2019年冠状病毒病(COVID-19)
医学
肺炎
严重急性呼吸综合征冠状病毒2型(SARS-CoV-2)
肺
放射性武器
2019-20冠状病毒爆发
间质性肺病
放射科
内科学
病理
疾病
爆发
传染病(医学专业)
作者
Nicolò Cardobi,Giulio Benetti,Giuseppe Cardano,Cinzia Arena,Claudio Micheletto,Carlo Cavedon,Stefania Montemezzi
出处
期刊:Radiologia Medica
[Springer Nature]
日期:2021-05-27
卷期号:126 (8): 1037-1043
被引量:18
标识
DOI:10.1007/s11547-021-01370-8
摘要
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 involvement. A subgroup of C−, COVID-19-like (CL), were considered as highly suggestive of COVID pneumonia at CT. Radiomic features were extracted from the whole lungs. A dual machine learning (ML) model approach was used. The first one excluded CL patients from the training set, eventually included on the test set. The second model included the CL patients also in the training set. Results The first model classified C + and C− pneumonias with AUC of 0.83. CL median response (0.80) was more similar to C + (0.92) compared to C− (0.17). Radiomic footprints of CL were similar to the C + ones (possibly false negative swab test). The second model, however, merging C + with CL patients in the training set, showed a slight decrease in classification performance (AUC = 0.81). Conclusion Whole lung ML models based on radiomics can classify C + and C− interstitial pneumonia. This may help in the correct management of patients with clinical and radiological stigmata of COVID-19, however presenting with a negative swab test. CL pneumonia was similar to C + pneumonia, albeit with slightly different radiomic footprints.
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