Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients
To develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history, laboratory tests, visual scoring by radiologists) and lung/lesion radiomic features extracted from chest CT images. Overall, 152 patients were enrolled...
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| Published in | Computers in biology and medicine Vol. 132; p. 104304 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Ltd
01.05.2021
Elsevier Limited The Author(s). Published by Elsevier Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0010-4825 1879-0534 1879-0534 |
| DOI | 10.1016/j.compbiomed.2021.104304 |
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| Abstract | To develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history, laboratory tests, visual scoring by radiologists) and lung/lesion radiomic features extracted from chest CT images.
Overall, 152 patients were enrolled in this study protocol. These were divided into 106 training/validation and 46 test datasets (untouched during training), respectively. Radiomic features were extracted from the segmented lungs and infectious lesions separately from chest CT images. Clinical data, including patients’ history and demographics, laboratory tests and radiological scores were also collected. Univariate analysis was first performed (q-value reported after false discovery rate (FDR) correction) to determine the most predictive features among all imaging and clinical data. Prognostic modeling of survival was performed using radiomic features and clinical data, separately or in combination. Maximum relevance minimum redundancy (MRMR) and XGBoost were used for feature selection and classification. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), sensitivity, specificity, and accuracy were used to assess the prognostic performance of the models on the test datasets.
For clinical data, cancer comorbidity (q-value < 0.01), consciousness level (q-value < 0.05) and radiological score involved zone (q-value < 0.02) were found to have high correlated features with outcome. Oxygen saturation (AUC = 0.73, q-value < 0.01) and Blood Urea Nitrogen (AUC = 0.72, q-value = 0.72) were identified as high clinical features. For lung radiomic features, SAHGLE (AUC = 0.70) and HGLZE (AUC = 0.67) from GLSZM were identified as most prognostic features. Amongst lesion radiomic features, RLNU from GLRLM (AUC = 0.73), HGLZE from GLSZM (AUC = 0.73) had the highest performance. In multivariate analysis, combining lung, lesion and clinical features was determined to provide the most accurate prognostic model (AUC = 0.95 ± 0.029 (95%CI: 0.95–0.96), accuracy = 0.88 ± 0.046 (95% CI: 0.88–0.89), sensitivity = 0.88 ± 0.066 (95% CI = 0.87–0.9) and specificity = 0.89 ± 0.07 (95% CI = 0.87–0.9)).
Combination of radiomic features and clinical data can effectively predict outcome in COVID-19 patients. The developed model has significant potential for improved management of COVID-19 patients.
[Display omitted]
•Radiomics–based machine learning models predict the survival of ccRCC patients.•Clinical/imaging features could potentially be used for COVID-19 patients management.•Radiomic and clinical features combination can effectively enhance outcome prediction.•Radiomic features of the lung provided more information than COVID-19 lesions. |
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| AbstractList | Image 1 ObjectiveTo develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history, laboratory tests, visual scoring by radiologists) and lung/lesion radiomic features extracted from chest CT images.MethodsOverall, 152 patients were enrolled in this study protocol. These were divided into 106 training/validation and 46 test datasets (untouched during training), respectively. Radiomic features were extracted from the segmented lungs and infectious lesions separately from chest CT images. Clinical data, including patients’ history and demographics, laboratory tests and radiological scores were also collected. Univariate analysis was first performed (q-value reported after false discovery rate (FDR) correction) to determine the most predictive features among all imaging and clinical data. Prognostic modeling of survival was performed using radiomic features and clinical data, separately or in combination. Maximum relevance minimum redundancy (MRMR) and XGBoost were used for feature selection and classification. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), sensitivity, specificity, and accuracy were used to assess the prognostic performance of the models on the test datasets.ResultsFor clinical data, cancer comorbidity (q-value < 0.01), consciousness level (q-value < 0.05) and radiological score involved zone (q-value < 0.02) were found to have high correlated features with outcome. Oxygen saturation (AUC = 0.73, q-value < 0.01) and Blood Urea Nitrogen (AUC = 0.72, q-value = 0.72) were identified as high clinical features. For lung radiomic features, SAHGLE (AUC = 0.70) and HGLZE (AUC = 0.67) from GLSZM were identified as most prognostic features. Amongst lesion radiomic features, RLNU from GLRLM (AUC = 0.73), HGLZE from GLSZM (AUC = 0.73) had the highest performance. In multivariate analysis, combining lung, lesion and clinical features was determined to provide the most accurate prognostic model (AUC = 0.95 ± 0.029 (95%CI: 0.95–0.96), accuracy = 0.88 ± 0.046 (95% CI: 0.88–0.89), sensitivity = 0.88 ± 0.066 (95% CI = 0.87–0.9) and specificity = 0.89 ± 0.07 (95% CI = 0.87–0.9)).ConclusionCombination