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Xu K, Kang H. A Review of Machine Learning Approaches for Brain Positron Emission Tomography Data Analysis. Nucl Med Mol Imaging 2024; 58:203-212. [PMID: 38932757 PMCID: PMC11196571 DOI: 10.1007/s13139-024-00845-6] [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: 10/16/2023] [Revised: 01/19/2024] [Accepted: 01/25/2024] [Indexed: 06/28/2024] Open
Abstract
Positron emission tomography (PET) imaging has moved forward the development of medical diagnostics and research across various domains, including cardiology, neurology, infection detection, and oncology. The integration of machine learning (ML) algorithms into PET data analysis has further enhanced their capabilities of including disease diagnosis and classification, image segmentation, and quantitative analysis. ML algorithms empower researchers and clinicians to extract valuable insights from complex big PET datasets, which enabling automated pattern recognition, predictive health outcome modeling, and more efficient data analysis. This review explains the basic knowledge of PET imaging, statistical methods for PET image analysis, and challenges of PET data analysis. We also discussed the improvement of analysis capabilities by combining PET data with machine learning algorithms and the application of this combination in various aspects of PET image research. This review also highlights current trends and future directions in PET imaging, emphasizing the driving and critical role of machine learning and big PET image data analytics in improving diagnostic accuracy and personalized medical approaches. Integration between PET imaging will shape the future of medical diagnosis and research.
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Affiliation(s)
- Ke Xu
- Department of Biostatistics, Vanderbilt University Medical Center, 2525 West End Avenue, Suite 1100, Nashville, TN 37203 USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, 2525 West End Avenue, Suite 1100, Nashville, TN 37203 USA
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Miller RJH, Bednarski BP, Pieszko K, Kwiecinski J, Williams MC, Shanbhag A, Liang JX, Huang C, Sharir T, Hauser MT, Dorbala S, Di Carli MF, Fish MB, Ruddy TD, Bateman TM, Einstein AJ, Kaufmann PA, Miller EJ, Sinusas AJ, Acampa W, Han D, Dey D, Berman DS, Slomka PJ. Clinical phenotypes among patients with normal cardiac perfusion using unsupervised learning: a retrospective observational study. EBioMedicine 2024; 99:104930. [PMID: 38168587 PMCID: PMC10794922 DOI: 10.1016/j.ebiom.2023.104930] [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: 08/31/2023] [Revised: 11/24/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Myocardial perfusion imaging (MPI) is one of the most common cardiac scans and is used for diagnosis of coronary artery disease and assessment of cardiovascular risk. However, the large majority of MPI patients have normal results. We evaluated whether unsupervised machine learning could identify unique phenotypes among patients with normal scans and whether those phenotypes were associated with risk of death or myocardial infarction. METHODS Patients from a large international multicenter MPI registry (10 sites) with normal perfusion by expert visual interpretation were included in this cohort analysis. The training population included 9849 patients, and external testing population 12,528 patients. Unsupervised cluster analysis was performed, with separate training and external testing cohorts, to identify clusters, with four distinct phenotypes. We evaluated the clinical and imaging features of clusters and their associations with death or myocardial infarction. FINDINGS Patients in Clusters 1 and 2 almost exclusively underwent exercise stress, while patients in Clusters 3 and 4 mostly required pharmacologic stress. In external testing, the risk for Cluster 4 patients (20.2% of population, unadjusted hazard ratio [HR] 6.17, 95% confidence interval [CI] 4.64-8.20) was higher than the risk associated with pharmacologic stress (HR 3.03, 95% CI 2.53-3.63), or previous myocardial infarction (HR 1.82, 95% CI 1.40-2.36). INTERPRETATION Unsupervised learning identified four distinct phenotypes of patients with normal perfusion scans, with a significant proportion of patients at very high risk of myocardial infarction or death. Our results suggest a potential role for patient phenotyping to improve risk stratification of patients with normal imaging results. FUNDING This work was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R35HL161195 to PS]. The REFINE SPECT database was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R01HL089765 to PS]. MCW was supported by the British Heart Foundation [FS/ICRF/20/26002].
