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Tabatabaei, Mohsen, Tasorian, Baharak, Goyal, Manu, Moini, Abdollatif, Sotoudeh, Houman. (1400). Feasibility of Radiomics to Differentiate Coronavirus Disease 2019 (COVID-19) from H1N1 Influenza Pneumonia on Chest Computed Tomography: A Proof of Concept. سامانه مدیریت نشریات علمی, 46(6), 420-427. doi: 10.30476/ijms.2021.88036.1858
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". سامانه مدیریت نشریات علمی, 46, 6, 1400, 420-427. doi: 10.30476/ijms.2021.88036.1858
Tabatabaei, Mohsen, Tasorian, Baharak, Goyal, Manu, Moini, Abdollatif, Sotoudeh, Houman. (1400). 'Feasibility of Radiomics to Differentiate Coronavirus Disease 2019 (COVID-19) from H1N1 Influenza Pneumonia on Chest Computed Tomography: A Proof of Concept', سامانه مدیریت نشریات علمی, 46(6), pp. 420-427. doi: 10.30476/ijms.2021.88036.1858
Tabatabaei, Mohsen, Tasorian, Baharak, Goyal, Manu, Moini, Abdollatif, Sotoudeh, Houman. Feasibility of Radiomics to Differentiate Coronavirus Disease 2019 (COVID-19) from H1N1 Influenza Pneumonia on Chest Computed Tomography: A Proof of Concept. سامانه مدیریت نشریات علمی, 1400; 46(6): 420-427. doi: 10.30476/ijms.2021.88036.1858

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

Iranian Journal of Medical Sciences
مقاله 2، دوره 46، شماره 6، بهمن 2021، صفحه 420-427    اصل مقاله (297.8 K)
نوع مقاله: Original Article(s)
شناسه دیجیتال (DOI): 10.30476/ijms.2021.88036.1858
نویسندگان
Mohsen Tabatabaei1؛ Baharak Tasorian2؛ Manu Goyal3؛ Abdollatif Moini4؛ Houman Sotoudeh* 5
1Health Information Management, Office of Vice Chancellor for Research, Arak University of Medical Sciences, Arak, Iran
2Internal Medicine Department, Arak University of Medical Sciences, Arak, Iran
3Postdoctoral Research Associate in Medical Imaging at Dartmouth College, Hanover, NH 03755 USA
4Department of Internal Medicine of Amir Al Momenin Hospital, Arak University of Medical Sciences, Arak, Iran
5Department of Radiology and Neurology; University of Alabama at Birmingham, Birmingham, Alabama, USA
چکیده
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.
کلیدواژه‌ها
COVID-19 ● Influenza؛ Human ● Artificial intelligence ● Tomography
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