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

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‐...

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Bibliographic Details
Published in:Journal of medical radiation sciences Vol. 70; no. 1; pp. 13 - 20
Main Authors: Nakashima, Maoko, Uchiyama, Yoshikazu, Minami, Hirotake, Kasai, Satoshi
Format: Journal Article
Language:English
Published: United States John Wiley & Sons, Inc 01-03-2023
John Wiley and Sons Inc
Wiley
Subjects:
Artificial intelligence
Coronaviruses
COVID-19
Diaphragm (Anatomy)
Discriminant analysis
Disease
Humans
Infections
Lung
Medical prognosis
Mortality
Original
Pneumonia
portable chest X‐ray
prognosis prediction
Radiography
Radiomics
Retrospective Studies
Severe acute respiratory syndrome
Severe acute respiratory syndrome coronavirus 2
Tomography, X-Ray Computed - methods
COVID-19
radiomics
portable chest X-ray
Artificial intelligence
prognosis prediction
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Summary: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. We developed an artificial intelligence approach capable of identifying patients at higher risk of developing more severe COVID‐19 by using radiomic features obtained from portable chest x‐ray images. Our approach may have important clinical relevance during the pandemic.
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ISSN:2051-3895
2051-3909
DOI:10.1002/jmrs.631