Thin-Section Chest CT Imaging of COVID-19 Pneumonia: A Comparison Between Patients with Mild and Severe Disease

COVID-19 Computed tomography patterns in renal replacement therapy patients

INTRODUCTION

Lung diseases are common in patients with end stage kidney disease (ESKD), making differential diagnosis with COVID-19 a challenge. This study describes pulmonary chest tomography (CT) findings in hospitalized ESKD patients on renal replacement therapy (RRT) with clinical suspicion of COVID-19.

METHODS

ESKD individuals referred to emergency department older than 18 years with clinical suspicion of COVID-19 were recruited. Epidemiological baseline clinical information was extracted from electronic health records. Pulmonary CT was classified as typical, indeterminate, atypical or negative. We then compared the CT findings of positive and negative COVID-19 patients.

RESULTS

We recruited 109 patients (62.3% COVID-19-positive) between March and December 2020, mean age 60 ± 12.5 years, 43% female. The most common etiology of ESKD was diabetes. Median time on dialysis was 36 months, interquartile range = 12-84. The most common pulmonary lesion on CT was ground glass opacities. Typical CT pattern was more common in COVID-19 patients (40 (61%) vs 0 (0%) in non-COVID-19 patients, p < 0.001). Sensitivity was 60.61% (40/66) and specificity was 100% (40/40). Positive predictive value and negative predictive value were 100% and 62.3%, respectively. Atypical CT pattern was more frequent in COVID-19-negative patients (9 (14%) vs 24 (56%) in COVID-19-positive, p < 0.001), while the indeterminate pattern was similar in both groups (13 (20%) vs 6 (14%), p = 0.606), and negative pattern was more common in COVID-19-negative patients (4 (6%) vs 12 (28%), p = 0.002).

CONCLUSIONS

In hospitalized ESKD patients on RRT, atypical chest CT pattern cannot adequately rule out the diagnosis of COVID-19.

Novel machine-learning analysis of SARS-CoV-2 infection in a subclinical nonhuman primate model using radiomics and blood biomarkers

Detection of the physiological response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is challenging in the absence of overt clinical signs but remains necessary to understand a full subclinical disease spectrum. In this study, our objective was to use radiomics (from computed tomography images) and blood biomarkers to predict SARS-CoV-2 infection in a nonhuman primate model (NHP) with inapparent clinical disease. To accomplish this aim, we built machine-learning models to predict SARS-CoV-2 infection in a NHP model of subclinical disease using baseline-normalized radiomic and blood sample analyses data from SARS-CoV-2-exposed and control (mock-exposed) crab-eating macaques. We applied a novel adaptation of the minimum redundancy maximum relevance (mRMR) feature-selection technique, called mRMR-permute, for statistically-thresholded and unbiased feature selection. Through performance comparison of eight machine-learning models trained on 14 feature sets, we demonstrated that a logistic regression model trained on the mRMR-permute feature set can predict SARS-CoV-2 infection with very high accuracy. Eighty-nine percent of mRMR-permute selected features had strong and significant class effects. Through this work, we identified a key set of radiomic and blood biomarkers that can be used to predict infection status even in the absence of clinical signs. Furthermore, we proposed and demonstrated the utility of a novel feature-selection technique called mRMR-permute. This work lays the foundation for the prediction and classification of SARS-CoV-2 disease severity.

Unilateral Pleural Effusion after Third Dose of BNT162b2 mRNA Vaccination: Case Report

Vaccination remains the best strategy against coronavirus disease 2019 (COVID-19) in terms of prevention. The efficacy and safety of COVID-19 vaccines is supported by well-designed clinical trials that recruited many participants. It is well-known that vaccination is associated with local side effects related to the injection site, and mild, systemic side effects. However, there has been an increase in the occurrence of what is known as infrequent adverse effects in the population of vaccinated individuals in real life. We present the case of a 46-year-old woman with no past medical history, who presented with a sharp chest pain with deep inspiration, a few days after receiving the third dose of the Pfizer-BioNTech COVID-19 mRNA vaccine (BNT162b2). There is an association between the BNT16b2 vaccination and myocarditis, pericarditis, and even bilateral pleural effusions. To the best of our knowledge, this is the first report featuring a unilateral pleural effusion in a patient with no known past medical history, who did not develop cardiac involvement nor have any viral infection. The aim of our report is to inform health professionals of the possibility of encountering this rare adverse event in their daily practice, as the population of individuals who are receiving additional vaccine doses is increasing steadily.

The role of Lung Ultrasound in the diagnosis of SARS-COV-2 disease in pregnant women

AIM

To evaluate the role of lung ultrasound (LUS) in recognizing lung abnormalities in pregnant women affected by COVID-19 pneumonia.

MATERIALS AND METHODS

An observational study analyzing LUS patterns in 60 consecutively enrolled pregnant women affected by COVID-19 infection was performed. LUS was performed by using a standardized protocol by Soldati et al. The scoring system of LUS findings ranged from 0 to 3 in increasing alteration severity. The highest score obtained from each landmark was reported and the sum of the 12 zones examined was calculated.

