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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 2024:110217. [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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Corti A, Iacono FL, Ronchetti F, Mushtaq S, Pontone G, Colombo GI, Corino VDA. Enhancing Cardiovascular Risk Stratification: Radiomics of Coronary Plaque and Perivascular Adipose Tissue - Current Insights and Future Perspectives. Trends Cardiovasc Med 2024:S1050-1738(24)00058-6. [PMID: 38960074 DOI: 10.1016/j.tcm.2024.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 06/25/2024] [Accepted: 06/28/2024] [Indexed: 07/05/2024]
Abstract
Radiomics, the quantitative extraction and mining of features from radiological images, has recently emerged as a promising source of non-invasive image-based cardiovascular biomarkers, potentially revolutionizing diagnostics and risk assessment. This review explores its application within coronary plaques and pericoronary adipose tissue, particularly focusing on plaque characterization and cardiac events prediction. By shedding light on the current state-of-the-art, achievements, and prospective avenues, this review contributes to a deeper understanding of the evolving landscape of radiomics in the context of coronary arteries. Finally, open challenges and existing gaps are emphasized to underscore the need for future efforts aimed at ensuring the robustness and reliability of radiomics studies, facilitating their clinical translation.
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Affiliation(s)
- Anna Corti
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
| | - Francesca Lo Iacono
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Francesca Ronchetti
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Saima Mushtaq
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Gianluca Pontone
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy; Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
| | - Gualtiero I Colombo
- Unit of Immunology and Functional Genomics, Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Valentina D A Corino
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy; Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
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Lo Iacono F, Maragna R, Pontone G, Corino VDA. A Novel Data Augmentation Method for Radiomics Analysis Using Image Perturbations. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01013-0. [PMID: 38710969 DOI: 10.1007/s10278-024-01013-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 12/20/2023] [Accepted: 12/22/2023] [Indexed: 05/08/2024]
Abstract
Radiomics extracts hundreds of features from medical images to quantitively characterize a region of interest (ROI). When applying radiomics, imbalanced or small dataset issues are commonly addressed using under or over-sampling, the latter being applied directly to the extracted features. Aim of this study is to propose a novel balancing and data augmentation technique by applying perturbations (erosion, dilation, contour randomization) to the ROI in cardiac computed tomography images. From the perturbed ROIs, radiomic features are extracted, thus creating additional samples. This approach was tested addressing the clinical problem of distinguishing cardiac amyloidosis (CA) from aortic stenosis (AS) and hypertrophic cardiomyopathy (HCM). Twenty-one CA, thirty-two AS and twenty-one HCM patients were included in the study. From each original and perturbed ROI, 107 radiomic features were extracted. The CA-AS dataset was balanced using the perturbation-based method along with random over-sampling, adaptive synthetic (ADASYN) and the synthetic minority oversampling technique (SMOTE). The same methods were tested to perform data augmentation dealing with CA and HCM. Features were submitted to robustness, redundancy, and relevance analysis testing five feature selection methods (p-value, least absolute shrinkage and selection operator (LASSO), semi-supervised LASSO, principal component analysis (PCA), semi-supervised PCA). Support vector machine performed the classification tasks, and its performance were evaluated by means of a 10-fold cross-validation. The perturbation-based approach provided the best performances in terms of f1 score and balanced accuracy in both CA-AS (f1 score: 80%, AUC: 0.91) and CA-HCM (f1 score: 86%, AUC: 0.92) classifications. These results suggest that ROI perturbations represent a powerful approach to address both data balancing and augmentation issues.
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Affiliation(s)
- F Lo Iacono
- Department of Electronics, Information and Bioengineering, Politecnico Di Milano, Milan, Italy.
