COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and...

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Published in:	Computers in biology and medicine Vol. 145; p. 105467
Main Authors:	Shiri, Isaac, Salimi, Yazdan, Pakbin, Masoumeh, Hajianfar, Ghasem, Avval, Atlas Haddadi, Sanaat, Amirhossein, Mostafaei, Shayan, Akhavanallaf, Azadeh, Saberi, Abdollah, Mansouri, Zahra, Askari, Dariush, Ghasemian, Mohammadreza, Sharifipour, Ehsan, Sandoughdaran, Saleh, Sohrabi, Ahmad, Sadati, Elham, Livani, Somayeh, Iranpour, Pooya, Kolahi, Shahriar, Khateri, Maziar, Bijari, Salar, Atashzar, Mohammad Reza, Shayesteh, Sajad P., Khosravi, Bardia, Babaei, Mohammad Reza, Jenabi, Elnaz, Hasanian, Mohammad, Shahhamzeh, Alireza, Foroghi Ghomi, Seyaed Yaser, Mozafari, Abolfazl, Teimouri, Arash, Movaseghi, Fatemeh, Ahmari, Azin, Goharpey, Neda, Bozorgmehr, Rama, Shirzad-Aski, Hesamaddin, Mortazavi, Roozbeh, Karimi, Jalal, Mortazavi, Nazanin, Besharat, Sima, Afsharpad, Mandana, Abdollahi, Hamid, Geramifar, Parham, Radmard, Amir Reza, Arabi, Hossein, Rezaei-Kalantari, Kiara, Oveisi, Mehrdad, Rahmim, Arman, Zaidi, Habib
Format:	Journal Article
Language:	English
Published:	United States Elsevier Ltd 01.06.2022 Elsevier Limited The Authors. Published by Elsevier Ltd
Subjects:	Algorithms Artificial intelligence Classifiers Coronaviruses COVID-19 COVID-19 - diagnostic imaging Datasets Deep learning Feature extraction Humans Internal Medicine Lung Neoplasms Lungs Machine Learning Medical prognosis Modelling Other Patients Polymerase chain reaction Prognosis Radiomics Retrospective Studies Sensitivity Statistical analysis Tomography, X-Ray Computed - methods Variance analysis X-ray CT COVID-19 X-ray CT Prognosis Machine learning Radiomics
ISSN:	0010-4825, 1879-0534, 1879-0534
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Abstract	We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81–0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81–0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients. [Display omitted] •CT images of 14′339 COVID-19 patients with known outcome from 19 centers were enrolled.•28 combinations of feature selection and classification approaches were implemented.•The models were evaluated using 10 different splitting and cross-validation strategies.•Lung CT radiomics features are promising for generalizable prognostic modeling.
AbstractList	Image 1 We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients.BACKGROUNDWe aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients.Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported.METHODSWhole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported.In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81-0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance.RESULTSIn the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81-0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance.Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients.CONCLUSIONLung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients. BackgroundWe aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients.MethodsWhole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported.ResultsIn the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81–0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81–0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance.ConclusionLung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients. We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81-0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients. We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81–0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81–0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients. [Display omitted] •CT images of 14′339 COVID-19 patients with known outcome from 19 centers were enrolled.•28 combinations of feature selection and classification approaches were implemented.•The models were evaluated using 10 different splitting and cross-validation strategies.•Lung CT radiomics features are promising for generalizable prognostic modeling. AbstractBackgroundWe aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. MethodsWhole lung segmentations were performed automatically using a deep learning-based model to extract 105 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. ResultsIn the test dataset (4301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81–0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81–0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. ConclusionLung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients.
