A Radiomics Signature to Quantitatively Analyze COVID-19-Infected Pulmonary Lesions

Assessing pulmonary lesions using computed tomography (CT) images is of great significance to the severity diagnosis and treatment of coronavirus disease 2019 (COVID-19)-infected patients. Such assessment mainly depends on radiologists’ subjective judgment, which is inefficient and presents difficulty for those with low levels of experience, especially in rural areas. This work focuses on developing a radiomics signature to quantitatively analyze whether COVID-19-infected pulmonary lesions are mild (Grade I) or moderate/severe (Grade II). We retrospectively analyzed 1160 COVID-19-infected pulmonary lesions from 16 hospitals. First, texture features were extracted from the pulmonary lesion regions of CT images. Then, feature preselection was performed and a radiomics signature was built using a stepwise logistic regression. The stepwise logistic regression also calculated the correlation between the radiomics signature and the grade of a pulmonary lesion. Finally, a logistic regression model was trained to classify the grades of pulmonary lesions. Given a significance level of α = 0.001, the stepwise logistic regression achieved an R (multiple correlation coefficient) of 0.70, which is much larger than Rα = 0.18 (the critical value of R). In the classification, the logistic regression model achieved an AUC of 0.87 on an independent test set. Overall, the radiomics signature is significantly correlated with the grade of a pulmonary lesion in COVID-19 infection. The classification model is interpretable and can assist radiologists in quickly and efficiently diagnosing pulmonary lesions. Graphic Abstract This work aims to develop a CT-based radiomics signature to quantitatively analyze whether COVID-19-infected pulmonary lesions are mild (Grade I) or moderate/severe (Grade II). The logistic regression model established based on this radiomics signature can assist radiologists to quickly and efficiently diagnose the grades of pulmonary lesions. The model calculates a radiomics score for a lesion and is interpretable and appropriate for clinical use Supplementary Information The online version contains supplementary material available at 10.1007/s12539-020-00410-7.