement (LGE) status in patients with hypertrophic cardiomyopathy. • The TPOT could serve as an adjuvant method to detect LGE by using information from native T1 maps, thus avoiding the need for contrast agent. • The TPOT also detects native T1 map alterations in LGE-negative patients with hypertrophic cardiomyopathy.
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Guo R, Weingärtner S, Šiurytė P, T Stoeck C, Füetterer M, E Campbell-Washburn A, Suinesiaputra A, Jerosch-Herold M, Nezafat R. Emerging Techniques in Cardiac Magnetic Resonance Imaging. J Magn Reson Imaging 2021; 55:1043-1059. [PMID: 34331487 DOI: 10.1002/jmri.27848] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 07/08/2021] [Accepted: 07/09/2021] [Indexed: 11/10/2022] Open
Abstract
Cardiovascular disease is the leading cause of death and a significant contributor of health care costs. Noninvasive imaging plays an essential role in the management of patients with cardiovascular disease. Cardiac magnetic resonance (MR) can noninvasively assess heart and vascular abnormalities, including biventricular structure/function, blood hemodynamics, myocardial tissue composition, microstructure, perfusion, metabolism, coronary microvascular function, and aortic distensibility/stiffness. Its ability to characterize myocardial tissue composition is unique among alternative imaging modalities in cardiovascular disease. Significant growth in cardiac MR utilization, particularly in Europe in the last decade, has laid the necessary clinical groundwork to position cardiac MR as an important imaging modality in the workup of patients with cardiovascular disease. Although lack of availability, limited training, physician hesitation, and reimbursement issues have hampered widespread clinical adoption of cardiac MR in the United States, growing clinical evidence will ultimately overcome these challenges. Advances in cardiac MR techniques, particularly faster image acquisition, quantitative myocardial tissue characterization, and image analysis have been critical to its growth. In this review article, we discuss recent advances in established and emerging cardiac MR techniques that are expected to strengthen its capability in managing patients with cardiovascular disease. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Rui Guo
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Sebastian Weingärtner
- Department of Imaging Physics, Magnetic Resonance Systems Lab, Delft University of Technology, Delft, The Netherlands
| | - Paulina Šiurytė
- Department of Imaging Physics, Magnetic Resonance Systems Lab, Delft University of Technology, Delft, The Netherlands
| | - Christian T Stoeck
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Maximilian Füetterer
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Adrienne E Campbell-Washburn
- Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Avan Suinesiaputra
- Faculty of Engineering and Physical Sciences, University of Leeds, Leeds, UK
| | - Michael Jerosch-Herold
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Reza Nezafat
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
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Roll W, Schindler P, Masthoff M, Seifert R, Schlack K, Bögemann M, Stegger L, Weckesser M, Rahbar K. Evaluation of 68Ga-PSMA-11 PET-MRI in Patients with Advanced Prostate Cancer Receiving 177Lu-PSMA-617 Therapy: A Radiomics Analysis. Cancers (Basel) 2021; 13:cancers13153849. [PMID: 34359750 PMCID: PMC8345703 DOI: 10.3390/cancers13153849] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 07/27/2021] [Indexed: 12/14/2022] Open
Abstract
177Lutetium PSMA-617 (Lu-PSMA) therapy in patients with metastatic castration resistant prostate cancer (mCRPC) has gained visibility through the ongoing phase III trial. The data on prediction of therapy outcome and survival out of pretherapeutic imaging parameters is still sparse. In this study, the predictive and prognostic value of radiomic features from 68Ga-PSMA-11 PET-MRI are analyzed. In total, 21 patients with mCRPC underwent 68Ga-PSMA-11 PET-MRI before Lu-PSMA therapy. The PET-positive tumor volume was defined and transferred to whole-body T2-, T1- and contrast-enhanced T1-weighted MRI-sequences. The radiomic features from PET and MRI sequences were extracted by using a freely available software package. For selecting features that allow differentiation of biochemical response (PSA decrease > 50%), a stepwise dimension reduction was performed. Logistic regression models were fitted, and selected features were tested for their prognostic value (overall survival) in all patients. Eight patients achieved biochemical response after Lu-PSMA therapy. Ten independent radiomic features differentiated well between responders and non-responders. The logistic regression model, including the feature interquartile range from T2-weighted images, revealed the highest accuracy (AUC = 0.83) for the prediction of biochemical response after Lu-PSMA therapy. Within the final model, patients with a biochemical response (p = 0.003) and higher T2 interquartile range values in pre-therapeutic imaging (p = 0.038) survived significantly longer. This proof-of-concept study provides first evidence on a potential predictive and prognostic value of radiomic analysis of pretherapeutic 68Ga-PSMA-11 PET-MRI before Lu-PSMA therapy.