| | - R Maragna
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - G Pontone
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
| | - V D A Corino
- Department of Electronics, Information and Bioengineering, Politecnico Di Milano, Milan, Italy
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
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Cundari G, Marchitelli L, Pambianchi G, Catapano F, Conia L, Stancanelli G, Catalano C, Galea N. Imaging biomarkers in cardiac CT: moving beyond simple coronary anatomical assessment. LA RADIOLOGIA MEDICA 2024; 129:380-400. [PMID: 38319493 PMCID: PMC10942914 DOI: 10.1007/s11547-024-01771-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 01/03/2024] [Indexed: 02/07/2024]
Abstract
Cardiac computed tomography angiography (CCTA) is considered the standard non-invasive tool to rule-out obstructive coronary artery disease (CAD). Moreover, several imaging biomarkers have been developed on cardiac-CT imaging to assess global CAD severity and atherosclerotic burden, including coronary calcium scoring, the segment involvement score, segment stenosis score and the Leaman-score. Myocardial perfusion imaging enables the diagnosis of myocardial ischemia and microvascular damage, and the CT-based fractional flow reserve quantification allows to evaluate non-invasively hemodynamic impact of the coronary stenosis. The texture and density of the epicardial and perivascular adipose tissue, the hypodense plaque burden, the radiomic phenotyping of coronary plaques or the fat radiomic profile are novel CT imaging features emerging as biomarkers of inflammation and plaque instability, which may implement the risk stratification strategies. The ability to perform myocardial tissue characterization by extracellular volume fraction and radiomic features appears promising in predicting arrhythmogenic risk and cardiovascular events. New imaging biomarkers are expanding the potential of cardiac CT for phenotyping the individual profile of CAD involvement and opening new frontiers for the practice of more personalized medicine.
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Affiliation(s)
- Giulia Cundari
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Livia Marchitelli
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Giacomo Pambianchi
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Federica Catapano
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini, 4, Pieve Emanuele, 20090, Milano, Italy
- Humanitas Research Hospital IRCCS, Via Alessandro Manzoni, 56, Rozzano, 20089, Milano, Italy
| | - Luca Conia
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Giuseppe Stancanelli
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Carlo Catalano
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Nicola Galea
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy.
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Zhu MM, Zhu XM, Lin SS, Dong ST, Liu WY, zhang JY, Xu Y. The incremental value of CCTA-derived myocardial radiomics signature for ischemia diagnosis with reference to CT myocardial perfusion imaging. Br J Radiol 2023; 96:20220971. [PMID: 37191174 PMCID: PMC10392656 DOI: 10.1259/bjr.20220971] [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: 10/18/2022] [Revised: 02/17/2023] [Accepted: 04/28/2023] [Indexed: 05/17/2023] Open
Abstract
OBJECTIVES To explore the incremental value of myocardial radiomics signature derived from static coronary computed tomography angiography (CCTA) for identifying myocardial ischemia based on stress dynamic CT myocardial perfusion imaging (CT-MPI). METHODS Patients who underwent CT-MPI and CCTA were retrospectively enrolled from two independent institutions, one used as training and the other as testing. Based on CT-MPI, coronary artery supplying area with relative myocardial blood flow (rMBF) value <0.8 was considered ischemia. Conventional imaging features of target plaques which caused the most severe narrowing of the vessel included area stenosis, lesion length (LL), total plaque burden, calcification burden, non-calcification burden, high-risk plaque (HRP) score, and CT fractional flow reserve (CT-FFR). Myocardial radiomics features were extracted at three vascular supply areas from CCTA images. The optimized radiomics signature was added to the conventional CCTA features to build the combined model (radiomics + conventional). RESULTS There were 168 vessels from 56 patients enrolled in the training set, and the testing set consisted of 135 vessels from 45 patients. From either cohort, HRP score, LL, stenosis ≥50% and CT-FFR ≤0.80 were associated with ischemia. The optimal myocardial radiomics signature consisted of nine features. The detection of ischemia using the combined model was significantly improved compared with conventional model in both training and testing set (AUC 0.789 vs 0.608, p < 0.001; 0.726 vs 0.637, p = 0.045). CONCLUSIONS Myocardial radiomics signature extracted from static CCTA combining with conventional features could provide incremental value to diagnose specific ischemia. ADVANCES IN KNOWLEDGE Myocardial radiomics signature extracted from CCTA may capture myocardial characteristics and provide incremental value to detect specific ischemia when combined with conventional features.