RESULTS

Patients were divided into two groups: 26 (43.3%) patients with respiratory symptoms and 32 (53.3%) patients without respiratory symptoms; 2 patients were asymptomatic (3.3%). Among the patients with respiratory symptoms 3 (12.5%) had dyspnea that required a mild Oxygen therapy. A significant correlation was found between respiratory symptoms and LUS score (p < 0.001) and between gestational weeks and respiratory symptoms (p = 0.023). Regression analysis showed that age and respiratory symptoms were risk factors for highest LUS score (p < 0.005).

DISCUSSION

LUS can affect the clinical decision course and can help in stratifying patients according to its findings. The lack of ionizing radiation and its repeatability makes it a reliable diagnostic tool in the management of pregnant women.

Significance of hematologic abnormalities in COVID-19 severity among infected patients in Lagos, Nigeria

BACKGROUND

There have been suggestions that hematologic abnormalities in COVID-19 are linked with the progression and severity of diseases and mortality. Lymphopenia, sepsis, and thrombocytopenia were highly reported in patients with COVID-19. This study investigated the significance of hematologic abnormalities in patients with COVID-19 in Lagos, Nigeria, and its potential as a diagnostic tool for COVID-19 severity.

RESULTS

This was a retrospective observational study with a total of 340 patients with COVID-19 (236 patients included in the analysis). These patients were categorized into two groups, comprising 71 patients with severe COVID-19 (SCP) and 165 patients with non-severe COVID-19 (NSCP). The majority were males in both categories (SCP 74.6% and NSCP 63.6%). The mean ± SD ages for SCP and NSCP were 52.28 ± 16.87 and 42.44 ± 17.18 years, respectively. The SCP (52.1%) and NSCP (20.0%) had underlying health conditions. The SCP exhibited significantly higher neutrophil counts (P < 0.05) and significantly lower mean hemoglobin, red blood cell (RBC), packed cell volume (PCV), and lymphocyte values (P < 0.05). Anemia and lymphocytopenia were more prominent in the SCP group than in the NSCP group (P < 0.05). Hemoglobin, RBC, PCV, and lymphocytes were inversely correlated with age-group in the SCP, while only lymphocytes and platelets were inversely correlated with age-group in the NSCP. The highest area under the ROC curve (AUC) for neutrophils was 0.739 with a sensitivity of 62.0% and specificity of 80.0%, while white blood cells had an AUC of 0.722 with a sensitivity of 73.2% and specificity of 61.2%. The AUC for neutrophil-lymphocyte ratio (NLR) was 0.766 with a sensitivity of 63.3% and specificity of 83.5%, while that for the platelet-lymphocyte ratio (PLR) was 0.695 with a sensitivity and specificity of 61.7% and 77.8%.

CONCLUSIONS

COVID-19 affected the levels of hemoglobin, RBC, PCV, and lymphocytes in the blood, and the differences were significant between the SCP and NSCP. The significant changes in neutrophil and lymphocyte counts may be useful in the prognosis and management of COVID-19 severity in hospital settings. Furthermore, NLR and PLR may be used in the prognosis and management of severe COVID-19 infection, as well as provide an objective basis for early identification and management in low-resource settings.

Correlation between the initial CT chest findings and short-term prognosis in Egyptian patients with COVID-19 pneumonia

Background

The recent pandemic of COVID‐19 has thrown the world into chaos due to its high rate of transmissions. This study aimed to highlight the encountered CT findings in 910 patients with COVID-19 pneumonia in Egypt including the mean severity score and also correlation between the initial CT finding and the short-term prognosis in 320 patients.

Results

All patients had confirmed COVID-19 infection. Non-contrast CT chest was performed for all cases; in addition, the correlation between each CT finding and disease severity or the short-term prognosis was reported. The mean age was higher for patients with unfavorable prognosis (P < 0.01). The patchy pattern was the most common, found in 532/910 patients (58.4%), the nodular pattern was the least common 123/910 (13.5%). The diffuse pattern was reported in 124 (13.6%). The ground glass density was the most common reported density in the study 512/910 (56.2%). The crazy pavement sign was reported more frequently in patients required hospitalization or ICU and was reported in 53 (56.9%) of patients required hospitalization and in 29 (40.2%) patients needed ICU, and it was reported in 11 (39.2%) deceased patients. Air bronchogram was reported more frequently in patients with poor prognosis than patients with good prognosis (16/100; 26% Vs 12/220; 5.4%). The mean CT severity score for patients with poor prognosis was 15.2. The mean CT severity score for patients with good prognosis 8.7., with statistically significant difference (P = 0.001).

Conclusion

Our results confirm the important role of the initial CT findings in the prediction of clinical outcome and short-term prognosis. Some signs like subpleural lines, halo sign, reversed halo sign and nodular shape of the lesions predict mild disease and favorable prognosis. The crazy paving sign, dense vessel sign, consolidation, diffuse shape and high severity score predict more severe disease and probably warrant early hospitalization. The high severity score is most important in prediction of unfavorable prognosis. The nodular shape of the lesions is the most important predictor of good prognosis.

