A Comparison of Clinical and Chest CT Findings in Patients With Influenza A (H1N1) Virus Infection and Coronavirus Disease (COVID-19). AJR Am J Roentgenol. 2020;215:1065-1071. [PMID: 32452731 DOI: 10.2214/ajr.20.23214]

Improving Respiratory Infection Diagnosis with Deep Learning and Combinatorial Fusion: A Two-Stage Approach Using Chest X-ray Imaging

The challenges of respiratory infections persist as a global health crisis, placing substantial stress on healthcare infrastructures and necessitating ongoing investigation into efficacious treatment modalities. The persistent challenge of respiratory infections, including COVID-19, underscores the critical need for enhanced diagnostic methodologies to support early treatment interventions. This study introduces an innovative two-stage data analytics framework that leverages deep learning algorithms through a strategic combinatorial fusion technique, aimed at refining the accuracy of early-stage diagnosis of such infections. Utilizing a comprehensive dataset compiled from publicly available lung X-ray images, the research employs advanced pre-trained deep learning models to navigate the complexities of disease classification, addressing inherent data imbalances through methodical validation processes. The core contribution of this work lies in its novel application of combinatorial fusion, integrating select models to significantly elevate diagnostic precision. This approach not only showcases the adaptability and strength of deep learning in navigating the intricacies of medical imaging but also marks a significant step forward in the utilization of artificial intelligence to improve outcomes in healthcare diagnostics. The study's findings illuminate the path toward leveraging technological advancements in enhancing diagnostic accuracies, ultimately contributing to the timely and effective treatment of respiratory diseases.

A Comparison of the Epidemiological Characteristics Between Influenza and COVID-19 Patients: A Retrospective, Observational Cohort Study

Background and objective It is crucial to make early differentiation between coronavirus disease 2019 (COVID-19) and seasonal influenza infections at the time of a patient's presentation to the emergency department (ED). In light of this, this study aimed to identify key epidemiological, initial laboratory, and radiological differences that would enable early recognition during co-circulation. Methods This was a retrospective, observational cohort study. All adult patients presenting to our ED at the Watford General Hospital, UK, with a laboratory-confirmed diagnosis of COVID-19 (2019/20) or influenza (2018/19) infection were included in this study. Demographic, laboratory, and radiological data were collected. Binary logistic regression was employed to determine features associated with COVID-19 infection rather than influenza. Results Chest radiographs suggestive of viral pneumonitis and older age (≥80 years) were associated with increased odds of having COVID-19 [odds ratio (OR): 47.00, 95% confidence interval (CI): 21.63-102.13 and OR: 64.85, 95% CI: 19.96-210.69 respectively]. Low eosinophils (<0.02 x 109/L) were found to increase the odds of COVID-19 (OR: 2.12, 95% CI: 1.44-3.10, p<0.001). Conclusions Gaining awareness about the epidemiological, biological, and radiologic presentation of influenza-like illness can be useful for clinicians in ED to differentiate between COVID-19 and influenza. This study showed that older age, eosinopenia, and radiographic evidence of viral pneumonitis significantly increase the odds of having COVID-19 compared to influenza. Further research is needed to determine if these findings are affected by acquired or natural immunity.

Current and Emerging Knowledge in COVID-19

COVID-19 has emerged as a pandemic leading to a global public health crisis of unprecedented morbidity. A comprehensive insight into the imaging of COVID-19 has enabled early diagnosis, stratification of disease severity, and identification of potential sequelae. The evolution of COVID-19 can be divided into early infectious, pulmonary, and hyperinflammatory phases. Clinical features, imaging features, and management are different among the three phases. In the early stage, peripheral ground-glass opacities are predominant CT findings, and therapy directly targeting SARS-CoV-2 is effective. In the later stage, organizing pneumonia or diffuse alveolar damage pattern are predominant CT findings and anti-inflammatory therapies are more beneficial. The risk of severe disease or hospitalization is lower in breakthrough or Omicron variant infection compared with nonimmunized or Delta variant infections. The protection rates of the fourth dose of mRNA vaccination were 34% and 67% against overall infection and hospitalizations for severe illness, respectively. After acute COVID-19 pneumonia, most residual CT abnormalities gradually decreased in extent, but they may remain as linear or multifocal reticular or cystic lesions. Advanced insights into the pathophysiologic and imaging features of COVID-19 along with vaccine benefits have improved patient care, but emerging knowledge of post-COVID-19 condition, or long COVID, also presents radiology with new challenges.

Community-Acquired Pneumonia: Postpandemic, Not Post-COVID-19

The coronavirus disease 2019 (COVID-19) pandemic upended our approach to imaging community-acquired pneumonia, and this will alter our diagnostic algorithms for years to come. In light of these changes, it is worthwhile to consider several postpandemic scenarios of community-acquired pneumonia: (1) patient with pneumonia and recent positive COVID-19 testing; (2) patient with air space opacities and history of prior COVID-19 pneumonia (weeks earlier); (3) multifocal pneumonia with negative or unknown COVID-19 status; and (4) lobar or sublobar pneumonia with negative or unknown COVID-19 status. In the setting of positive COVID-19 testing and typical radiologic findings, the diagnosis of COVID-19 pneumonia is generally secure. The diagnosis prompts vigilance for thromboembolic disease acutely and, in severely ill patients, for invasive fungal disease. Persistent or recurrent air space opacities following COVID-19 infection may more often represent organizing pneumonia than secondary infection. When COVID-19 status is unknown or negative, widespread airway-centric disease suggests infection with mycoplasma, Haemophilus influenzae, or several respiratory viruses. Necrotizing pneumonia favors infection with pneumococcus, Staphylococcus, Klebsiella, and anaerobes. Lobar or sublobar pneumonia will continue to suggest the diagnosis of pneumococcus or consideration of other pathogens in the setting of local outbreaks. A positive COVID-19 test accompanied by these imaging patterns may suggest coinfection with one of the above pathogens, or when the prevalence of COVID-19 is very low, a false positive COVID-19 test. Clinicians may still proceed with testing for COVID-19 when radiologic patterns are atypical for COVID-19, dependent on the patient's exposure history and the local epidemiology of the virus.

Comparative analysis of elderly hospitalized patients with COVID-19 or influenza A H1N1 virus infections

OBJECTIVES

This study aimed to investigate the differences between elderly patients hospitalized with COVID-19 or influenza A H1N1 virus infections.

METHODS

We contrasted two absolute groups of patients (age ≥60 years) infected with either COVID-19 (n = 222) or influenza A H1N1 virus infections (n = 96). Propensity score matching was used to reduce the imbalance between the two matched groups. The clinical features, imaging presentations, therapies, and prognosis data were compared between the two groups.

RESULTS

The patients with influenza showed higher proportions of cough, expectoration, fatigue, and shortness of breath. Higher counts of lymphocytes, hemoglobin, and creatine kinase and lower counts of white blood cells, neutrophils, blood urea nitrogen, and C-reactive protein were found in the patients with COVID-19. Regarding the imaging characteristics, bilateral pneumonia was the most abnormal pattern in the two groups of patients. The incidence of acute respiratory distress syndrome or death was lower among the patients with COVID-19.

CONCLUSION

The clinical manifestations of patients with COVID-19 are more concealed than those of patients with influenza. Fewer symptoms of sputum production, fatigue, and shortness of breath, combined with lower counts of white blood cells, neutrophils, and C-reactive protein are the possible predictive factors of COVID-19 among elderly patients.

