Result: Automated Data Transformation and Feature Extraction for Oxygenation-Sensitive Cardiovascular Magnetic Resonance Images.
Title:
Automated Data Transformation and Feature Extraction for Oxygenation-Sensitive Cardiovascular Magnetic Resonance Images.
Authors:
Plasa G; Research Institute of the McGill University Health Centre, Montreal, Canada.; Faculty of Science, Neuroscience, McGill University, Montreal, Canada.; Area 19 Medical, Montreal, Canada., Hillier E; Research Institute of the McGill University Health Centre, Montreal, Canada.; Department of Medicine and Health Sciences, Faculty of Medicine and Dentistry, McGill University, Montreal, Canada., Luu J; Research Institute of the McGill University Health Centre, Montreal, Canada., Boutet D; Faculty of Science, Neuroscience, McGill University, Montreal, Canada., Benovoy M; Research Institute of the McGill University Health Centre, Montreal, Canada.; Area 19 Medical, Montreal, Canada., Friedrich MG; Research Institute of the McGill University Health Centre, Montreal, Canada. mgwfriedrich@gmail.com.; Department of Medicine and Diagnostic Radiology, McGill University Health Centre, 1001 Decarie Blvd, Montreal, QC, H4A 3J1, Canada. mgwfriedrich@gmail.com.
Source:
Journal of cardiovascular translational research [J Cardiovasc Transl Res] 2024 Jun; Vol. 17 (3), pp. 705-715. Date of Electronic Publication: 2024 Jan 16.
Publication Type:
Journal Article; Research Support, Non-U.S. Gov't
Language:
English
Journal Info:
Publisher: Springer Country of Publication: United States NLM ID: 101468585 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1937-5395 (Electronic) Linking ISSN: 19375387 NLM ISO Abbreviation: J Cardiovasc Transl Res Subsets: MEDLINE
Imprint Name(s):
Original Publication: New York, NY : Springer
MeSH Terms:
References:
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Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O,…Cournapeau D. (n.d.) Scikit-learn: machine learning in Python. MACHINE LEARNING IN PYTHON.
Hillier E, Plasa G, Luu J, Benovoy M, Friedrich M G. Machine learning analysis of oxygenation-sensitive cardiovascular magnetic resonance imaging in patients with coronary artery stenosis. Society of Cardiovascular Magnetic Resonance Imaging. 24–26. San Diego, California. USA: Poster Presentation; 2023.
Ghantous CM, Kamareddine L, Farhat R, Zouein FA, Mondello S, Kobeissy F, Zeidan A. Advances in cardiovascular biomarker discovery. Biomedicines. 2020;8(12):552. https://doi.org/10.3390/biomedicines8120552 . (PMID: 10.3390/biomedicines8120552332658987759775)
Hathaway QA, Roth SM, Pinti MV, Sprando DC, Kunovac A, Durr AJ,…Hollander JM (2019) Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics. Cardiovasc Diabetol, 18(1), 78. https://doi.org/10.1186/s12933-019-0879-0.
Jack CR, Bennett DA, Blennow K, Carrillo MC, Feldman HH, Frisoni GB,…Dubois B (2016) A/T/N: an unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology, 87(5), 539–547. https://doi.org/10.1212/WNL.0000000000002923.
Pennell DJ, Sechtem UP, Higgins CB, Manning WJ, Pohost GM, Rademakers FE,…Yucel EK. Clinical indications for cardiovascular magnetic resonance (CMR): consensus panel report. Eur Heart J, 2004;25(21), 1940–1965. https://doi.org/10.1016/j.ehj.2004.06.040.