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Affiliation(s)
- Meng-meng Zhu
- Department of Medical Imaging, Medical Imaging Center, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China
| | - Xiao-mei Zhu
- Department of Radiology,, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Shu-shen Lin
- CT collaboration, Siemens Healthineers Ltd, Shanghai, China
| | - Si-ting Dong
- Department of Radiology,, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Wang-yan Liu
- Department of Radiology,, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jia-yin zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yi Xu
- Department of Radiology,, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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Lo Iacono F, Maragna R, Pontone G, Corino VDA. A robust radiomic-based machine learning approach to detect cardiac amyloidosis using cardiac computed tomography. FRONTIERS IN RADIOLOGY 2023; 3:1193046. [PMID: 37588665 PMCID: PMC10426499 DOI: 10.3389/fradi.2023.1193046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 05/26/2023] [Indexed: 08/18/2023]
Abstract
Introduction Cardiac amyloidosis (CA) shares similar clinical and imaging characteristics (e.g., hypertrophic phenotype) with aortic stenosis (AS), but its prognosis is generally worse than severe AS alone. Recent studies suggest that the presence of CA is frequent (1 out of 8 patients) in patients with severe AS. The coexistence of the two diseases complicates the prognosis and therapeutic management of both conditions. Thus, there is an urgent need to standardize and optimize the diagnostic process of CA and AS. The aim of this study is to develop a robust and reliable radiomics-based pipeline to differentiate the two pathologies. Methods Thirty patients were included in the study, equally divided between CA and AS. For each patient, a cardiac computed tomography (CCT) was analyzed by extracting 107 radiomics features from the LV wall. Feature robustness was evaluated by means of geometrical transformations to the ROIs and intra-class correlation coefficient (ICC) computation. Various correlation thresholds (0.80, 0.85, 0.90, 0.95, 1), feature selection methods [p-value, least absolute shrinkage and selection operator (LASSO), semi-supervised LASSO, principal component analysis (PCA), semi-supervised PCA, sequential forwards selection] and machine learning classifiers (k-nearest neighbors, support vector machine, decision tree, logistic regression and gradient boosting) were assessed using a leave-one-out cross-validation. Data augmentation was performed using the synthetic minority oversampling technique. Finally, explainability analysis was performed by using the SHapley Additive exPlanations (SHAP) method. Results Ninety-two radiomic features were selected as robust and used in the further steps. Best performances of classification were obtained using a correlation threshold of 0.95, PCA (keeping 95% of the variance, corresponding to 9 PCs) and support vector machine classifier reaching an accuracy, sensitivity and specificity of 0.93. Four PCs were found to be mainly dependent on textural features, two on first-order statistics and three on shape and size features. Conclusion These preliminary results show that radiomics might be used as non-invasive tool able to differentiate CA from AS using clinical routine available images.
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Affiliation(s)
- Francesca Lo Iacono
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Riccardo Maragna
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Gianluca Pontone
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
| | - Valentina D. A. Corino
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
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Baeßler B, Götz M, Antoniades C, Heidenreich JF, Leiner T, Beer M. Artificial intelligence in coronary computed tomography angiography: Demands and solutions from a clinical perspective. Front Cardiovasc Med 2023; 10:1120361. [PMID: 36873406 PMCID: PMC9978503 DOI: 10.3389/fcvm.2023.1120361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 01/25/2023] [Indexed: 02/18/2023] Open
Abstract
Coronary computed tomography angiography (CCTA) is increasingly the cornerstone in the management of patients with chronic coronary syndromes. This fact is reflected by current guidelines, which show a fundamental shift towards non-invasive imaging - especially CCTA. The guidelines for acute and stable coronary artery disease (CAD) of the European Society of Cardiology from 2019 and 2020 emphasize this shift. However, to fulfill this new role, a broader availability in adjunct with increased robustness of data acquisition and speed of data reporting of CCTA is needed. Artificial intelligence (AI) has made enormous progress for all imaging methodologies concerning (semi)-automatic tools for data acquisition and data post-processing, with outreach toward decision support systems. Besides onco- and neuroimaging, cardiac imaging is one of the main areas of application. Most current AI developments in the scenario of cardiac imaging are related to data postprocessing. However, AI applications (including radiomics) for CCTA also should enclose data acquisition (especially the fact of dose reduction) and data interpretation (presence and extent of CAD). The main effort will be to integrate these AI-driven processes into the clinical workflow, and to combine imaging data/results with further clinical data, thus - beyond the diagnosis of CAD- enabling prediction and forecast of morbidity and mortality. Furthermore, data fusing for therapy planning (e.g., invasive angiography/TAVI planning) will be warranted. The aim of this review is to present a holistic overview of AI applications in CCTA (including radiomics) under the umbrella of clinical workflows and clinical decision-making. The review first summarizes and analyzes applications for the main role of CCTA, i.e., to non-invasively rule out stable coronary artery disease. In the second step, AI applications for additional diagnostic purposes, i.e., to improve diagnostic power (CAC = coronary artery classifications), improve differential diagnosis (CT-FFR and CT perfusion), and finally improve prognosis (again CAC plus epi- and pericardial fat analysis) are reviewed.