Co-infection of SARS-CoV-2 with other respiratory pathogens in patients with liver disease

Respiratory illness caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) was first documented in Wuhan, China, in December 2019, followed by its rapid spread across the globe. Accumulating evidence has demonstrated viral/bacterial co-infection in the respiratory tract could modulate disease severity and its outcome in COVID-19 infection. In this retrospective study, 300 chronic liver disease patients with radiologically confirmed lower respiratory tract infection were enrolled from September 2020 to December 2021. In all of them, along with SARS-CoV-2, other respiratory viral/bacterial pathogens were studied. In total, 23.7 % (n=71) patients were positive for SARS-CoV-2. Among the positive patients, 23.9 % (n=17) had co-infection with other respiratory pathogens, bacterial co-infections being dominant. The SARS-CoV-2 negative cohort had 39.7  % positivity (n=91) for other respiratory pathogens, the most common being those of the rhinovirus/enterovirus family. Ground glass opacity (GGO) with consolidation was found to be the most common radiological finding among SARS-CoV-2 positive co-infected patients, as compared to only GGO among SARS-CoV-2 mono-infected patients. Accurate diagnosis of co-infections, especially during pandemics including COVID-19, can ameliorate the treatment and management of suspected cases.

AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions

Background and objective

In recent years, Artificial Intelligence has had an evident impact on the way research addresses challenges in different domains. It has proven to be a huge asset, especially in the medical field, allowing for time-efficient and reliable solutions. This research aims to spotlight the impact of deep learning and machine learning models in the detection of COVID-19 from medical images. This is achieved by conducting a review of the state-of-the-art approaches proposed by the recent works in this field.

Methods

The main focus of this study is the recent developments of classification and segmentation approaches to image-based COVID-19 detection. The study reviews 140 research papers published in different academic research databases. These papers have been screened and filtered based on specified criteria, to acquire insights prudent to image-based COVID-19 detection.

Results

The methods discussed in this review include different types of imaging modality, predominantly X-rays and CT scans. These modalities are used for classification and segmentation tasks as well. This review seeks to categorize and discuss the different deep learning and machine learning architectures employed for these tasks, based on the imaging modality utilized. It also hints at other possible deep learning and machine learning architectures that can be proposed for better results towards COVID-19 detection. Along with that, a detailed overview of the emerging trends and breakthroughs in Artificial Intelligence-based COVID-19 detection has been discussed as well.

Conclusion

This work concludes by stipulating the technical and non-technical challenges faced by researchers and illustrates the advantages of image-based COVID-19 detection with Artificial Intelligence techniques.

Semi-quantitative evaluation of chest computed tomography for coronavirus disease 2019 in a critical care unit: A case-control study

The spread of abnormal opacity on chest computed tomography (CT) has been reported as a predictor of coronavirus disease 2019 (COVID-19) severity; however, the relationship between CT findings and prognosis in patients with severe COVID-19 remains unclear. The objective of this study was to evaluate the extent of abnormal opacity on chest CT and its association with prognosis in patients with COVID-19 in a critical care medical center, using a simple semi-quantitative method. This single-center case-control study included patients diagnosed with severe COVID-19 pneumonia who were admitted to a critical care center. The diagnosis of COVID-19 was based on positive results of a reverse transcription polymerase chain reaction test. All patients underwent non-contrast whole-body CT upon admission. Six representative axial chest CT images were selected for each patient to evaluate the extent of lung lesions. The percentage of the area involved in the representative CT images was visually assessed by 2 radiologists and scored on 4-point scale to obtain the bedside CT score, which was compared between patients who survived and those who died using the Mann-Whitney U test. A total of 63 patients were included in this study: 51 survived and 12 died after intensive treatment. The inter-rater reliability of bedside scores between the 2 radiologists was acceptable. The median bedside CT score of the survival group was 12.5 and that of the mortality group was 16.5; the difference between the 2 groups was statistically significant. The degree of opacity can be easily scored using representative CT images in patients with severe COVID-19 pneumonia, without sophisticated software. A greater extent of abnormal opacity is associated with poorer prognosis. Predicting the prognosis of patients with severe COVID-19 could facilitate prompt and appropriate treatment.

The Caliber of Segmental and Subsegmental Vessels in COVID-19 Pneumonia Is Enlarged: A Distinctive Feature in Comparison with Other Forms of Inflammatory and Thromboembolic Diseases

Background: The purpose of this study was to compare COVID-19 patients’ vessel caliber with that of normal lungs and lungs affected by other inflammatory and thromboembolic processes. Methods: between March and April 2020, 42 patients affected by COVID-19 pneumonia (COV-P) underwent CT scans of the lungs at Verona University Hospital for clinical indications. The lung images of four different groups of patients were compared (normal lung (NL), distal thromboembolism (DTE), and bacterial and fungal pneumonia (Bact-P, Fung-P)) by a radiologist with four years of experience. Results: The COV-P patients’ segmental and subsegmental vessels, evaluated as the ratio with the corresponding bronchial branch (V/B ratio), were larger, with respect to the NL the DTE groups, in the apparently healthy parenchyma, a result confirmed in the zones of opacification with respect to the Bact-P and Fung-P groups. Conclusions: This was the first study to show, by comparative analysis, that COVID-19 patients’ segmental and subsegmental vessel calibers are significantly enlarged. This is a distinctive feature of COVID-19 pneumonia, suggesting its distinct pathophysiology as compared to other inflammatory and thromboembolic diseases and alerting radiologists to consider it when evaluating the CT scans of suspected patients.