Hillier E, Friedrich MG. The potential of oxygenation-sensitive CMR in Heart failure. Curr Heart Fail Rep. 2021. https://doi.org/10.1007/s11897-021-00525-y . (PMID: 10.1007/s11897-021-00525-y34378154)
Fischer K, Yamaji K, Luescher S, Ueki Y, Jung B, von Tengg-Kobligk H, Windecker S, Friedrich MG, Eberle B, Guensch DP. Feasibility of cardiovascular magnetic resonance to detect oxygenation deficits in patients with multi-vessel coronary artery disease triggered by breathing maneuvers. J Cardiovasc Magn Reson. 2018;20(1):31. https://doi.org/10.1186/s12968-018-0446-y.PMID:29730991;PMCID:PMC5937049 . (PMID: 10.1186/s12968-018-0446-y.PMID:29730991;PMCID:PMC5937049297309915937049)
Fischer K, Guensch DP, Jung B, King I, von Tengg-Kobligk H, Giannetti N, Eberle B, Friedrich MG. Insights into myocardial oxygenation and cardiovascular magnetic resonance tissue biomarkers in heart failure with preserved ejection fraction. Circ Heart Fail. 2022;15(4):e008903. https://doi.org/10.1161/CIRCHEARTFAILURE.121.008903 . (PMID: 10.1161/CIRCHEARTFAILURE.121.00890335038887)
Friedrich MG, Karamitsos TD. Oxygenation-sensitive cardiovascular magnetic resonance. J Cardiovasc Magn Reson. 2013;15(1):43. https://doi.org/10.1186/1532-429X-15-43.PMID:23706167;PMCID:PMC3681671 . (PMID: 10.1186/1532-429X-15-43.PMID:23706167;PMCID:PMC3681671237061673681671)
Dharmakumar R, Qi X, Hong J, Wright GA. Detecting microcirculatory changes in blood oxygen state with steady-state free precession imaging. Magn Reson Med. 2006;55(6):1372–80. https://doi.org/10.1002/mrm.20911 . (PMID: 10.1002/mrm.2091116680697)
Hillier E, Covone J, Friedrich MG. Oxygenation-sensitive cardiac MRI with vasoactive breathing maneuvers for the non-invasive assessment of coronary microvascular dysfunction. JoVE (Journal of Visualized Experiments). 2022;186:e64149. https://doi.org/10.3791/64149 . (PMID: 10.3791/64149)
Leiner T, Rueckert D, Suinesiaputra A, Baeßler B, Nezafat R, Išgum I, Young AA. Machine learning in cardiovascular magnetic resonance: basic concepts and applications. J Cardiovasc Magn Reson. 2019;21(1):61. https://doi.org/10.1186/s12968-019-0575-y . (PMID: 10.1186/s12968-019-0575-y315906646778980)
Fotaki A, Puyol-Antón E, Chiribiri A, Botnar R, Pushparajah K, Prieto C. Artificial intelligence in cardiac MRI: is clinical adoption forthcoming? Front Cardiovasc Med. 2022;8:818765. https://doi.org/10.3389/fcvm.2021.818765 . (PMID: 10.3389/fcvm.2021.818765350833038785419)
Ravi D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, Yang G-Z. Deep learning for health informatics. IEEE J Biomed Health Inform. 2017;21(1):4–21. https://doi.org/10.1109/JBHI.2016.2636665 . (PMID: 10.1109/JBHI.2016.263666528055930)
Baeßler B, Weiss K, Pinto Dos Santos D. Robustness and reproducibility of radiomics in magnetic resonance imaging: a phantom study. Invest Radiol. 2019;54(4):221–8. https://doi.org/10.1097/RLI.0000000000000530 . (PMID: 10.1097/RLI.000000000000053030433891)
Neisius U, El-Rewaidy H, Nakamori S, Rodriguez J, Manning WJ, Nezafat R. Radiomic analysis of myocardial native T1 imaging discriminates between hypertensive heart disease and hypertrophic cardiomyopathy. JACC: Cardiovasc Imaging. 2019;12(10):1946–54. (PMID: 30660549)
Cerqueira MD, Weissman NJ, Dilsizian V, Jacobs AK, Kaul S, Laskey WK,...& American Heart Association Writing Group on Myocardial Segmentation and Registration for Cardiac Imaging. Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. Circulation, 2022;105(4), 539-542.
