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.

Radiological changes of serial CT examinations in patients recovering from coronavirus disease 2019 (COVID-19) (preprint)

Purpose: To find the pulmonary CT imaging characteristics in patients recovering from coronavirus disease 2019 (COVID-19).Method: Twenty patients with confirmed COVID-19 were enrolled. We analyzed the changes of four pulmonary CT imaging manifestations (ground glass opacity, consolidation, crazy paving sign and cord/band sign) in patients during hospitalization. The disease course was divided into four stages: early stage (0-4 days), progressive stage (5-8 days), peak stage (9-13 days) and absorption stage (≥14 days).Results: There were 12 male and 8 female with an average age of 45±16 years. In the first three stages, GGO was the most common sign on CT imaging. Then, the proportion of GGO decreased in the absorption stage compared with the first three stages (P<0.05). The proportion of crazy paving sign peaked in the progressive stage and then declined, with statistical difference between the progressive stage and the absorption stage (P<0.05). Cord/band sign was increasing from the early stage to the absorption stage, and statistical differences were found between the early stage and the peak stage (P<0.05), as well as the absorption stage and the first three stages (P<0.05). No statistical differences of consolidation proportion were found among the four stages.Conclusions: CT imaging showed different characteristics during the four stages. The proportion of cord/band sign significantly increased in the third stage, which might be an indicator of COVID-19 improvement.