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Zhou C, Xiao Y, Li L, Liu Y, Zhu F, Zhou W, Yi X, Zhao M. Radiomics Nomogram Derived from Gated Myocardial Perfusion SPECT for Identifying Ischemic Cardiomyopathy. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01145-3. [PMID: 38806952 DOI: 10.1007/s10278-024-01145-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 05/05/2024] [Accepted: 05/15/2024] [Indexed: 05/30/2024]
Abstract
Personalized management involving heart failure (HF) etiology is crucial for better prognoses. We aim to evaluate the utility of a radiomics nomogram based on gated myocardial perfusion imaging (GMPI) in distinguishing ischemic from non-ischemic origins of HF. A total of 172 heart failure patients with reduced left ventricular ejection fraction (HFrEF) who underwent GMPI scan were divided into training (n = 122) and validation sets (n = 50) based on chronological order of scans. Radiomics features were extracted from the resting GMPI. Four machine learning algorithms were used to construct radiomics models, and the model with the best performances were selected to calculate the Radscore. A radiomics nomogram was constructed based on the Radscore and independent clinical factors. Finally, the model performance was validated using operating characteristic curves, calibration curve, decision curve analysis, integrated discrimination improvement values (IDI), and the net reclassification index (NRI). Three optimal radiomics features were used to build a radiomics model. Total perfusion deficit (TPD) was identified as the independent factors of conventional GMPI metrics for building the GMPI model. In the validation set, the radiomics nomogram integrating the Radscore, age, systolic blood pressure, and TPD significantly outperformed the GMPI model in distinguishing ischemic cardiomyopathy (ICM) from non-ischemic cardiomyopathy (NICM) (AUC 0.853 vs. 0.707, p = 0.038). IDI analysis indicated that the nomogram improved diagnostic accuracy by 28.3% compared to the GMPI model in the validation set. By combining radiomics signatures with clinical indicators, we developed a GMPI-based radiomics nomogram that helps to identify the ischemic etiology of HFrEF.
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Affiliation(s)
- Chunqing Zhou
- Department of Nuclear Medicine, The Third Xiangya Hospital of Central South University, No.138, Tongzipo Road, Changsha, Hunan Province, 410013, China
| | - Yi Xiao
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, Changsha, China
| | - Longxi Li
- School of Computer and Communication Engineering, Zhenzhou University of Light Industry, Zhengzhou, 450002, Henan, China
| | - Yanyun Liu
- School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, China
| | - Fubao Zhu
- School of Computer and Communication Engineering, Zhenzhou University of Light Industry, Zhengzhou, 450002, Henan, China
| | - Weihua Zhou
- Department of Applied Computing, Michigan Technological University, Houghton, MI, USA
| | - Xiaoping Yi
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Min Zhao
- Department of Nuclear Medicine, The Third Xiangya Hospital of Central South University, No.138, Tongzipo Road, Changsha, Hunan Province, 410013, China.
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, Changsha, China.
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Zhang Y, Bos E, Clarkin O, Wilson T, Small GR, Wells RG, Lu L, Chow BJW. Interpretation of SPECT wall motion with deep learning. J Nucl Cardiol 2024:101881. [PMID: 38723886 DOI: 10.1016/j.nuclcard.2024.101881] [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: 01/12/2024] [Revised: 03/12/2024] [Accepted: 05/01/2024] [Indexed: 05/27/2024]
Abstract
OBJECTIVES We sought to develop a novel deep learning (DL) workflow to interpret single-photon emission computed tomography (SPECT) wall motion. BACKGROUND Wall motion assessment with SPECT is limited by image temporal and spatial resolution. Visual interpretation of wall motion can be subjective and prone to error. Artificial intelligence (AI) may improve accuracy of wall motion assessment. METHODS A total of 1038 patients undergoing rest electrocardiogram (ECG)-gated SPECT and echocardiography were included. Using echocardiography as truth, a DL-model (DL-model 1) was trained to predict the probability of abnormal wall motion. Of the 1038 patients, 317 were used to train a DL-model (DL-model 2) to assess regional wall motion. A 10-fold cross-validation was adopted. Diagnostic performance of DL was compared with human readers and quantitative parameters. RESULTS The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) of DL model (AUC: .82 [95% CI: .79-.85]; ACC: .88) were higher than human (AUC: .77 [95% CI: .73-.81]; ACC: .82; P < .001) and quantitative parameter (AUC: .74 [95% CI: .66-.81]; ACC: .78; P < .05). The net reclassification index (NRI) was 7.7%. The AUC and accuracy of DL model for per-segment and per-vessel territory diagnosis were also higher than human reader. The DL model generated results within 30 seconds with operable guided user interface (GUI) and therefore could provide preliminary interpretation. CONCLUSIONS DL can be used to improve interpretation of rest SPECT wall motion as compared with current human readers and quantitative parameter diagnosis.
