Intersections between pneumonia, lowered oxygen saturation percentage and immune activation mediate depression, anxiety, and chronic fatigue syndrome-like symptoms due to COVID-19: A nomothetic network approach

BACKGROUND

COVID-19 is associated with neuropsychiatric symptoms including increased depressive, anxiety and chronic fatigue-syndrome (CFS)-like and physiosomatic symptoms.

AIMS

To delineate the associations between affective and CFS-like symptoms in COVID-19 and chest computed tomography scan anomalies (CCTAs), oxygen saturation (SpO₂), interleukin (IL)-6, IL-10, C-Reactive Protein (CRP), albumin, calcium, magnesium, soluble angiotensin converting enzyme (ACE2) and soluble advanced glycation products (sRAGEs).

METHOD

The above biomarkers were assessed in 60 COVID-19 patients and 30 healthy controls who had measurements of the Hamilton Depression (HDRS) and Anxiety (HAM-A) and the Fibromyalgia and Chronic Fatigue (FF) Rating Scales.

RESULTS

Partial Least Squares-SEM analysis showed that reliable latent vectors could be extracted from a) key depressive and anxiety and physiosomatic symptoms (the physio-affective or PA-core), b) IL-6, IL-10, CRP, albumin, calcium, and sRAGEs (the immune response core); and c) different CCTAs (including ground glass opacities, consolidation, and crazy paving) and lowered SpO₂% (lung lesions). PLS showed that 70.0% of the variance in the PA-core was explained by the regression on the immune response and lung lesions latent vectors. One common "infection-immune-inflammatory (III) core" underpins pneumonia-associated CCTAs, lowered SpO₂ and immune activation, and this III core explains 70% of the variance in the PA core, and a relevant part of the variance in melancholia, insomnia, and neurocognitive symptoms.

DISCUSSION

Acute SARS-CoV-2 infection is accompanied by lung lesions and lowered SpO₂ which may cause activated immune-inflammatory pathways, which mediate the effects of the former on the PA-core and other neuropsychiatric symptoms due to SARS-CoV-2 infection.

Imaging Primer on Chimeric Antigen Receptor T-Cell Therapy for Radiologists

Chimeric antigen receptor (CAR) T-cell therapy is a recently approved breakthrough treatment that has become a new paradigm in treatment of recurrent or refractory B-cell lymphomas and pediatric or adult acute lymphoid leukemia. CAR T cells are a type of cellular immunotherapy that artificially enhances T cells to boost eradication of malignancy through activation of the native immune system. The CAR construct is a synthetically created functional cell receptor grafted onto previously harvested patient T cells, which bind to preselected tumor-associated antigens and thereby activate host immune signaling cascades to attack tumor cells. Advantages include a single treatment episode of 2-3 weeks and durable disease elimination, with remission rates of over 80%. Responses to therapy are more rapid than with conventional chemotherapy or immunotherapy, with intervening short-interval edema. CAR T-cell administration is associated with therapy-related toxic effects in a large percentage of patients, notably cytokine release syndrome, immune effect cell-associated neurotoxicity syndrome, and infections related to immunosuppression. Knowledge of the expected evolution of therapy response and potential adverse events in CAR T-cell therapy and correlation with the timeline of treatment are important to optimize patient care. Some toxic effects are radiologically evident, and familiarity with their imaging spectrum is key to avoiding misinterpretation. Other clinical toxic effects may be occult at imaging and are diagnosed on the basis of clinical assessment. Future directions for CAR T-cell therapy include new indications and expanded tumor targets, along with novel ways to capture T-cell activation with imaging. An invited commentary by Ramaiya and Smith is available online. Online supplemental material is available for this article. ^©RSNA, 2022.

Distinguishing COVID-19 From Influenza Pneumonia in the Early Stage Through CT Imaging and Clinical Features

BACKGROUND

Both coronavirus disease 2019 (COVID-19) and influenza pneumonia are highly contagious and present with similar symptoms. We aimed to identify differences in CT imaging and clinical features between COVID-19 and influenza pneumonia in the early stage and to identify the most valuable features in the differential diagnosis.

METHODS

Seventy-three patients with COVID-19 confirmed by real-time reverse transcription-polymerase chain reaction (RT-PCR) and 48 patients with influenza pneumonia confirmed by direct/indirect immunofluorescence antibody staining or RT-PCR were retrospectively reviewed. Clinical data including course of disease, age, sex, body temperature, clinical symptoms, total white blood cell (WBC) count, lymphocyte count, lymphocyte ratio, neutrophil count, neutrophil ratio, and C-reactive protein, as well as 22 qualitative and 25 numerical imaging features from non-contrast-enhanced chest CT images were obtained and compared between the COVID-19 and influenza pneumonia groups. Correlation tests between feature metrics and diagnosis outcomes were assessed. The diagnostic performance of each feature in differentiating COVID-19 from influenza pneumonia was also evaluated.

RESULTS

Seventy-three COVID-19 patients including 41 male and 32 female with mean age of 41.9 ± 14.1 and 48 influenza pneumonia patients including 30 male and 18 female with mean age of 40.4 ± 27.3 were reviewed. Temperature, WBC count, crazy paving pattern, pure GGO in peripheral area, pure GGO, lesion sizes (1-3 cm), emphysema, and pleural traction were significantly independent associated with COVID-19. The AUC of clinical-based model on the combination of temperature and WBC count is 0.880 (95% CI: 0.819-0.940). The AUC of radiological-based model on the combination of crazy paving pattern, pure GGO in peripheral area, pure GGO, lesion sizes (1-3 cm), emphysema, and pleural traction is 0.957 (95% CI: 0.924-0.989). The AUC of combined model based on the combination of clinical and radiological is 0.991 (95% CI: 0.980-0.999).

CONCLUSION

COVID-19 can be distinguished from influenza pneumonia based on CT imaging and clinical features, with the highest AUC of 0.991, of which crazy-paving pattern and WBC count play most important role in the differential diagnosis.

Prognostic impact of CT severity score in childhood cancer with SARS-CoV-2

Background

CT chest severity score (CTSS) is a semi-quantitative measure done to correlate the severity of the pulmonary involvement on the CT with the severity of the disease.

The objectives of this study are to describe chest CT criteria and CTSS of the COVID-19 infection in pediatric oncology patients, to find a cut-off value of CTSS that can differentiate mild COVID-19 cases that can be managed at home and moderate to severe cases that need hospital care.

A retrospective cohort study was conducted on 64 pediatric oncology patients with confirmed COVID-19 infection between 1 April and 30 November 2020. They were classified clinically into mild, moderate, and severe groups. CT findings were evaluated for lung involvement and CTSS was calculated and range from 0 (clear lung) to 20 (all lung lobes were affected).

Results

Overall, 89% of patients had hematological malignancies and 92% were under active oncology treatment. The main CT findings were ground-glass opacity (70%) and consolidation patches (62.5%). In total, 85% of patients had bilateral lung involvement, ROC curve  showed that the area under the curve of CTSS for diagnosing severe type was 0.842 (95% CI 0.737–0.948). The CTSS cut-off of 6.5 had 90.9% sensitivity and 69% specificity, with 41.7% positive predictive value (PPV) and 96.9% negative predictive value (NPV). According to the Kaplan–Meier analysis, mortality risk was higher in patients with CT score > 7 than in those with CTSS < 7.

Conclusion

Pediatric oncology patients, especially those with hematological malignancies, are more vulnerable to COVID-19 infection. Chest CT severity score > 6.5 (about 35% lung involvement) can be used as a predictor of the need for hospitalization.

