The coronavirus disease 2019 (COVID-19) is now a global pandemic. Tens of millions of people have been confirmed infection, and more people are suspected. Chest computed tomography (CT) is recognized as an important tool for COVID-19 severity assessment. As the number of chest CT images increase rapidly, manual severity assessment becomes a labor-intensive task, delaying appropriate isolation and treatment. In this paper, a study of automatic severity assessment for COVID-19 is presented. Specifically, chest CT images of 118 patients (age 46.5±16.5 years, 64 male and 54 female) with confirmed COVID-19 infection are used, from which 63 quantitative features and 110 Radiomics features are derived. Besides the chest CT image features, 36 laboratory indices of each patient are also used, which can provide complementary information from a different view. A random forest (RF) model is trained to assess the severity (non-severe or severe) according to the chest CT image features and laboratory indices. Importance of each chest CT image feature and laboratory index, which reflects the correlation to the severity of COVID-19, is also calculated from the RF model. Using three-fold cross-validation, the RF model shows promising results: 0.910 (true positive ratio), 0.858 (true negative ratio) and 0.890 (accuracy), along with AUC of 0.98. Moreover, several chest CT image features and laboratory indices are found to be important to COVID-19 severity, which could be valuable for the clinical diagnosis of COVID-19.
Keywords: COVID-19; Chest CT image features; Laboratory indices; Random forest; Severity assessment.
© 2020 Institute of Physics and Engineering in Medicine.