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Affiliation(s)
- Yangmei Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Emma Bos
- Department of Physics, Engineering Physics & Astronomy, Queen's University, Canada
| | - Owen Clarkin
- Department of Medicine (Cardiology), University of Ottawa Heart Institute, Canada
| | - Tyler Wilson
- Department of Applied Science in Computer Engineering, Queen's University, Canada
| | - Gary R Small
- Department of Medicine (Cardiology), University of Ottawa Heart Institute, Canada
| | - R Glenn Wells
- Department of Medicine (Cardiology), University of Ottawa Heart Institute, Canada
| | - Lijun Lu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China; Pazhou Lab, Guangzhou, China
| | - Benjamin J W Chow
- Department of Medicine (Cardiology), University of Ottawa Heart Institute, Canada; Department of Radiology, University of Ottawa, Canada.
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Bors S, Abler D, Dietz M, Andrearczyk V, Fageot J, Nicod-Lalonde M, Schaefer N, DeKemp R, Kamani CH, Prior JO, Depeursinge A. Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [ 82Rb] PET for MACE prediction. Sci Rep 2024; 14:9644. [PMID: 38671059 PMCID: PMC11053111 DOI: 10.1038/s41598-024-60095-6] [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: 08/30/2023] [Accepted: 04/18/2024] [Indexed: 04/28/2024] Open
Abstract
Assessing the individual risk of Major Adverse Cardiac Events (MACE) is of major importance as cardiovascular diseases remain the leading cause of death worldwide. Quantitative Myocardial Perfusion Imaging (MPI) parameters such as stress Myocardial Blood Flow (sMBF) or Myocardial Flow Reserve (MFR) constitutes the gold standard for prognosis assessment. We propose a systematic investigation of the value of Artificial Intelligence (AI) to leverage [82 Rb] Silicon PhotoMultiplier (SiPM) PET MPI for MACE prediction. We establish a general pipeline for AI model validation to assess and compare the performance of global (i.e. average of the entire MPI signal), regional (17 segments), radiomics and Convolutional Neural Network (CNN) models leveraging various MPI signals on a dataset of 234 patients. Results showed that all regional AI models significantly outperformed the global model ( p < 0.001 ), where the best AUC of 73.9% (CI 72.5-75.3) was obtained with a CNN model. A regional AI model based on MBF averages from 17 segments fed to a Logistic Regression (LR) constituted an excellent trade-off between model simplicity and performance, achieving an AUC of 73.4% (CI 72.3-74.7). A radiomics model based on intensity features revealed that the global average was the least important feature when compared to other aggregations of the MPI signal over the myocardium. We conclude that AI models can allow better personalized prognosis assessment for MACE.