of radiomic features and clinical data can effectively predict outcome in COVID-19 patients. The developed model has significant potential for improved management of COVID-19 patients. To develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history, laboratory tests, visual scoring by radiologists) and lung/lesion radiomic features extracted from chest CT images. Overall, 152 patients were enrolled in this study protocol. These were divided into 106 training/validation and 46 test datasets (untouched during training), respectively. Radiomic features were extracted from the segmented lungs and infectious lesions separately from chest CT images. Clinical data, including patients’ history and demographics, laboratory tests and radiological scores were also collected. Univariate analysis was first performed (q-value reported after false discovery rate (FDR) correction) to determine the most predictive features among all imaging and clinical data. Prognostic modeling of survival was performed using radiomic features and clinical data, separately or in combination. Maximum relevance minimum redundancy (MRMR) and XGBoost were used for feature selection and classification. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), sensitivity, specificity, and accuracy were used to assess the prognostic performance of the models on the test datasets. For clinical data, cancer comorbidity (q-value < 0.01), consciousness level (q-value < 0.05) and radiological score involved zone (q-value < 0.02) were found to have high correlated features with outcome. Oxygen saturation (AUC = 0.73, q-value < 0.01) and Blood Urea Nitrogen (AUC = 0.72, q-value = 0.72) were identified as high clinical features. For lung radiomic features, SAHGLE (AUC = 0.70) and HGLZE (AUC = 0.67) from GLSZM were identified as most prognostic features. Amongst lesion radiomic features, RLNU from GLRLM (AUC = 0.73), HGLZE from GLSZM (AUC = 0.73) had the highest performance. In multivariate analysis, combining lung, lesion and clinical features was determined to provide the most accurate prognostic model (AUC = 0.95 ± 0.029 (95%CI: 0.95–0.96), accuracy = 0.88 ± 0.046 (95% CI: 0.88–0.89), sensitivity = 0.88 ± 0.066 (95% CI = 0.87–0.9) and specificity = 0.89 ± 0.07 (95% CI = 0.87–0.9)). Combination of radiomic features and clinical data can effectively predict outcome in COVID-19 patients. The developed model has significant potential for improved management of COVID-19 patients. [Display omitted] •Radiomics–based machine learning models predict the survival of ccRCC patients.•Clinical/imaging features could potentially be used for COVID-19 patients management.•Radiomic and clinical features combination can effectively enhance outcome prediction.•Radiomic features of the lung provided more information than COVID-19 lesions. AbstractObjectiveTo develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history, laboratory tests, visual scoring by radiologists) and lung/lesion radiomic features extracted from chest CT images. MethodsOverall, 152 patients were enrolled in this study protocol. These were divided into 106 training/validation and 46 test datasets (untouched during training), respectively. Radiomic features were extracted from the segmented lungs and infectious lesions separately from chest CT images. Clinical data, including patients’ history and demographics, laboratory tests and radiological scores were also collected. Univariate analysis was first performed (q-value reported after false discovery rate (FDR) correction) to determine the most predictive features among all imaging and clinical data. Prognostic modeling of survival was performed using radiomic features and clinical data, separately or in combination. Maximum relevance minimum redundancy (MRMR) and XGBoost were used for feature selection and classification. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), sensitivity, specificity, and accuracy were used to assess the prognostic performance of the models on the test datasets. ResultsFor clinical data, cancer comorbidity (q-value < 0.01), consciousness level (q-value < 0.05) and radiological score involved zone (q-value < 0.02) were found to have high correlated features with outcome. Oxygen saturation (AUC = 0.73, q-value < 0.01) and Blood Urea Nitrogen (AUC = 0.72, q-value = 0.72) were identified as high clinical features. For lung radiomic features, SAHGLE (AUC = 0.70) and HGLZE (AUC = 0.67) from GLSZM were identified as most prognostic features. Amongst lesion radiomic features, RLNU from GLRLM (AUC = 0.73), HGLZE from GLSZM (AUC = 0.73) had the highest performance. In multivariate analysis, combining lung, lesion and clinical features was determined to provide the most accurate prognostic model (AUC = 0.95 ± 0.029 (95%CI: 0.95–0.96), accuracy = 0.88 ± 0.046 (95% CI: 0.88–0.89), sensitivity = 0.88 ± 0.066 (95% CI = 0.87–0.9) and specificity = 0.89 ± 0.07 (95% CI = 0.87–0.9)). ConclusionCombination of radiomic features and clinical data can effectively predict outcome in COVID-19 patients. The developed model has significant potential for improved management of COVID-19 patients. To develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history, laboratory tests, visual scoring by radiologists) and lung/lesion radiomic features extracted from chest CT images. Overall, 152 patients were enrolled in this study protocol. These were divided into 106 training/validation and 46 test datasets (untouched during training), respectively. Radiomic features were extracted from the segmented lungs and infectious lesions separately from chest CT images. Clinical data, including patients' history and demographics, laboratory tests and radiological scores were also collected. Univariate analysis was first performed (q-value reported after false discovery rate (FDR) correction) to determine the most predictive features among all imaging and clinical data. Prognostic modeling of survival was performed using radiomic features and clinical data, separately or in combination. Maximum relevance minimum redundancy (MRMR) and XGBoost were used for feature selection and classification. