Objectives: To develop and validate a CT radiomics signature for diagnosing COVID-19 pneumonia compared with clinical model and COVID-19 reporting and data system (CO-RADS).

Methods: This two-center retrospective study enrolled 115 laboratory-confirmed COVID-19 patients with 1127 lesions and 435 non-COVID-19 pneumonia patients with 842 lesions. In study 1, a radiomics signature and a clinical model was developed and validated in the training and internal validation cohorts (patient/lesion [n] = 379/1325, n = 131/505) for identifying COVID-19 pneumonia. In study 2, the developed radiomics signature was tested in another independent cohort including all viral pneumonia (n = 40/139), compared with clinical model and CO-RADS approach. The predictive performance was assessed by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA).

Results: Twenty-three texture features were selected to construct the radiomics model. Radiomics model outperformed the clinical model in diagnosing COVID-19 pneumonia with an area under the ROC (AUC) of 0.98 and good calibration in the internal validation cohort. Radiomics model also performed better in the testing cohort to distinguish COVID-19 from other viral pneumonia with an AUC of 0.96 compared with 0.75 (P=0.007) for clinical model, and 0.69 (P=0.002) or 0.82 (P=0.04) for two trained radiologists using CO-RADS approach. The sensitivity and specificity of radiomics model can be improved to 0.90 and 1.00. The DCA confirmed the clinical utility of radiomics model. 

Conclusions: The proposed radiomics signature outperformed clinical model and CO-RADS approach for diagnosing COVID-19, which can facilitate rapid and accurate detection of COVID-19 pneumonia.

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accurate detection of covid19 pneumoniastrong strongp receiver operating characteristics curve roc analysis calibration curve radiomics signature outperformed clinical model and corads approach 842 texture features area under the roc auc diagnosing covid19 pneumonia 1127 lesions covid19 reporting and data system corads

Liu, Huanhuan, Hou, Liang, Zhang, Shuhai, Li, Huimin, Wang, Dengbin, Chi, Runmin, Chen, Yanhong, Duan, Shaofeng, Xie, Zongyu, Li, Jinning, Xu, He, Wu, Zengbin, Zheng, Hui, Ren, Hua,.Development and validation of a CT radiomics signature for diagnosing COVID-19 pneumonia compared with CO-RADS. (),.

Liu, Huanhuan, et al."Development and validation of a CT radiomics signature for diagnosing COVID-19 pneumonia compared with CO-RADS"

Liu, Huanhuan, Hou, Liang, Zhang, Shuhai, Li, Huimin, Wang, Dengbin, Chi, Runmin, Chen, Yanhong, Duan, Shaofeng, Xie, Zongyu, Li, Jinning, Xu, He, Wu, Zengbin, Zheng, Hui, Ren, Hua,"Development and validation of a CT radiomics signature for diagnosing COVID-19 pneumonia compared with CO-RADS"