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.

Container CT scanner: a solution for modular emergency radiology department during the COVID-19 pandemic.

During the coronavirus disease 2019 (COVID-19) pandemic period, container computed tomography (CT) scanners were developed and used for the first time in China to perform CT examinations for patients with clinically mild to moderate COVID-19 who did not need to be hospitalized for comprehensive treatment, but needed to be isolated in Fangcang shelter hospitals (also known as makeshift hospitals) to receive some supportive treatment. The container CT is a multidetector CT scanner installed within a radiation-protected stand-alone container (a detachable lead shielding room) that is deployed outside the makeshift hospital buildings. The container CT approach provided various medical institutions with the solution not only for rapid CT installation and high adaptability to site environments, but also for significantly minimizing the risk of cross-infection between radiological personnel and patients during CT examination in the pandemic. In this article, we described the typical setup of a container CT and how it worked for chest CT examinations in Wuhan city, the epicenter of COVID-19 outbreak.

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.

A precision medicine approach to managing 2019 novel coronavirus pneumonia.

In December 2019, several patients with pneumonia of an unknown cause were detected in Wuhan, China. On 7 January 2020, the causal organism was identified as a new coronavirus, later named as the 2019 novel coronavirus (2019-nCoV). Genome sequencing found the genetic sequence of 2019-nCoV homologous to that of severe acute respiratory syndrome-associated coronavirus. As of 29 January 2020, the virus had been diagnosed in more than 7000 patients in China and 77 patients in other countries. It is reported that both symptomatic and asymptomatic patients with 2019-nCoV can play a role in disease transmission via airborne and contact. This finding has caused a great concern about the prevention of illness spread. The clinical features of the infection are not specific and are often indistinguishable from those of other respiratory infections, making it difficult to diagnose. Given that the virus has a strong ability to spread between individuals, it is of top priority to identify potential or suspected patients as soon as possible-or the virus may cause a serious pandemic. Therefore, a precision medicine approach to managing this disease is urgently needed for detecting and controlling the spread of the virus. In this article, we present such an approach to managing 2019-nCoV-related pneumonia based on the unique traits of the virus recently revealed and on our experience with coronaviruses at West China Hospital in Chengdu, China.

Coronavirus disease 2019 (COVID-19): two case reports from a family cluster.

The coronavirus disease 2019 (COVID-19) is a new infectious disease, firstly appeared in Wuhan city and has rapidly spread to 114 countries outside China, which is receiving worldwide attention. As two important means of examination, computed tomography (CT) and real-time reverse transcription polymerase chain reaction (RT-PCR) have always been controversial in the clinical diagnosis of COVID-19 pneumonia. Here, we report a family cluster case of a father and a son diagnosed as COVID-19 at our hospital, and described the clinical manifestations, laboratory results, CT changes, diagnosis and treatment strategy of these two patients. Focus on the value of these two methods in the diagnosis and treatment of diseases, as well as their respective deficiencies. For patient 1 (father), the efficacy of RT-PCR is not satisfactory either in terms of diagnosis or follow-up, which may cause misdiagnosis and delay treatment. For patient 2 (son), the clinical symptoms were not obvious, but CT imaging clearly displayed dynamic changes of the lung lesions. Meanwhile, the two patients respectively underwent five chest CT examinations during their hospitalization and discharge follow-up, showing the potential harm of radiation. Therefore, in clinical work, doctors should make full use of the advantages of CT and RT-PCR, and take other measures to make up for their disadvantages.

The Battle Against Coronavirus Disease 2019 (COVID-19): Emergency Management and Infection Control in a Radiology Department.

OBJECTIVE: To describe the strategy and the emergency management and infection control procedure of our radiology department during the coronavirus disease 2019 (COVID-19) outbreak. METHODS: We set up emergency management and sensing control teams. The team formulated various measures: reconfiguration of the radiology department, personal protection and training of staff, examination procedures for patients suspected of or confirmed with COVID-19 as well as patients without an exposure history or symptoms. Those with suspected or confirmed COVID-19 infection were scanned in the designated fever-CT unit. RESULTS: From January 21, 2020, to March 9, 2020, 3,083 people suspected or confirmed to be infected with COVID-19 underwent fever-CT examinations. Including initial examinations and re-examinations, the total number of fever-CT examinations numbered 3,340. As a result of our precautions, none of the staff of the radiology department were infected with COVID-19. CONCLUSION: Strategic planning and adequate protections can help protect patients and staff against a highly infectious disease while maintaining function at a high-volume capacity.

La batalla contra la enfermedad del Coronavirus 2019 (COVID-19): Manejo de Emergencia y Control de Infecciones en un Departamento de Radiología

Objetivo Describir las estrategias, manejo de emergencias y los procedimientos de control de infecciones de nuestro departamento durante el brote de la enfermedad por coronavirus 2019 (COVID-19). Métodos Creamos un equipo de manejo de emergencias. El equipo estableció varias medidas: Reconfiguración del flujo de trabajo en el departamento de radiología, distribución de material de protección personal y adiestramiento del personal, procedimientos para la obtención de imágenes en pacientes sospechosos o confirmados con COVID-19, así como para pacientes sin historial de exposición o síntomas. Aquellos con sospecha o confirmación de COVID-19 fueron escaneados en una unidad dedicada para ello. Result ados: Del 21 de enero del 2020 hasta el 9 de marzo del 2020, 3,083 personas con sospecha o confirmación de COVID-19 recibieron CT de torax. Incluyendo los exámenes iniciales y repetidos, el número total de CT fue 3,340. Como resultado de nuestras medidas de precaución, ninguno de los miembros del personal del departamento de radiología fue infectado con COVID-19. Conclusión Las estrategias de planificación y las protecciones adecuadas pueden ayudar a proteger a los pacientes y al personal contra una enfermedad altamente infecciosa. Y a la misma vez ayudar a mantener la capacidad de atender un volumen alto de pacientes.