A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images

Coronavirus disease-19 (COVID-19) is a severe respiratory viral disease first reported in late 2019 that has spread worldwide. Although some wealthy countries have made significant progress in detecting and containing this disease, most underdeveloped countries are still struggling to identify COVID-19 cases in large populations. With the rising number of COVID-19 cases, there are often insufficient COVID-19 diagnostic kits and related resources in such countries. However, other basic diagnostic resources often do exist, which motivated us to develop Deep Learning models to assist clinicians and radiologists to provide prompt diagnostic support to the patients. In this study, we have developed a deep learning-based COVID-19 case detection model trained with a dataset consisting of chest CT scans and X-ray images. A modified ResNet50V2 architecture was employed as deep learning architecture in the proposed model. The dataset utilized to train the model was collected from various publicly available sources and included four class labels: confirmed COVID-19, normal controls and confirmed viral and bacterial pneumonia cases. The aggregated dataset was preprocessed through a sharpening filter before feeding the dataset into the proposed model. This model attained an accuracy of 96.452% for four-class cases (COVID-19/Normal/Bacterial pneumonia/Viral pneumonia), 97.242% for three-class cases (COVID-19/Normal/Bacterial pneumonia) and 98.954% for two-class cases (COVID-19/Viral pneumonia) using chest X-ray images. The model acquired a comprehensive accuracy of 99.012% for three-class cases (COVID-19/Normal/Community-acquired pneumonia) and 99.99% for two-class cases (Normal/COVID-19) using CT-scan images of the chest. This high accuracy presents a new and potentially important resource to enable radiologists to identify and rapidly diagnose COVID-19 cases with only basic but widely available equipment.

COVID-19 pneumonia: a pictorial review of CT findings and differential diagnosis

Background

The gold standard for verifying COVID-19 mostly depends on microbiological tests like real-time polymerase chain reaction (RT-PCR). However, the availability of RT-PCR kits can be known as a problem and false negative results may be encountered. Although CT scan is not a screening tool for the diagnosis of COVID-19 pneumonia, given the widespread acquisition of it in the pandemic state, familiarity with different CT findings and possible differential diagnosis is essential in this regard.

Main text

In this review, we introduced the typical and atypical CT features of COVID-19 pneumonia, and discussed the main differential diagnosis of COVID-19 pneumonia.

Conclusions

The imaging findings in this viral pneumonia showed a broad spectrum, and there are no pathognomonic imaging findings for COVID-19 pneumonia. Although CT scan is not a diagnostic and screening tool, familiarity with different imaging findings and their differential diagnosis can be helpful in a rapid and accurate decision-making.

Early CT features of COVID-19 pneumonia, association with patients’ age and duration of presenting complaint

Background

Coronavirus disease (COVID-19) is a respiratory syndrome with a variable degree of severity. Imaging is a vital component of disease monitoring and follow-up in coronavirus pulmonary syndromes. The study of temporal changes of CT findings of COVID-19 pneumonia can help in better understanding of disease pathogenesis and prediction of disease prognosis. In this study, we aim to determine the typical and atypical CT imaging features of COVID-19 and discuss the association of typical CT imaging features with the duration of the presenting complaint and patients’ age.

Results

The lesions showed unilateral distribution in 20% of cases and bilateral distribution in 80% of cases. The lesions involved the lower lung lobes in 30% of cases and showed diffuse involvement in 58.2% of cases. The lesions showed peripheral distribution in 74.5% of cases. The most common pattern was multifocal ground glass opacity found in 72.7% of cases. Atypical features like cavitation and pleural effusion can occur early in the disease course. There was significant association between increased number of the lesions, bilaterality, diffuse pattern of lung involvement and older age group (≥ 50 years old) and increased duration of presenting complaint (≥ 4 days). There was significant association between crazy-paving pattern and increased duration of presenting complaint. No significant association could be detected between any CT pattern and increased patient age.

Conclusion

The most common CT feature of COVID-19 was multifocal ground glass opacity. Atypical features like cavitation and pleural effusion can occur early in the course of the disease. Our cases showed more extensive lesions with bilateral and diffuse patterns of distribution in the older age group and with increased duration of presenting complaint. There was a significant association between crazy-paving pattern and increased duration of presenting complaint. No significant association could be detected between any CT pattern and increased patient age.

Chest CT features associated with the clinical characteristics of patients with COVID-19 pneumonia

OBJECTIVES

Coronavirus disease 2019 (COVID-19) has rapidly swept across the world. This study aimed to explore the relationship between the chest CT findings and clinical characteristics of COVID-19 patients.

METHODS

Patients with COVID-19 confirmed by next-generation sequencing or RT-PCR who had undergone more than 4 serial chest CT procedures were retrospectively enrolled.

RESULTS

This study included 361 patients - 192 men and 169 women. On initial chest CT, more lesions were identified as multiple bilateral lungs lesions and localised in the peripheral lung. The predominant patterns of abnormality were ground-glass opacities (GGO) (28.5%), consolidation (13.0%), nodule (23.0%), fibrous stripes (5.3%) and mixed (30.2%). Severe cases were more common in patients with a mixed pattern (21.1%) and less common in patients with nodules (2.4%). During follow-up CT, the mediumtotal severity score (TSS) in patients with nodules and fibrous strips was significantly lower than that in patients with mixed patterns in all three stages (p < .01).

CONCLUSION

Chest CT plays an important role in diagnosing COVID-19. The CT features may vary by age. Different CT features are not only associated with clinical manifestation but also patient prognosis. Key messages The initial chest CT findings of COVID-19 could help us monitor and predict the outcome. Nodules were more common in non severe cases and had a favorable prognosis. The mixed pattern was more common in severe cases and usually had a relatively poor outcome.

What are the factors that affect post COVID 1st month's continuing symptoms?

AIM

The aim of our research was to investigate retrospectively the relationship between the symptoms and general characteristics, initial laboratory values and treatments in patients who had COVID-19 and who applied to the chest diseases outpatient clinic for control after 1 month.

METHOD

Three hundred fifteen patients who were diagnosed with COVID-19 and applied to the chest diseases outpatient clinic between May 2020 and August 2020 for control in the 1st month were included in the study. Patient information was collected from the hospital information system and the e-pulse system.

RESULTS

Females accounted for 50.2% of our patients and their mean age was 47.9 ± 14.8 (19-88) years. About 14.3% (n: 45) of the individuals were 65 years of age and older, 20.6% (n: 65) of our patients were smoking and 70.2% (n: 221) of our patients were treated at home. A total of 133 patients had at least one comorbid disease. The patients most frequently reported cough, dyspnoea, weakness, myalgia and diarrhoea. The most common symptoms were cough, dyspnoea, weakness and myalgia in the 1st month. Initial D-dimer, initial CRP and the values of platelet, D dimer and CRP in the 1st month were detected to be higher in patients with persistent symptoms when the laboratory values of patients whose symptoms continue after 1 month were examined. It was determined that the symptoms had persisted in patients who had been hospitalised, had dual therapy, had comorbid diseases and had more common pathologies in their pulmonary imaging.

CONCLUSION

Symptoms may persist for a long time in hospitalised patients, in patients with COVID-19-related pneumonia and concomitant chronic diseases and in patients with high D-dimer and high CRP at the time of admission. Patients are informed that their symptoms may last for a long time, unnecessary hospital admissions can be avoided.

RSNA and BSTI grading systems of COVID-19 pneumonia: comparison of the diagnostic performance and interobserver agreement

Background

This study aimed to compare the performance and interobservers agreement of cases with findings on chest CT based on the British Society of Thoracic Imaging (BSTI) guideline statement of COVID-19 and the Radiological Society of North America (RSNA) expert consensus statement.

Methods

In this study, 903 patients who had admitted to the emergency department with a pre-diagnosis of COVID-19 between 1 and 18 July 2020 and had chest CT. Two radiologists classified the chest CT findings according to the RSNA and BSTI consensus statements. The performance, sensitivity and specificity values of the two classification systems were calculated and the agreement between the observers was compared by using kappa analysis.

Results

Considering RT-PCR test result as a gold standard, the sensitivity, specificity and positive predictive values were significantly higher for the two observers according to the BSTI guidance statement and the RSNA expert consensus statement (83.3%, 89.7%, 89.0%; % 81.2,% 89.7,% 88.7, respectively). There was a good agreement in the PCR positive group (κ: 0.707; p < 0.001 for BSTI and κ: 0.716; p < 0.001 for RSNA), a good agreement in the PCR negative group (κ: 0.645; p < 0.001 for BSTI and κ: 0.743; p < 0.001 for RSNA) according to the BSTI and RSNA classification between the two radiologists.