Mediastinal lymph node enlargement in COVID-19: Relationships with mortality and CT findings

BACKGROUND

The presence of mediastinal lymph node enlargement (MLNE) in computed tomography (CT) of Coronavirus disease 2019 (COVID-19) patients can be associated with disease severity.

OBJECTIVES

To investigate the relationship between MLNE with intensive care unit admission (ICU), mortality rates, and CT findings, especially in early-stage COVID-19 patients.

METHODS

This single-center retrospective case-control study, included aged ≥18 years, 250 COVID-19 patients with positive RT-PCR tests. We included two patient groups, 125/250 with and without MLNE. Demographic information of the patients, laboratory findings, length of stay in hospital or ICU, mortality rates, initial CT imaging findings and CT severity scores (CT-SS) were recorded and their relationship with MLNE was investigated.

RESULTS

Patients with MLNE were older (69.61 ± 11.16; p < 0.001) and had a higher CT-SS (14.67 ± 7.55; p < 0.001). There was a significant difference between the presence of MLNE with mortality (58/77, 75.3%; p < 0.001) and ICU admission (49/61, 80.3%; p < 0.001). Also, a statistical association was found between MLNE with ICU admission (p = 0.001) and (p < 0.001) mortality rates in patients with CORADS≤2 CT findings. In multivariate logistic regression analysis, MLNE was 8.8-fold (95% CI: 1.62-47.86, p =  0.01) more correlated with linear opacity and 0.25-fold with bronchial wall thickening (95% CI: 0.07-0.92, p =  0.04).

CONCLUSION

Mediastinal lymph node enlargement is an important CT finding that can predict the severe prognosis of COVID-19 patients. Even in patients without lung involvement on initial CT, the presence of MLNE should be carefully examined as it is associated with disease severity.

Abnormal Chest Computed Tomography Findings among Admitted Symptomatic COVID-19 Patients in a Tertiary Care Centre: A Descriptive Cross-sectional Study

Introduction

COVID-19 has emerged as a pandemic and has varied clinical presentation. Computed Tomography scans of the chest play an important role in evaluating the lung parenchymal changes and aids in better planning the management of COVID-19 patients. The purpose of this study was to find the prevalence of abnormal chest computed tomography findings among admitted symptomatic COVID-19 patients in a tertiary care centre.

Methods

This descriptive cross-sectional study was conducted from 25 October 2020 to January 2021 in a tertiary care hospital. Ethical approval was taken from the Institutional Review Committee (Registration number: 348). Convenience sampling method was used. Chest computed tomography findings of the admitted symptomatic COVID-19 patients were evaluated for abnormal findings. Point estimate and 95% Confidence Interval were calculated.

Results

Among 153 patients, abnormal chest computed tomography findings were seen in 147 (96.07%) (92.99-99.15, 95% Confidence Interval). The findings of ground-glass opacities with consolidations were seen in 78 (53.06%) patients.

Conclusions

The prevalence of abnormal chest findings among symptomatic COVID-19 patients in our study was similar to the studies done in other countries in similar settings. Majority of the symptomatic COVID-19 patients showed abnormal chest computed tomography scan findings in the form of ground glass opacities and consolidations.

Keywords

COVID-19; Nepal; pneumonia; prevalence.

Utilizing Electrochemical-Based Sensing Approaches for the Detection of SARS-CoV-2 in Clinical Samples: A Review

The development of precise and efficient diagnostic tools enables early treatment and proper isolation of infected individuals, hence limiting the spread of coronavirus disease 2019 (COVID-19). The standard diagnostic tests used by healthcare workers to diagnose severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection have some limitations, including longer detection time, the need for qualified individuals, and the use of sophisticated bench-top equipment, which limit their use for rapid SARS-CoV-2 assessment. Advances in sensor technology have renewed the interest in electrochemical biosensors miniaturization, which provide improved diagnostic qualities such as rapid response, simplicity of operation, portability, and readiness for on-site screening of infection. This review gives a condensed overview of the current electrochemical sensing platform strategies for SARS-CoV-2 detection in clinical samples. The fundamentals of fabricating electrochemical biosensors, such as the chosen electrode materials, electrochemical transducing techniques, and sensitive biorecognition molecules, are thoroughly discussed in this paper. Furthermore, we summarised electrochemical biosensors detection strategies and their analytical performance on diverse clinical samples, including saliva, blood, and nasopharyngeal swab. Finally, we address the employment of miniaturized electrochemical biosensors integrated with microfluidic technology in viral electrochemical biosensors, emphasizing its potential for on-site diagnostics applications.