Bronchiectasis and COVID-19 infection: a two-way street

ABSTRACT

Bronchiectasis (BE) has been linked to past viral infections such as influenza, measles, or adenovirus. Two years ago, a new pandemic viral infection severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) broke out and it still persists today, and a significant proportion of surviving patients have radiological and clinical sequelae, including BE. Our aim was to thoroughly review the information available in the literature on the bidirectional relationship between SARS-CoV-2 infection and the development of BE, as well as the impact of this infection on patients already suffering from BE. Available information indicates that only a small percentage of patients in the acute phase of coronavirus disease 2019 (COVID-19) pneumonia develop BE, although the latter is recognized as one of the radiological sequelae of COVID-19 pneumonia, especially when it is caused by traction. The severity of the initial pneumonia is the main risk factor for the development of future BE, but during the COVID-19 pandemic, exacerbations in BE patients were reduced by approximately 50%. Finally, the impact of BE on the prognosis of patients with COVID-19 pneumonia is not yet known.

Infectivity versus fatality of SARS-CoV-2 mutations and influenza

OBJECTIVES

Because of the spread of the Omicron variant, many countries have experienced COVID-19 case numbers unseen since the start of the pandemic. We aimed to compare the epidemiological characteristics of Omicron with previous variants and different strains of influenza to provide context for public health responses.

METHODS

We developed transmission models for SARS-CoV-2 variants and influenza, in which transmission, death, and vaccination rates were taken to be time-varying. We fit our model based on publicly available data in South Africa, the United States, and Canada. We used this model to evaluate the relative transmissibility and mortality of Omicron compared with previous variants and influenza.

RESULTS

We found that Omicron is more transmissible and less fatal than both seasonal and 2009 H1N1 influenza and the Delta variant; these characteristics make Omicron epidemiologically more similar to influenza than it is to Delta. We estimate that as of February 7, 2022, booster doses have prevented 4.29×107 and 1.14×106 Omicron infections in the United States and Canada, respectively.

CONCLUSION

Our findings indicate that the high infectivity of Omicron will keep COVID-19 endemic, similar to influenza. However, because of Omicron's lower fatality rate, our work suggests that human populations living with SARS-CoV-2 are most likely.

Black Fungus and beyond: COVID-19 associated infections

Globally, many hospitalized COVID-19 patients can experience an unexpected acute change in status, prompting rapid and expert clinical assessment. Superimposed infections can be a significant cause of clinical and radiologic deviations in this patient population, further worsening clinical outcome and muddling the differential diagnosis. As thrombotic, inflammatory, and medication-induced complications can also trigger an acute change in COVID-19 patient status, imaging early and often plays a vital role in distinguishing the cause of patient decline and monitoring patient outcome. While the common radiologic findings of COVID-19 infection are now widely reported, little is known about the clinical manifestations and imaging findings of superimposed infection. By discussing case studies of patients who developed bacterial, fungal, parasitic, and viral co-infections and identifying the most frequently reported imaging findings of superimposed infections, physicians will be more familiar with common infectious presentations and initiate a directed workup sooner. Ultimately, any abrupt changes in the expected COVID-19 imaging presentation, such as the presence of new consolidations or cavitation, should prompt further workup to exclude superimposed opportunistic infection.

Thoracic Computed Tomography to Assess ARDS and COVID-19 Lungs

This review was designed to discuss the role of thoracic-computed tomography (CT) in the evaluation and treatment of patients with ARDS and COVID-19 lung disease. Non-aerated lungs characterize the ARDS lungs, compared to normal lungs in the lowermost lung regions, compressive atelectasis. Heterogenous ARDS lungs have a tomographic vertical gradient characterized by progressively more aerated lung tissues from the gravity-dependent to gravity-independent lungs levels. The application of positive pressure ventilation to these heterogeneous ARDS lungs provides some areas of high shear stress, others of tidal hyperdistension or tidal recruitment that increases the chances of appearance and perpetuation of ventilator-induced lung injury. Other than helping to the correct diagnosis of ARDS, thoracic-computed tomography can help to the adjustments of PEEP, ideal tidal volume, and a better choice of patient position during invasive mechanical ventilation. Thoracic tomography can also help detect possible intra-thoracic complications and in the follow-up of the ARDS patients’ evolution during their hospital stay. In COVID-19 patients, thoracic-computed tomography was the most sensitive imaging technique for diagnosing pulmonary involvement. The most common finding is diffuse pulmonary infiltrates, ranging from ground-glass opacities to parenchymal consolidations, especially in the lower portions of the lungs’ periphery. Tomographic lung volume loss was associated with an increased risk for oxygenation support and patient intubation and the use of invasive mechanical ventilation. Pulmonary dual-energy angio-tomography in COVID-19 patients showed a significant number of pulmonary ischemic areas even in the absence of visible pulmonary arterial thrombosis, which may reflect micro-thrombosis associated with COVID-19 pneumonia. A greater thoracic tomography severity score in ARDS was independently related to poor outcomes.

A deep learning-based approach for diagnosing COVID-19 on chest x-ray images, and a test study with clinical experts

Pneumonia is among the common symptoms of the virus that causes COVID-19, which has turned into a worldwide pandemic. It is possible to diagnose pneumonia by examining chest radiographs. Chest x-ray (CXR) is a fast, low-cost, and practical method widely used in this field. The fact that different pathogens other than COVID-19 also cause pneumonia and the radiographic images of all are similar make it difficult to detect the source of the disease. In this study, automatic detection of COVID-19 cases over CXR images was tried to be performed using convolutional neural network (CNN), a deep learning technique. Classifications were carried out using six different architectures on the dataset consisting of 15,153 images of three different types: healthy, COVID-19, and other viral-induced pneumonia. In the classifications performed with five different state-of-art models, ResNet18, GoogLeNet, AlexNet, VGG16, and DenseNet161, and a minimal CNN architecture specific to this study, the most successful result was obtained with the ResNet18 architecture as 99.25% accuracy. Although the minimal CNN model developed for this study has a simpler structure, it was observed that it has a success to compete with more complex models. The performances of the models used in this study were compared with similar studies in the literature and it was revealed that they generally achieved higher success. The model with the highest success was transformed into a test application, tested by 10 volunteer clinicians, and it was concluded that it provides 99.06% accuracy in practical use. This result reveals that the conducted study can play the role of a successful decision support system for experts.

Exploring New Characteristics: Using Deep Learning and 3D Reconstruction to Compare the Original COVID-19 and Its Delta Variant Based on Chest CT

Purpose: Computer-aided diagnostic methods were used to compare the characteristics of the Original COVID-19 and its Delta Variant.

Methods: This was a retrospective study. A deep learning segmentation model was applied to segment lungs and infections in CT. Three-dimensional (3D) reconstruction was used to create 3D models of the patient’s lungs and infections. A stereoscopic segmentation method was proposed, which can subdivide the 3D lung into five lobes and 18 segments. An expert-based CT scoring system was improved and artificial intelligence was used to automatically score instead of visual score. Non-linear regression and quantitative analysis were used to analyze the dynamic changes in the percentages of infection (POI).

Results: The POI in the five lung lobes of all patients were calculated and converted into CT scores. The CT scores of Original COVID-19 patients and Delta Variant patients since the onset of initial symptoms were fitted over time, respectively. The peak was found to occur on day 11 in Original COVID-19 patients and on day 15 in Delta Variant patients. The time course of lung changes in CT of Delta Variant patients was redetermined as early stage (0–3 days), progressive and peak stage (4–16 days), and absorption stage (17–42 days). The first RT-PCR negative time in Original COVID-19 patients appeared earlier than in Delta Variant patients (22 [17–30] vs. 39 [31–44], p < 0.001). Delta Variant patients had more re-detectable positive RT-PCR test results than Original COVID-19 patients after the first negative RT-PCR time (30.5% vs. 17.1%). In the early stage, CT scores in the right lower lobe were significantly different (Delta Variant vs. Original COVID-19, 0.8 ± 0.6 vs. 1.3 ± 0.6, p = 0.039). In the absorption stage, CT scores of the right middle lobes were significantly different (Delta Variant vs. Original COVID-19, 0.6 ± 0.7 vs. 0.3 ± 0.4, p = 0.012). The left and the right lower lobes contributed most to lung involvement at any given time.