Conclusion

As a result, RSNA and BSTI statement provided reasonable performance and interobservers agreement in reporting CT findings of COVID-19. However, the number of patients defined as false negative and indeterminate in both classification systems is at a level that cannot be neglected.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12880-021-00668-3.

Complications of Lung Transplantation: Update on Imaging Manifestations and Management

As lung transplantation has become the most effective definitive treatment option for end-stage chronic respiratory diseases, yearly rates of this surgery have been steadily increasing. Despite improvement in surgical techniques and medical management of transplant recipients, complications from lung transplantation are a major cause of morbidity and mortality. Some of these complications can be classified on the basis of the time they typically occur after lung transplantation, while others may occur at any time. Imaging studies, in conjunction with clinical and laboratory evaluation, are key components in diagnosing and monitoring these conditions. Therefore, radiologists play a critical role in recognizing and communicating findings suggestive of lung transplantation complications. A description of imaging features of the most common lung transplantation complications, including surgical, medical, immunologic, and infectious complications, as well as an update on their management, will be reviewed here. Keywords: Pulmonary, Thorax, Surgery, Transplantation Supplemental material is available for this article. © RSNA, 2021.

Clinical and CT findings of adenovirus pneumonia in immunocompetent adults

INTRODUCTION

Adenovirus pneumonia is not uncommon in children and immunocompromised patients. However, the study of the clinical and computed tomography (CT) characteristics of Adenovirus pneumonia in immunocompetent adults is still limited.

OBJECTIVE

The objective of this study was to retrospectively observe the clinical and CT characteristics as well as their dynamic change of Adenovirus pneumonia in immunocompetent adults.

METHODS

Twenty patients (18 males, median age, 36 years old) with Adenovirus pneumonia were retrospectively included from January 2018 to December 2019. Clinical information and chest CT at admission of all patients were reviewed. Twelve patients underwent serial CT scans, and the temporal changes of CT findings were summarized. Pneumonia severity index (PSI) was calculated according to clinical information.

RESULTS

The median time interval from illness onset to admission was 6 days (interquartile range [IQR], 5-7.5 days). The clinical characteristics included the high fever (39.2 ± 0.8°C) with the normal white blood cell count, the decreased lymphocyte, and elevated C-reactive protein. Ten cases complicated with mycoplasma infection at admission. Thirteen patients were mechanically ventilated, and two patients died during hospitalization. Consolidation was a predominant pattern found during the first 2 weeks and then resolved to minimal consolidation after the fourth week. There was no significant correlation between CT score and PSI score (r = 0.15, p = 0.41).

CONCLUSIONS

Predominant radiological finding of Adenovirus pneumonia was consolidation. Multilobular involvement, higher CT scores, and pleural effusion were found in more severe patients. The abnormal opacity peaked in 2 weeks of illness onset and gradually resolved after the third week. The temporal changes of radiological score are consistent with clinical findings.

Similarities and Differences of CT Features between COVID-19 Pneumonia and Heart Failure

Aims: During the COVID-19 epidemic, chest computed tomography (CT) has been highly recommended for screening of patients with suspected COVID-19 because of an unclear contact history, overlapping clinical features, and an overwhelmed health system. However, there has not been a full comparison of CT for diagnosis of heart failure or COVID-19 pneumonia.Methods: Patients with heart failure (n=23) or COVID-19 pneumonia (n=23) and one patient with both diseases were retrospectively enrolled. Clinical information and chest CT images were obtained and analyzed.Results: There was no difference in ground-glass opacity, consolidation, crazy paving pattern, the lobes affected, and septal thickening between heart failure and COVID-19 pneumonia. However, a less rounded morphology (4% vs. 70%, P=0.00092), more peribronchovascular thickening (70% vs. 35%, P=0.018) and fissural thickening (43% vs. 4%, P=0.002), and less peripheral distribution (30% vs. 87%, P=0.00085) were found in the heart failure group than in the COVID-19 group. Importantly, there were also more patients with upper pulmonary vein enlargement (61% vs. 4%, P=0.00087), subpleural effusion (50% vs. 0%, P=0.00058), and cardiac enlargement (61% vs. 4%, P=0.00075) in the heart failure group than in the COVID-19 group. Besides, more fibrous lesions were found in the COVID-19 group, although there was no statistical difference (22% vs. 4%, P=0.080).Conclusions: Although there is some overlap of CT features between heart failure and COVID-19, CT is still a useful tool for differentiating COVID-19 pneumonia.

Organizing pneumonia associated with SARS-CoV-2 infection

Organizing pneumonia is a nonspecific pulmonary response pattern associated with a variety of clinical contexts including viral infections. The classic radiological manifestations are peribronchovascular/peripheral ground glass opacities or consolidations and may be accompanied by nodules, masses, and interstitial opacities. We describe the case of a 62-year-old male patient with SARS-CoV-2 pneumonia and torpid clinical and radiological evolution in whom organizing pneumonia was documented through transbronchial biopsy and imaging findings, with a good response to corticosteroids. The importance of recognizing the development of organizing pneumonia lies in the better prognosis and outcome in those patients who receive treatment with corticosteroids, however, the clinical and radiological suspicion must be confirmed with biopsy because radiological findings associated with bacterial coinfection may overlap.

Pseudocavitation sign at chest CT scan due to COVID-19 pneumonia: A report of 5 cases and literature review

Coronavirus disease 2019 (COVID-19) pneumonia computed tomography imaging features have been described in detail in many studies. The pseudocavitation sign has not been described in the previous COVID-19 studies. We present chest computed tomography scans of 5 reverse transcriptase-polymerase chain reaction positive patients with COVID-19 pneumonia who has bare areas among pulmonary infiltrates. All 5 also had previous scans with similarly sized low attenuated areas in the same location prior to the addition of pulmonary infiltrates. The pre-existing cystic changes had become remarkable due to the contrast around them after the pulmonary infiltrates added. Therefore, they should be termed as “pseodocavity” according to Fleischner Society glossary. Small air-containing spaces between pulmonary infiltrates have been termed in previous COVID-19 studies as a new sign called “round cystic changes/air bubble sign/vacuolar sign.” We would like to draw attention that the vacuolar sign and the synonyms may be the pseudocavity sign that is due to pre-existing changes rather than a new defined sign.

New challenges for management of COVID-19 patients: Analysis of MDCT based "Automated pneumonia analysis program"

PURPOSE

The aim of this study is to define the role of an "Automated Multi Detector Computed Tomography (MDCT) Pneumonia Analysis Program'' as an early outcome predictor for COVID-19 pneumonia in hospitalized patients.

MATERIALS AND METHODS

A total of 96 patients who had RT-PCR proven COVID-19 pneumonia diagnosed by non-contrast enhanced chest MDCT and hospitalized were enrolled in this retrospective study. An automated CT pneumonia analysis program was used for each patient to see the extent of disease. Patients were divided into two clinical subgroups upon their clinical status as good and bad clinical course. Total opacity scores (TOS), intensive care unit (ICU) entry, and mortality rates were measured for each clinical subgroups and also laboratory values were used to compare each subgroup.

RESULTS

Left lower lobe was the mostly effected side with a percentage of 78.12 % and followed up by right lower lobe with 73.95 %. TOS, ICU entry, and mortality rates were higher in bad clinical course subgroup. TOS values were also higher in patients older than 60 years and in patients with comorbidities including, Hypertension (HT), Diabetes Mellitus (DM), Chronic Obstructive Pulmonary Disease (COPD), Chronic Heart Failure (CHF) and malignancy.

CONCLUSION

Automated MDCT analysis programs for pneumonia are fast and an objective way to define the disease extent in COVID-19 pneumonia and it is highly correlated with the disease severity and clinical outcome thus providing physicians with valuable knowledge from the time of diagnosis.