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Affiliation(s)
- Bettina Baeßler
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Michael Götz
- Division of Experimental Radiology, Department for Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| | - Charalambos Antoniades
- British Heart Foundation Chair of Cardiovascular Medicine, Cardiovascular Medicine, University of Oxford, Oxford, United Kingdom
| | - Julius F. Heidenreich
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Tim Leiner
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Meinrad Beer
- Department for Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
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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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Shang J, Guo Y, Ma Y, Hou Y. Cardiac computed tomography radiomics: a narrative review of current status and future directions. Quant Imaging Med Surg 2022; 12:3436-3453. [PMID: 35655815 PMCID: PMC9131324 DOI: 10.21037/qims-21-1022] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 03/23/2022] [Indexed: 08/18/2023]
Abstract
BACKGROUND AND OBJECTIVE In an era of profound growth of medical data and rapid development of advanced imaging modalities, precision medicine increasingly requires further expansion of what can be interpreted from medical images. However, the current interpretation of cardiac computed tomography (CT) images mainly depends on subjective and qualitative analysis. Radiomics uses advanced image analysis to extract numerous quantitative features from digital images that are unrecognizable to the naked eye. Visualization of these features can reveal underlying connections between image phenotyping and biological characteristics and support clinical outcomes. Although research into radiomics on cardiovascular disease began only recently, several studies have indicated its potential clinical value in assessing future cardiac risk and guiding prevention and management strategies. Our review aimed to summarize the current applications of cardiac CT radiomics in the cardiovascular field and discuss its advantages, challenges, and future directions. METHODS We searched for English-language articles published between January 2010 and August 2021 in the databases of PubMed, Embase, and Google Scholar. The keywords used in the search included computed tomography or CT, radiomics, cardiovascular or cardiac. KEY CONTENT AND FINDINGS The current applications of radiomics in cardiac CT were found to mainly involve research into coronary plaques, perivascular adipose tissue (PVAT), myocardial tissue, and intracardiac lesions. Related findings on cardiac CT radiomics suggested the technique can assist the identification of vulnerable plaques or patients, improve cardiac risk prediction and stratification, discriminate myocardial pathology and etiologies behind intracardiac lesions, and offer new perspective and development prospects to personalized cardiovascular medicine. CONCLUSIONS Cardiac CT radiomics can gather additional disease-related information at a microstructural level and establish a link between imaging phenotyping and tissue pathology or biology alone. Therefore, cardiac CT radiomics has significant clinical implications, including a contribution to clinical decision-making. Along with advancements in cardiac CT imaging, cardiac CT radiomics is expected to provide more precise phenotyping of cardiovascular disease for patients and doctors, which can improve diagnostic, prognostic, and therapeutic decision making in the future.
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Affiliation(s)
- Jin Shang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yan Guo
- GE Healthcare, Beijing, China
| | - Yue Ma
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yang Hou
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
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Lee S, Han K, Suh YJ. Quality assessment of radiomics research in cardiac CT: a systematic review. Eur Radiol 2022; 32:3458-3468. [PMID: 34981135 DOI: 10.1007/s00330-021-08429-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 09/13/2021] [Accepted: 10/22/2021] [Indexed: 01/01/2023]
Abstract
OBJECTIVES To assess the quality of current radiomics research on cardiac CT using radiomics quality score (RQS) and Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) systems. METHODS Systematic searches of PubMed and EMBASE were performed to identify all potentially relevant original research articles about cardiac CT radiomics. Fifteen original research articles were selected. Two cardiac radiologists assessed the quality of the methodology adopted in those studies according to the RQS and TRIPOD guidelines. Basic adherence rates for the following six key domains were evaluated: image protocol and reproducibility, feature reduction and validation, biologic/clinical utility, performance index, high level of evidence, and open science. RESULTS Among the 15 included articles, six (40%) were about coronary artery disease and six (40%) were about myocardial infarction. The mean RQS was 9.9 ± 7.3 (27.4% of the ideal score of 36), and the basic adherence rate was 44.6%. Fourteen (93.3%) and nine (60%) studies performed feature selection and validation, but only two (13.3%) of them performed external validation. Two studies (13.3%) were prospective, and only one study (6.7%) conducted calibration analysis and stated the potential clinical utility. None of the studies conducted phantom study and cost-effective analysis. The overall adherence rate for TRIPOD was 63%. CONCLUSION The quality of radiomics studies in cardiac CT is currently insufficient. A higher level of evidence is required, and analysis of clinical utility and calibration of model performance need to be improved. KEY POINTS • The quality of science of radiomics studies in cardiac CT is currently insufficient. • No study conducted a phantom study or cost-effective analysis, with further limitations being demonstrated in a high level of evidence for radiomics studies. • Analysis of clinical utility and calibration of model performance need to be improved, and a higher level of evidence is required.
