Feasibility of Radiomics to Differentiate Coronavirus Disease 2019 (COVID-19) from H1N1 Influenza Pneumonia on Chest Computed Tomography: A Proof of Concept

Background: Chest computed tomography (CT) plays an essential role in diagnosing coronavirus disease 2019 (COVID-19). However, CT findings are often nonspecific among different viral pneumonia conditions. The differentiation between COVID-19 and influenza can be challenging when seasonal influenza c...

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Autores principales:	Mohsen Tabatabaei, Baharak Tasorian, Manu Goyal, Abdollatif Moini, Houman Sotoudeh
Formato:	article
Lenguaje:	EN
Publicado:	Shiraz University of Medical Sciences 2021
Materias:	covid-19 ● influenza human ● artificial intelligence ● tomography Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/2191d04e179a4ad699cec3ba68f3c58e
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spelling	oai:doaj.org-article:2191d04e179a4ad699cec3ba68f3c58e2021-11-09T06:23:38ZFeasibility of Radiomics to Differentiate Coronavirus Disease 2019 (COVID-19) from H1N1 Influenza Pneumonia on Chest Computed Tomography: A Proof of Concept0253-07161735-368810.30476/ijms.2021.88036.1858https://doaj.org/article/2191d04e179a4ad699cec3ba68f3c58e2021-11-01T00:00:00Zhttps://ijms.sums.ac.ir/article_47399_db7caa48a635b08c3450221f569cc308.pdfhttps://doaj.org/toc/0253-0716https://doaj.org/toc/1735-3688Background: Chest computed tomography (CT) plays an essential role in diagnosing coronavirus disease 2019 (COVID-19). However, CT findings are often nonspecific among different viral pneumonia conditions. The differentiation between COVID-19 and influenza can be challenging when seasonal influenza concurs with the COVID-19 pandemic. This study was conducted to test the ability of radiomics-artificial intelligence (AI) to perform this task.Methods: In this retrospective study, chest CT images from 47 patients with COVID-19 (after February 2020) and 19 patients with H1N1 influenza (before September 2019) pneumonia were collected from three hospitals affiliated with Arak University of Medical Sciences, Arak, Iran. All pulmonary lesions were segmented on CT images. Multiple radiomics features were extracted from the lesions and used to develop support-vector machine (SVM), k-nearest neighbor (k-NN), decision tree, neural network, adaptive boosting (AdaBoost), and random forest.Results: The patients with COVID-19 and H1N1 influenza were not significantly different in age and sex (P=0.13 and 0.99, respectively). Nonetheless, the average time between initial symptoms/hospitalization and chest CT was shorter in the patients with COVID-19 (P=0.001 and 0.01, respectively). After the implementation of the inclusion and exclusion criteria, 453 pulmonary lesions were included in this study. On the harmonized features, random forest yielded the highest performance (area under the curve=0.97, sensitivity=89%, precision=90%, F1 score=89%, and classification accuracy=89%).Conclusion: In our preliminary study, radiomics feature extraction, conjoined with AI, especially random forest and neural network, appeared to yield very promising results in the differentiation between COVID-19 and H1N1 influenza on chest CT.Mohsen TabatabaeiBaharak TasorianManu GoyalAbdollatif MoiniHouman SotoudehShiraz University of Medical Sciencesarticlecovid-19 ● influenzahuman ● artificial intelligence ● tomographyMedicine (General)R5-920ENIranian Journal of Medical Sciences, Vol 46, Iss 6, Pp 420-427 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	covid-19 ● influenza human ● artificial intelligence ● tomography Medicine (General) R5-920
spellingShingle	covid-19 ● influenza human ● artificial intelligence ● tomography Medicine (General) R5-920 Mohsen Tabatabaei Baharak Tasorian Manu Goyal Abdollatif Moini Houman Sotoudeh Feasibility of Radiomics to Differentiate Coronavirus Disease 2019 (COVID-19) from H1N1 Influenza Pneumonia on Chest Computed Tomography: A Proof of Concept
description	Background: Chest computed tomography (CT) plays an essential role in diagnosing coronavirus disease 2019 (COVID-19). However, CT findings are often nonspecific among different viral pneumonia conditions. The differentiation between COVID-19 and influenza can be challenging when seasonal influenza concurs with the COVID-19 pandemic. This study was conducted to test the ability of radiomics-artificial intelligence (AI) to perform this task.Methods: In this retrospective study, chest CT images from 47 patients with COVID-19 (after February 2020) and 19 patients with H1N1 influenza (before September 2019) pneumonia were collected from three hospitals affiliated with Arak University of Medical Sciences, Arak, Iran. All pulmonary lesions were segmented on CT images. Multiple radiomics features were extracted from the lesions and used to develop support-vector machine (SVM), k-nearest neighbor (k-NN), decision tree, neural network, adaptive boosting (AdaBoost), and random forest.Results: The patients with COVID-19 and H1N1 influenza were not significantly different in age and sex (P=0.13 and 0.99, respectively). Nonetheless, the average time between initial symptoms/hospitalization and chest CT was shorter in the patients with COVID-19 (P=0.001 and 0.01, respectively). After the implementation of the inclusion and exclusion criteria, 453 pulmonary lesions were included in this study. On the harmonized features, random forest yielded the highest performance (area under the curve=0.97, sensitivity=89%, precision=90%, F1 score=89%, and classification accuracy=89%).Conclusion: In our preliminary study, radiomics feature extraction, conjoined with AI, especially random forest and neural network, appeared to yield very promising results in the differentiation between COVID-19 and H1N1 influenza on chest CT.
format	article
author	Mohsen Tabatabaei Baharak Tasorian Manu Goyal Abdollatif Moini Houman Sotoudeh
author_facet	Mohsen Tabatabaei Baharak Tasorian Manu Goyal Abdollatif Moini Houman Sotoudeh
author_sort	Mohsen Tabatabaei
title	Feasibility of Radiomics to Differentiate Coronavirus Disease 2019 (COVID-19) from H1N1 Influenza Pneumonia on Chest Computed Tomography: A Proof of Concept
title_short	Feasibility of Radiomics to Differentiate Coronavirus Disease 2019 (COVID-19) from H1N1 Influenza Pneumonia on Chest Computed Tomography: A Proof of Concept
title_full	Feasibility of Radiomics to Differentiate Coronavirus Disease 2019 (COVID-19) from H1N1 Influenza Pneumonia on Chest Computed Tomography: A Proof of Concept
title_fullStr	Feasibility of Radiomics to Differentiate Coronavirus Disease 2019 (COVID-19) from H1N1 Influenza Pneumonia on Chest Computed Tomography: A Proof of Concept
title_full_unstemmed	Feasibility of Radiomics to Differentiate Coronavirus Disease 2019 (COVID-19) from H1N1 Influenza Pneumonia on Chest Computed Tomography: A Proof of Concept
title_sort	feasibility of radiomics to differentiate coronavirus disease 2019 (covid-19) from h1n1 influenza pneumonia on chest computed tomography: a proof of concept
publisher	Shiraz University of Medical Sciences
publishDate	2021
url	https://doaj.org/article/2191d04e179a4ad699cec3ba68f3c58e
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Feasibility of Radiomics to Differentiate Coronavirus Disease 2019 (COVID-19) from H1N1 Influenza Pneumonia on Chest Computed Tomography: A Proof of Concept

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