Conclusion: Compared with the Original COVID-19, the Delta Variant has a longer lung change duration, more re-detectable positive RT-PCR test results, different locations of pneumonia, and more lesions in the early stage, and the peak of infection occurred later.

Clinical data to be used as a foundation to combat Covid-19 vaccine hesitancy

The coronavirus has become the paramount subject in peoples' lives, affecting and disrupting virtually every aspect of society, as the pandemic casts a shadow over the world. The facts, myths, and conspiracy theories centered on the Covid-19 pandemic have dominated social media accounts, local and national newspapers, as well as television programs. Strategies need to be evolved to counter Covid-19 vaccine hesitancy and mitigate health disparities in at-risk populations. Overcoming misinformation and distrust will require an interdisciplinary approach to deal with Covid-19. The purpose of this review is to offer a factual basis to all healthcare providers to assist in framing strategies to mitigate vaccine hesitancy and achieve herd immunity to combat the deadly Covid-19 pandemic. First an overview of the discovery of the viruses and their molecular structures will be presented. Secondly, a historical perspective is offered, comparing the differences between the 1918 flu pandemic and the current covid-19 pandemic. Lastly, an overview for proposed techniques and methods to counter and or mitigate covid-19 vaccine misinformation that may be used by an interdisciplinary team will be offered narratively and graphically.

Pneumonie à Sars-CoV-2 : broncho-pneumonie ou vasculopathie ? Focus sur le signe scanographique du « vaisseau élargi » et corrélations radio-histologiques

Introduction

La pandémie de Sars-CoV-2 évolue depuis un an et, à ce jour, la physiopathologie des lésions engendrées par cette atteinte virale n’est que partiellement élucidée. Le scanner thoracique est un outil diagnostique et pronostique essentiel de la prise en charge des patients et les lésions typiques de la pneumonie à Sars-CoV-2 sont à présent bien établies. Cependant, certaines anomalies vasculaires rencontrées chez la plupart des patients, qui se traduisent par un aspect épaissi et irrégulier des vaisseaux pulmonaires au sein des zones pathologiques, sont sous-estimées et peu connues des radiologues.

Données récentes

Les études histologiques soulignent la prépondérance des atteintes pariétales vasculaires au niveau pulmonaire qui peuvent être corrélées aux anomalies scanographiques. Ces dernières permettent d’orienter le diagnostic en cas de doute biologique ou de progression des lésions en verre dépoli. Elles suggèrent que l’atteinte alvéolo-interstitielle, sans anomalie bronchique ou bronchiolaire associée, pourrait être secondaire aux lésions vasculaires. Enfin, des études complémentaires sont nécessaires pour rechercher un éventuel intérêt pronostique de la quantification de ces lésions.

Conclusion

Ce travail illustre les corrélations radio-histologiques des atteintes vasculaires et pulmonaires du Sars-Cov-2 et propose une iconographie didactique des principales lésions rencontrées.

COVID-19 pandemic in flu season. Chest computed tomography - what we know so far

Chest computed tomography (CT) is proven to have high sensitivity in COVID-19 diagnosis. It is available in most emergency wards, and in contrast to polymerase chain reaction (PCR) it can be obtained in several minutes. However, its imaging features change during the course of the disease and overlap with other viral pneumonias, including influenza pneumonia. In this brief analysis we review the recent literature about chest CT features, useful radiological scales, and COVID-19 differentiation with other viral infections.

Registration of computed tomography images of a lung infected with COVID-19 based in the new meta-heuristic algorithm HPSGWO

Computed tomography (CT) helps the radiologist in the rapid and correct detection of a person infected with the coronavirus disease 2019 (COVID-19), and this by showing the presence of the ground-glass opacity in the lung of with the virus. Tracking the evolution of the spread of the ground-glass opacity (GGO) in the lung of the person infected with the virus needs to study more than one image in different times. The various CT images must be registration to identify the evolution of the ground glass in the lung and to facilitate the study and identification of the virus. Due to the process of registration images is essentially an improvement problem, we present in this paper a new HPSGWO algorithm for registration CT images of a lung infected with the COVID-19. This algorithm is a hybridization of the two algorithms Particle swarm optimization (PSO) and Grey wolf optimizer (GWO). The simulation results obtained after applying the algorithm to the test images show that the proposed approach achieved high-precision and robust registration compared to other methods such as GWO, PSO, Firefly Algorithm (FA), and Crow Searcha Algorithms (CSA).

Place des virus respiratoires dans les pneumonies aiguës communautaires de l'adulte : quels changements depuis la Covid-19 ?

L’émergence du SARS-CoV-2 a renforcé l'intérêt pour la place des virus respiratoires, dans les pneumonies aiguës communautaires, en mettant en exergue de nombreux points encore mal connus tels que la part des infections asymptomatiques, les interactions entre virus respiratoires et pathogènes non viraux, leurs périodes d'incubation, leur pathogénicité ou encore la durée d'excrétion variable.

La présentation clinique et radiologique des pneumonies aiguës communautaires ne permet pas toujours de distinguer l'origine virale de l'origine bactérienne.

L'absence de réelle conséquence thérapeutique semble un frein à l'utilisation des PCR multiplex dans la pratique quotidienne. Toutefois, l'amélioration en termes de délai de rendu des résultats et du nombre de pathogènes inclus dans les panels, ainsi que l'accumulation récente de données épidémiologiques et cliniques, devraient aider à rationaliser l'utilisation de ces tests, faciliter l'interprétation de leurs résultats et guider l'utilisation des molécules antivirales en développement.

Comparison of Clinical Characteristics for Distinguishing COVID-19 From Influenza During the Early Stages in Guangdong, China

Background: To explore the differences in clinical manifestations and infection marker determination for early diagnosis of coronavirus disease-2019 (COVID-19) and influenza (A and B).

Methods: A hospital-based retrospective cohort study was designed. Patients with COVID-19 and inpatients with influenza at a sentinel surveillance hospital were recruited. Demographic data, medical history, laboratory findings, and radiographic characteristics were summarized and compared between the two groups. The chi-square test or Fisher's exact test was used for categorical variables, and Kruskal–Wallis H-test was used for continuous variables in each group. Receiver operating characteristic curve (ROC) was used to differentiate the intergroup characteristics. The Cox proportional hazards model was used to analyze the predisposing factors.

Results: About 23 patients with COVID-19 and 74 patients with influenza were included in this study. Patients with influenza exhibited more symptoms of cough and sputum production than COVID-19 (p < 0.05). CT showed that consolidation and pleural effusion were more common in influenza than COVID-19 (p < 0.05). Subgroup analysis showed that patients with influenza had high values of infection and coagulation function markers, but low values of blood routine and biochemical test markers than patients with COVID-19 (mild or moderate groups) (p < 0.05). In patients with COVID-19, the ROC analysis showed positive predictions of albumin and hematocrit, but negative predictions of C-reactive protein (CRP), procalcitonin (PCT), lactate dehydrogenase (LDH), hydroxybutyrate dehydrogenase (HBDH), and erythrocyte sedimentation rate. Multivariate analysis revealed that influenza might associate with risk of elevated CRP, PCT, and LDH, whereas COVID-19 might associated with high HBDH.

Conclusion: Patients with influenza had more obvious clinical symptoms but less common consolidation lesions and pleural effusion than those with COVID-19. These findings suggested that influenza likely presents with stronger inflammatory reactions than COVID-19, which provides some insights into the pathogenesis of these two contagious respiratory illnesses.

Imaging of Pulmonary Superinfections and Co-Infections in COVID-19

New challenges in imaging and management of COVID-19 pneumonia emerge as the pandemic continues across the globe. These arise not only due to the COVID-19 pneumonia but also related to various superinfections and co-infections. Limited use of bronchoscopic and other aerosol generating procedures to obtain representative lower respiratory samples from these patient groups for accurate identification of organism, increases the responsibility of radiologists in suggesting the most likely cause of secondary infection. Imaging features of many of these infections overlap with features of COVID-19 pneumonia. In this review, we highlight imaging findings that can aid in the diagnosis of superinfections and co-infections in patients with COVID-19 pneumonia, and also help in predicting the likely causative organism.