ArticleNumber	105467
Author	Mostafaei, Shayan Sharifipour, Ehsan Kolahi, Shahriar Saberi, Abdollah Radmard, Amir Reza Movaseghi, Fatemeh Besharat, Sima Jenabi, Elnaz Pakbin, Masoumeh Mansouri, Zahra Ahmari, Azin Sadati, Elham Shahhamzeh, Alireza Ghasemian, Mohammadreza Livani, Somayeh Bijari, Salar Bozorgmehr, Rama Salimi, Yazdan Shirzad-Aski, Hesamaddin Mortazavi, Nazanin Shayesteh, Sajad P. Khateri, Maziar Zaidi, Habib Shiri, Isaac Akhavanallaf, Azadeh Goharpey, Neda Afsharpad, Mandana Hajianfar, Ghasem Mortazavi, Roozbeh Geramifar, Parham Karimi, Jalal Teimouri, Arash Arabi, Hossein Khosravi, Bardia Sohrabi, Ahmad Rahmim, Arman Babaei, Mohammad Reza Hasanian, Mohammad Sanaat, Amirhossein Abdollahi, Hamid Atashzar, Mohammad Reza Askari, Dariush Foroghi Ghomi, Seyaed Yaser Iranpour, Pooya Avval, Atlas Haddadi Oveisi, Mehrdad Sandoughdaran, Saleh Rezaei-Kalantari, Kiara Mozafari, Abolfazl
Author_xml	– sequence: 1 givenname: Isaac surname: Shiri fullname: Shiri, Isaac organization: Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland – sequence: 2 givenname: Yazdan surname: Salimi fullname: Salimi, Yazdan organization: Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland – sequence: 3 givenname: Masoumeh surname: Pakbin fullname: Pakbin, Masoumeh organization: Imaging Department, Qom University of Medical Sciences, Qum, Iran – sequence: 4 givenname: Ghasem surname: Hajianfar fullname: Hajianfar, Ghasem organization: Rajaie Cardiovascular, Medical & Research Center, Iran University of Medical Science, Tehran, Iran – sequence: 5 givenname: Atlas Haddadi surname: Avval fullname: Avval, Atlas Haddadi organization: School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran – sequence: 6 givenname: Amirhossein surname: Sanaat fullname: Sanaat, Amirhossein organization: Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland – sequence: 7 givenname: Shayan surname: Mostafaei fullname: Mostafaei, Shayan organization: Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden – sequence: 8 givenname: Azadeh surname: Akhavanallaf fullname: Akhavanallaf, Azadeh organization: Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland – sequence: 9 givenname: Abdollah surname: Saberi fullname: Saberi, Abdollah organization: Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland – sequence: 10 givenname: Zahra surname: Mansouri fullname: Mansouri, Zahra organization: Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland – sequence: 11 givenname: Dariush surname: Askari fullname: Askari, Dariush organization: Department of Radiology Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran – sequence: 12 givenname: Mohammadreza surname: Ghasemian fullname: Ghasemian, Mohammadreza organization: Department of Radiology, Shahid Beheshti Hospital, Qom University of Medical Sciences, Qum, Iran – sequence: 13 givenname: Ehsan surname: Sharifipour fullname: Sharifipour, Ehsan organization: Neuroscience Research Center, Qom University of Medical Sciences, Qum, Iran – sequence: 14 givenname: Saleh surname: Sandoughdaran fullname: Sandoughdaran, Saleh organization: Men's Health and Reproductive Health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran – sequence: 15 givenname: Ahmad surname: Sohrabi fullname: Sohrabi, Ahmad organization: Cancer Control Research Center, Cancer Control Foundation, Iran University of Medical Sciences, Tehran, Iran – sequence: 16 givenname: Elham surname: Sadati fullname: Sadati, Elham organization: Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran – sequence: 17 givenname: Somayeh surname: Livani fullname: Livani, Somayeh organization: Clinical Research Development Unit (CRDU), Sayad Shirazi Hospital, Golestan University of Medical Sciences, Gorgan, Iran – sequence: 18 givenname: Pooya surname: Iranpour fullname: Iranpour, Pooya organization: Medical Imaging Research Center, Department of Radiology, Shiraz University of Medical Sciences, Shiraz, Iran – sequence: 19 givenname: Shahriar surname: Kolahi fullname: Kolahi, Shahriar organization: Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran – sequence: 20 givenname: Maziar surname: Khateri fullname: Khateri, Maziar organization: Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Tehran, Iran – sequence: 21 givenname: Salar surname: Bijari fullname: Bijari, Salar organization: Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran – sequence: 22 givenname: Mohammad Reza surname: Atashzar fullname: Atashzar, Mohammad Reza organization: Department of Immunology, School of Medicine, Fasa University of Medical Sciences, Fasa, Iran – sequence: 23 givenname: Sajad P. surname: Shayesteh fullname: Shayesteh, Sajad P. organization: Department of Physiology, Pharmacology and Medical Physics, Alborz University of Medical Sciences, Karaj, Iran – sequence: 24 givenname: Bardia surname: Khosravi fullname: Khosravi, Bardia organization: Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran – sequence: 25 givenname: Mohammad Reza surname: Babaei fullname: Babaei, Mohammad Reza organization: Department of Interventional Radiology, Firouzgar Hospital, Iran University of Medical Sciences, 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Teimouri fullname: Teimouri, Arash organization: Medical Imaging Research Center, Department of Radiology, Shiraz University of Medical Sciences, Shiraz, Iran – sequence: 32 givenname: Fatemeh surname: Movaseghi fullname: Movaseghi, Fatemeh organization: Department of Medical Sciences, Qom Branch, Islamic Azad University, Qum, Iran – sequence: 33 givenname: Azin surname: Ahmari fullname: Ahmari, Azin organization: Ayatolah Khansary Hospital, Arak University of Medical Sciences, Arak, Iran – sequence: 34 givenname: Neda surname: Goharpey fullname: Goharpey, Neda organization: Department of Radiation Oncology, Shohadaye Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran – sequence: 35 givenname: Rama surname: Bozorgmehr fullname: Bozorgmehr, Rama organization: Clinical Research Development Unit, Shohadaye Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran – sequence: 36 givenname: Hesamaddin surname: Shirzad-Aski fullname: Shirzad-Aski, Hesamaddin organization: Infectious Diseases Research Center, Golestan University of Medical Sciences, Gorgan, Iran – sequence: 37 givenname: Roozbeh surname: Mortazavi fullname: Mortazavi, Roozbeh organization: Department of Internal Medicine, Shiraz University of Medical Sciences, Shiraz, Iran – sequence: 38 givenname: Jalal surname: Karimi fullname: Karimi, Jalal organization: Department of Infectious Disease, School of Medicine, Fasa University of Medical Sciences, Fasa, Iran – sequence: 39 givenname: Nazanin surname: Mortazavi fullname: Mortazavi, Nazanin organization: Dental Research Center, Golestan University of Medical Sciences, Gorgan, Iran – sequence: 40 givenname: Sima surname: Besharat fullname: Besharat, Sima organization: Golestan Research Center of Gastroenterology and Hepatology, Golestan University of Medical Sciences, Gorgan, Iran – sequence: 41 givenname: Mandana surname: Afsharpad fullname: Afsharpad, Mandana organization: Cancer Control Research Center, Cancer Control Foundation, Iran University of Medical Sciences, Tehran, Iran – sequence: 42 givenname: Hamid surname: Abdollahi fullname: Abdollahi, Hamid organization: Department of Radiologic Technology, Faculty of Allied Medical Sciences, Kerman University of Medical Sciences, Kerman, Iran – sequence: 43 givenname: Parham surname: Geramifar fullname: Geramifar, Parham organization: Research Centre for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran – sequence: 44 givenname: Amir Reza surname: Radmard fullname: Radmard, Amir Reza organization: Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran – sequence: 45 givenname: Hossein surname: Arabi fullname: Arabi, Hossein organization: Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland – sequence: 46 givenname: Kiara surname: Rezaei-Kalantari fullname: Rezaei-Kalantari, Kiara organization: Rajaie Cardiovascular, Medical & Research Center, Iran University of Medical Science, Tehran, Iran – sequence: 47 givenname: Mehrdad surname: Oveisi fullname: Oveisi, Mehrdad organization: Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine, King’s College London, London, United Kingdom – sequence: 48 givenname: Arman surname: Rahmim fullname: Rahmim, Arman organization: Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada – sequence: 49 givenname: Habib orcidid: 0000-0001-7559-5297 surname: Zaidi fullname: Zaidi, Habib email: habib.zaidi@hcuge.ch organization: Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
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Keywords	COVID-19 X-ray CT Prognosis Machine learning Radiomics
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Snippet	We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. Whole lung segmentations were performed... AbstractBackgroundWe aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. MethodsWhole lung segmentations... BackgroundWe aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients.MethodsWhole lung segmentations were... We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients.BACKGROUNDWe aimed to analyze the prognostic power... Image 1
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SubjectTerms	Algorithms Artificial intelligence Classifiers Coronaviruses COVID-19 COVID-19 - diagnostic imaging Datasets Deep learning Feature extraction Humans Internal Medicine Lung Neoplasms Lungs Machine Learning Medical prognosis Modelling Other Patients Polymerase chain reaction Prognosis Radiomics Retrospective Studies Sensitivity Statistical analysis Tomography, X-Ray Computed - methods Variance analysis X-ray CT
Title	COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients
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