Radiomics Is Effective for Distinguishing Coronavirus Disease 2019 Pneumonia From Influenza Virus Pneumonia - Lin, Liaoyi; Liu, Jinjin; Deng, Qingshan; Li, Na; Pan, Jingye; Sun, Houzhang; Quan, Shichao (Frontiers in Public Health, 2021)
Objectives: To develop and validate a radiomics model for distinguishing coronavirus disease 2019 (COVID-19) pneumonia from influenza virus pneumonia. Materials and Methods: A radiomics model was developed on the basis of 56 patients with COVID-19 pneumonia and 90 patients with influenza virus pneumonia in this retrospective study. Radiomics features were extracted from CT images. The radiomics features were reduced by Max-Relevance and Min-Redundancy algorithm and least absolute shrinkage and selection operator method. Radiomics model was built using the multi-variate backward stepwise logistic regression. A nomogram of radiomics model was established and the decision curve showed the clinical usefulness of the radiomics nomogram. Results: The radiomics features, consisting of 9 selected features, was significantly different between the COVID-19 pneumonia and influenza virus pneumonia in both training and validation data sets. The receiver operator characteristic curve of the radiomics model showed good discrimination in the training sample (area under the receiver operating characteristic curve [AUC], 0.909; 95% confidence interval [CI], 0.859-0.958) and in the validation sample (AUC, 0.911; 95% CI, 0.753–1.000). The nomogram was established and had good calibration. Decision curve analysis showed that the radiomics nomogram was clinically useful. Conclusions: The radiomics model has good performance for distinguishing COVID-19 pneumonia from influenza virus pneumonia and may aid in the diagnosis of COVID-19 pneumonia.