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Affiliation(s)
- Sacha Bors
- Nuclear Medicine and Molecular Imaging Department, Lausanne University Hospital, Lausanne, Switzerland
- Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
| | - Daniel Abler
- Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
- Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
| | - Matthieu Dietz
- INSERM U1060, CarMeN laboratory, University of Lyon, Lyon, France
| | - Vincent Andrearczyk
- Nuclear Medicine and Molecular Imaging Department, Lausanne University Hospital, Lausanne, Switzerland
- Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
| | - Julien Fageot
- AudioVisual Communications Laboratory (LCAV), EPFL, Lausanne, Switzerland
| | - Marie Nicod-Lalonde
- Nuclear Medicine and Molecular Imaging Department, Lausanne University Hospital, Lausanne, Switzerland
- University of Lausanne, Lausanne, Switzerland
| | - Niklaus Schaefer
- Nuclear Medicine and Molecular Imaging Department, Lausanne University Hospital, Lausanne, Switzerland
- University of Lausanne, Lausanne, Switzerland
| | - Robert DeKemp
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, ON, Canada
| | - Christel H Kamani
- Department of Cardiology, Lausanne University Hospital, Lausanne, Switzerland
| | - John O Prior
- Nuclear Medicine and Molecular Imaging Department, Lausanne University Hospital, Lausanne, Switzerland.
- University of Lausanne, Lausanne, Switzerland.
| | - Adrien Depeursinge
- Nuclear Medicine and Molecular Imaging Department, Lausanne University Hospital, Lausanne, Switzerland
- Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
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Wang H, Chen Y, Qiu J, Xie J, Lu W, Ma J, Jia M. Machine learning based on SPECT/CT to differentiate bone metastasis and benign bone lesions in lung malignancy patients. Med Phys 2024; 51:2578-2588. [PMID: 37966123 DOI: 10.1002/mp.16839] [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: 07/07/2023] [Accepted: 10/30/2023] [Indexed: 11/16/2023] Open
Abstract
BACKGROUND Bone metastasis is a common event in lung cancer progression. Early diagnosis of lung malignant tumor with bone metastasis is crucial for selecting effective treatment strategies. However, 14.3% of patients are still difficult to diagnose after SPECT/CT examination. PURPOSE Machine learning analysis of [99mTc]-methylene diphosphate (99mTc-MDP) SPECT/CT scans to distinguish bone metastases from benign bone lesions in patients with lung cancer. METHODS One hundred forty-one patients (69 with bone metastases and 72 with benign bone lesions) were randomly assigned to the training group or testing group in a 7:3 ratio. Lesions were manually delineated using ITK-SNAP, and 944 radiomics features were extracted from SPECT and CT images. The least absolute shrinkage and selection operator (LASSO) regression was used to select the radiomics features in the training set, and the single/bimodal radiomics models were established based on support vector machine (SVM). To further optimize the model, the best bimodal radiomics features were combined with clinical features to establish an integrated Radiomics-clinical model. The diagnostic performance of models was evaluated using receiver operating characteristic (ROC) curve and confusion matrix, and performance differences between models were evaluated using the Delong test. RESULTS The optimal radiomics model comprised of structural modality (CT) and metabolic modality (SPECT), with an area under curve (AUC) of 0.919 and 0.907 for the training and testing set, respectively. The integrated model, which combined SPECT, CT, and two clinical features, exhibited satisfactory differentiation in the training and testing set, with AUC of 0.939 and 0.925, respectively. CONCLUSIONS The machine learning can effectively differentiate between bone metastases and benign bone lesions. The Radiomics-clinical integrated model demonstrated the best performance.
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Affiliation(s)
- Huili Wang
- College of Preventive Medicine & Institute of Radiation Medicine, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China
| | - Yiru Chen
- Department of Nuclear Medicine, The Second Affiliated Hospital of Shandong First Medical University, Taian, China
| | - Jianfeng Qiu
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, China
| | - Jindong Xie
- College of Preventive Medicine & Institute of Radiation Medicine, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China
| | - Weizhao Lu
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, China
| | - Junchi Ma
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, China
| | - Mingsheng Jia
- Department of Nuclear Medicine, The Second Affiliated Hospital of Shandong First Medical University, Taian, China
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Miller RJH, Slomka PJ. Artificial Intelligence in Nuclear Cardiology: An Update and Future Trends. Semin Nucl Med 2024:S0001-2998(24)00015-1. [PMID: 38521708 DOI: 10.1053/j.semnuclmed.2024.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 02/19/2024] [Indexed: 03/25/2024]
Abstract
Myocardial perfusion imaging (MPI), using either single photon emission computed tomography (SPECT) or positron emission tomography (PET), is one of the most commonly ordered cardiac imaging tests, with prominent clinical roles for disease diagnosis and risk prediction. Artificial intelligence (AI) could potentially play a role in many steps along the typical MPI workflow, from image acquisition through to clinical reporting and risk estimation. AI can be utilized to improve image quality, reducing radiation exposure and image acquisition times. Once images are acquired, AI can help optimize motion correction and image registration during image reconstruction or provide direct image attenuation correction. Utilizing these image sets, AI can segment a number of anatomic features from associated computed tomographic imaging or even generate synthetic attenuation imaging. Lastly, AI may play an important role in disease diagnosis or risk prediction by combining the large number of potentially important clinical, stress, and imaging-related variables. This review will focus on the most recent developments in the field, providing clinicians and researchers with a timely update on the field. Additionally, it will discuss future trends including applications of AI during multiple points of the typical MPI workflow to maximize clinical utility and methods to maximize the information that can be obtained from hybrid imaging.
