CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS (preprint)

Background: Limited data was available for rapid and accurate detection of COVID-19 using CT-based machine learning model. This study aimed to investigate the value of chest CT radiomics for diagnosing COVID-19 pneumonia compared with clinical model and COVID-19 reporting and data system (CO-RADS), and develop an open-source diagnostic tool with the constructed radiomics model. Methods This study enrolled 115 laboratory-confirmed COVID-19 and 435 non-COVID-19 pneunomia patients (training dataset, n = 379; validation dataset, n = 131; testing dataset, n = 40). Key radiomics features extracted from chest CT images were selected to build a radiomics signature using least absolute shrinkage and selection operator (LASSO) regression. Clinical and clinico-radiomics combined models were constructed. The combined model was further validated in the viral pneumonia cohort, and compared with performance of two radiologists with CO-RADS. The diagnostic performance was assessed by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). Results Eight radiomics features and 5 clinical variables were selected to construct the combined radiomics model, which outperformed the clinical model in diagnosing COVID-19 pneumonia with an area under the ROC (AUC) of 0.98 and good calibration in the validation cohort. The combined model also performed better in distinguishing COVID-19 from other viral pneumonia with an AUC of 0.93 compared with 0.75 ( P  = 0.03) for clinical model, and 0.69 ( P  = 0.008) or 0.82 ( P  = 0.15) for two trained radiologists using CO-RADS. The sensitivity and specificity of the combined model can be achieved to 0.85 and 0.90. The DCA confirmed the clinical utility of the combined model. An easy-to-use open-source diagnostic tool was developed using the combined model. Conclusions The combined radiomics model outperformed clinical model and CO-RADS for diagnosing COVID-19 pneumonia, which can facilitate more rapid and accurate detection.

Development and validation of a CT radiomics signature for diagnosing COVID-19 pneumonia compared with CO-RADS (preprint)

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.

Indomethacin has a potent antiviral activity against SARS CoV-2 in vitro and canine coronavirus in vivo (preprint)

BackgroundThe outbreak of SARS CoV-2 has caused ever-increasing attention and public panic all over the world. Currently, there is no specific treatment against the SARS CoV-2. Therefore, identifying effective antiviral agents to combat the disease is urgently needed. Previous studies found that indomethacin has the ability to inhibit the replication of several unrelated DNA and RNA viruses, including SARS-CoV. MethodsSARS CoV-2 pseudovirus-infected African green monkey kidney VERO E6 cells treated with different concentrations of indomethacin or aspirin at 48 hours post infection (p.i). The level of cell infection was determined by luciferase activity. Anti-coronavirus efficacy in vivo was confirmed by evaluating the time of recovery in canine coronavirus (CCV) infected dogs treated orally with 1mg/kg body weight indomethacin. ResultsWe found that indomethacin has a directly and potently antiviral activity against the SARS CoV-2 pseudovirus (reduce relative light unit to zero). In CCV-infected dogs, recovery occurred significantly sooner with symptomatic treatment + oral indomethacin (1 mg/kg body weight) daily treatments than with symptomatic treatment + ribavirin (10-15 mg/kg body weight) daily treatments (P =0.0031), but was not significantly different from that with symptomatic treatment + anti-canine coronavirus serum + canine hemoglobin + canine blood immunoglobulin + interferon treatments (P =0.7784). ConclusionThe results identify indomethacin as a potent inhibitor of SARS CoV-2.