Derivation and Validation of a Predictive Score for Respiratory Failure Worsening Leading to Secondary Intubation in COVID-19: The CERES Score

Predictive scores assessing the risk of respiratory failure in COVID-19 mostly focused on the prediction of early intubation. A combined assessment of clinical parameters and biomarkers of endotheliopathy could allow to predict late worsening of acute respiratory failure (ARF), subsequently warranting intubation in COVID-19. Retrospective single-center derivation (n = 92 subjects) and validation cohorts (n = 59 subjects), including severe COVID-19 patients with non-invasive respiratory support, were assessed for at least 48 h following intensive care unit (ICU) admission. We used stepwise regression to construct the COVID endothelial and respiratory failure (CERES) score in a derivation cohort, and secondly assessed its accuracy for the prediction of late ARF worsening, requiring intubation within 15 days following ICU admission in an independent validation cohort. Platelet count, fraction of inspired oxygen, and endocan measured on ICU admission were identified as the top three predictive variables for late ARF worsening and subsequently included in the CERES score. The area under the ROC curve of the CERES score to predict late ARF worsening was calculated in the derivation and validation cohorts at 0.834 and 0.780, respectively. The CERES score is a simple tool with good performances to predict respiratory failure worsening, leading to secondary intubation, in COVID-19 patients.

Prevalence and clinical outcomes of pleural effusion in COVID-19 patients: A systematic review and meta-analysis

Observational studies indicate that pleural effusion has an association with risk and the clinical prognosis of COVID-19 disease; however, the available literature on this area is inconsistent. The objective of this systematic review and meta-analysis is to evaluate the correlation between COVID-19 disease and pleural effusion. A rigorous literature search was conducted using multiple databases. All eligible observational studies were included from around the globe. The pooled prevalence and associated 95% confidence interval (CI) were calculated using the random effect model. Mantel-Haenszel odds ratios were produced to report overall effect size using random effect models for severity and mortality outcomes. Funnel plots, Egger regression tests, and Begg-Mazumdar's rank correlation test were used to appraise publication bias. Data from 23 studies including 6234 COVID-19 patients was obtained. The overall prevalence of pleural effusion in COVID-19 patients was 9.55% (95% CI, I2  = 92%). Our findings also indicated that the presence of pleural effusions associated with increased risk of severity of disease(OR = 5.08, 95% CI 3.14-8.22, I2  = 77.4%) and mortality due to illness(OR = 4.53, 95% CI 2.16-9.49, I2  = 66%) compared with patients without pleural effusion. Sensitivity analyses illustrated a similar effect size while decreasing the heterogeneity. No significant publication bias was evident in the meta-analysis. The presence of pleural effusion can assist as a prognostic factor to evaluate the risk of worse outcomes in COVID-19 patients hence, it is recommended that hospitalized COVID-19 patients with pleural effusion should be managed on an early basis.

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.

Differences in Dynamics of Lung Computed Tomography Patterns between Survivors and Deceased Adult Patients with COVID-19

This study’s aim was to investigate CT (computed tomography) pattern dynamics differences within surviving and deceased adult patients with COVID-19, revealing new prognostic factors and reproducing already known data with our patients’ cohort: 635 hospitalized patients (55.3% of them were men, 44.7%—women), of which 87.3% had a positive result of RT-PCR (reverse transcription-polymerase chain reaction) at admission. The number of deaths was 53 people (69.8% of them were men and 30.2% were women). In total, more than 1500 CT examinations were performed on patients, using a GE Optima CT 660 computed tomography (General Electric Healthcare, Chicago, IL, USA). The study was performed at hospital admission, the frequency of repetitive scans further varied based on clinical need. The interpretation of the imaging data was carried out by 11 radiologists with filling in individual registration cards that take into account the scale of the lesion, the location, contours, and shape of the foci, the dominating types of changes, as well as the presence of additional findings and the dynamics of the process—a total of 45 parameters. Statistical analysis was performed using the software packages SPSS Statistics version 23.0 (IBM, Armonk, NY, USA) and R software version 3.3.2. For comparisons in pattern dynamics across hospitalization we used repeated measures general linear model with outcome and disease phase as factors. The crazy paving pattern, which is more common and has a greater contribution to the overall CT picture in different phases of the disease in deceased patients, has isolated prognostic significance and is probably a reflection of faster dynamics of the process with a long phase of progression of pulmonary parenchyma damage with an identical trend of changes in the scale of the lesion (as recovered) in this group of patients. Already known data on typical pulmonological CT manifestations of infection, frequency of occurrence, and the prognostic significance of the scale of the lesion were reproduced, new differences in the dynamics of the process between recovered and deceased adult patients were also found that may have prognostic significance and can be reflected in clinical practice.

Relationship between chest computed tomography findings and clinical conditions of coronavirus disease (COVID-19): A multicentre experience

AIMS

This study aimed to investigate the clinical and chest computed tomography (CT) features associated with clinical parameters for coronavirus disease (COVID-19) in the capital of Turkey, Ankara.

MATERIALS AND METHODS

Epidemiological, clinical features, laboratory findings and radiological characteristics of 1563 hospitalised patients with COVID-19 in Ankara were collected, reviewed and analysed in this study. The risk factors associated with disease severity were investigated.