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Affiliation(s)
- Robert J H Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA; Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada
| | - Piotr J Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA.
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Nigam S, Gjelaj E, Wang R, Wei GW, Wang P. Machine Learning and Deep Learning Applications in Magnetic Particle Imaging. J Magn Reson Imaging 2024. [PMID: 38358090 DOI: 10.1002/jmri.29294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/29/2024] [Accepted: 01/31/2024] [Indexed: 02/16/2024] Open
Abstract
In recent years, magnetic particle imaging (MPI) has emerged as a promising imaging technique depicting high sensitivity and spatial resolution. It originated in the early 2000s where it proposed a new approach to challenge the low spatial resolution achieved by using relaxometry in order to measure the magnetic fields. MPI presents 2D and 3D images with high temporal resolution, non-ionizing radiation, and optimal visual contrast due to its lack of background tissue signal. Traditionally, the images were reconstructed by the conversion of signal from the induced voltage by generating system matrix and X-space based methods. Because image reconstruction and analyses play an integral role in obtaining precise information from MPI signals, newer artificial intelligence-based methods are continuously being researched and developed upon. In this work, we summarize and review the significance and employment of machine learning and deep learning models for applications with MPI and the potential they hold for the future. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Saumya Nigam
- Precision Health Program, Michigan State University, East Lansing, Michigan, USA
- Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, Michigan, USA
| | - Elvira Gjelaj
- Precision Health Program, Michigan State University, East Lansing, Michigan, USA
- Lyman Briggs College, Michigan State University, East Lansing, Michigan, USA
| | - Rui Wang
- Department of Mathematics, College of Natural Science, Michigan State University, East Lansing, Michigan, USA
| | - Guo-Wei Wei
- Department of Mathematics, College of Natural Science, Michigan State University, East Lansing, Michigan, USA
- Department of Electrical and Computer Engineering, College of Engineering, Michigan State University, East Lansing, Michigan, USA
- Department of Biochemistry and Molecular Biology, College of Natural Science, Michigan State University, East Lansing, Michigan, USA
| | - Ping Wang
- Precision Health Program, Michigan State University, East Lansing, Michigan, USA
- Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, Michigan, USA
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7
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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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Hajianfar G, Khorgami M, Rezaei Y, Amini M, Samiei N, Tabib A, Borji BK, Kalayinia S, Shiri I, Hosseini S, Oveisi M. Comparison of Machine Learning Algorithms Using Manual/Automated Features on 12-Lead Signal Electrocardiogram Classification: A Large Cohort Study on Students Aged Between 6 to 18 Years Old. Cardiovasc Eng Technol 2023; 14:786-800. [PMID: 37848737 DOI: 10.1007/s13239-023-00687-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 09/13/2023] [Indexed: 10/19/2023]
Abstract
PROPOSE An electrocardiogram (ECG) has been extensively used to detect rhythm disturbances. We sought to determine the accuracy of different machine learning in distinguishing abnormal ECGs from normal ones in children who were examined using a resting 12-Lead ECG machine, and we also compared the manual and automated measurement using the modular ECG Analysis System (MEANS) algorithm of ECG features. METHODS Altogether, 10745 ECGs were recorded for students aged 6 to 18. Manual and automatic ECG features were extracted for each participant. Features were normalized using Z-score normalization and went through the student's t-test and chi-squared test to measure their relevance. We applied the Boruta algorithm for feature selection and then implemented eight classifier algorithms. The dataset was split into training (80%) and test (20%) partitions. The performance of the classifiers was evaluated on the test data (unseen data) by 1000 bootstrap, and sensitivity (SEN), specificity (SPE), AUC, and accuracy (ACC) were reported. RESULTS In univariate analysis, the highest performance was heart rate and RR interval in the manual