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Affiliation(s)
- Suji Lee
- Department of Radiology, Center for Clinical Imaging Data Science, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Kyunghwa Han
- Department of Radiology, Center for Clinical Imaging Data Science, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Young Joo Suh
- Department of Radiology, Center for Clinical Imaging Data Science, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
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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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Yang YC, Wei XY, Tang XQ, Yin RH, Zhang M, Duan SF, Pan CJ. Exploring value of CT coronary imaging combined with machine-learning methods to predict myocardial ischemia. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2022; 30:767-776. [PMID: 35527621 DOI: 10.3233/xst-221160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
PURPOSE To establish a machine-learning (ML) model based on coronary computed tomography angiography (CTA) images for evaluating myocardial ischemia in patients diagnosed with coronary atherosclerosis. METHODS This retrospective analysis includes CTA images acquired from 110 patients. Among them, 58 have myocardial ischemia and 52 have normal myocardial blood supply. The patients are divided into training and test datasets with a ratio 7 : 3. Deep learning model-based CQK software is used to automatically segment myocardium on CTA images and extract texture features. Then, seven ML models are constructed to classify between myocardial ischemia and normal myocardial blood supply cases. Predictive performance and stability of the classifiers are determined by receiver operating characteristic curve with cross validation. The optimal ML model is then validated using an independent test dataset. RESULTS Accuracy and areas under ROC curves (AUC) obtained from the support vector machine with extreme gradient boosting linear method are 0.821 and 0.777, respectively, while accuracy and AUC achieved by the neural network (NN) method are 0.818 and 0.757, respectively. The naive Bayes model yields the highest sensitivity (0.942), and the random forest model yields the highest specificity (0.85). The k-nearest neighbors model yields the lowest accuracy (0.74). Additionally, NN model demonstrates the lowest relative standard deviations (0.16 for accuracy and 0.08 for AUC) indicating the high stability of this model, and its AUC applying to the independent test dataset is 0.72. CONCLUSION The NN model demonstrates the best performance in predicting myocardial ischemia using radiomics features computed from CTA images, which suggests that this ML model has promising potential in guiding clinical decision-making.
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Affiliation(s)
- You-Chang Yang
- Department of Radiology, Changzhou Second People's Hospital, Changzhou, China
| | - Xiao-Yu Wei
- Department of Radiology, Changzhou Second People's Hospital, Changzhou, China
| | - Xiao-Qiang Tang
- Department of Radiology, Changzhou Second People's Hospital, Changzhou, China
| | - Ruo-Han Yin
- Department of Radiology, Changzhou Second People's Hospital, Changzhou, China
| | - Ming Zhang
- Department of Radiology, Changzhou Second People's Hospital, Changzhou, China
| | - Shao-Feng Duan
- Precision Health Institution, GE Healthcare (China), Shanghai, China
| | - Chang-Jie Pan
- Department of Radiology, Changzhou Second People's Hospital, Changzhou, China
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Infante T, Cavaliere C, Punzo B, Grimaldi V, Salvatore M, Napoli C. Radiogenomics and Artificial Intelligence Approaches Applied to Cardiac Computed Tomography Angiography and Cardiac Magnetic Resonance for Precision Medicine in Coronary Heart Disease: A Systematic Review. Circ Cardiovasc Imaging 2021; 14:1133-1146. [PMID: 34915726 DOI: 10.1161/circimaging.121.013025] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The risk of coronary heart disease (CHD) clinical manifestations and patient management is estimated according to risk scores accounting multifactorial risk factors, thus failing to cover the individual cardiovascular risk. Technological improvements in the field of medical imaging, in particular, in cardiac computed tomography angiography and cardiac magnetic resonance protocols, laid the development of radiogenomics. Radiogenomics aims to integrate a huge number of imaging features and molecular