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), sensitivity, specificity, and accuracy were used to assess the prognostic performance of the models on the test datasets. For clinical data, cancer comorbidity (q-value < 0.01), consciousness level (q-value < 0.05) and radiological score involved zone (q-value < 0.02) were found to have high correlated features with outcome. Oxygen saturation (AUC = 0.73, q-value < 0.01) and Blood Urea Nitrogen (AUC = 0.72, q-value = 0.72) were identified as high clinical features. For lung radiomic features, SAHGLE (AUC = 0.70) and HGLZE (AUC = 0.67) from GLSZM were identified as most prognostic features. Amongst lesion radiomic features, RLNU from GLRLM (AUC = 0.73), HGLZE from GLSZM (AUC = 0.73) had the highest performance. In multivariate analysis, combining lung, lesion and clinical features was determined to provide the most accurate prognostic model (AUC = 0.95 ± 0.029 (95%CI: 0.95-0.96), accuracy = 0.88 ± 0.046 (95% CI: 0.88-0.89), sensitivity = 0.88 ± 0.066 (95% CI = 0.87-0.9) and specificity = 0.89 ± 0.07 (95% CI = 0.87-0.9)). Combination of radiomic features and clinical data can effectively predict outcome in COVID-19 patients. The developed model has significant potential for improved management of COVID-19 patients. To develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history, laboratory tests, visual scoring by radiologists) and lung/lesion radiomic features extracted from chest CT images.OBJECTIVETo develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history, laboratory tests, visual scoring by radiologists) and lung/lesion radiomic features extracted from chest CT images.Overall, 152 patients were enrolled in this study protocol. These were divided into 106 training/validation and 46 test datasets (untouched during training), respectively. Radiomic features were extracted from the segmented lungs and infectious lesions separately from chest CT images. Clinical data, including patients' history and demographics, laboratory tests and radiological scores were also collected. Univariate analysis was first performed (q-value reported after false discovery rate (FDR) correction) to determine the most predictive features among all imaging and clinical data. Prognostic modeling of survival was performed using radiomic features and clinical data, separately or in combination. Maximum relevance minimum redundancy (MRMR) and XGBoost were used for feature selection and classification. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), sensitivity, specificity, and accuracy were used to assess the prognostic performance of the models on the test datasets.METHODSOverall, 152 patients were enrolled in this study protocol. These were divided into 106 training/validation and 46 test datasets (untouched during training), respectively. Radiomic features were extracted from the segmented lungs and infectious lesions separately from chest CT images. Clinical data, including patients' history and demographics, laboratory tests and radiological scores were also collected. Univariate analysis was first performed (q-value reported after false discovery rate (FDR) correction) to determine the most predictive features among all imaging and clinical data. Prognostic modeling of survival was performed using radiomic features and clinical data, separately or in combination. Maximum relevance minimum redundancy (MRMR) and XGBoost were used for feature selection and classification. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), sensitivity, specificity, and accuracy were used to assess the prognostic performance of the models on the test datasets.For clinical data, cancer comorbidity (q-value < 0.01), consciousness level (q-value < 0.05) and radiological score involved zone (q-value < 0.02) were found to have high correlated features with outcome. Oxygen saturation (AUC = 0.73, q-value < 0.01) and Blood Urea Nitrogen (AUC = 0.72, q-value = 0.72) were identified as high clinical features. For lung radiomic features, SAHGLE (AUC = 0.70) and HGLZE (AUC = 0.67) from GLSZM were identified as most prognostic features. Amongst lesion radiomic features, RLNU from GLRLM (AUC = 0.73), HGLZE from GLSZM (AUC = 0.73) had the highest performance. In multivariate analysis, combining lung, lesion and clinical features was determined to provide the most accurate prognostic model (AUC = 0.95 ± 0.029 (95%CI: 0.95-0.96), accuracy = 0.88 ± 0.046 (95% CI: 0.88-0.89), sensitivity = 0.88 ± 0.066 (95% CI = 0.87-0.9) and specificity = 0.89 ± 0.07 (95% CI = 0.87-0.9)).RESULTSFor clinical data, cancer comorbidity (q-value < 0.01), consciousness level (q-value < 0.05) and radiological score involved zone (q-value < 0.02) were found to have high correlated features with outcome. Oxygen saturation (AUC = 0.73, q-value < 0.01) and Blood Urea Nitrogen (AUC = 0.72, q-value = 0.72) were identified as high clinical features. For lung radiomic features, SAHGLE (AUC = 0.70) and HGLZE (AUC = 