dataset and heart rate in an automated dataset with AUC of 0.72 and 0.71, respectively. The best classifiers in the manual dataset were random forest (RF) and quadratic-discriminant-analysis (QDA) with AUC, ACC, SEN, and SPE equal to 0.93, 0.98, 0.69, 0.99, and 0.90, 0.95, 0.75, 0.96, respectively. In the automated dataset, QDA (AUC: 0.89, ACC:0.92, SEN:0.71, SPE:0.93) and stack learning (SL) (AUC:0.89, ACC:0.96, SEN:0.61, SPE:0.99) reached best performances. CONCLUSION This study demonstrated that the manual measurement of 12-Lead ECG features had better performance than the automated measurement (MEANS algorithm), but some classifiers had promising results in discriminating between normal and abnormal cases. Further studies can help us evaluate the applicability and efficacy of machine-learning approaches for distinguishing abnormal ECGs in community-based investigations in both adults and children.
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Affiliation(s)
- Ghasem Hajianfar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Niayesh Highway, Valiasr Ave., Tehran, 19969111541, Iran
| | - Mohammadrafie Khorgami
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Niayesh Highway, Valiasr Ave., Tehran, 19969111541, Iran.
| | - Yousef Rezaei
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Niayesh Highway, Valiasr Ave., Tehran, 19969111541, Iran
- Behyan Clinic, Pardis New Town, Tehran, Iran
| | - Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Niloufar Samiei
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Niayesh Highway, Valiasr Ave., Tehran, 19969111541, Iran
| | - Avisa Tabib
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Niayesh Highway, Valiasr Ave., Tehran, 19969111541, Iran
| | - Bahareh Kazem Borji
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Niayesh Highway, Valiasr Ave., Tehran, 19969111541, Iran
| | - Samira Kalayinia
- Cardiogenetic Research Center, Rajaie Cardiovascular, Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
- Department of Cardiology, Inselspital, University of Bern, Bern, Switzerland
| | - Saeid Hosseini
- Heart Valve Disease Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Mehrdad Oveisi
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
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Hajianfar G, Haddadi Avval A, Hosseini SA, Nazari M, Oveisi M, Shiri I, Zaidi H. Time-to-event overall survival prediction in glioblastoma multiforme patients using magnetic resonance imaging radiomics. LA RADIOLOGIA MEDICA 2023; 128:1521-1534. [PMID: 37751102 PMCID: PMC10700216 DOI: 10.1007/s11547-023-01725-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 09/05/2023] [Indexed: 09/27/2023]
Abstract
PURPOSE Glioblastoma Multiforme (GBM) represents the predominant aggressive primary tumor of the brain with short overall survival (OS) time. We aim to assess the potential of radiomic features in predicting the time-to-event OS of patients with GBM using machine learning (ML) algorithms. MATERIALS AND METHODS One hundred nineteen patients with GBM, who had T1-weighted contrast-enhanced and T2-FLAIR MRI sequences, along with clinical data and survival time, were enrolled. Image preprocessing methods included 64 bin discretization, Laplacian of Gaussian (LOG) filters with three Sigma values and eight variations of Wavelet Transform. Images were then segmented, followed by the extraction of 1212 radiomic features. Seven feature selection (FS) methods and six time-to-event ML algorithms were utilized. The combination of preprocessing, FS, and ML algorithms (12 × 7 × 6 = 504 models) was evaluated by multivariate analysis. RESULTS Our multivariate analysis showed that the best prognostic FS/ML combinations are the Mutual Information (MI)/Cox Boost, MI/Generalized Linear Model Boosting (GLMB) and MI/Generalized Linear Model Network (GLMN), all of which were done via the LOG (Sigma = 1 mm) preprocessing method (C-index = 0.77). The LOG filter with Sigma = 1 mm preprocessing method, MI, GLMB and GLMN achieved significantly higher C-indices than other preprocessing, FS, and ML methods (all p values < 0.05, mean C-indices of 0.65, 0.70, and 0.64, respectively). CONCLUSION ML algorithms are capable of predicting the time-to-event OS of patients using MRI-based radiomic and clinical features. MRI-based radiomics analysis in combination with clinical variables might appear promising in assisting clinicians in the survival prediction of patients with GBM. Further research is needed to establish the applicability of radiomics in the management of GBM in the clinic.
