2021
DOI: 10.1007/s11547-021-01370-8
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CT radiomic models to distinguish COVID-19 pneumonia from other interstitial pneumonias

Abstract: Purpose To classify COVID-19, COVID-19-like and non-COVID-19 interstitial pneumonia using lung CT radiomic features. Material and Methods CT data of 115 patients with respiratory symptoms suspected for COVID-19 disease were retrospectively analyzed. Based on the results of nasopharyngeal swab, patients were divided into two main groups, COVID-19 positive (C +) and COVID-19 negative (C−), respectively. C− patients, however, presented with interstitial lung … Show more

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Cited by 20 publications
(13 citation statements)
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“…Data from these countries have proven the benefit of a booster dose in reducing symptomatic infection and offering a significant decrease in critical outcomes [ 49 , 50 , 51 , 52 , 53 , 54 ]. Moreover, the protection level offered by previous SARS-CoV-2 infection, both in terms of infection and disease severity and, therefore, of outcome, is still unclear [ 55 , 56 , 57 , 58 , 59 , 60 , 61 ]. In this scenario, the main essential element leading to the evolution of SARS-CoV-2 infection is the interaction with the host’s immune system.…”
Section: Introductionmentioning
confidence: 99%
“…Data from these countries have proven the benefit of a booster dose in reducing symptomatic infection and offering a significant decrease in critical outcomes [ 49 , 50 , 51 , 52 , 53 , 54 ]. Moreover, the protection level offered by previous SARS-CoV-2 infection, both in terms of infection and disease severity and, therefore, of outcome, is still unclear [ 55 , 56 , 57 , 58 , 59 , 60 , 61 ]. In this scenario, the main essential element leading to the evolution of SARS-CoV-2 infection is the interaction with the host’s immune system.…”
Section: Introductionmentioning
confidence: 99%
“…Cardobi et al [ 34 ] extracted RF using PyRadiomics tool from automatic lung segmentations and developed a model to distinguish CT images of COVID-19 patients from other interstitial pneumonias with AUC values of 0.77. However, the limited number of CT scans employed in this work ( n = 115) and the lack of independent validation raise some caveats to its clinical implementation.…”
Section: Discussionmentioning
confidence: 99%
“…Therefore, in our study, the effectiveness of artificial intelligence in distinguishing between CAP subgroups and COVID-19 pneumonia was not examined. Cordoba et al 29 evaluated whether artificial intelligence application is useful for differentiating COVID-19 pneumonia from non-COVID-19 interstitial pneumonia. In this study which included 115 patients, it was determined that a machine learning model based on whole lung HRCT radiomic footprint could be useful in differentiating COVID-19 pneumonia from non-COVID-19 interstitial pneumonia.…”
Section: Discussionmentioning
confidence: 99%