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Affiliation(s)
- Wolfgang Roll
- Department of Nuclear Medicine, University Hospital Muenster, 48149 Muenster, Germany; (R.S.); (L.S.); (M.W.); (K.R.)
- Correspondence: ; Tel.: +49-251-8347362; Fax: +49-251-8347363
| | - Philipp Schindler
- Department of Radiology, University Hospital Muenster, 48149 Muenster, Germany; (P.S.); (M.M.)
| | - Max Masthoff
- Department of Radiology, University Hospital Muenster, 48149 Muenster, Germany; (P.S.); (M.M.)
| | - Robert Seifert
- Department of Nuclear Medicine, University Hospital Muenster, 48149 Muenster, Germany; (R.S.); (L.S.); (M.W.); (K.R.)
- Department of Nuclear Medicine, University Hospital Essen, 45147 Essen, Germany
| | - Katrin Schlack
- Department of Urology, University Hospital Muenster, 48149 Muenster, Germany; (K.S.); (M.B.)
| | - Martin Bögemann
- Department of Urology, University Hospital Muenster, 48149 Muenster, Germany; (K.S.); (M.B.)
| | - Lars Stegger
- Department of Nuclear Medicine, University Hospital Muenster, 48149 Muenster, Germany; (R.S.); (L.S.); (M.W.); (K.R.)
| | - Matthias Weckesser
- Department of Nuclear Medicine, University Hospital Muenster, 48149 Muenster, Germany; (R.S.); (L.S.); (M.W.); (K.R.)
| | - Kambiz Rahbar
- Department of Nuclear Medicine, University Hospital Muenster, 48149 Muenster, Germany; (R.S.); (L.S.); (M.W.); (K.R.)
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Fan ZY, Wu CW, An DA, Chen BH, Wesemann LD, He J, Hu JN, Bu J, Xu JR, Zhou Y, Wu LM. Myocardial area at risk and salvage in reperfused acute MI measured by texture analysis of cardiac T2 mapping and its prediction value of functional recovery in the convalescent stage. Int J Cardiovasc Imaging 2021; 37:3549-3560. [PMID: 34279752 DOI: 10.1007/s10554-021-02336-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Accepted: 06/29/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVES We sought to distinguish area at risk from salvage myocardial zone and to predict left ventricle functional recovery in the convalescent stage by Texture Analysis (TA) of T2-Mapping. METHODS One hundred and six patients diagnosed with AMI and treated with percutaneous coronary intervention (PCI) underwent acute cardiac magnetic resonance imaging (CMR) and 45 of whom had a subsequent CMR scan following recovery. Cine imaging, T2-Mapping, T2-weighted STIR imaging, and LGE imaging were performed. In the texture analysis, regions of interest (infarcted, salvageable, and remote) were drawn by two blinded, independent readers. RESULTS Seven independent texture features on T2-Mapping were selected: Perc.50%, S(2,2)InvDfMom, S(2.-2)AngScMom, S(4,0)Entropy, 45dgrLngREmph, 45dgr_Fraction and 135dr_GLevNonU. Among them, the average value of 135dr_GLevNonU in the infarct zone, AAR zone, and the remote zone was: 61.96±26.03, 31.811±18.933 and 99.839±26.231, respectively. Additionally, 135dr_GLevNonU provided the highest area under the curve (AUC) from the receiver operating characteristic curve (ROC curve) for distinguishing AAR from the infarct zone in each subgroup (all patients, patients with MVO and)were 0.845 ± 0.052 0.855 ± 0.083 and 0.845 ± 0.066, respectively, and were more promise than T2-Mapping mean (p<0.001). The AUC for differentiating AAR from the remote zone is 0.942±0.041. Texture features are not associated with convalescent decreased strain, ejection fraction (EF) or left ventricle remodeling (LVR) in analysis (p>0.05). CONCLUSION TA of T2-mapping can distinguish AAR from both the infarct zone and the remote myocardial zone without LGE imaging in reperfused AMI. However, these features are not able to predict patients' functional recovery in the convalescent stage.
