Prediction of COVID‐19 patients in danger of death using radiomic features of portable chest radiographs

INTRODUCTION: Computer‐aided diagnostic systems have been developed for the detection and differential diagnosis of coronavirus disease 2019 (COVID‐19) pneumonia using imaging studies to characterise a patient's current condition. In this radiomic study, we propose a system for predicting COVID‐19 patients in danger of death using portable chest X‐ray images. METHODS: In this retrospective study, we selected 100 patients, including ten that died and 90 that recovered from the COVID‐19‐AR database of the Cancer Imaging Archive. Since it can be difficult to analyse portable chest X‐ray images of patients with COVID‐19 because bone components overlap with the abnormal patterns of this disease, we employed a bone‐suppression technique during pre‐processing. A total of 620 radiomic features were measured in the left and right lung regions, and four radiomic features were selected using the least absolute shrinkage and selection operator technique. We distinguished death from recovery cases using a linear discriminant analysis (LDA) and a support vector machine (SVM). The leave‐one‐out method was used to train and test the classifiers, and the area under the receiver‐operating characteristic curve (AUC) was used to evaluate discriminative performance. RESULTS: The AUCs for LDA and SVM were 0.756 and 0.959, respectively. The discriminative performance was improved when the bone‐suppression technique was employed. When the SVM was used, the sensitivity for predicting disease severity was 90.9% (9/10), and the specificity was 95.6% (86/90). CONCLUSIONS: We believe that the radiomic features of portable chest X‐ray images can predict COVID‐19 patients in danger of death.