Combining initial chest CT with clinical variables in differentiating coronavirus disease 2019 (COVID-19) pneumonia from influenza pneumonia.

Coronavirus disease 2019 (COVID-19) has spread in more than 100 countries and regions around the world, raising grave global concerns. COVID-19 has a similar pattern of infection, clinical symptoms, and chest imaging findings to influenza pneumonia. In this retrospective study, we analysed clinical and chest CT data of 24 patients with COVID-19 and 79 patients with influenza pneumonia. Univariate analysis demonstrated that the temperature, systolic pressure, cough and sputum production could distinguish COVID-19 from influenza pneumonia. The diagnostic sensitivity and specificity for the clinical features are 0.783 and 0.747, and the AUC value is 0.819. Univariate analysis demonstrates that nine CT features, central-peripheral distribution, superior-inferior distribution, anterior-posterior distribution, patches of GGO, GGO nodule, vascular enlargement in GGO, air bronchogram, bronchiectasis within focus, interlobular septal thickening, could distinguish COVID-19 from influenza pneumonia. The diagnostic sensitivity and specificity for the CT features are 0.750 and 0.962, and the AUC value is 0.927. Finally, a multivariate logistic regression model combined the variables from the clinical variables and CT features models was made. The combined model contained six features: systolic blood pressure, sputum production, vascular enlargement in the GGO, GGO nodule, central-peripheral distribution and bronchiectasis within focus. The diagnostic sensitivity and specificity for the combined features are 0.87 and 0.96, and the AUC value is 0.961. In conclusion, some CT features or clinical variables can differentiate COVID-19 from influenza pneumonia. Moreover, CT features combined with clinical variables had higher diagnostic performance.

A Quantitative and Radiomics approach to monitoring ARDS in COVID-19 patients based on chest CT: a retrospective cohort study.

Rationale: Acute respiratory distress syndrome (ARDS) is one of the major reasons for ventilation and intubation management of COVID-19 patients but there is no noninvasive imaging monitoring protocol for ARDS. In this study, we aimed to develop a noninvasive ARDS monitoring protocol based on traditional quantitative and radiomics approaches from chest CT. Methods: Patients diagnosed with COVID-19 from Jan 20, 2020 to Mar 31, 2020 were enrolled in this study. Quantitative and radiomics data were extracted from automatically segmented regions of interest (ROIs) of infection regions in the lungs. ARDS existence was measured by Pa02/Fi02 <300 in artery blood samples. Three different models were constructed by using the traditional quantitative imaging metrics, radiomics features and their combinations, respectively. Receiver operating characteristic (ROC) curve analysis was used to assess the effectiveness of the models. Decision curve analysis (DCA) was used to test the clinical value of the proposed model. Results: The proposed models were constructed using 352 CT images from 86 patients. The median age was 49, and the male proportion was 61.9%. The training dataset and the validation dataset were generated by randomly sampling the patients with a 2:1 ratio. Chi-squared test showed that there was no significant difference in baseline of the enrolled patients between the training and validation datasets. The areas under the ROC curve (AUCs) of the traditional quantitative model, radiomics model and combined model in the validation dataset was 0.91, 0.91 and 0.94, respectively. Accordingly, the sensitivities were 0.55, 0.82 and 0.58, while the specificities were 0.97, 0.86 and 0.98. The DCA curve showed that when threshold probability for a doctor or patients is within a range of 0 to 0.83, the combined model adds more net benefit than "treat all" or "treat none" strategies, while the traditional quantitative model and radiomics model could add benefit in all threshold probability. Conclusions: It is feasible to monitor ARDS from CT images using radiomics or traditional quantitative analysis in COVID-19. The radiomics model seems to be the most practical one for possible clinical use. Multi-center validation with a larger number of samples is recommended in the future.

Quantitative analysis of chest CT imaging findings with the risk of ARDS in COVID-19 patients: a preliminary study.

BACKGROUND: The coronavirus disease 2019 (COVID-19) has rapidly become a pandemic worldwide. The value of chest computed tomography (CT) is debatable during the treatment of COVID-19 patients. Compared with traditional chest X-ray radiography, quantitative CT may supply more information, but its value on COVID-19 patients was still not proven. METHODS: An automatic quantitative analysis model based on a deep network called VB-Net for infection region segmentation was developed. A quantitative analysis was performed for patients diagnosed as severe COVID 19. The quantitative assessment included volume and density among the infectious area. The primary clinical outcome was the existence of acute respiratory distress syndrome (ARDS). A univariable and multivariable logistic analysis was done to explore the relationship between the quantitative results and ARDS existence. RESULTS: The VB-Ne model was sensitive and stable for pulmonary lesion segmentation, and quantitative analysis indicated that the total volume and average density of the lung lesions were not related to ARDS. However, lesions with specific density changes showed some influence on the risk of ARDS. The proportion of lesion density from -549 to -450 Hounsfield unit (HU) was associated with increased risk of ARDS, while the density was ranging from -149 to -50 HU was related to a lowered risk of ARDS. CONCLUSIONS: The automatic quantitative model based on VB-Ne can supply useful information for ARDS risk stratification in COVID-19 patients during treatment.

Imaging features and mechanisms of novel coronavirus pneumonia (COVID-19): Study Protocol Clinical Trial (SPIRIT Compliant).

INTRODUCTION: A novel coronavirus, tentatively designated as 2019 Novel Coronavirus (2019-nCoV), now called severe acute respiratory syndrome coronavirus 2, emerged in Wuhan, China, at the end of 2019 and which continues to expand. On February 11, 2020, the World Health Organization (WHO) named the disease coronavirus disease 2019 (COVID-19). On February 28, WHO increased our assessment of the risk of spread and the risk of impact of COVID-19 to very high at a global level. The COVID-19 poses significant threats to international health.Computed tomography (CT) has been an important imaging modality in assisting in the diagnosis and management of patients withCOVID-19. Some retrospective imaging studies have reported chest CT findings of COVID-19 in the past 2 months, suggesting that several CT findings may be characteristic. To our knowledge, there has been no prospective multicentre imaging study of COVID-19 to date.We proposed a hypothesis: There are some specific CT features on Chest CT of COVID-19 patients. And the mechanism of these CT features is explicable based on pathological findings. OBJECTIVE: To investigate the specific CT features of COVID-19 and the formation mechanism of these CT features. METHOD: This study is a prospective multicenter observational study. We will recruit 100 patients with COVID-19 at 55 hospitals. All patients undergo chest CT examination with the same scan protocol. The distribution and morphology of lesions on chest CT, clinical data will be recorded. A number of patients will be pathologically examined after permission is granted. The data of these three aspects will be analyzed synthetically. DISCUSSION: This study will help us to identify the chest CT features of COVID-19 and its mechanism. ETHICS AND DISSEMINATION: This retrospective study was approved by the Biomedical Research Ethics Committee of West China Hospital of Sichuan University (No. 2020-140). Written informed consent will be obtained from all study participants prior to enrollment in the study. To protect privacy of participants, all private information were kept anonymous. The results will be published in a peer-reviewed journal and will be disseminated electronically and in print regardless of results.