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Affiliation(s)
- Zi-Yang Fan
- Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Chong-Wen Wu
- Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Dong-Aolei An
- Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Bing-Hua Chen
- Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Luke D Wesemann
- Department of Radiology, School of Medicine, Wayne State University, Detroit, MI, 48201, USA
| | - Jie He
- Department of Cardiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Jia-Ni Hu
- Department of Radiology, School of Medicine, Wayne State University, Detroit, MI, 48201, USA
| | - Jun Bu
- Department of Cardiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Jian-Rong Xu
- Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China.
| | - Yan Zhou
- Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China.
| | - Lian-Ming Wu
- Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China.
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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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Infante T, Francone M, De Rimini ML, Cavaliere C, Canonico R, Catalano C, Napoli C. Machine learning and network medicine: a novel approach for precision medicine and personalized therapy in cardiomyopathies. J Cardiovasc Med (Hagerstown) 2021; 22:429-440. [PMID: 32890235 DOI: 10.2459/jcm.0000000000001103] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The early identification of pathogenic mechanisms is essential to predict the incidence and progression of cardiomyopathies and to plan appropriate preventive interventions. Noninvasive cardiac imaging such as cardiac computed tomography, cardiac magnetic resonance, and nuclear imaging plays an important role in diagnosis and management of cardiomyopathies and provides useful prognostic information. Most molecular factors exert their functions by interacting with other cellular components, thus many diseases reflect perturbations of intracellular networks. Indeed, complex diseases and traits such as cardiomyopathies are caused by perturbations of biological networks. The network medicine approach, by integrating systems biology, aims to identify pathological interacting genes and proteins, revolutionizing the way to know cardiomyopathies and shifting the understanding of their pathogenic phenomena from a reductionist to a holistic approach. In addition, artificial intelligence tools, applied to morphological and functional imaging, could allow imaging scans to be automatically analyzed to extract new parameters and features for cardiomyopathy evaluation. The aim of this review is to discuss the tools of network medicine in cardiomyopathies that could reveal new candidate genes and artificial intelligence imaging-based features with the aim to translate into clinical practice as diagnostic, prognostic, and predictive biomarkers and shed new light on the clinical setting of cardiomyopathies. The integration and elaboration of clinical habits, molecular big data, and imaging into machine learning models could provide better disease phenotyping, outcome prediction, and novel drug targets, thus opening a new scenario for the implementation of precision medicine for cardiomyopathies.