RESULTS

Non-severe (1214; 77.7%) and severe cases (349; 22.3%) were enrolled in the study. Compared with the non-severe group, the severe group were significantly older and had more comorbidities (ie, hypertension, diabetes mellitus, cardiovascular disease and chronic kidney disease). Smoking was more common in the severe group. Severe patients had higher respiratory rates and higher incidences of cough and dyspnoea compared with non-severe patients. Compared with the non-severe patients, the severe patients had increased C-reactive protein (CRP), procalcitonin, neutrophil to lymphocyte ratio (NLR) and CRP/albumin ratio and decreased albumin. The occurrence rates of consolidation, subpleural sparing, crazy-paving pattern, cavity, halo sign, reversed halo sign, air bronchogram, pleural thickening, micronodule, subpleural curvilinear line and multilobar and bilateral involvement in the CT finding of the severe patients were significantly higher than those of the non-severe patients.

CONCLUSIONS

Many factors are related to the severity of COVID-19, which can help clinicians judge the severity of the patient and evaluate the prognosis. This cohort study revealed that male sex, age (≥55 years), patients with any comorbidities, especially those with cardiovascular disease, dyspnoea, increased CRP, D-dimer and NLR, and decreased lymphocyte count and CT findings of consolidation and multilobar involvement were predictors of severe COVID-19.

Interplay between hypoxia and inflammation contributes to the progression and severity of respiratory viral diseases

History of pandemics is dominated by viral infections and specifically respiratory viral diseases like influenza and COVID-19. Lower respiratory tract infection is the fourth leading cause of death worldwide. Crosstalk between resultant inflammation and hypoxic microenvironment may impair ventilatory response of lungs. This reduces arterial partial pressure of oxygen, termed as hypoxemia, which is observed in a section of patients with respiratory virus infections including SARS-CoV-2 (COVID-19). In this review, we describe the interplay between inflammation and hypoxic microenvironment in respiratory viral infection and its contribution to disease pathogenesis.

Evaluation of initial chest computed tomography (CT) findings of COVID-19 pneumonia in 117 deceased patients: a retrospective study

Background/aim

There is no study in the literature in which only chest computed tomography (CT) findings of deceased cases obtained at admission were examined, and the relationship between these findings and mortality was evaluated.

Materials and methods

In this retrospective study, a total of 117 deceased patients with COVID-19 infection confirmed by positive polymerase chain reaction and undergone chest CT were enrolled. We evaluated initial chest CT findings and their relationship, location, prevalence, and the frequency with mortality.

Results

The mean age of patients was 73 ±18 years; 71 of all patients were male and 46 were female. The predominant feature was pure ground-glass opacity (GGO) lesion (82.0%), and 59.8% of cases had pure consolidation. There was no cavitation or architectural distorsion. Pericardial effusion was found in 9.4% the patients, and pleural effusions were found in 15.3% of them. Mediastinal lymphadenopathy was only 11.9% in total.

Conclusion

In deceased patients, on admission CTs, pure consolidation, pleural and pericardial effusion, mediastinal LAP were more common than ordinary cases. It was these findings that should also raise the concern when they were seen on chest CT; therefore, these radiologic features have the potential to represent prognostic imaging markers in patients with COVID-19 pneumonia.

An Illustrated Guide to the Imaging Evolution of COVID in Non-Epidemic Areas of Southeast China

Purpose: By analyzing the CT manifestations and evolution of COVID in non-epidemic areas of southeast China, analyzing the developmental abnormalities and accompanying signs in the early and late stages of the disease, providing imaging evidence for clinical diagnosis and identification, and assisting in judging disease progression and monitoring prognosis. Methods: This retrospective and multicenter study included 1,648 chest CT examinations from 693 patients with laboratory-confirmed COVID-19 infection from 16 hospitals of southeast China between January 19 and March 27, 2020. Six trained radiologists analyzed and recorded the distribution and location of the lesions in the CT images of these patients. The accompanying signs include crazy-paving sign, bronchial wall thickening, microvascular thickening, bronchogram sign, fibrous lesions, halo and reverse-halo signs, nodules, atelectasis, and pleural effusion, and at the same time, they analyze the evolution of the abovementioned manifestations over time. Result: There were 1,500 positive findings in 1,648 CT examinations of 693 patients; the average age of the patients was 46 years, including 13 children; the proportion of women was 49%. Early CT manifestations are single or multiple nodular, patchy, or flaky ground-glass-like density shadows. The frequency of occurrence of ground-glass shadows (47.27%), fibrous lesions (42.60%), and microvascular thickening (40.60%) was significantly higher than that of other signs. Ground-glass shadows increase and expand 3-7 days after the onset of symptoms. The distribution and location of lesions were not significantly related to the appearance time. Ground-glass shadow is the most common lesion, with an average absorption time of 6.2 days, followed by consolidation, with an absorption time of about 6.3 days. It takes about 8 days for pure ground-glass lesions to absorb. Consolidation change into ground glass or pure ground glass takes 10-14 days. For ground-glass opacity to evolve into pure ground-glass lesions, it takes an average of 17 days. For ground-glass lesions to evolve into consolidation, it takes 7 days, pure ground-glass lesions need 8 days to evolve into ground-glass lesions. The average time for CT signs to improve is 10-15 days, and the first to improve is the crazy-paving sign and nodules; while the progression of the disease is 6-12 days, the earliest signs of progression are air bronchogram signs, bronchial wall thickening, and bronchiectasis. There is no severe patient in this study. Conclusion: This study depicts the CT manifestation and evolution of COVID in non-epidemic origin areas, and provides valuable first-hand information for clinical diagnosis and judgment of patient's disease evolution and prediction.

Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images

Computer-aided diagnosis for the reliable and fast detection of coronavirus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the healthcare system. Chest X-ray (CXR) imaging has several advantages over other imaging and detection techniques. Numerous works have been reported on COVID-19 detection from a smaller set of original X-ray images. However, the effect of image enhancement and lung segmentation of a large dataset in COVID-19 detection was not reported in the literature. We have compiled a large X-ray dataset (COVQU) consisting of 18,479 CXR images with 8851 normal, 6012 non-COVID lung infections, and 3616 COVID-19 CXR images and their corresponding ground truth lung masks. To the best of our knowledge, this is the largest public COVID positive database and the lung masks. Five different image enhancement techniques: histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE), image complement, gamma correction, and balance contrast enhancement technique (BCET) were used to investigate the effect of image enhancement techniques on COVID-19 detection. A novel U-Net model was proposed and compared with the standard U-Net model for lung segmentation. Six different pre-trained Convolutional Neural Networks (CNNs) (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and ChexNet) and a shallow CNN model were investigated on the plain and segmented lung CXR images. The novel U-Net model showed an accuracy, Intersection over Union (IoU), and Dice coefficient of 98.63%, 94.3%, and 96.94%, respectively for lung segmentation. The gamma correction-based enhancement technique outperforms other techniques in detecting COVID-19 from the plain and the segmented lung CXR images. Classification performance from plain CXR images is slightly better than the segmented lung CXR images; however, the reliability of network performance is significantly improved for the segmented lung images, which was observed using the visualization technique. The accuracy, precision, sensitivity, F1-score, and specificity were 95.11%, 94.55%, 94.56%, 94.53%, and 95.59% respectively for the segmented lung images. The proposed approach with very reliable and comparable performance will boost the fast and robust COVID-19 detection using chest X-ray images.

Imaging the COVID-19: a practical guide

The Coronavirus Disease 2019 (COVID-19) represents the first medical catastrophe of the new millennium. Although imaging is not a screening test for COVID-19, it plays a crucial role in evaluation and follow-up of COVID-19 patients. In this paper, we will review typical and atypical imaging findings of COVID-19.

A Comprehensive Overview of the COVID-19 Literature: Machine Learning-Based Bibliometric Analysis

BACKGROUND

Shortly after the emergence of COVID-19, researchers rapidly mobilized to study numerous aspects of the disease such as its evolution, clinical manifestations, effects, treatments, and vaccinations. This led to a rapid increase in the number of COVID-19-related publications. Identifying trends and areas of interest using traditional review methods (eg, scoping and systematic reviews) for such a large domain area is challenging.

OBJECTIVE

We aimed to conduct an extensive bibliometric analysis to provide a comprehensive overview of the COVID-19 literature.

METHODS

We used the COVID-19 Open Research Dataset (CORD-19) that consists of a large number of research articles related to all coronaviruses. We used a machine learning-based method to analyze the most relevant COVID-19-related articles and extracted the most prominent topics. Specifically, we used a clustering algorithm to group published articles based on the similarity of their abstracts to identify research hotspots and current research directions. We have made our software accessible to the community via GitHub.

RESULTS

Of the 196,630 publications retrieved from the database, we included 28,904 in our analysis. The mean number of weekly publications was 990 (SD 789.3). The country that published the highest number of COVID-19-related articles was China (2950/17,270, 17.08%). The highest number of articles were published in bioRxiv. Lei Liu affiliated with the Southern University of Science and Technology in China published the highest number of articles (n=46). Based on titles and abstracts alone, we were able to identify 1515 surveys, 733 systematic reviews, 512 cohort studies, 480 meta-analyses, and 362 randomized control trials. We identified 19 different topics covered among the publications reviewed. The most dominant topic was public health response, followed by clinical care practices during the COVID-19 pandemic, clinical characteristics and risk factors, and epidemic models for its spread.

CONCLUSIONS

We provide an overview of the COVID-19 literature and have identified current hotspots and research directions. Our findings can be useful for the research community to help prioritize research needs and recognize leading COVID-19 researchers, institutes, countries, and publishers. Our study shows that an AI-based bibliometric analysis has the potential to rapidly explore a large corpus of academic publications during a public health crisis. We believe that this work can be used to analyze other eHealth-related literature to help clinicians, administrators, and policy makers to obtain a holistic view of the literature and be able to categorize different topics of the existing research for further analyses. It can be further scaled (for instance, in time) to clinical summary documentation. Publishers should avoid noise in the data by developing a way to trace the evolution of individual publications and unique authors.

Central pulmonary embolism in patients with Covid-19

BACKGROUND

The purpose was to evaluate central pulmonary embolism (PE) in patients with Covid-19. The association with severe radiological pulmonary changes, prophylactic anticoagulation and ICU care was assessed.