profiles to identify optimal radiomic/biomarker signatures. In addition, supervised and unsupervised artificial intelligence algorithms have the potential to combine different layers of data (imaging parameters and features, clinical variables and biomarkers) and elaborate complex and specific CHD risk models allowing more accurate diagnosis and reliable prognosis prediction. Literature from the past 5 years was systematically collected from PubMed and Scopus databases, and 60 studies were selected. We speculated the applicability of radiogenomics and artificial intelligence through the application of machine learning algorithms to identify CHD and characterize atherosclerotic lesions and myocardial abnormalities. Radiomic features extracted by cardiac computed tomography angiography and cardiac magnetic resonance showed good diagnostic accuracy for the identification of coronary plaques and myocardium structure; on the other hand, few studies exploited radiogenomics integration, thus suggesting further research efforts in this field. Cardiac computed tomography angiography resulted the most used noninvasive imaging modality for artificial intelligence applications. Several studies provided high performance for CHD diagnosis, classification, and prognostic assessment even though several efforts are still needed to validate and standardize algorithms for CHD patient routine according to good medical practice.
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Affiliation(s)
- Teresa Infante
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Naples, Italy (T.I., C.N.)
| | | | - Bruna Punzo
- IRCCS SDN, Naples, Italy (C.C., B.P., V.G., M.S., C.N.)
| | | | | | - Claudio Napoli
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Naples, Italy (T.I., C.N.).,IRCCS SDN, Naples, Italy (C.C., B.P., V.G., M.S., C.N.)
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14
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Freitas SA, Nienow D, da Costa CA, Ramos GDO. Functional Coronary Artery Assessment: a Systematic Literature Review. Wien Klin Wochenschr 2021; 134:302-318. [PMID: 34870740 DOI: 10.1007/s00508-021-01970-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 10/11/2021] [Indexed: 11/28/2022]
Abstract
Cardiovascular diseases represent the number one cause of death in the world, including the most common disorders in the heart's health, namely coronary artery disease (CAD). CAD is mainly caused by fat accumulated in the arteries' internal walls, creating an atherosclerotic plaque that impacts the blood flow functional behavior. Anatomical plaque characteristics are essential but not sufficient for a complete functional assessment of CAD. In fact, plaque analysis and visual inspection alone have proven insufficient to determine the lesion severity and hemodynamic repercussion. Furthermore, the fractional flow reserve (FFR) exam, which is considered the gold standard for stenosis functional impair determination, is invasive and contains several limitations. Such a panorama evidences the need for new techniques applied to image exams to improve CAD functional assessment. In this article, we perform a systematic literature review on emerging methods determining CAD significance, thus delivering a unique base for comparing these methods, qualitatively and quantitatively. Our goal is to guide further studies with evidence from the most promising methods, highlighting the benefits from both areas. We summarize benchmarks, metrics for evaluation, and challenges already faced, thus shedding light on the requirements for a valid, meaningful, and accepted technique for functional assessment evaluation. We create a base of comparison based on quantitative and qualitative indicators and highlight the most relevant geometrical metrics that correlate with lesion significance. Finally, we point out future benchmarks based on recent literature.
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Affiliation(s)
- Samuel A Freitas
- Software Innovation Laboratory, Graduate Program in Applied Computing, Universidade do Vale do Rio dos Sinos, São Leopoldo, Brazil
| | - Débora Nienow
- Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Cristiano A da Costa
- Software Innovation Laboratory, Graduate Program in Applied Computing, Universidade do Vale do Rio dos Sinos, São Leopoldo, Brazil
| | - Gabriel de O Ramos
- Software Innovation Laboratory, Graduate Program in Applied Computing, Universidade do Vale do Rio dos Sinos, São Leopoldo, Brazil.