CT Examinations for COVID-19: A Systematic Review of Protocols, Radiation Dose, and Numbers Needed to Diagnose and Predict

Purpose

Although chest CT has been discussed as a first-line test for coronavirus disease 2019 (COVID-19), little research has explored the implications of CT exposure in the population. To review chest CT protocols and radiation doses in COVID-19 publications and explore the number needed to diagnose (NND) and the number needed to predict (NNP) if CT is used as a first-line test.

Materials and Methods

We searched nine highly cited radiology journals to identify studies discussing the CT-based diagnosis of COVID-19 pneumonia. Study-level information on the CT protocol and radiation dose was collected, and the doses were compared with each national diagnostic reference level (DRL). The NND and NNP, which depends on the test positive rate (TPR), were calculated, given a CT sensitivity of 94% (95% confidence interval [CI]: 91%-96%) and specificity of 37% (95% CI: 26%-50%), and applied to the early outbreak in Wuhan, New York, and Italy.

Results

From 86 studies, the CT protocol and radiation dose were reported in 81 (94.2%) and 17 studies (19.8%), respectively. Low-dose chest CT was used more than twice as often as standard-dose chest CT (39.5% vs.18.6%), while the remaining studies (44.2%) did not provide relevant information. The radiation doses were lower than the national DRLs in 15 of the 17 studies (88.2%) that reported doses. The NND was 3.2 scans (95% CI: 2.2-6.0). The NNPs at TPRs of 50%, 25%, 10%, and 5% were 2.2, 3.6, 8.0, 15.5 scans, respectively. In Wuhan, 35418 (TPR, 58%; 95% CI: 27710-56755) to 44840 (TPR, 38%; 95% CI: 35161-68164) individuals were estimated to have undergone CT examinations to diagnose 17365 patients. During the early surge in New York and Italy, daily NNDs changed up to 5.4 and 10.9 times, respectively, within 10 weeks.

Conclusion

Low-dose CT protocols were described in less than half of COVID-19 publications, and radiation doses were frequently lacking. The number of populations involved in a first-line diagnostic CT test could vary dynamically according to daily TPR; therefore, caution is required in future planning.

COVID-19 pneumonia and its lookalikes: How radiologists perform in differentiating atypical pneumonias

Purpose

To examine the performance of radiologists in differentiating COVID-19 from non-COVID-19 atypical pneumonia and to perform an analysis of CT patterns in a study cohort including viral, fungal and atypical bacterial pathogens.

Methods

Patients with positive RT-PCR tests for COVID-19 pneumonia (n = 90) and non-COVID-19 atypical pneumonia (n = 294) were retrospectively included. Five radiologists, blinded to the pathogen test results, assessed the CT scans and classified them as COVID-19 or non-COVID-19 pneumonia. For both groups specific CT features were recorded and a multivariate logistic regression model was used to calculate their ability to predict COVID-19 pneumonia.

Results

The radiologists differentiated between COVID-19 and non-COVID-19 pneumonia with an overall accuracy, sensitivity, and specificity of 88% ± 4 (SD), 79% ± 6 (SD), and 90% ± 6 (SD), respectively. The percentage of correct ratings was lower in the early and late stage of COVID-19 pneumonia compared to the progressive and peak stage (68 and 71% vs 85 and 89%). The variables associated with the most increased risk of COVID-19 pneumonia were band like subpleural opacities (OR 5.55, p < 0.001), vascular enlargement (OR 2.63, p = 0.071), and subpleural curvilinear lines (OR 2.52, p = 0.021). Bronchial wall thickening and centrilobular nodules were associated with decreased risk of COVID-19 pneumonia with OR of 0.30 (p = 0.013) and 0.10 (p < 0.001), respectively.

Conclusions

Radiologists can differentiate between COVID-19 and non-COVID-19 atypical pneumonias at chest CT with high overall accuracy, although a lower performance was observed in the early and late stage of COVID 19 pneumonia. Specific CT features might help to make the correct diagnosis.

Differentiation of Chest CT Findings Between Influenza Pneumonia and COVID-19: Interobserver Agreement Between Radiologists

OBJECTIVES

To investigate the chest CT and clinical characteristics of COVID-19 pneumonia and H1N1 influenza, and explore the radiologist diagnosis differences between COVID-19 and influenza.

MATERIALS AND METHODS

This cross-sectional study included a total of 43 COVID-19-confirmed patients (24 men and 19 women, 49.90 ± 18.70 years) and 41 influenza-confirmed patients (17 men and 24 women, 61.53 ± 19.50 years). Afterwards, the chest CT findings were recorded and 3 radiologists recorded their diagnoses of COVID-19 or of H1N1 influenza based on the CT findings.

RESULTS

The most frequent clinical symptom in patients with COVID-19 and H1N1 pneumonia were dyspnea (96.6%) and cough (62.5%), respectively. The CT findings showed that the COVID-19 group was characterized by GGO (88.1%), while the influenza group had features such as GGO (68.4%) and consolidation (66.7%). Compared to the influenza group, the COVID-19 group was more likely to have GGO (88.1% vs. 68.4%, p = 0.032), subpleural sparing (69.0% vs. 7.7%, p <0.001) and subpleural band (50.0% vs. 20.5%, p = 0.006), but less likely to have pleural effusion (4.8% vs. 33.3%, p = 0.001). The agreement rate between the 3 radiologists was 65.8%.

CONCLUSION

Considering similarities of respiratory infections especially H1N1 and COVID-19, it is essential to introduce some clinical and para clinical modalities to help differentiating them. In our study we extracted some lung CT scan findings from patients suspected to COVID-19 as a newly diagnosed infection comparing with influenza pneumonia patients.

Chest CT findings in patients with coronavirus disease 2019 (COVID-19): a comprehensive review

The objective of this review was to summarize the most pertinent CT imaging findings in patients with coronavirus disease 2019 (COVID-19). A literature search retrieved eligible studies in PubMed, EMBASE, Cochrane Library and Web of Science up to June 1, 2020. A comprehensive review of publications of the Chinese Medical Association about COVID-19 was also performed. A total of 84 articles with more than 5340 participants were included and reviewed. Chest CT comprised 92.61% of abnormal CT findings overall. Compared with real-time polymerase chain reaction result, CT findings has a sensitivity of 96.14% but a low specificity of 40.48% in diagnosing COVID-19. Ground glass opacity (GGO), pure (57.31%) or mixed with consolidation (41.51%) were the most common CT features with a majority of bilateral (80.32%) and peripheral (66.21%) lung involvement. The opacity might associate with other imaging features, including air bronchogram (41.07%), vascular enlargement (54.33%), bronchial wall thickening (19.12%), crazy-paving pattern (27.55%), interlobular septal thickening (42.48%), halo sign (25.48%), reverse halo sign (12.29%), bronchiectasis (32.44%), and pulmonary fibrosis (26.22%). Other accompanying signs including pleural effusion, lymphadenopathy and pericardial effusion were rare, but pleural thickening was common. The younger or early stage patients tended to have more GGOs, while extensive/multilobar involvement with consolidation was prevalent in the older or severe population. Children with COVID-19 showed significantly lower incidences of some ancillary findings than those of adults and showed a better performance on CT during follow up. Follow-up CT showed GGO lesions gradually decreased, and the consolidation lesions first increased and then remained relatively stable at 6-13 days, and then absorbed and fibrosis increased after 14 days. Chest CT imaging is an important component in the diagnosis, staging, disease progression and follow-up of patients with COVID-19.

Triage Modeling for Differential Diagnosis Between COVID-19 and Human Influenza A Pneumonia: Classification and Regression Tree Analysis

Background: The coronavirus disease 2019 (COVID-19) pandemic has lasted much longer than an influenza season, but the main signs, symptoms, and some imaging findings are similar in COVID-19 and influenza patients. The aim of the current study was to construct an accurate and robust model for initial screening and differential diagnosis of COVID-19 and influenza A.

