Background Convenient and precise assessment of the severity in coronavirus disease 2019 (COVID-19) contributes to the timely patient treatment and prognosis improvement. We aimed to evaluate the ability of CT-based radiomics nomogram in discriminating the severity of patients with COVID-19 Pneumonia. Methods A total of 150 patients (training cohort n = 105; test cohort n = 45) with COVID-19 confirmed by reverse transcription polymerase chain reaction (RT-PCR) test were enrolled. Two feature selection methods, Max-Relevance and Min-Redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO), were used to extract features from CT images and construct model. A total of 30 radiomic features were finally retained. Rad-score was calculated by summing the selected features weighted by their coefficients. The radiomics nomogram incorporating clinical-radiological features was eventually constructed by multivariate regression analysis. Nomogram, calibration, and decision-curve analysis were all assessed. Results In both cohorts, 40 patients with COVID-19 pneumonia were severe and 110 patients were non-severe. By combining the 30 radiomic features extracted from CT images, the radiomics signature showed high discrimination between severe and non-severe patients in the training set [Area Under the Curve (AUC), 0.857; 95% confidence interval (CI), 0.775–0.918] and the test set (AUC, 0.867; 95% CI, 0.732–949). The final combined model that integrated age, comorbidity, CT scores, number of lesions, ground glass opacity (GGO) with consolidation, and radiomics signature, improved the AUC to 0.952 in the training cohort and 0.98 in the test cohort. The nomogram based on the combined model similarly exhibited excellent discrimination performance in both training and test cohorts. Conclusions The developed model based on a radiomics signature derived from CT images can be a reliable marker for discriminating the severity of COVID-19 pneumonia.
【저자키워드】 COVID-19, nomogram, tomography, Radiomics, X-ray computed, 【초록키워드】 coronavirus disease, Coronavirus disease 2019, COVID-19 pneumonia, Prognosis, Pneumonia, severity, Comorbidity, nomogram, RT-PCR, reverse transcription polymerase chain reaction, polymerase chain reaction, Cohort, severity of COVID-19, Patient, age, reverse transcription, GGO, consolidation, multivariate regression analysis, marker, AUC, Chain Reaction, training set, regression analysis, Ground glass opacity, 95% CI, 95% confidence interval, calibration, lesions, cohorts, Final, severe and non-severe patients, LASSO, operator, decision-curve analysis, coefficients, patient treatment, feature, polymerase chain, Result, enrolled, selected, evaluate, exhibited, calculated, were used, contribute, retained, Curve, non-severe patient, patients with COVID-19, with COVID-19, 【제목키워드】 severity,