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15
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Zhao H, Yuan L, Chen Z, Liao Y, Lin J. Exploring the diagnostic effectiveness for myocardial ischaemia based on CCTA myocardial texture features. BMC Cardiovasc Disord 2021; 21:416. [PMID: 34465308 PMCID: PMC8406838 DOI: 10.1186/s12872-021-02206-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Accepted: 08/11/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND To explore the characteristics of myocardial textures on coronary computed tomography angiography (CCTA) images in patients with coronary atherosclerotic heart disease, a classification model was established, and the diagnostic effectiveness of CCTA for myocardial ischaemia patients was explored. METHODS This was a retrospective analysis of the CCTA images of 155 patients with clinically diagnosed coronary heart disease from September 2019 to January 2020, 79 of whom were considered positive (myocardial ischaemia) and 76 negative (normal myocardial blood supply) according to their clinical diagnoses. By using the deep learning model-based CQK software, the myocardium was automatically segmented from the CCTA images and used to extract texture features. All patients were randomly divided into a training cohort and a test cohort at a 7:3 ratio. The Spearman correlation and least absolute shrinkage and selection operator (LASSO) method were used for feature selection. Based on the selected features of the training cohort, a multivariable logistic regression model was established. Finally, the test cohort was used to verify the regression model. RESULTS A total of 387 features were extracted from the CCTA images of the 155 coronary heart disease patients. After performing dimensionality reduction with the Spearman correlation and LASSO, three texture features were selected. The accuracy, area under the curve, specificity, sensitivity, positive predictive value and negative predictive value of the constructed multivariable logistic regression model with the test cohort were 0.783, 0.875, 0.733, 0.875, 0.650 and 0.769, respectively. CONCLUSION CCTA imaging texture features of the myocardium have potential as biomarkers for diagnosing myocardial ischaemia.
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Affiliation(s)
- Hengyu Zhao
- Xiamen Cardiovascular Hospital Xiamen University, Xiamen, 361006, Fujian, China. .,Key Laboratory of Functional and Clinical Translational Medicine, Fujian Province University, Xiamen Medical College, Xiamen, China. .,Xiamen Key Laboratory of Precision Medicine for Cardiovascular Disease, Xiamen, China.
| | - Lijie Yuan
- Department of Molecular Biology, Xiamen Medical College, Xiamen, China
| | - Zhishang Chen
- Xiamen Cardiovascular Hospital Xiamen University, Xiamen, 361006, Fujian, China.,Xiamen Key Laboratory of Precision Medicine for Cardiovascular Disease, Xiamen, China
| | | | - Jiangzhou Lin
- Xiamen Cardiovascular Hospital Xiamen University, Xiamen, 361006, Fujian, China.,Xiamen Key Laboratory of Precision Medicine for Cardiovascular Disease, Xiamen, China
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Li L, Hu X, Tao X, Shi X, Zhou W, Hu H, Hu X. Radiomic features of plaques derived from coronary CT angiography to identify hemodynamically significant coronary stenosis, using invasive FFR as the reference standard. Eur J Radiol 2021; 140:109769. [PMID: 33992980 DOI: 10.1016/j.ejrad.2021.109769] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Revised: 05/02/2021] [Accepted: 05/05/2021] [Indexed: 01/14/2023]
Abstract
OBJECTIVE This study aimed to investigate the diagnostic performance of radiomics features derived from coronary computed tomography angiography (CCTA) in the identification of ischemic coronary stenosis plaque using invasive fractional flow reserve (FFR) as the reference standard. MATERIALS AND METHODS 174 plaques of 149 patients (age: 62.21 ± 8.47 years, 96 males) with at least one lesion stenosis degree between 30 % and 90 % were retrospectively included. Stenosis degree and plaque characteristics were recorded, and a conventional multivariate logistic model was established. Over 1000 radiomics features of the plaque were derived from CCTA images. The plaques were randomly divided into training set (n = 139) and validation set (n = 35). A random forest model was built. The area under the curve (AUC) of the models was compared. RESULTS Fifty-eight radiomics features were correlated with functionally significant stenosis (p < 0.05), wherein 56 features had an AUC of >0.6. NCP volume, NRS, remodeling index, and spotty calcification were included in the conventional model. Ultimately, 14 features were integrated to build the radiomics model. The AUC showed an improvement: 0.71 vs 0.82 for the training set and 0.70 vs 0.77 for the validation set (conventional model and radiomics model, respectively); however, it was not statistically significant (p = 0.58). CONCLUSION The radiomics analysis of plaques showed improvement compared with conventional plaques assessment in identifying hemodynamically significant coronary stenosis. The statistical advancement of machine learning for plaques to predict hemodynamic stenosis with a noninvasive approach still needs further studies on a large-scale dataset.