Methods: All patients in the study were diagnosed at Fuyang No. 2 People's Hospital, and they included 151 with COVID-19 and 155 with influenza A. The patients were randomly assigned to training set or a testing set at a 4:1 ratio. Predictor variables were selected based on importance, assessed by random forest algorithms, and analyzed to develop classification and regression tree models.

Results: In the optimal model A, the best single predictor of COVID-19 patients was a normal or high level of low-density lipoprotein cholesterol, followed by low level of creatine kinase, then the presence of <3 respiratory symptoms, then a highest temperature on the first day of admission <38°C. In the suboptimal model B, the best single predictor of COVID-19 was a low eosinophil count, then a normal monocyte ratio, then a normal hematocrit value, then a highest temperature on the first day of admission of <37°C, then a complete lack of respiratory symptoms.

Conclusions: The two models provide clinicians with a rapid triage tool. The optimal model can be used to developed countries/regions and major hospitals, and the suboptimal model can be used in underdeveloped regions and small hospitals.

Pulmonary imaging in coronavirus disease 2019 (COVID-19): a series of 140 Latin American children

BACKGROUND

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which resulted in the worldwide coronavirus disease 2019 (COVID-19) pandemic of 2020, has particularly affected Latin America.

OBJECTIVE

The purpose of the study was to analyze the imaging findings of pulmonary COVID-19 in a large pediatric series.

MATERIALS AND METHODS

Children with SARS-CoV-2 infection confirmed by either quantitative reverse transcription-polymerase chain reaction from nasopharyngeal swabs or presence of circulating immunoglobulin M (IgM) antibodies and who underwent chest radiograph or CT or both were included in this retrospective multicenter study. Three pediatric radiologists independently reviewed radiographs and CTs to identify the presence, localization, distribution and extension of pulmonary lesions.

RESULTS

We included 140 children (71 female; median age 6.3 years, interquartile range 1.6-12.1 years) in the study. Peribronchial thickening (93%), ground-glass opacities (79%) and vascular engorgement (63%) were the most frequent findings on 131 radiographs. Ground-glass opacities (91%), vascular engorgement (84%) and peribronchial thickening (72%) were the most frequent findings on 32 CTs. Peribronchial thickening (100%), ground-glass opacities (83%) and pulmonary vascular engorgement (79%) were common radiograph findings in asymptomatic children (n=25). Ground-glass opacity and consolidation were significantly higher in children who needed intensive care admission or died (92% and 48%), in contrast with children with a favorable outcome (71% and 24%, respectively; P<0.05).

CONCLUSION

Asymptomatic children and those with mild symptoms of COVID-19 showed mainly peribronchial thickening, ground-glass opacities and pulmonary vascular engorgement on radiographs. Ground-glass opacity and consolidation were more common in children who required intensive care admission or died.

Computational pathology reveals unique spatial patterns of immune response in H&E images from COVID-19 autopsies: preliminary findings

Purpose: We used computerized image analysis and machine learning approaches to characterize spatial arrangement features of the immune response from digitized autopsied H&E tissue images of the lung in coronavirus disease 2019 (COVID-19) patients. Additionally, we applied our approach to tease out potential morphometric differences from autopsies of patients who succumbed to COVID-19 versus H1N1. Approach: H&E lung whole slide images from autopsy specimens of nine COVID-19 and two H1N1 patients were computationally interrogated. 606 image patches ( ∼ 55 per patient) of 1024 × 882    pixels were extracted from the 11 autopsied patient studies. A watershed-based segmentation approach in conjunction with a machine learning classifier was employed to identify two types of nuclei families: lymphocytes and non-lymphocytes (i.e., other nucleated cells such as pneumocytes, macrophages, and neutrophils). Based off the proximity of the individual nuclei, clusters for each nuclei family were constructed. For each of the resulting clusters, a series of quantitative measurements relating to architecture and density of nuclei clusters were calculated. A receiver operating characteristics-based feature selection method, violin plots, and the t-distributed stochastic neighbor embedding algorithm were employed to study differences in immune patterns. Results: In COVID-19, the immune response consistently showed multiple small-size lymphocyte clusters, suggesting that lymphocyte response is rather modest, possibly due to lymphocytopenia. In H1N1, we found larger lymphocyte clusters that were proximal to large clusters of non-lymphocytes, a possible reflection of increased prevalence of macrophages and other immune cells. Conclusion: Our study shows the potential of computational pathology to uncover immune response features that may not be obvious by routine histopathology visual inspection.

Imaging in the COVID-19 era: Lessons learned during a pandemic

The first year of the coronavirus disease 2019 (COVID-19) pandemic has been a year of unprecedented changes, scientific breakthroughs, and controversies. The radiology community has not been spared from the challenges imposed on global healthcare systems. Radiology has played a crucial part in tackling this pandemic, either by demonstrating the manifestations of the virus and guiding patient management, or by safely handling the patients and mitigating transmission within the hospital. Major modifications involving all aspects of daily radiology practice have occurred as a result of the pandemic, including workflow alterations, volume reductions, and strict infection control strategies. Despite the ongoing challenges, considerable knowledge has been gained that will guide future innovations. The aim of this review is to provide the latest evidence on the role of imaging in the diagnosis of the multifaceted manifestations of COVID-19, and to discuss the implications of the pandemic on radiology departments globally, including infection control strategies and delays in cancer screening. Lastly, the promising contribution of artificial intelligence in the COVID-19 pandemic is explored.

Differential Diagnosis of COVID-19 Pneumonia From Influenza A (H1N1) Pneumonia Using a Model Based on Clinicoradiologic Features

Objectives: Both coronavirus disease 2019 (COVID-19) pneumonia and influenza A (H1N1) pneumonia are highly contagious diseases. We aimed to characterize initial computed tomography (CT) and clinical features and to develop a model for differentiating COVID-19 pneumonia from H1N1 pneumonia.

Methods: In total, we enrolled 291 patients with COVID-19 pneumonia from January 20 to February 13, 2020, and 97 patients with H1N1 pneumonia from May 24, 2009, to January 29, 2010 from two hospitals. Patients were randomly grouped into a primary cohort and a validation cohort using a seven-to-three ratio, and their clinicoradiologic data on admission were compared. The clinicoradiologic features were optimized by the least absolute shrinkage and selection operator (LASSO) logistic regression analysis to generate a model for differential diagnosis. Receiver operating characteristic (ROC) curves were plotted for assessing the performance of the model in the primary and validation cohorts.

Results: The COVID-19 pneumonia mainly presented a peripheral distribution pattern (262/291, 90.0%); in contrast, H1N1 pneumonia most commonly presented a peribronchovascular distribution pattern (52/97, 53.6%). In LASSO logistic regression, peripheral distribution patterns, older age, low-grade fever, and slightly elevated aspartate aminotransferase (AST) were associated with COVID-19 pneumonia, whereas, a peribronchovascular distribution pattern, centrilobular nodule or tree-in-bud sign, consolidation, bronchial wall thickening or bronchiectasis, younger age, hyperpyrexia, and a higher level of AST were associated with H1N1 pneumonia. For the primary and validation cohorts, the LASSO model containing above eight clinicoradiologic features yielded an area under curve (AUC) of 0.963 and 0.943, with sensitivity of 89.7 and 86.2%, specificity of 89.7 and 89.7%, and accuracy of 89.7 and 87.1%, respectively.

Conclusions: Combination of distribution pattern and category of pulmonary opacity on chest CT with clinical features facilitates the differentiation of COVID-19 pneumonia from H1N1 pneumonia.

RETRAIT : Pneumonie à Sars-CoV-2: broncho-pneumonie ou vasculopathie ? Focus sur le signe scanographique du « vaisseau élargi » et corrélations radio-histologiques

Disponible en ligne : 4 juin 2021.