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Affiliation(s)
- Ghasem Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland
| | | | - Seyyed Ali Hosseini
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montréal, QC, Canada
| | - Mostafa Nazari
- Department of Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mehrdad Oveisi
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, 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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Li J, Yang G, Zhang L. Artificial Intelligence Empowered Nuclear Medicine and Molecular Imaging in Cardiology: A State-of-the-Art Review. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:586-596. [PMID: 38223683 PMCID: PMC10781930 DOI: 10.1007/s43657-023-00137-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 10/13/2023] [Accepted: 10/16/2023] [Indexed: 01/16/2024]
Abstract
Nuclear medicine and molecular imaging plays a significant role in the detection and management of cardiovascular disease (CVD). With recent advancements in computer power and the availability of digital archives, artificial intelligence (AI) is rapidly gaining traction in the field of medical imaging, including nuclear medicine and molecular imaging. However, the complex and time-consuming workflow and interpretation involved in nuclear medicine and molecular imaging, limit their extensive utilization in clinical practice. To address this challenge, AI has emerged as a fundamental tool for enhancing the role of nuclear medicine and molecular imaging. It has shown promising applications in various crucial aspects of nuclear cardiology, such as optimizing imaging protocols, facilitating data processing, aiding in CVD diagnosis, risk classification and prognosis. In this review paper, we will introduce the key concepts of AI and provide an overview of its current progress in the field of nuclear cardiology. In addition, we will discuss future perspectives for AI in this domain.
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Affiliation(s)
- Junhao Li
- Department of Nuclear Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, 210002 Jiangsu China
| | - Guifen Yang
- Department of Nuclear Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, 210002 Jiangsu China
| | - Longjiang Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, 210002 Jiangsu China
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Khodabakhshi Z, Amini M, Hajianfar G, Oveisi M, Shiri I, Zaidi H. Dual-Centre Harmonised Multimodal Positron Emission Tomography/Computed Tomography Image Radiomic Features and Machine Learning Algorithms for Non-small Cell Lung Cancer Histopathological Subtype Phenotype Decoding. Clin Oncol (R Coll Radiol) 2023; 35:713-725. [PMID: 37599160 DOI: 10.1016/j.clon.2023.08.003] [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: 09/27/2022] [Revised: 06/10/2023] [Accepted: 08/05/2023] [Indexed: 08/22/2023]
Abstract
AIMS We aimed to build radiomic models for classifying non-small cell lung cancer (NSCLC) histopathological subtypes through a dual-centre dataset and comprehensively evaluate the effect of ComBat harmonisation on the performance of single- and multimodality radiomic models. MATERIALS AND METHODS A public dataset of NSCLC patients from two independent centres was used. Two image fusion methods, namely guided filtering-based fusion and image fusion based on visual saliency map and weighted least square optimisation, were used. Radiomic features were extracted from each scan, including first-order, texture and moment-invariant features. Subsequently, ComBat harmonisation was applied to the extracted features from computed tomography (CT), positron emission tomography (PET) and fused images to correct the centre effect. For feature selection, least absolute shrinkage and selection operator (Lasso) and recursive feature elimination (RFE) were investigated. For machine learning, logistic regression (LR), support vector machine (SVM) and AdaBoost were evaluated for classifying NSCLC subtypes. Training and evaluation of the models were carried out in a robust framework to offset plausible errors and performance was reported using area under the curve, balanced accuracy, sensitivity and specificity before and after harmonisation. N-way ANOVA was used to assess the effect of different factors on the performance of the models. RESULTS Support vector machine fed with selected features by recursive feature elimination from a harmonised PET feature set achieved the highest performance (area under the curve = 0.82) in classifying NSCLC histopathological subtypes. Although the performance of the models did not significantly improve for CT images after harmonisation, the performance of PET and guided filtering-based fusion feature signatures significantly improved for almost all models. Although the selection of the image modality and feature selection methods was effective on the performance of the model (ANOVA P-values <0.001), machine learning and harmonisation did not change the performance significantly (ANOVA P-values = 0.839 and 0.292, respectively). CONCLUSION This study confirmed the potential of radiomic analysis on PET, CT and hybrid images for histopathological classification of NSCLC subtypes.