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Affiliation(s)
- Lin Li
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 East Qingchun Road, Hangzhou, Zhejiang, 310010, China.
| | - Xi Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 East Qingchun Road, Hangzhou, Zhejiang, 310010, China.
| | - Xinwei Tao
- Siemens Healthineers China, No.278, Road Zhouzhu, Shanghai, 201314, China.
| | - Xiaozhe Shi
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 East Qingchun Road, Hangzhou, Zhejiang, 310010, China.
| | - Wenli Zhou
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 East Qingchun Road, Hangzhou, Zhejiang, 310010, China.
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 East Qingchun Road, Hangzhou, Zhejiang, 310010, China.
| | - Xiuhua Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 East Qingchun Road, Hangzhou, Zhejiang, 310010, China.
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Texture analysis of delayed contrast-enhanced computed tomography to diagnose cardiac sarcoidosis. Jpn J Radiol 2021; 39:442-450. [PMID: 33483941 DOI: 10.1007/s11604-020-01086-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 12/27/2020] [Indexed: 01/09/2023]
Abstract
PURPOSE To investigate the diagnostic value of texture analysis to differentiate cardiac sarcoidosis (CS) from other non-ischemic cardiomyopathies (non-CS). MATERIALS AND METHODS Twenty CS patients and 15 non-CS patients who had undergone myocardial CT delayed enhancement (CTDE) were included. A total of 36 texture features were calculated according to the CT attenuation of CTDE. We investigated the diagnostic value to differentiate CS from non-CS. We also assessed the intra- and inter-rater reproducibility for each feature and inter-observer agreement for visual assessment. RESULTS Seven extracted features had significantly higher run length non-uniformity (RLNU) values (5.4 × 102 ± 6.2 × 102 vs. 11.2 × 102 ± 4.9 × 102, p = 0.037) and significantly lower low gray-level zone emphasis (LGZE) values (7.1 × 10-3 ± 8.6 × 10-3 vs. 18.1 × 10-3 ± 16.9 × 10-3, p = 0.017) in CS than in non-CS. Intra- and inter-rater reproducibility of RLNU and LGZE were excellent (ICCs > 0.8), while inter-observer agreement of visual assessment was poor (kappa = 0.19). The accuracies of texture analysis were 69% with RLNU and 71% with LGZE, which were better than that of visual assessment. CONCLUSION Texture analysis of CTDE could differentiate CS from non-CS with high reproducibility.
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CT texture analysis of vulnerable plaques on optical coherence tomography. Eur J Radiol 2021; 136:109551. [PMID: 33485126 DOI: 10.1016/j.ejrad.2021.109551] [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: 10/12/2020] [Revised: 12/16/2020] [Accepted: 01/12/2021] [Indexed: 02/02/2023]
Abstract
PURPOSE To explore whether CT texture analysis can identify thin-cap fibroatheroma (TCFA) determined by optical coherence tomography (OCT). METHODS Thirty-three patients with 43 lesions who underwent both CCTA and OCT within 3 months were retrospectively included. 12 conventional CT-derived plaque features, fat attenuation index (FAI) and 1691 plaque radiomics features were extracted to discriminate TCFA lesions and non-TCFA lesions determined by OCT. Minimum redundancy and maximum relevance (mRMR) method was employed to select radiomics features. The top ranked features were used to construct a forward stepwise logistic radiomics model. The performance of radiomics model was compared with the conventional high-risk plaque (HRP) features model and FAI model for the detection of TCFA. RESULTS Out of 1691 features, 35 features were significantly different between TCFA and non-TCFA lesions (all p<0.05) while only low attenuation plaque (LAP) was more frequent in TCFA group (p = 0.004). There was no significant difference in FAI between TCFA and non-TCFA lesions. Five features were ultimately integrated into the radiomics model after mRMR analysis, which demonstrated significantly higher AUC for the detection of TCFA (0.952; 95 % CI: 0.897-1.000) compared with the conventional HRP features model (0.621; 95 % CI: 0.469-0.773, p < 0.001) and FAI model (0.52; 95 % CI: 0.33-0.70, p < 0.001). CONCLUSION CT texture analysis performs better at identifying TCFA determined by OCT compared with conventional CT-derived plaque parameters and FAI. Texture analysis may serve as a potential non-invasive method of evaluating vulnerable plaque.
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