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Affiliation(s)
- Teresa Infante
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Marco Francone
- Department of Radiological, Oncological, and Pathological Sciences, La Sapienza University, Rome
| | | | | | - Raffaele Canonico
- U.O.C. of Dietetics, Sport Medicine and Psychophysical Wellbeing, Department of Experimental Medicine, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Carlo Catalano
- Department of Radiological, Oncological, and Pathological Sciences, La Sapienza University, Rome
| | - Claudio Napoli
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania 'Luigi Vanvitelli', Naples, Italy
- IRCCS SDN
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50
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Qin L, Chen C, Gu S, Zhou M, Xu Z, Ge Y, Yan F, Yang W. A radiomic approach to predict myocardial fibrosis on coronary CT angiography in hypertrophic cardiomyopathy. Int J Cardiol 2021; 337:113-118. [PMID: 33961944 DOI: 10.1016/j.ijcard.2021.04.060] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 04/21/2021] [Accepted: 04/28/2021] [Indexed: 01/14/2023]
Abstract
BACKGROUND Late gadolinium enhancement (LGE) derived from cardiac magnetic resonance (CMR) represents myocardial fibrosis (MF) and is associated with prognosis in hypertrophic cardiomyopathy (HCM). However, it cannot be evaluated when CMR is unavailable. Hence, we aimed to investigate the ability of radiomic features derived from coronary computed tomography angiography (CCTA) to detect the presence and extent of MF in HCM, with LGE as references. METHODS 161 patients with HCM who underwent CCTA and CMR were retrospectively enrolled and randomly divided into training (107 patients, 1712 segments) and testing cohorts (54 patients, 864 segments). Segments were obtained according to AHA 17-segment method. Radiomic features were extracted from per-segment and entire myocardium regions, and multiple machine-learning algorithms were used for radiomic signatures (Rad-sig) generation and model building. Four models were established by multivariable logistic regression using Rad-sig (R-model), clinical characteristic (C-model), echocardiography parameters (E-model), and all features integrated (Integ-model) to identify LGE/left ventricular mass ≥ 15%. RESULTS The model achieved good diagnostic accuracy in both training (area under the curve [AUC]:0.81, 95% confidence interval [CI]: 0.78-0.83) and testing cohort (AUC: 0.78, 95%CI: 0.75-0.81) on a per-segment basis for the presence of MF. The Integ-model owned the highest discriminative ability for patients with LGE/left ventricular mass ≥ 15% in both training and testing cohorts with AUC of 0.94 (95%CI: 0.89-0.98) and 0.92 (95%CI: 0.85-0.99), respectively. CONCLUSIONS Our radiomic models were considered as useful and complementary biomarkers for the evaluation of the presence and extent of MF on CCTA, facilitating clinical decision-making and risk stratification in HCM patients.
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Affiliation(s)
- Le Qin
- Department of Radiology, Ruijin Hospital Affiliated to Shanghai Jiaotong University School of Medicine, No. 197 Ruijin 2nd Rd, Shanghai 200025, China
| | - Chihua Chen
- Department of Radiology, Ruijin Hospital Affiliated to Shanghai Jiaotong University School of Medicine, No. 197 Ruijin 2nd Rd, Shanghai 200025, China
| | - Shengjia Gu
- Department of Radiology, Ruijin Hospital Affiliated to Shanghai Jiaotong University School of Medicine, No. 197 Ruijin 2nd Rd, Shanghai 200025, China
| | - Mi Zhou
- Department of Cardiovascular Surgery, Ruijin Hospital Affiliated to Shanghai Jiaotong University School of Medicine, No. 197 Ruijin 2nd Rd, Shanghai 200025, China
| | - Zhihan Xu
- Siemens Healthcare Ltd., No. 278 Zhouzhu Road, Shanghai 201318, China
| | - Yingqian Ge
- Siemens Healthcare Ltd., No. 278 Zhouzhu Road, Shanghai 201318, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital Affiliated to Shanghai Jiaotong University School of Medicine, No. 197 Ruijin 2nd Rd, Shanghai 200025, China
| | - Wenjie Yang
- Department of Radiology, Ruijin Hospital Affiliated to Shanghai Jiaotong University School of Medicine, No. 197 Ruijin 2nd Rd, Shanghai 200025, China.
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