Next Generation of Computationally Optimized Broadly Reactive HA Vaccines Elicited Cross-Reactive Immune Responses and Provided Protection against H1N1 Virus Infection

Vaccination is the best way to prevent influenza virus infections, but the diversity of antigenically distinct isolates is a persistent challenge for vaccine development. In order to conquer the antigenic variability and improve influenza virus vaccine efficacy, our research group has developed computationally optimized broadly reactive antigens (COBRAs) in the form of recombinant hemagglutinins (rHAs) to elicit broader immune responses. However, previous COBRA H1N1 vaccines do not elicit immune responses that neutralize H1N1 virus strains in circulation during the recent years. In order to update our COBRA vaccine, two new candidate COBRA HA vaccines, Y2 and Y4, were generated using a new seasonal-based COBRA methodology derived from H1N1 isolates that circulated during 2013-2019. In this study, the effectiveness of COBRA Y2 and Y4 vaccines were evaluated in mice, and the elicited immune responses were compared to those generated by historical H1 COBRA HA and wild-type H1N1 HA vaccines. Mice vaccinated with the next generation COBRA HA vaccines effectively protected against morbidity and mortality after infection with H1N1 influenza viruses. The antibodies elicited by the COBRA HA vaccines were highly cross-reactive with influenza A (H1N1) pdm09-like viruses isolated from 2009 to 2021, especially with the most recent circulating viruses from 2019 to 2021. Furthermore, viral loads in lungs of mice vaccinated with Y2 and Y4 were dramatically reduced to low or undetectable levels, resulting in minimal lung injury compared to wild-type HA vaccines following H1N1 influenza virus infection.

[Computed tomographic features of coronavirus disease-2019 (COVID-19) pneumonia in three groups of Iranian patients: A single center study]

INTRODUCTION AND OBJECTIVES

The pivotal role of chest computed tomographic (CT) to diagnosis and prognosis coronavirus disease-2019 (COVID-19) is still an open field to be explored. This study was conducted to assess the CT features in confirmed cases with COVID-19.

MATERIALS AND METHODS

Retrospectively, initial chest CT data of 363 confirmed cases with COVID-19 were reviewed. All subjects were stratified into three groups based on patients' clinical outcomes; non-critical group (n=194), critical group (n=65), and death group (n=104). The detailed of CT findings were collected from patients' medical records and then evaluated for each group. In addition, multinomial logistic regression was used to analyze risk factors according to CT findings in three groups of patients with COVID-19.

RESULTS

Compared with the non-critical group, mixed ground-glass opacities (GGO) and consolidation lesion, pleural effusion lesion, presence of diffuse opacity in cases, more than 2 lobes involved and opacity scores were significantly higher in the critical and death groups (P<0.05). Having more mixed GGO with consolidation, pleural effusion, lack of pure GGO, more diffuse opacity, involvement of more than 2 lobes and high opacity score identified as independent risk factors of critical and death groups.

CONCLUSION

CT images of non-critical, critical and death groups with COVID-19 had definite characteristics. CT examination plays a vital role in managing the current COVID-19 outbreak, for early detection of COVID-19 pneumonia. In addition, initial CT findings may be useful to stratify patients, which have a potentially important utility in the current global medical situation.

Dexamethasone for Severe COVID-19: How Does It Work at Cellular and Molecular Levels?

The coronavirus disease 2019 (COVID-19) caused by infection of the severe respiratory syndrome coronavirus-2 (SARS-CoV-2) significantly impacted human society. Recently, the synthetic pure glucocorticoid dexamethasone was identified as an effective compound for treatment of severe COVID-19. However, glucocorticoids are generally harmful for infectious diseases, such as bacterial sepsis and severe influenza pneumonia, which can develop respiratory failure and systemic inflammation similar to COVID-19. This apparent inconsistency suggests the presence of pathologic mechanism(s) unique to COVID-19 that renders this steroid effective. We review plausible mechanisms and advance the hypothesis that SARS-CoV-2 infection is accompanied by infected cell-specific glucocorticoid insensitivity as reported for some other viruses. This alteration in local glucocorticoid actions interferes with undesired glucocorticoid to facilitate viral replication but does not affect desired anti-inflammatory properties in non-infected organs/tissues. We postulate that the virus coincidentally causes glucocorticoid insensitivity in the process of modulating host cell activities for promoting its replication in infected cells. We explore this tenet focusing on SARS-CoV-2-encoding proteins and potential molecular mechanisms supporting this hypothetical glucocorticoid insensitivity unique to COVID-19 but not characteristic of other life-threatening viral diseases, probably due to a difference in specific virally-encoded molecules and host cell activities modulated by them.

Role of Chest Imaging in Viral Lung Diseases

The infection caused by novel beta-coronavirus (SARS-CoV-2) was officially declared a pandemic by the World Health Organization in March 2020. However, in the last 20 years, this has not been the only viral infection to cause respiratory tract infections leading to hundreds of thousands of deaths worldwide, referring in particular to severe acute respiratory syndrome (SARS), influenza H1N1 and Middle East respiratory syndrome (MERS). Although in this pandemic period SARS-CoV-2 infection should be the first diagnosis to exclude, many other viruses can cause pulmonary manifestations and have to be recognized. Through the description of the main radiological patterns, radiologists can suggest the diagnosis of viral pneumonia, also combining information from clinical and laboratory data.

Toward understanding COVID-19 pneumonia: a deep-learning-based approach for severity analysis and monitoring the disease

We report a new approach using artificial intelligence (AI) to study and classify the severity of COVID-19 using 1208 chest X-rays (CXRs) of 396 COVID-19 patients obtained through the course of the disease at Emory Healthcare affiliated hospitals (Atlanta, GA, USA). Using a two-stage transfer learning technique to train a convolutional neural network (CNN), we show that the algorithm is able to classify four classes of disease severity (normal, mild, moderate, and severe) with the average Area Under the Curve (AUC) of 0.93. In addition, we show that the outputs of different layers of the CNN under dominant filters provide valuable insight about the subtle patterns in the CXRs, which can improve the accuracy in the reading of CXRs by a radiologist. Finally, we show that our approach can be used for studying the disease progression in a single patient and its influencing factors. The results suggest that our technique can form the foundation of a more concrete clinical model to predict the evolution of COVID-19 severity and the efficacy of different treatments for each patient through using CXRs and clinical data in the early stages of the disease. This use of AI to assess the severity and possibly predicting the future stages of the disease early on, will be essential in dealing with the upcoming waves of COVID-19 and optimizing resource allocation and treatment.

Image-based deep learning in diagnosing the etiology of pneumonia on pediatric chest X-rays

PURPOSE

Comparing the efficacy of a deep-learning model in classifying the etiology of pneumonia on pediatric chest X-rays (CXRs) with that of human readers.

METHODS

We built a clinical-pediatric CXR set containing 4035 patients to exploit a deep-learning model called Resnet-50 for differentiating viral from bacterial pneumonia. The dataset was split into training (80%) and validation (20%). Model performance was assessed by receiver operating characteristic curve and area under the curve (AUC) on the first test set of 400 CXRs collected from different studies. For the second test set composed of 100 independent examinations obtained from the daily clinical practice at our institution, the kappa coefficient was selected to measure the interrater agreement in a pairwise fashion for the reference standard, all reviewers, and the model. Gradient-weighted class activation mapping was used to visualize the significant areas contributing to the model prediction.

RESULTS

On the first test set, the best-performing classifier achieved an AUC of 0.919 (p < .001), with a sensitivity of 79.0% and specificity of 88.9%. On the second test set, the classifier achieved performance similar to that of human experts, which resulted in a sensitivity of 74.3% and specificity of 90.8%, positive and negative likelihood ratios of 8.1 and 0.3, respectively. Contingence tables and kappa values further revealed that expert reviewers and model reached substantial agreements on differentiating the etiology of pediatric pneumonia.

CONCLUSIONS

This study demonstrated that the model performed similarly as human reviewers and recognized the regions of pathology on CXRs.

Effectiveness of COVID-19 diagnosis and management tools: A review

OBJECTIVE

To review the available literature concerning the effectiveness of the COVID-19 diagnostic tools.