0.67) from GLSZM were identified as most prognostic features. Amongst lesion radiomic features, RLNU from GLRLM (AUC = 0.73), HGLZE from GLSZM (AUC = 0.73) had the highest performance. In multivariate analysis, combining lung, lesion and clinical features was determined to provide the most accurate prognostic model (AUC = 0.95 ± 0.029 (95%CI: 0.95-0.96), accuracy = 0.88 ± 0.046 (95% CI: 0.88-0.89), sensitivity = 0.88 ± 0.066 (95% CI = 0.87-0.9) and specificity = 0.89 ± 0.07 (95% CI = 0.87-0.9)).Combination of radiomic features and clinical data can effectively predict outcome in COVID-19 patients. The developed model has significant potential for improved management of COVID-19 patients.CONCLUSIONCombination of radiomic features and clinical data can effectively predict outcome in COVID-19 patients. The developed model has significant potential for improved management of COVID-19 patients. |
| ArticleNumber | 104304 |
| Author | Rahmim, Arman Zaidi, Habib Shiri, Isaac Kasaeian, Amir Nazari, Mostafa Aghaghazvini, Leila Hajianfar, Ghasem Sorouri, Majid Abdollahi, Hamid Geramifar, Parham Askari, Dariush Radmard, Amir Reza Salimi, Yazdan Abdollahi, Mohammad Arabi, Hossein Khosravi, Bardia |
| Author_xml | – sequence: 1 givenname: Isaac surname: Shiri fullname: Shiri, Isaac organization: Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland – sequence: 2 givenname: Majid surname: Sorouri fullname: Sorouri, Majid organization: Digestive Diseases Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran – sequence: 3 givenname: Parham surname: Geramifar fullname: Geramifar, Parham organization: Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran – sequence: 4 givenname: Mostafa surname: Nazari fullname: Nazari, Mostafa organization: Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran – sequence: 5 givenname: Mohammad surname: Abdollahi fullname: Abdollahi, Mohammad organization: Digestive Diseases Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran – sequence: 6 givenname: Yazdan surname: Salimi fullname: Salimi, Yazdan organization: Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland – sequence: 7 givenname: Bardia surname: Khosravi fullname: Khosravi, Bardia organization: Digestive Diseases Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran – sequence: 8 givenname: Dariush surname: Askari fullname: Askari, Dariush organization: Department of Radiology Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran – sequence: 9 givenname: Leila surname: Aghaghazvini fullname: Aghaghazvini, Leila organization: Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran – sequence: 10 givenname: Ghasem surname: Hajianfar fullname: Hajianfar, Ghasem organization: Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran – sequence: 11 givenname: Amir surname: Kasaeian fullname: Kasaeian, Amir organization: Digestive Diseases Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran – sequence: 12 givenname: Hamid surname: Abdollahi fullname: Abdollahi, Hamid organization: Department of Radiologic Sciences and Medical Physics, Kerman University of Medical Sciences, Kerman, Iran – sequence: 13 givenname: Hossein surname: Arabi fullname: Arabi, Hossein organization: Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland – sequence: 14 givenname: Arman orcidid: 0000-0002-9980-2403 surname: Rahmim fullname: Rahmim, Arman organization: Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada – sequence: 15 givenname: Amir Reza surname: Radmard fullname: Radmard, Amir Reza email: radmard@tums.ac.ir organization: Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran – sequence: 16 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, CH-1211, Geneva, Switzerland |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33691201$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | 2021 The Author(s) The Author(s) Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved. 2021. The Author(s) 2021 The Author(s) 2021 |
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| DOI | 10.1016/j.compbiomed.2021.104304 |
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| Keywords | COVID-19 Computed tomography (CT) Prognosis Modeling Radiomics |
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| License | This is an open access article under the CC BY-NC-ND license. Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. cc-by-nc-nd |
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Rep. doi: 10.1038/s41598-019-50886-7 |
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| Snippet | To develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history, laboratory... AbstractObjectiveTo develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and... ObjectiveTo develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history,... Image 1 |
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| SubjectTerms | Algorithms Artificial intelligence Blood pressure Chest Computed tomography Computed tomography (CT) Coronaviruses COVID-19 Datasets Demography Disease Feature extraction Hospitals Internal Medicine Laboratories Laboratory tests Learning algorithms Lesions Lungs Machine learning Medical imaging Medical prognosis Modeling Modelling Multivariate analysis Other Oxygen content Patients Pneumonia Prognosis Radiomics Redundancy Severe acute respiratory syndrome coronavirus 2 Software Survival Training Tuberculosis Urea |
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| Title | Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients |
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