L’éditeur a le regret de vous informer que cet article ayant déjà été mis en ligne dans la Journal d'imagerie diagnostique et interventionnelle, le 25 août 2021, https://doi.org/10.1016/j.jidi.2021.06.001.

Chest-CT mimics of COVID-19 pneumonia-a review article

Coronavirus disease 2019 (COVID-19) emerged in early December 2019 in China, as an acute lower respiratory tract infection and spread rapidly worldwide being declared a pandemic in March 2020. Chest-computed tomography (CT) has been utilized in different clinical settings of COVID-19 patients; however, COVID-19 imaging appearance is highly variable and nonspecific. Indeed, many pulmonary infections and non-infectious diseases can show similar CT findings and mimic COVID-19 pneumonia. In this review, we discuss clinical conditions that share a similar imaging appearance with COVID-19 pneumonia, in order to identify imaging and clinical characteristics useful in the differential diagnosis.

Pneumonia Caused by Severe Acute Respiratory Syndrome Coronavirus 2 and Influenza Virus: A Multicenter Comparative Study

Background

Detailed differences in clinical information between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia (CP), which is the main phenotype of SARS-CoV-2 disease, and influenza pneumonia (IP) are still unclear.

Methods

A prospective, multicenter cohort study was conducted by including patients with CP who were hospitalized between January and June 2020 and a retrospective cohort of patients with IP hospitalized from 2009 to 2020. We compared the clinical presentations and studied the prognostic factors of CP and IP.

Results

Compared with the IP group (n = 66), in the multivariate analysis, the CP group (n = 362) had a lower percentage of patients with underlying asthma or chronic obstructive pulmonary disease (P < .01), lower neutrophil-to-lymphocyte ratio (P < .01), lower systolic blood pressure (P < .01), higher diastolic blood pressure (P < .01), lower aspartate aminotransferase level (P < .05), higher serum sodium level (P < .05), and more frequent multilobar infiltrates (P < .05). The diagnostic scoring system based on these findings showed excellent differentiation between CP and IP (area under the receiver operating characteristic curve, 0.889). Moreover, the prognostic predictors were different between CP and IP.

Conclusions

Comprehensive differences between CP and IP were revealed, highlighting the need for early differentiation between these 2 pneumonias in clinical settings.

Patient hematology during hospitalization for viral pneumonia caused by SARS-CoV-2 and non-SARS-CoV-2 agents: a retrospective study

Background

Hematological comparison of coronavirus disease (COVID-19) and other viral pneumonias can provide insights into COVID-19 treatment.

Methods

In this retrospective case–control single-center study, we compared the data of 126 patients with viral pneumonia during different outbreaks [severe acute respiratory syndrome (SARS) in 2003, influenza A (H1N1) in 2009, human adenovirus type 7 in 2018, and COVID-19 in 2020].

Results

One of the COVID-19 characteristics was a continuous decline in the hemoglobin level. The neutrophil count was related to the aggravation of COVID-19 and SARS. Thrombocytopenia occurred in patients with SARS and severe COVID-19 even at the recovery stage. Lymphocytes were related to the entire course of adenovirus infection, recovery of COVID-19, and disease development of SARS.

Conclusions

Dynamic changes in hematological counts could provide a reference for the pathogenesis and prognosis of pneumonia caused by respiratory viruses in clinics.

Cytokine Storm: The Primary Determinant for the Pathophysiological Evolution of COVID-19 Deterioration

The coronavirus disease 2019 (COVID-19) pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an ongoing major threat to global health and has posed significant challenges for the treatment of severely ill COVID-19 patients. Several studies have reported that cytokine storms are an important cause of disease deterioration and death in COVID-19 patients. Consequently, it is important to understand the specific pathophysiological processes underlying how cytokine storms promote the deterioration of COVID-19. Here, we outline the pathophysiological processes through which cytokine storms contribute to the deterioration of SARS-CoV-2 infection and describe the interaction between SARS-CoV-2 and the immune system, as well as the pathophysiology of immune response dysfunction that leads to acute respiratory distress syndrome (ARDS), multi-organ dysfunction syndrome (MODS), and coagulation impairment. Treatments based on inhibiting cytokine storm-induced deterioration and occurrence are also described.

Comparison of Chest CT Findings of COVID-19, Influenza, and Organizing Pneumonia: A Multireader Study

Background: Previous studies have compared CT findings of COVID-19 pneumonia with those of other infections; however, to our knowledge, no studies have included non-infectious organizing pneumonia (OP) as a comparison group. Objective: To compare chest CT features of COVID-19, influenza, and OP using a multireader design, and to assess radiologists' performance in distinguishing between these conditions. Methods: This retrospective study included 150 chest CT examinations in 150 patients (mean age 58±16 years) with diagnosis of COVID-19, influenza, or non-infectious OP (50 randomly selected abnormal CT examinations per diagnosis). Six thoracic radiologists independently assessed CT examinations for 14 individual CT findings and Radiologic Society of North America (RSNA) COVID-19 category and recorded a favored diagnosis. CT characteristics of the three diagnoses were compared using random effects models; readers' diagnostic performance was assessed. Results: COVID-19 pneumonia was significantly different (p<.05) from influenza pneumonia for seven of 14 chest CT findings, though different (p<.05) from OP for 4 of 14 findings [central or diffuse distribution in 10% and 7% of COVID-19 vs 20% and 21% of OP; unilateral distribution in 1% of COVID-19 vs 7% of OP; non-tree-in-bud nodules in 32% of COVID-19 vs 53% of OP; tree-in-bud nodules in 6% of COVID-19 vs 14% of OP]. A total of 70% of cases of COVID-19, 33% of influenza, and 47% of OP had an RSNA COVID-19 category of typical (p<.001). Mean percentage of correct favored diagnoses compared to actual diagnoses was 44% for COVID-19, 29% for influenza, and 39% for OP. Mean diagnostic accuracy of favored diagnoses was 70% for COVID-19 pneumonia and 68% for both influenza and OP. Conclusion: CT findings of COVID-19 substantially overlap with influenza and, to a greater extent, with OP. Radiologists' diagnostic accuracy was low in a study sample containing equal proportions of these three types of pneumonia. Clinical Impact: Recognized challenges in diagnosing COVID-19 by CT are furthered by our observed strong overlap between CT appearances of COVID-19 and OP. This challenge may be particularly evident in clinical settings with substantial proportions of patients with potential causes of OP such as ongoing cancer therapy or autoimmune conditions.

A Comparative Systematic Review of COVID-19 and Influenza

Background: Both SARS-CoV-2 and influenza virus share similarities such as clinical features and outcome, laboratory, and radiological findings. Methods: Literature search was done using PubMed to find MEDLINE indexed articles relevant to this study. As of 25 November 2020, the search has been conducted by combining the MeSH words “COVID-19” and “Influenza”. Results: Eighteen articles were finally selected in adult patients. Comorbidities such as cardiovascular diseases, diabetes, and obesity were significantly higher in COVID-19 patients, while pulmonary diseases and immunocompromised conditions were significantly more common in influenza patients. The incidence rates of fever, vomiting, ocular and otorhinolaryngological symptoms were found to be significantly higher in influenza patients when compared with COVID-19 patients. However, neurologic symptoms and diarrhea were statistically more frequent in COVID-19 patients. The level of white cell count and procalcitonin was significantly higher in influenza patients, whereas thrombopenia and elevated transaminases were significantly more common in COVID-19 patients. Ground-grass opacities, interlobular septal thickening, and a peripheral distribution were more common in COVID-19 patients than in influenza patients where consolidations and linear opacities were described instead. COVID-19 patients were significantly more often transferred to intensive care unit with a higher rate of mortality. Conclusions: This study estimated differences of COVID-19 and influenza patients which can help clinicians during the co-circulation of the two viruses.

The incidence of pleural effusion in COVID-19 pneumonia: State-of-the-art review

BACKGROUND

COVID-19-related pleural effusions are frequently described during the ongoing pandemic.