BACKGROUND

With the absence of specific treatment/vaccines for the coronavirus COVID-19, the most appropriate approach to control this infection is to quarantine people and isolate symptomatic people and suspected or infected cases. Although real-time reverse transcription-polymerase chain reaction (RT-PCR) assay is considered the first tool to make a definitive diagnosis of COVID-19 disease, the high false negative rate, low sensitivity, limited supplies and strict requirements for laboratory settings might delay accurate diagnosis. Computed tomography (CT) has been reported as an important tool to identify and investigate suspected patients with COVID-19 disease at early stage.

KEY FINDINGS

RT-PCR shows low sensitivity (60-71%) in diagnosing patients with COVID-19 infection compared to the CT chest. Several studies reported that chest CT scans show typical imaging features in all patients with COVID-19. This high sensitivity and initial presentation in CT chest can be helpful in rectifying false negative results obtained from RT-PCR. As COVID-19 has similar manifestations to other pneumonia diseases, artificial intelligence (AI) might help radiologists to differentiate COVID-19 from other pneumonia diseases.

CONCLUSION

Although CT scan is a powerful tool in COVID-19 diagnosis, it is not sufficient to detect COVID-19 alone due to the low specificity (25%), and challenges that radiologists might face in differentiating COVID-19 from other viral pneumonia on chest CT scans. AI might help radiologists to differentiate COVID-19 from other pneumonia diseases.

IMPLICATION FOR PRACTICE

Both RT-PCR and CT tests together would increase sensitivity and improve quarantine efficacy, an impact neither could achieve alone.

Quantitative assessment of lung involvement on chest CT at admission: Impact on hypoxia and outcome in COVID-19 patients

BACKGROUND

The aim of this study was to quantify COVID-19 pneumonia features using CT performed at time of admission to emergency department in order to predict patients' hypoxia during the hospitalization and outcome.

METHODS

Consecutive chest CT performed in the emergency department between March 1st and April 7th 2020 for COVID-19 pneumonia were analyzed. The three features of pneumonia (GGO, semi-consolidation and consolidation) and the percentage of well-aerated lung were quantified using a HU threshold based software. ROC curves identified the optimal cut-off values of CT parameters to predict hypoxia worsening and hospital discharge. Multiple Cox proportional hazards regression was used to analyze the capability of CT quantitative features, demographic and clinical variables to predict the time to hospital discharge.

RESULTS

Seventy-seven patients (median age 56-years-old, 51 men) with COVID-19 pneumonia at CT were enrolled. The quantitative features of COVID-19 pneumonia were not associated to age, sex and time-from-symptoms onset, whereas higher number of comorbidities was correlated to lower well-aerated parenchyma ratio (rho = -0.234, p = 0.04) and increased semi-consolidation ratio (rho = -0.303, p = 0.008). Well-aerated lung (≤57%), semi-consolidation (≥17%) and consolidation (≥9%) predicted worst hypoxemia during hospitalization, with moderate areas under curves (AUC 0.76, 0.75, 0.77, respectively). Multiple Cox regression identified younger age (p < 0.01), female sex (p < 0.001), longer time-from-symptoms onset (p = 0.049), semi-consolidation ≤17% (p < 0.01) and consolidation ≤13% (p = 0.03) as independent predictors of shorter time to hospital discharge.

CONCLUSION

Quantification of pneumonia features on admitting chest CT predicted hypoxia worsening during hospitalization and time to hospital discharge in COVID-19 patients.

CT Imaging Features of Patients Infected with 2019 Novel Coronavirus

Novel coronavirus pneumonia is an acute, infectious pneumonia caused by a novel coronavirus infection. Computed tomographic (CT) imaging is one of the main methods to screen and diagnose patients with this disease. Here, the importance and clinical value of chest CT examination in the diagnosis of COVID-19 is expounded, and the pulmonary CT findings of COVID-19 patients in different stages are briefly summarized, thus providing a reference document for the CT diagnosis of COVID-19 patients.

Computed tomographic features of coronavirus disease-2019 (COVID-19) pneumonia in three groups of Iranian patients: A single center study

Introduction and objectives

The pivotal role of chest computed tomographic (CT) to diagnosis and prognosis coronavirus disease-2019 (COVID-19) is still an open field to be explored. This study was conducted to assess the CT features in confirmed cases with COVID-19.

Materials and methods

Retrospectively, initial chest CT data of 363 confirmed cases with COVID-19 were reviewed. All subjects were stratified into three groups based on patients’ clinical outcomes; non-critical group (n = 194), critical group (n = 65), and death group (n = 104). The detailed of CT findings were collected from patients’ medical records and then evaluated for each group. In addition, multinomial logistic regression was used to analyze risk factors according to CT findings in three groups of patients with COVID-19.

Results

Compared with the non-critical group, mixed ground-glass opacities (GGO) and consolidation lesion, pleural effusion lesion, presence of diffuse opacity in cases, more than 2 lobes involved and opacity scores were significantly higher in the critical and death groups (P < 0.05). Having more mixed GGO with consolidation, pleural effusion, lack of pure GGO, more diffuse opacity, involvement of more than 2 lobes and high opacity score identified as independent risk factors of critical and death groups.

Conclusion

CT images of non-critical, critical and death groups with COVID-19 had definite characteristics. CT examination plays a vital role in managing the current COVID-19 outbreak, for early detection of COVID-19 pneumonia. In addition, initial CT findings may be useful to stratify patients, which have a potentially important utility in the current global medical situation.

Clinical Factors and Quantitative CT Parameters Associated With ICU Admission in Patients of COVID-19 Pneumonia: A Multicenter Study

The clinical spectrum of COVID-19 pneumonia is varied. Thus, it is important to identify risk factors at an early stage for predicting deterioration that require transferring the patients to ICU. A retrospective multicenter study was conducted on COVID-19 patients admitted to designated hospitals in China from Jan 17, 2020, to Feb 17, 2020. Clinical presentation, laboratory data, and quantitative CT parameters were also collected. The result showed that increasing risks of ICU admission were associated with age > 60 years (odds ratio [OR], 12.72; 95% confidence interval [CI], 2.42-24.61; P = 0.032), coexisting conditions (OR, 5.55; 95% CI, 1.59-19.38; P = 0.007) and CT derived total opacity percentage (TOP) (OR, 8.0; 95% CI, 1.45-39.29; P = 0.016). In conclusion, older age, coexisting conditions, larger TOP at the time of hospital admission are associated with ICU admission in patients with COVID-19 pneumonia. Early monitoring the progression of the disease and implementing appropriate therapies are warranted.

Automated CT Lung Density Analysis of Viral Pneumonia and Healthy Lungs Using Deep Learning-Based Segmentation, Histograms and HU Thresholds

CT patterns of viral pneumonia are usually only qualitatively described in radiology reports. Artificial intelligence enables automated and reliable segmentation of lungs with chest CT. Based on this, the purpose of this study was to derive meaningful imaging biomarkers reflecting CT patterns of viral pneumonia and assess their potential to discriminate between healthy lungs and lungs with viral pneumonia. This study used non-enhanced and CT pulmonary angiograms (CTPAs) of healthy lungs and viral pneumonia (SARS-CoV-2, influenza A/B) identified by radiology reports and RT-PCR results. After deep learning segmentation of the lungs, histogram-based and threshold-based analyses of lung attenuation were performed and compared. The derived imaging biomarkers were correlated with parameters of clinical and biochemical severity (modified WHO severity scale; c-reactive protein). For non-enhanced CTs (n = 526), all imaging biomarkers significantly differed between healthy lungs and lungs with viral pneumonia (all p < 0.001), a finding that was not reproduced for CTPAs (n = 504). Standard deviation (histogram-derived) and relative high attenuation area [600-0 HU] (HU-thresholding) differed most. The strongest correlation with disease severity was found for absolute high attenuation area [600-0 HU] (r = 0.56, 95% CI = 0.46-0.64). Deep-learning segmentation-based histogram and HU threshold analysis could be deployed in chest CT evaluation for the differentiating of healthy lungs from AP lungs.