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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, USA; Department of Cardiac Sciences, University of Calgary and Libin Cardiovascular Institute, Calgary, AB, Canada
| | - Bryan P Bednarski
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Konrad Pieszko
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jacek Kwiecinski
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Interventional Cardiology and Angiology, Institute of Cardiology, Warsaw, Poland
| | - Michelle C Williams
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA; British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Aakash Shanbhag
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Joanna X Liang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Cathleen Huang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Tali Sharir
- Department of Nuclear Cardiology, Assuta Medical Centers, Tel Aviv, Israel; Israel and Ben Gurion University of the Negev, Beer Sheba, Israel
| | - M Timothy Hauser
- Department of Nuclear Cardiology, Oklahoma Heart Hospital, Oklahoma City, OK, USA
| | - Sharmila Dorbala
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Marcelo F Di Carli
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Mathews B Fish
- Oregon Heart and Vascular Institute, Sacred Heart Medical Center, Springfield, OR, USA
| | - Terrence D Ruddy
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, ON, Canada
| | | | - Andrew J Einstein
- Division of Cardiology, Department of Medicine and Department of Radiology, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, NY, USA
| | - Philipp A Kaufmann
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Zurich, Switzerland
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Albert J Sinusas
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Wanda Acampa
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Donghee Han
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Damini Dey
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Daniel S Berman
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Piotr J Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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Kigka VI, Georga E, Tsakanikas V, Kyriakidis S, Tsompou P, Siogkas P, Michalis LK, Naka KK, Neglia D, Rocchiccioli S, Pelosi G, Fotiadis DI, Sakellarios A. Machine Learning Coronary Artery Disease Prediction Based on Imaging and Non-Imaging Data. Diagnostics (Basel) 2022; 12:diagnostics12061466. [PMID: 35741275 PMCID: PMC9221964 DOI: 10.3390/diagnostics12061466] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 06/06/2022] [Accepted: 06/11/2022] [Indexed: 11/16/2022] Open
Abstract
The prediction of obstructive atherosclerotic disease has significant clinical meaning for the decision making. In this study, a machine learning predictive model based on gradient boosting classifier is presented, aiming to identify the patients of high CAD risk and those of low CAD risk. The machine learning methodology includes five steps: the preprocessing of the input data, the class imbalance handling applying the Easy Ensemble algorithm, the recursive feature elimination technique implementation, the implementation of gradient boosting classifier, and finally the model evaluation, while the fine tuning of the presented model was implemented through a randomized search optimization of the model’s hyper-parameters over an internal 3-fold cross-validation. In total, 187 participants with suspicion of CAD previously underwent CTCA during EVINCI and ARTreat clinical studies and were prospectively included to undergo follow-up CTCA. The predictive model was trained using imaging data (geometrical and blood flow based) and non-imaging data. The overall predictive accuracy of the model was 0.81, using both imaging and non-imaging data. The innovative aspect of the proposed study is the combination of imaging-based data with the typical CAD risk factors to provide an integrated CAD risk-predictive model.
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Affiliation(s)
- Vassiliki I. Kigka
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece; (V.I.K.); (E.G.); (V.T.); (S.K.); (P.T.); (P.S.); (D.I.F.)
- Institute of Molecular Biology and Biotechnology, Department of Biomedical Research—FORTH, University Campus of Ioannina, GR 45110 Ioannina, Greece
| | - Eleni Georga
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece; (V.I.K.); (E.G.); (V.T.); (S.K.); (P.T.); (P.S.); (D.I.F.)
- Institute of Molecular Biology and Biotechnology, Department of Biomedical Research—FORTH, University Campus of Ioannina, GR 45110 Ioannina, Greece
| | - Vassilis Tsakanikas
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece; (V.I.K.); (E.G.); (V.T.); (S.K.); (P.T.); (P.S.); (D.I.F.)
- Institute of Molecular Biology and Biotechnology, Department of Biomedical Research—FORTH, University Campus of Ioannina, GR 45110 Ioannina, Greece
| | - Savvas Kyriakidis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece; (V.I.K.); (E.G.); (V.T.); (S.K.); (P.T.); (P.S.); (D.I.F.)
- Institute of Molecular Biology and Biotechnology, Department of Biomedical Research—FORTH, University Campus of Ioannina, GR 45110 Ioannina, Greece
| | - Panagiota Tsompou
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece; (V.I.K.); (E.G.); (V.T.); (S.K.); (P.T.); (P.S.); (D.I.F.)
| | - Panagiotis Siogkas
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece; (V.I.K.); (E.G.); (V.T.); (S.K.); (P.T.); (P.S.); (D.I.F.)
- Institute of Molecular Biology and Biotechnology, Department of Biomedical Research—FORTH, University Campus of Ioannina, GR 45110 Ioannina, Greece
| | - Lampros K. Michalis
- Department of Cardiology, Medical School, University of Ioannina, GR 45110 Ioannina, Greece; (L.K.M.); (K.K.N.)
| | - Katerina K. Naka
- Department of Cardiology, Medical School, University of Ioannina, GR 45110 Ioannina, Greece; (L.K.M.); (K.K.N.)
| | - Danilo Neglia
- Fondazione Toscana Gabriele Monasterio, IT 56126 Pisa, Italy;
| | - Silvia Rocchiccioli
- Institute of Clinical Physiology, National Research Council, IT 56124 Pisa, Italy; (S.R.); (G.P.)
| | - Gualtiero Pelosi
- Institute of Clinical Physiology, National Research Council, IT 56124 Pisa, Italy; (S.R.); (G.P.)
| | - Dimitrios I. Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece; (V.I.K.); (E.G.); (V.T.); (S.K.); (P.T.); (P.S.); (D.I.F.)
- Institute of Molecular Biology and Biotechnology, Department of Biomedical Research—FORTH, University Campus of Ioannina, GR 45110 Ioannina, Greece
| | - Antonis Sakellarios
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece; (V.I.K.); (E.G.); (V.T.); (S.K.); (P.T.); (P.S.); (D.I.F.)
- Institute of Molecular Biology and Biotechnology, Department of Biomedical Research—FORTH, University Campus of Ioannina, GR 45110 Ioannina, Greece
- Correspondence: ; Tel.: +30-26510-07848
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