OBJECTIVES

We described the incidence, characteristics, and outcomes of COVID-19-related pleural effusions based on the current evidence available in the literature.

METHODS

We searched MEDLINE, Pubmed, and Google Scholar databases using keywords of "coronavirus disease 2019 (COVID-19)," "severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)," "pleural effusion," "pleural fluid," and "pleura" from January 1st, 2020 to January 31st, 2021.

RESULTS

The incidence of pleural effusions was low at 7.3% among the 47 observational studies. Pleural effusions were commonly observed in critically ill patients and had Multisystem Inflammatory Syndrome (MIS). COVID-19-related pleural effusions were identified 5-7 days and 11 days, after hospital admission and onset of COVD-19 symptoms. The characteristic findings of pleural fluid were exudative, lymphocytic or neutrophilic-predominant pleural fluid with markedly elevated lactate dehydrogenase (LDH) levels and pleural fluid to serum LDH ratio.

CONCLUSION

A well-designed study is required to assess the significance of COVID-19-related pleural effusions during this current pandemic.

Differences in Clinical Characteristics and Chest Images between Coronavirus Disease 2019 and Influenza-Associated Pneumonia

Background: Concerns are arising about the simultaneous occurrence of the coronavirus disease 2019 (COVID-19) pandemic and the influenza epidemic, the so-called “twindemic”. In this study, we compared clinical characteristics and chest images from patients with COVID-19 and influenza. Methods: We conducted a case-control study of COVID-19 and age- and sex-matched influenza patients. Clinical characteristics and chest imaging findings between patients with COVID-19 and matched influenza patient controls were compared. Results: A total of 47 patients were enrolled in each group. Anosmia (14.9%) and ageusia (21.3%) were only observed in COVID-19 patients. There were 31 (66%) and 23 (48.9%) patients with COVID-19 and influenza who had pulmonary lesions confirmed by chest computed tomography (CT), respectively. The interval between symptom onset and pneumonia was significantly longer in patients with COVID-19. Round opacities were more common in images from COVID-19 patients (41.9% vs. 8.7%, p = 0.007), whereas pure consolidation (0% vs. 34.9%, p < 0.001) and pleural effusion (0% vs. 17.4%, p = 0.028) were more common in images from influenza patients. Notably, the difference in the number of involved pulmonary lobes observed on CT and pulmonary fields observed on radiographic images was significantly higher in COVID-19-associated pneumonia than that in influenza-associated pneumonia (2.32 ± 1.14 vs. 1.48 ± 0.99, p = 0.010). Conclusions: Chest images and thorough review of clinical findings could provide value for proper differential diagnoses of COVID-19 patients, but they are not sufficiently sensitive for initial diagnoses. In addition, chest radiography could underestimate COVID-19 lung involvement because of the lesion characteristics of COVID-19-associated pneumonia.

A systematic review and meta-analysis comparing the diagnostic accuracy of initial RT-PCR and CT scan in suspected COVID-19 patients

OBJECTIVE

To perform a systematic review and meta-analysis to compare the diagnostic accuracy of CT and initial reverse transcriptase polymerase chain reaction (RT-PCR) for detecting COVID-19 infection.

METHODS

We searched three databases, PubMed, EMBASE, and EMCARE, to identify studies reporting diagnostic accuracy of both CT and RT-PCR in detecting COVID-19 infection between December 2019 and May 2020. For accurate comparison, only those studies that had patients undergoing both CT and RT-PCR were included. Pooled diagnostic accuracy of both the tests was calculated by using a bivariate random effects model.

RESULTS

Based on inclusion criteria, only 11 studies consisting of 1834 patients were included in the final analysis that reported diagnostic accuracy of both CT and RT-PCR, in the same set of patients. Sensitivity estimates for CT scan ranged from 0.69 to 1.00 and for RT-PCR varied ranging from 0.47 to 1.00. The pooled estimates of sensitivity for CT and RT-PCR were 0.91 [95% CI (0.84-0.97)] and 0.84 [95% CI (0.71-0.94)], respectively. On subgroup analysis, pooled sensitivity of CT and RT-PCR was 0.95 [95% CI (0.88-0.98)] and 0.91 [95% CI (0.80-0.96), p = o.ooo1]. The pooled specificity of CT and RT-PCR was 0.31 [95% CI (0.035-0.84)] and 1.00 [95% CI (0.96-1.00)].

CONCLUSION

CT is more sensitive than RT-PCR in detecting COVID-19 infection, but has a very low specificity.

ADVANCES IN KNOWLEDGE

Since the results of a CT scan are available quickly, it can be used as an adjunctive initial diagnostic test for patients with a history of positive contact or epidemiological history.

Pre-test probability for SARS-Cov-2-related infection score: The PARIS score

INTRODUCTION

In numerous countries, large population testing is impossible due to the limited availability of RT-PCR kits and CT-scans. This study aimed to determine a pre-test probability score for SARS-CoV-2 infection.

METHODS

This multicenter retrospective study (4 University Hospitals) included patients with clinical suspicion of SARS-CoV-2 infection. Demographic characteristics, clinical symptoms, and results of blood tests (complete white blood cell count, serum electrolytes and CRP) were collected. A pre-test probability score was derived from univariate analyses of clinical and biological variables between patients and controls, followed by multivariate binary logistic analysis to determine the independent variables associated with SARS-CoV-2 infection.

RESULTS

605 patients were included between March 10th and April 30th, 2020 (200 patients for the training cohort, 405 consecutive patients for the validation cohort). In the multivariate analysis, lymphocyte (<1.3 G/L), eosinophil (<0.06 G/L), basophil (<0.04 G/L) and neutrophil counts (<5 G/L) were associated with high probability of SARS-CoV-2 infection but no clinical variable was statistically significant. The score had a good performance in the validation cohort (AUC = 0.918 (CI: [0.891-0.946]; STD = 0.014) with a Positive Predictive Value of high-probability score of 93% (95%CI: [0.89-0.96]). Furthermore, a low-probability score excluded SARS-CoV-2 infection with a Negative Predictive Value of 98% (95%CI: [0.93-0.99]). The performance of the score was stable even during the last period of the study (15-30th April) with more controls than infected patients.

CONCLUSIONS

The PARIS score has a good performance to categorize the pre-test probability of SARS-CoV-2 infection based on complete white blood cell count. It could help clinicians adapt testing and for rapid triage of patients before test results.

Review of the Chest CT Differential Diagnosis of Ground-Glass Opacities in the COVID Era

Coronavirus disease 2019 (COVID-19), a recently emerged lower respiratory tract illness, has quickly become a pandemic. The purpose of this review is to discuss and differentiate typical imaging findings of COVID-19 from those of other diseases, which can appear similar in the first instance. The typical CT findings of COVID-19 are bilateral and peripheral predominant ground-glass opacities. As per the Fleischner Society consensus statement, CT is appropriate in certain scenarios, including for patients who are at risk for and/or develop clinical worsening. The probability that CT findings represent COVID-19, however, depends largely on the pretest probability of infection, which is in turn defined by community prevalence of infection. When the community prevalence of COVID-19 is low, a large gap exists between positive predictive values of chest CT versus those of reverse transcriptase polymerase chain reaction. This implies that with use of chest CT there are a large number of false-positive results. Imaging differentiation is important for management and isolation purposes and for appropriate disposition of patients with false-positive CT findings. Herein the authors discuss differential pathology with close imaging resemblance to typical CT imaging features of COVID-19 and highlight CT features that may help differentiate COVID-19 from other conditions.

Influenza H1N1 virus-associated pneumonia often resembles rapidly progressive interstitial lung disease seen in collagen vascular diseases and COVID-19 pneumonia; CT-pathologic correlation in 24 patients

Purpose

To describe computed tomography (CT) findings of influenza H1N1 virus-associated pneumonia (IH1N1VAP), and to correlate CT findings to pathological ones.