Generalized additive mixed model to evaluate the association between total pulmonary infection volume and volume ratio, and clinical types, in patients with COVID-19 pneumonia: a propensity score analysis

Objectives

To investigate the association between longitudinal total pulmonary infection volume and volume ratio over time and clinical types in COVID-19 pneumonia patients.

Methods

This retrospective review included 367 patients with COVID-19 pneumonia. All patients underwent CT examination at baseline and/or one or more follow-up CT. Patients were categorized into two clinical types (moderate and severe groups). The severe patients were matched to the moderate patients via propensity scores (PS). The association between total pulmonary infection volume and volume ratio and clinical types was analyzed using a generalized additive mixed model (GAMM).

Results

Two hundred and seven moderate patients and 160 severe patients were enrolled. The baseline clinical and imaging variables were balanced using PS analysis to avoid patient selection bias. After PS analysis, 172 pairs of moderate patients were allocated to the groups; there was no difference in the clinical and CT characteristics between the two groups (p > 0.05). A total of 332 patients, including 396 CT scans, were assessed. The impact of total pulmonary infection volume and volume ratio with time was significantly affected by clinical types (p for interaction = 0.01 and 0.01, respectively) using GAMM. Total pulmonary infection volume and volume ratio of the severe group increased by 14.66 cm³ (95% confidence interval [CI]: 3.92 to 25.40) and 0.45% (95% CI: 0.13 to 0.77) every day, respectively, compared to that of the moderate group.

Conclusions

Longitudinal total pulmonary infection volume and volume ratio are independently associated with the clinical types of COVID-19 pneumonia.

Key Points

• The impact of total pulmonary infection volume and volume ratio over time was significantly affected by the clinical types (p for interaction = 0.01 and 0.01, respectively) using the GAMM.

• Total pulmonary infection volume and volume ratio of the severe group increased by 14.66 cm³(95% CI: 3.92 to 25.40) and 0.45% (95% CI: 0.13 to 0.77) every day, respectively, compared to that of the moderate group.

Atypical Pneumonia: Definition, Causes, and Imaging Features

Pneumonia is among the most common causes of death worldwide. The epidemiologic and clinical heterogeneity of pneumonia results in challenges in diagnosis and treatment. There is inconsistency in the definition of the group of microorganisms that cause "atypical pneumonia." Nevertheless, the use of this term in the medical and radiologic literature is common. Among the causes of community-acquired pneumonia, atypical bacteria are responsible for approximately 15% of cases. Zoonotic and nonzoonotic bacteria, as well as viruses, have been considered among the causes of atypical pneumonia in a patient who is immunocompetent and have been associated with major community outbreaks of respiratory infection, with relevant implications in public health policies. Considering the difficulty of isolating atypical microorganisms and the significant overlap in clinical manifestations, a targeted empirical therapy is not possible. Imaging plays an important role in the diagnosis and management of atypical pneumonia, as in many cases its findings may first suggest the possibility of an atypical infection. Clarifying and unifying the definition of atypical pneumonia among the medical community, including radiologists, are of extreme importance. The prompt diagnosis and prevention of community spread of some atypical microorganisms can have a relevant impact on local, regional, and global health policies. ^©RSNA, 2021.

Clinical differences in chest CT characteristics between the progression and remission stages of patients with COVID-19 pneumonia

INTRODUCTION

Computed tomography (CT) can be effective for the early screening and diagnosis of COVID-19. This study aimed to investigate the distinctive CT characteristics of two stages of the disease (progression and remission).

METHODS

We included all COVID-19 patients admitted to Wenzhou Central Hospital from January to February, 2020. Patients underwent multiple chest CT scans at intervals of 3-10 days. CT features were recorded, such as the lesion lobe, distribution characteristics (subpleural, scattered or diffused), shape of the lesion, maximum size of the lesion, lesion morphology (ground-glass opacity, GGO) and consolidation features. When consolidation was positive, the boundary was identified to determine its clarity.

RESULTS

The ratios of some representative features differed between the remission stage and the progression phase, such as round-shape lesion (8.0% vs 34.4%), GGO (65.0% vs 87.5%), consolidation (62.0% vs 31.3%), large cable sign (59.0% vs 9.4%) and crazy-paving sign (20.0% vs 50.0%). Using these features, we pooled all the CT data (n = 132) and established a logistic regression model to predict the current development stage. The variables consolidation, boundary feature, large cable sign and crazy-paving sign were the most significant factors, based on a variable named "prediction of progression or remission" (PPR) that we constructed. The ROC curve showed that PPR had an AUC of 0.882 (cutoff value = 0.66, sensitivity = 0.75, specificity = 0.875).

CONCLUSION

CT characteristics, in particular, round shape, GGO, consolidation, large cable sign, and crazy-paving sign, may increase the recognition of the intrapulmonary development of COVID-19.

Accuracy of Chest CT for Differentiating COVID-19 from COVID-19 Mimics

PURPOSE

To determine the performance of radiologists with different levels of expertise regarding the differentiation of COVID-19 from other atypical pneumonias. Chest CT to identify patients suffering from COVID-19 has been reported to be limited by its low specificity for distinguishing COVID-19 from other atypical pneumonias ("COVID-19 mimics"). Meanwhile, the understanding of the morphologic patterns of COVID-19 has improved and they appear to be fairly specific.

MATERIALS AND METHODS

Between 02/2020 and 04/2020, 60 patients with COVID-19 pneumonia underwent chest CT in our department. Cases were matched with a comparable control group of 60 patients of similar age, sex, and comorbidities, who underwent chest CT prior to 01/2020 for atypical pneumonia caused by other pathogens. Included were other viral, fungal, and bacterial pathogens. All 120 cases were blinded to patient history and were reviewed independently by two radiologists and two radiology residents. Readers rated the probability of COVID-19 pneumonia according to the COV-RADS classification system. Results were analyzed using Clopper-Pearson 95 % confidence intervals, Youden's Index for test quality criteria, and Fleiss' kappa statistics.

RESULTS

Overall, readers were able to correctly identify the presence of COVID-19 pneumonia in 219/240 (sensitivity: 91 %; 95 %-CI; 86.9 %-94.5 %), and to correctly attribute CT findings to COVID-19 mimics in 159/240 ratings (specificity: 66.3 %; 59.9 %-72.2 %), yielding an overall diagnostic accuracy of 78.8 % (378/480; 74.8 %-82.3 %). Individual reader accuracy ranged from 74.2 % (89/120) to 84.2 % (101/120) and did not correlate significantly with reader expertise. Youden's Index was 0.57. Between-reader agreement was moderate (κ = 0.53).

CONCLUSION

In this enriched cohort, radiologists were able to distinguish COVID-19 from "COVID-19 mimics" with moderate diagnostic accuracy. Accuracy did not correlate with reader expertise.

KEY POINTS

· In a scenario of direct comparison (no negative findings), CT allows the differentiation of COVID-19 from other atypical pneumonias ("COVID mimics") with moderate accuracy.. · Reader expertise did not significantly influence these results.. · Despite similar patterns and distributions of pulmonary findings, radiologists were able to estimate the probability of COVID-19 pneumonia using the COV-RADS classification in a standardized manner in the larger proportion of cases..

CITATION FORMAT

· Sähn M, Yüksel C, Keil S et al. Accuracy of Chest CT for Differentiating COVID-19 from COVID-19 Mimics. Fortschr Röntgenstr 2021; 193: 1081 - 1091.

Artificial Intelligence Applied to Chest X-ray for Differential Diagnosis of COVID-19 Pneumonia

We assessed the role of artificial intelligence applied to chest X-rays (CXRs) in supporting the diagnosis of COVID-19. We trained and cross-validated a model with an ensemble of 10 convolutional neural networks with CXRs of 98 COVID-19 patients, 88 community-acquired pneumonia (CAP) patients, and 98 subjects without either COVID-19 or CAP, collected in two Italian hospitals. The system was tested on two independent cohorts, namely, 148 patients (COVID-19, CAP, or negative) collected by one of the two hospitals (independent testing I) and 820 COVID-19 patients collected by a multicenter study (independent testing II). On the training and cross-validation dataset, sensitivity, specificity, and area under the curve (AUC) were 0.91, 0.87, and 0.93 for COVID-19 versus negative subjects, 0.85, 0.82, and 0.94 for COVID-19 versus CAP. On the independent testing I, sensitivity, specificity, and AUC were 0.98, 0.88, and 0.98 for COVID-19 versus negative subjects, 0.97, 0.96, and 0.98 for COVID-19 versus CAP. On the independent testing II, the system correctly diagnosed 652 COVID-19 patients versus negative subjects (0.80 sensitivity) and correctly differentiated 674 COVID-19 versus CAP patients (0.82 sensitivity). This system appears promising for the diagnosis and differential diagnosis of COVID-19, showing its potential as a second opinion tool in conditions of the variable prevalence of different types of infectious pneumonia.