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Affiliation(s)
- Z Khodabakhshi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - M Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - G Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - M Oveisi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran; 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
| | - I Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - H Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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Amini M, Pursamimi M, Hajianfar G, Salimi Y, Saberi A, Mehri-Kakavand G, Nazari M, Ghorbani M, Shalbaf A, Shiri I, Zaidi H. Machine learning-based diagnosis and risk classification of coronary artery disease using myocardial perfusion imaging SPECT: A radiomics study. Sci Rep 2023; 13:14920. [PMID: 37691039 PMCID: PMC10493219 DOI: 10.1038/s41598-023-42142-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: 04/04/2023] [Accepted: 09/06/2023] [Indexed: 09/12/2023] Open
Abstract
This study aimed to investigate the diagnostic performance of machine learning-based radiomics analysis to diagnose coronary artery disease status and risk from rest/stress Myocardial Perfusion Imaging (MPI) single-photon emission computed tomography (SPECT). A total of 395 patients suspicious of coronary artery disease who underwent 2-day stress-rest protocol MPI SPECT were enrolled in this study. The left ventricle myocardium, excluding the cardiac cavity, was manually delineated on rest and stress images to define a volume of interest. Added to clinical features (age, sex, family history, diabetes status, smoking, and ejection fraction), a total of 118 radiomics features, were extracted from rest and stress MPI SPECT images to establish different feature sets, including Rest-, Stress-, Delta-, and Combined-radiomics (all together) feature sets. The data were randomly divided into 80% and 20% subsets for training and testing, respectively. The performance of classifiers built from combinations of three feature selections, and nine machine learning algorithms was evaluated for two different diagnostic tasks, including 1) normal/abnormal (no CAD vs. CAD) classification, and 2) low-risk/high-risk CAD classification. Different metrics, including the area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE), were reported for models' evaluation. Overall, models built on the Stress feature set (compared to other feature sets), and models to diagnose the second task (compared to task 1 models) revealed better performance. The Stress-mRMR-KNN (feature set-feature selection-classifier) reached the highest performance for task 1 with AUC, ACC, SEN, and SPE equal to 0.61, 0.63, 0.64, and 0.6, respectively. The Stress-Boruta-GB model achieved the highest performance for task 2 with AUC, ACC, SEN, and SPE of 0.79, 0.76, 0.75, and 0.76, respectively. Diabetes status from the clinical feature family, and dependence count non-uniformity normalized, from the NGLDM family, which is representative of non-uniformity in the region of interest were the most frequently selected features from stress feature set for CAD risk classification. This study revealed promising results for CAD risk classification using machine learning models built on MPI SPECT radiomics. The proposed models are helpful to alleviate the labor-intensive MPI SPECT interpretation process regarding CAD status and can potentially expedite the diagnostic process.
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Affiliation(s)
- Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Mohamad Pursamimi
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ghasem Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Abdollah Saberi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Ghazal Mehri-Kakavand
- Department of Medical Physics, School of Medicine, Semnan University of Medical Sciences, Semnan, Iran
| | - Mostafa Nazari
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mahdi Ghorbani
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
- Department of Cardiology, Inselspital, University of Bern, Bern, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.
- University Research and Innovation Center, Obuda University, Budapest, Hungary.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University of Medical Center Groningen, Groningen, The Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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