Methods

The study included 24 patients with IH1N1VAP. Two observers independently evaluated the presence, distribution, and extent of CT findings. CT features were divided into either classical form (C-form) or non-classical form (NC-form). C-form included: A.) broncho-bronchiolitis and bronchopneumonia type, whereas NC-forms included: B.) diffuse peribronchovascular type, simulating subacute rheumatoid arthritis-associated (RA) interstitial lung disease (ILD) and C.) lower peripheral and/or peribronchovascular type, resembling dermatomyositis-associated ILD and COVID-19 pneumonia. In 10 cases with IH1N1VAP where lung biopsy was performed, CT and pathology findings were correlated.

Results

The most common CT findings were ground-glass opacities (24/24, 100 %) and airspace consolidation (23/24, 96 %). C-form was found in 11 (46 %) patients while NC-form in 13 (54 %). Types A, B, and C were seen in 11(46 %), 4 (17 %), and 9 (38 %) patients, respectively. The lung biopsy revealed organizing pneumonia in all patients and 6 patients (60 %) showed incorporated type organizing pneumonia that was common histological findings of rapidly progressive ILD.

Conclusion

In almost half of patients of IH1N1VAP, CT images show NC-form pneumonia pattern resembling either acute or subacute RA or dermatomyositis-associated ILD and COVID-19 pneumonia.

Contribution of CT Features in the Diagnosis of COVID-19

The outbreak of novel coronavirus disease 2019 (COVID-19) first occurred in Wuhan, Hubei Province, China, and spread across the country and worldwide quickly. It has been defined as a major global health emergency by the World Health Organization (WHO). As this is a novel virus, its diagnosis is crucial to clinical treatment and management. To date, real-time reverse transcription-polymerase chain reaction (RT-PCR) has been recognized as the diagnostic criterion for COVID-19. However, the results of RT-PCR can be complemented by the features obtained in chest computed tomography (CT). In this review, we aim to discuss the diagnosis and main CT features of patients with COVID-19 based on the results of the published literature, in order to enhance the understanding of COVID-19 and provide more detailed information regarding treatment.

The value of the platelet count and platelet indices in differentiation of COVID-19 and influenza pneumonia

It is difficult to distinguish coronavirus disease-2019 (COVID-19) from other viral respiratory tract infections owing to the similarities in clinical and radiological findings. This study aims to determine the clinical importance of platelet count and platelet indices in the differentiation of COVID-19 from influenza and the value of these parameters in the differential diagnosis of COVID-19. The medical records of the patients and the electronic patient monitoring system were retrospectively analyzed. Demographic characteristics, admission symptoms, laboratory findings, radiological involvement, comorbidities, and mortality of the patients were recorded. Forty-three patients diagnosed with influenza and 54 diagnosed with COVID-19 were included in the study. The average age of the COVID-19 patients was lower than that of the influenza patients (influenza: 60.5 years, COVID-19: 52.4 years; pp = 0.024),.024), and the male gender was predominant in the COVID-19 group (influenza: 42%, COVID-19: 56%). According to laboratory findings, the mean platelet volume (MPV) and MPV/platelet ratio were statistically significantly lower, whereas the eosinophil count and platelet distribution width levels were significantly higher (p < 0.05) in the COVID-19 group. It was found that the most common symptom in both groups was dyspnea and that the symptom was more prevalent among influenza patients. In the diagnosis of COVID-19, the platelet count and platelet indices are easily accessible, inexpensive, and important parameters in terms of differential diagnosis and can help in the differentiation of COVID-19 from influenza during seasonal outbreaks of the latter.

Chest CT in COVID-19: What the Radiologist Needs to Know

Chest CT has a potential role in the diagnosis, detection of complications, and prognostication of coronavirus disease 2019 (COVID-19). Implementation of appropriate precautionary safety measures, chest CT protocol optimization, and a standardized reporting system based on the pulmonary findings in this disease will enhance the clinical utility of chest CT. However, chest CT examinations may lead to both false-negative and false-positive results. Furthermore, the added value of chest CT in diagnostic decision making is dependent on several dynamic variables, most notably available resources (real-time reverse transcription-polymerase chain reaction [RT-PCR] tests, personal protective equipment, CT scanners, hospital and radiology personnel availability, and isolation room capacity) and the prevalence of both COVID-19 and other diseases with overlapping manifestations at chest CT. Chest CT is valuable to detect both alternative diagnoses and complications of COVID-19 (acute respiratory distress syndrome, pulmonary embolism, and heart failure), while its role for prognostication requires further investigation. The authors describe imaging and managing care of patients with COVID-19, with topics including (a) chest CT protocol, (b) chest CT findings of COVID-19 and its complications, (c) the diagnostic accuracy of chest CT and its role in diagnostic decision making and prognostication, and (d) reporting and communicating chest CT findings. The authors also review other specific topics, including the pathophysiology and clinical manifestations of COVID-19, the World Health Organization case definition, the value of performing RT-PCR tests, and the radiology department and personnel impact related to performing chest CT in COVID-19. ©RSNA, 2020.

Not all that glitters is COVID! Differential diagnosis of FDG-avid interstitial lung disease in low-prevalence regions

Coronavirus disease-19 (COVID-19) is only one of the many possible infectious and non-infectious diseases that may occur with similar imaging features in patients undergoing [¹⁸F]-fluorodeoxyglucose (¹⁸FDG) monitoring, particularly in the most fragile oncologic patients. We briefly summarise some key radiological elements of differential diagnosis of interstitial lung diseases which, in our opinion, could be extremely useful for physicians reporting ¹⁸FDG PET/CT scans, not only during the COVID-19 pandemic, but also for their normal routine activity.

Using the Past to Maximize the Success Probability of Future Anti-Viral Vaccines

Rapid obtaining of safe, effective, anti-viral vaccines has recently risen to the top of the international agenda. To maximize the success probability of future anti-viral vaccines, the anti-viral vaccines successful in the past are summarized here by virus type and vaccine type. The primary focus is on viruses with both single-stranded RNA genomes and a membrane envelope, given the pandemic past of influenza viruses and coronaviruses. The following conclusion is reached, assuming that success of future strategies is positively correlated with strategies successful in the past. The primary strategy, especially for emerging pandemic viruses, should be development of vaccine antigens that are live-attenuated viruses; the secondary strategy should be development of vaccine antigens that are inactivated virus particles. Support for this conclusion comes from the complexity of immune systems. These conclusions imply the need for a revision in current strategic planning.

Differential diagnosis of COVID-19 in symptomatic patients at the University Hospital Center Mohammed VI, Marrakesh

INTRODUCTION

coronavirus disease 2019 caused by severe acute respiratory syndrome coronavirus 2 was first reported in Wuhan, China. Clinical spectrum of this disease has nonspecific symptoms shared by many other frequent infectious diseases of the respiratory tract and other respiratory tract diseases. This study explains the importance of differential diagnosis between COVID-19 and other lung diseases.

METHODS

we analyzed in this study, the demographic features, clinical presentations, laboratory data and radiologic findings of the COVID-19 patients in comparison to those with other respiratory infections or diseases.

RESULTS

the mean age of all patients was 38.04 years; 35 patients were later confirmed to be positive for SARS-CoV-2 infection. The most common symptoms reported by both groups included nonproductive cough and myalgia. Two of the non-COVID-19 patients were having below 92% oxygen saturation and low systolic blood pressure. The patients shared relatively similar laboratory findings except 3% of the non-COVID-19 patients who had lympho-neutropenia and 22.6% had high levels of C-reactive protein. Pulmonary tuberculosis and autoimmune disease respiratory disorder were suspected in 2 of the non-COVID-19 patients respectively.

CONCLUSION

we emphasize the importance of good screening protocols, rapid detection of SARS-CoV-2 and other most common respiratory pathogens, which may help for a better control of COVID-19 spread and avoid delayed care of other lung diseases.