COVID-19 Research Paper Volume 13, Issue 7 pp 9186—9224

A COVID-19 risk score combining chest CT radiomics and clinical characteristics to differentiate COVID-19 pneumonia from other viral pneumonias

The receiver operating characteristic (ROC) curves and the decision curve analysis (DCA) for the lesion-based risk score and weighted support vector machine model using radiomic features alone. (A) ROC curve. (B) DCA analysis. In (B), the x-axis of the decision curve is the threshold of the predicted probability using the risk score to classify COVID-19 and non-COVID-19 patients. The y-axis shows the clinical decision net benefit for patients based on the classification result in this threshold. The decision curves of the treat-all scheme (the monotonically decreasing dash-line curve in the figure) and the treat-none scheme (the line when x equals zero) are used as references in the DCA. In this study, the treat-all scheme assumes that all patients had COVID-19; the treat-none scheme assumes that none of the patients had COVID-19. Abbreviations: AUC, area under the curve; 95% CI, 95% confidence interval.

Figure 5. The receiver operating characteristic (ROC) curves and the decision curve analysis (DCA) for the lesion-based risk score and weighted support vector machine model using radiomic features alone. (A) ROC curve. (B) DCA analysis. In (B), the x-axis of the decision curve is the threshold of the predicted probability using the risk score to classify COVID-19 and non-COVID-19 patients. The y-axis shows the clinical decision net benefit for patients based on the classification result in this threshold. The decision curves of the treat-all scheme (the monotonically decreasing dash-line curve in the figure) and the treat-none scheme (the line when x equals zero) are used as references in the DCA. In this study, the treat-all scheme assumes that all patients had COVID-19; the treat-none scheme assumes that none of the patients had COVID-19. Abbreviations: AUC, area under the curve; 95% CI, 95% confidence interval.