Differential diagnoses of acute ground-glass opacity in chest computed tomography: pictorial essay

Ground-glass opacity is a very frequent and unspecified finding in chest computed tomography. Therefore, it admits a wide range of differential diagnoses in the acute context, from viral pneumonias such as influenza virus, coronavirus disease 2019 and cytomegalovirus and even non-infectious lesions, such as vaping, pulmonary infarction, alveolar hemorrhage and pulmonary edema. For this diagnostic differentiation, ground glass must be correlated with other findings in imaging tests, with laboratory tests and with the patients' clinical condition. In the context of a pandemic, it is extremely important to remember the other pathologies with similar findings to coronavirus disease 2019 in the imaging exams.

Chest CT Imaging Features of Typical Covert COVID-19 Cases

Purpose: To analyze the chest CT imaging findings of patients with initial negative RT-PCR and to compare with the CT findings of the same sets of patients when the RT-PCR turned positive for SARS-CoV-2 a few days later. Materials and methods: A total of 32 patients (8 males and 24 females; 52.9±7years old) with COVID-19 from 27 January and 26 February 2020 were enrolled in this retrospective study. Clinical and radiological characteristics were analyzed. Results: The median period (25%, 75%) between initial symptoms and the first chest CT, the initial negative RT-PCR, the second CT and the positive RT-PCR were 7(4.25,11.75), 7(5,10.75), 15(11,23) and 14(10,22) days, respectively. Ground glass opacities was the most frequent CT findings at both the first and second CTs. Consolidation was more frequently observed on lower lobes, and more frequently detected during the second CT (64.0%) with positive RT-PCR than the first CT with initial negative RT-PCR (53.1%). The median of total lung severity score and the number of lobes affected had significant difference between twice chest CT (P=0.007 and P=0.011, respectively). Conclusion: In the first week of disease course, CT was sensitive to the COVID-19 with initial negative RT-PCR. Throat swab test turned positive while chest CT mostly demonstrated progression.

When pneumonia is not COVID-19

During the COVID-19 epidemic, the prevalence of the disease means that practically any lung opacity on an X-ray could represent pneumonia due to infection with SARS-CoV-2. Nevertheless, atypical radiologic findings add weight to negative microbiological or serological tests. Likewise, outside the epidemic wave and with the return of other respiratory diseases, radiologists can play an important role in decision making about diagnoses, treatment, or preventive measures (isolation), provided they know the key findings for entities that can simulate COVID-19 pneumonia. Unifocal opacities or opacities located in upper lung fields and predominant airway involvement, in addition to other key radiologic and clinical findings detailed in this paper, make it necessary to widen the spectrum of possible diagnoses.

When pneumonia is not COVID-19

During the COVID-19 epidemic, the prevalence of the disease means that practically any lung opacity on an X-ray could represent pneumonia due to infection with SARS-CoV-2. Nevertheless, atypical radiologic findings add weight to negative microbiological or serological tests. Likewise, outside the epidemic wave and with the return of other respiratory diseases, radiologists can play an important role in decision making about diagnoses, treatment, or preventive measures (isolation), provided they know the key findings for entities that can simulate COVID-19 pneumonia. Unifocal opacities or opacities located in upper lung fields and predominant airway involvement, in addition to other key radiologic and clinical findings detailed in this paper, make it necessary to widen the spectrum of possible diagnoses.

CO-RADS versus CT-SS scores in predicting severe COVID-19 patients: retrospective comparative study

Background

The role of CT in assessing and plotting viral pulmonary affection land marking is its potential among other investigation tools, and the aim of the study was to compare the ability of two different CT-based scoring systems in discriminating severe COVID-19 disease.

Results

Retrospective comparative study included 142 confirmed COVID-19 patients by real-time polymerase chain reaction (RT-PCR) test, with different degrees of disease (mild to severe), the data of patients collected from medical records, and patients with their first CT chest read for calculating CO-RADS and severity scoring system (CT-SS) score. The patients with severe COVID-19 disease were significantly older and had different comorbidities. The level of C-reactive protein, ESR, ferritin, and LDH were significantly higher in severe disease, P < 0.001. The ability of CT chest and its score bases (CT-SS and CO-RADS) were accurate in differentiation between mild/moderate and severe disease; AUC were 89% and 97%, respectively. The cutoff value of less than 7.5 and 4.5 for CT-SS and CO-RADS, respectively, can rule out severe COVID-19 by 90% and 97%, respectively.

Conclusions

CT chest play a segregate role in COVID-19 disease, add on an advantage in clinical data in triage, and highlight the decision of hospital admission.

Acute respiratory distress syndrome caused by varicella pneumonia in immunocompetent adult: Clinical case

Introduction

Varicella zona infection is a rare condition in immunocompetent adults. It can lead to severe and lethal complications including Varicella pneumonia that can rapidly progress to acute respiratory distress syndrome a rare and life-threatening situation.

Clinical case

A 63 years old man was admitted to the intensive care unit for pneumonia with generalized papulovesicular lesions. After investigations, the diagnosis of Varicella pneumonia complicated with acute respiratory distress syndrome was maintained and the patient was put on mechanical ventilation, and despite proper management (antiviral treatment; protective ventilation and prone position) the patient died 48 hours after his admission.

Conclusion

Despite its rarity, Varicella pneumonia can be a life-threatening situation in immunocompetent adults. The diagnosis must be evoked when the patient presented with respiratory manifestations with dermatologic lesions.

•

Varicella is a highly contagious disease caused by the initial infection with varicella-zoster virus (VZV).

•

The clinical symptomatology of Varicella infection is usually mild, however, it can manifest with serious complications including varicella pneumonia in immunocompromised patients.

•

In this paper we describe a rare clinical case of pnumonia varicella complicated with acute respiratory distress syndrom in immunocompetent adult.

RSNA Expert Consensus Statement on Reporting Chest CT Findings Related to COVID-19: Interobserver Agreement Between Chest Radiologists

PURPOSE

To assess the interobserver variability between chest radiologists in the interpretation of the Radiological Society of North America (RSNA) expert consensus statement reporting guidelines in patients with suspected coronavirus disease 2019 (COVID-19) pneumonia in a setting with limited reverse transcription polymerase chain reaction testing availability.

METHODS

Chest computed tomography (CT) studies in 303 consecutive patients with suspected COVID-19 were reviewed by 3 fellowship-trained chest radiologists. Cases were assigned an impression of typical, indeterminate, atypical, or negative for COVID-19 pneumonia according to the RSNA expert consensus statement reporting guidelines, and interobserver analysis was performed. Objective CT features associated with COVID-19 pneumonia and distribution of findings were recorded.

RESULTS

The Fleiss kappa for all observers was almost perfect for typical (0.815), atypical (0.806), and negative (0.962) COVID-19 appearances (P < .0001) and substantial (0.636) for indeterminate COVID-19 appearance (P < .0001). Using Cramer V analysis, there were very strong correlations between all radiologists' interpretations, statistically significant for all (typical, indeterminate, atypical, and negative) COVID-19 appearances (P < .001). Objective CT imaging findings were recorded in similar percentages of typical cases by all observers.

CONCLUSION

The RSNA expert consensus statement on reporting chest CT findings related to COVID-19 demonstrates substantial to almost perfect interobserver agreement among chest radiologists in a relatively large cohort of patients with clinically suspected COVID-19. It therefore serves as a reliable reference framework for radiologists to accurately communicate their level of suspicion based on the presence of evidence-based objective findings.

