Differentiation between COVID-19 and bacterial pneumonia using radiomics of chest computed tomography and clinical features

Abstract

To develop and validate an effective model for distinguishing COVID-19 from bacterial pneumonia. In the training group and internal validation group, all patients were randomly divided into a training group (n = 245) and a validation group (n = 105). The whole lung lesion on chest computed tomography (CT) was drawn as the region of interest (ROI) for each patient. Both feature selection and model construction were first performed in the training set and then further tested in the validation set with the same thresholds. Additional tests were conducted on an external multicentre cohort with 105 subjects. The diagnostic model of LightGBM showed the best performance, achieving a sensitivity of 0.941, specificity of 0.981, accuracy of 0.962 on the validation dataset. In this study, we established a differential model to distinguish between COVID-19 and bacterial pneumonia based on chest CT radiomics and clinical indexes.