Kraskov A, Stögbauer H, Grassberger P. Estimating mutual information. Phys Rev E. 2004;69(6):066138. https://doi.org/10.1103/PhysRevE.69.066138 . (PMID: 10.1103/PhysRevE.69.066138)
Chen YW, & Lin CJ (2006) Combining SVMs with various feature selection strategies. In I. Guyon, M. Nikravesh, S. Gunn, & L. A. Zadeh (Eds.), Feature Extraction (Vol. 207, pp. 315–324). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-35488-8_13.
Spencer R, Thabtah F, Abdelhamid N, Thompson M. Exploring feature selection and classification methods for predicting heart disease. Digital Health. 2020;6:2055207620914777. https://doi.org/10.1177/2055207620914777 . (PMID: 10.1177/2055207620914777322848737133070)
Pal M, Parija S. Prediction of heart diseases using random forest. J Phys: Conf Ser. 2021;1817(1):012009. https://doi.org/10.1088/1742-6596/1817/1/012009 . (PMID: 10.1088/1742-6596/1817/1/012009)
Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, van Mulbregt P. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods. 2020;17(3):261–72. https://doi.org/10.1038/s41592-019-0686-2 . (PMID: 10.1038/s41592-019-0686-2320155437056644)
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O,…Cournapeau D. (n.d.) Scikit-learn: machine learning in Python. MACHINE LEARNING IN PYTHON.
Hillier E, Plasa G, Luu J, Benovoy M, Friedrich M G. Machine learning analysis of oxygenation-sensitive cardiovascular magnetic resonance imaging in patients with coronary artery stenosis. Society of Cardiovascular Magnetic Resonance Imaging. 24–26. San Diego, California. USA: Poster Presentation; 2023.
Ghantous CM, Kamareddine L, Farhat R, Zouein FA, Mondello S, Kobeissy F, Zeidan A. Advances in cardiovascular biomarker discovery. Biomedicines. 2020;8(12):552. https://doi.org/10.3390/biomedicines8120552 . (PMID: 10.3390/biomedicines8120552332658987759775)
Hathaway QA, Roth SM, Pinti MV, Sprando DC, Kunovac A, Durr AJ,…Hollander JM (2019) Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics. Cardiovasc Diabetol, 18(1), 78. https://doi.org/10.1186/s12933-019-0879-0.
Jack CR, Bennett DA, Blennow K, Carrillo MC, Feldman HH, Frisoni GB,…Dubois B (2016) A/T/N: an unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology, 87(5), 539–547. https://doi.org/10.1212/WNL.0000000000002923.
Contributed Indexing:
Keywords: Coronary vascular function; Machine learning for medical imaging; Magnetic resonance imaging; Oxygenation-sensitive cardiovascular magnetic resonance
Substance Nomenclature:
S88TT14065 (Oxygen)
Entry Date(s):
Date Created: 20240116 Date Completed: 20240702 Latest Revision: 20241115
Update Code:
20250114
DOI:
10.1007/s12265-023-10474-7
PMID:
38229001
Database:
MEDLINE
Further Information
Oxygenation-sensitive cardiovascular magnetic resonance (OS-CMR) is a novel, powerful tool for assessing coronary function in vivo. The data extraction and analysis however are labor-intensive. The objective of this study was to provide an automated approach for the extraction, visualization, and biomarker selection of OS-CMR images. We created a Python-based tool to automate extraction and export of raw patient data, featuring 3336 attributes per participant, into a template compatible with common data analytics frameworks, including the functionality to select predictive features for the given disease state. Each analysis was completed in about 2 min. The features selected by both ANOVA and MIC significantly outperformed (p < 0.001) the null set and complete set of features in two datasets, with mean AUROC scores of 0.89eatures f 0.94lete set of features in two datasets, with mean AUROC scores that our tool is suitable for automated data extraction and analysis of OS-CMR images.
(© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)