The spider web sign in Covid-19 pneumonia: An interesting case studied to resolution with computed tomography

Since the widespread of acute respiratory syndrome infection caused by Coronavirus-19, unenhanced computed tomography (CT) was considered a useful imaging tool commonly used in early diagnosis and monitoring of patients with complicated Covid-19 pneumonia. Many typical imaging features of this disease were described such as bilateral multilobar ground-glass opacity (GGO) with a prevalent peripheral or posterior distribution, mainly in the lower lobes, and sometimes consolidative opacities superimposed on GGO. As less common findings were mentioned septal thickening, bronchiectasis, pleural thickening, and subpleural involvement. Here we describe the case of a patient, with Covid-19 pneumonia, that had the spider web sign, a triangular or angular GGO in the subpleural lung, documented at CT.

Utility of visual coronary artery calcification on non-cardiac gated thoracic CT in predicting clinical severity and outcome in COVID-19

Background

Assessment of visual-coronary artery calcification on non-cardiac gated CT in COVID-19 patients could provide an objective approach to rapidly identify and triage clinically severe patients for early hospital admission to avert worse prognosis.

Purpose

To ascertain the role of semi-quantitative scoring in visual-coronary artery calcification score (V-CACS) for predicting the clinical severity and outcome in patients with COVID-19.

Materials and methods

With institutional review board approval this study included 67 COVID-19 confirmed patients who underwent non-cardiac gated CT chest in an inpatient setting. Two blinded radiologist (Radiologist-1 &2) assessed the V-CACS, CT Chest severity score (CT-SS). The clinical data including the requirement for oxygen support, assisted ventilation, ICU admission and outcome was assessed, and patients were clinically subdivided depending on clinical severity. Logistic regression analyses were performed to identify independent predictors. ROC curves analysis is performed for the assessment of performance and Pearson correlation were performed to looks for the associations.

Results

V-CACS cut off value of 3 (82.67% sensitivity and 54.55% specificity; AUC 0.75) and CT-SS with a cut off value of 21.5 (95.7% sensitivity and 63.6% specificity; AUC 0.87) are independent predictors for clinical severity and also the need for ICU admission or assisted ventilation. The pooling of both CT-SS and V-CACS (82.67% sensitivity and 86.4% specificity; AUC 0.92) are more reliable in terms of predicting the primary outcome of COVID-19 patients. On regression analysis, V-CACS and CT-SS are individual independent predictors of clinical severity in COVID-19 (Odds ratio, 1.72; 95% CI, 0.99–2.98; p = 0.05 and Odds ratio, 1.22; 95% CI, 1.08–1.39; p = 0.001 respectively). The area under the curve (AUC) for pooled V-CACS and CT-SS was 0.96 (95% CI 0.84–0.98) which correctly predicted 82.1% cases.

Conclusion

Logistic regression model using pooled Visual-Coronary artery calcification score and CT Chest severity score in non-cardiac gated CT can predict clinical severity and outcome in patients with COVID-19.

Correlation between CT findings and outcomes in 46 patients with coronavirus disease 2019

The aim of this study was to analyze initial chest computed tomography (CT) findings in COVID-19 pneumonia and identify features associated with poor prognosis. Patients with RT-PCR-confirmed COVID-19 infection were assigned to recovery group if they made a full recovery and to death group if they died within 2 months of hospitalization. Chest CT examinations for ground-glass opacity, crazy-paving pattern, consolidation, and fibrosis were scored by two reviewers. The total CT score comprised the sum of lung involvement (5 lobes, scores 1–5 for each lobe, range; 0, none; 25, maximum). 40 patients who recovered from COVID-19 and six patients who died were enrolled. The initial chest CTs showed 27 (58.7%) patients had ground-glass opacity, 19 (41.3%) had ground glass and consolidation, and 35 (76.1%) patients had crazy-paving pattern. None of the patients who died had fibrosis in contrast to six (15%) patients who recovered from COVID-19. Most patients had subpleural lesions (89.0%) as well as bilateral (87.0%) and lower (93.0%) lung lobe involvement. Diffuse lesions were present in four (67%) patients who succumbed to coronavirus but only one (2.5%) patient who recovered (p < 0.001). In the death group of patients, the total CT score was higher than that of the recovery group (p = 0.005). Patients in the death group had lower lymphocyte count and higher C-reactive protein than those in the recovery group (p = 0.011 and p = 0.041, respectively). A high CT score and diffuse distribution of lung lesions in COVID-19 are indicative of disease severity and short-term mortality.

A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19)

This study aims to explore and compare a novel deep learning-based quantification with the conventional semi-quantitative computed tomography (CT) scoring for the serial chest CT scans of COVID-19. 95 patients with confirmed COVID-19 and a total of 465 serial chest CT scans were involved, including 61 moderate patients (moderate group, 319 chest CT scans) and 34 severe patients (severe group, 146 chest CT scans). Conventional CT scoring and deep learning-based quantification were performed for all chest CT scans for two study goals: (1) Correlation between these two estimations; (2) Exploring the dynamic patterns using these two estimations between moderate and severe groups. The Spearman's correlation coefficient between these two estimation methods was 0.920 (p < 0.001). predicted pulmonary involvement (CT score and percent of pulmonary lesions calculated using deep learning-based quantification) increased more rapidly and reached a higher peak on 23rd days from symptom onset in severe group, which reached a peak on 18th days in moderate group with faster absorption of the lesions. The deep learning-based quantification for COVID-19 showed a good correlation with the conventional CT scoring and demonstrated a potential benefit in the estimation of disease severities of COVID-19.

Deep learning for differentiating novel coronavirus pneumonia and influenza pneumonia

Background

Chest computed tomography (CT) has been found to have high sensitivity in diagnosing novel coronavirus pneumonia (NCP) at the early stage, giving it an advantage over nucleic acid detection during the current pandemic. In this study, we aimed to develop and validate an integrated deep learning framework on chest CT images for the automatic detection of NCP, focusing particularly on differentiating NCP from influenza pneumonia (IP).

Methods

A total of 148 confirmed NCP patients [80 male; median age, 51.5 years; interquartile range (IQR), 42.5–63.0 years] treated in 4 NCP designated hospitals between January 11, 2020 and February 23, 2020 were retrospectively enrolled as a training cohort, along with 194 confirmed IP patients (112 males; median age, 65.0 years; IQR, 55.0–78.0 years) treated in 5 hospitals from May 2015 to February 2020. An external validation set comprising 57 NCP patients and 50 IP patients from 8 hospitals was also enrolled. Two deep learning schemes (the Trinary scheme and the Plain scheme) were developed and compared using receiver operating characteristic (ROC) curves.

Results

Of the NCP lesions, 96.6% were >1 cm and 76.8% were of a density <−500 Hu, indicating them to have less consolidation than IP lesions, which had nodules ranging from 5–10 mm. The Trinary scheme accurately distinguished NCP from IP lesions, with an area under the curve (AUC) of 0.93. For patient-level classification in the external validation set, the Trinary scheme outperformed the Plain scheme (AUC: 0.87 vs. 0.71) and achieved human specialist-level performance.

Conclusions

Our study has potentially provided an accurate tool on chest CT for early diagnosis of NCP with high transferability and showed high efficiency in differentiating between NCP and IP; these findings could help to reduce misdiagnosis and contain the pandemic transmission.

Childhood interstitial lung disease: A case-based review of the imaging findings

Childhood interstitial lung disease (chILD) consists of a large, heterogeneous group of individually rare disorders. chILD demonstrates major differences in disease etiology, natural history, and management when compared with the adult group. It occurs primarily secondary to an underlying developmental or genetic abnormality affecting the growth and maturity of the pediatric lung. They present with different clinical, radiologic, and pathologic features. In this pictorial review article, we will divide chILD into those more prevalent in infancy and those not specific to infancy. We will use a case based approach to discuss relevant imaging findings including modalities such as radiograph and computed tomography in a wide variety of pathologies.