TY - JOUR ID - 47399 TI - Feasibility of Radiomics to Differentiate Coronavirus Disease 2019 (COVID-19) from H1N1 Influenza Pneumonia on Chest Computed Tomography: A Proof of Concept JO - Iranian Journal of Medical Sciences JA - IJMS LA - en SN - 0253-0716 AU - Tabatabaei, Mohsen AU - Tasorian, Baharak AU - Goyal, Manu AU - Moini, Abdollatif AU - Sotoudeh, Houman AD - Health Information Management, Office of Vice Chancellor for Research, Arak University of Medical Sciences, Arak, Iran AD - Internal Medicine Department, Arak University of Medical Sciences, Arak, Iran AD - Postdoctoral Research Associate in Medical Imaging at Dartmouth College, Hanover, NH 03755 USA AD - Department of Internal Medicine of Amir Al Momenin Hospital, Arak University of Medical Sciences, Arak, Iran AD - Department of Radiology and Neurology; University of Alabama at Birmingham, Birmingham, Alabama, USA Y1 - 2021 PY - 2021 VL - 46 IS - 6 SP - 420 EP - 427 KW - COVID-19 ● Influenza KW - Human ● Artificial intelligence ● Tomography DO - 10.30476/ijms.2021.88036.1858 N2 - 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. UR - https://ijms.sums.ac.ir/article_47399.html L1 - https://ijms.sums.ac.ir/article_47399_db7caa48a635b08c3450221f569cc308.pdf ER -