METHODS

From 1 March until 31 May 2020, all in-hospital patients with a positive PCR for SARS-CoV-2-RNA and PE diagnosed with computed tomography pulmonary angiography were identified through diagnostic codes in medical charts. PE was characterised as central/peripheral and unilateral/bilateral. Covid-19 related lung changes were evaluated scoring the proportion of affected lung (max-score score 25) for all five lobes in both lungs. ICU and non-ICU patients were included and anticoagulant regimens were assessed.

RESULTS

Of 1162 patients with Covid-19, 41 were diagnosed with PE (cumulative incidence 3.5%), and of these 63.4% (=overall 2.2%) had central PE. PE on admission was present in 46.3%. No differences were seen in the distribution of central vs. peripheral PE in relation to prophylactic anticoagulation (p=.317). Of ICU patients 82.4% were diagnosed with central PE compared to 50.0% among non-ICU patients (p=.05). No association was observed between the presence of central PE and the extent of radiological Covid-19 changes (p=.451). Mild (0-12 p) and severe (13-25 p) pulmonary changes were seen in 63.4% and 36.6% of patients respectively.

CONCLUSIONS

Overall, and especially in ICU-patients, a high proportion of central PE was seen and many were diagnosed at admission. No association between central PE and prophylactic anticoagulation, or the extent of pulmonary Covid-19 changes was observed.

An Interpretable Model-Based Prediction of Severity and Crucial Factors in Patients with COVID-19

This study established an interpretable machine learning model to predict the severity of coronavirus disease 2019 (COVID-19) and output the most crucial deterioration factors. Clinical information, laboratory tests, and chest computed tomography (CT) scans at admission were collected. Two experienced radiologists reviewed the scans for the patterns, distribution, and CT scores of lung abnormalities. Six machine learning models were established to predict the severity of COVID-19. After parameter tuning and performance comparison, the optimal model was explained using Shapley Additive explanations to output the crucial factors. This study enrolled and classified 198 patients into mild (n = 162; 46.93 ± 14.49 years old) and severe (n = 36; 60.97 ± 15.91 years old) groups. The severe group had a higher temperature (37.42 ± 0.99°C vs. 36.75 ± 0.66°C), CT score at admission, neutrophil count, and neutrophil-to-lymphocyte ratio than the mild group. The XGBoost model ranked first among all models, with an AUC, sensitivity, and specificity of 0.924, 90.91%, and 97.96%, respectively. The early stage of chest CT, total CT score of the percentage of lung involvement, and age were the top three contributors to the prediction of the deterioration of XGBoost. A higher total score on chest CT had a more significant impact on the prediction. In conclusion, the XGBoost model to predict the severity of COVID-19 achieved excellent performance and output the essential factors in the deterioration process, which may help with early clinical intervention, improve prognosis, and reduce mortality.

The mean severity score and its correlation with common computed tomography chest manifestations in Egyptian patients with COVID-2019 pneumonia

Background

Computed tomography (CT) is one of the main diagnostic tools for early detection and management of coronavirus disease 2019 (COVID-19) pneumonia. This study aims to highlight the commonly encountered CT findings in patients with COVID-19 pneumonia in Egypt and the mean severity score and its correlation with the imaging findings. This study involved 200 patients with pathologically confirmed COVID-19 infection; non-contrast CT chest was performed for all cases; in addition, CT findings and severity score (CT-SS) were then assessed using descriptive analysis, and the correlation between the CT findings and disease severity was assessed.

Results

The ground-glass densities and peripheral adhesions were the most typical CT findings. Prominent interlobular septations; bronchial thickening/dilatation; CT signs of crazy-paving, halo, and reversed halo; and reactive mediastinal lymphadenopathy were significantly correlated with disease severity. The mean CT-SS of Egyptian patients with COVID-19 pneumonia was 11.2 (mild to moderate severity).

Conclusion

Multislice CT played a vital role in the early identification of Egyptian patients with COVID-19 pneumonia. The assessment of the CT severity score of COVID-19 is essential for the extent of pneumonia involvement to help clinicians achieve the purpose of early diagnosis and accurate treatment.

Chest CT Imaging Signature of Coronavirus Disease 2019 Infection: In Pursuit of the Scientific Evidence

BACKGROUND

Chest CT may be used for the diagnosis of coronavirus disease 2019 (COVID-19), but clear scientific evidence is lacking. Therefore, we systematically reviewed and meta-analyzed the chest CT imaging signature of COVID-19.

RESEARCH QUESTION

What is the chest CT imaging signature of COVID-19 infection?

STUDY DESIGN AND METHODS

A systematic literature search was performed for original studies on chest CT imaging findings in patients with COVID-19. Methodologic quality of studies was evaluated. Pooled prevalence of chest CT imaging findings were calculated with the use of a random effects model in case of between-study heterogeneity (predefined as I2 ≥50); otherwise, a fixed effects model was used.

RESULTS

Twenty-eight studies were included. The median number of patients with COVID-19 per study was 124 (range, 50-476), comprising a total of 3,466 patients. Median prevalence of symptomatic patients was 99% (range, >76.3%-100%). Twenty-seven of the studies (96%) had a retrospective design. Methodologic quality concerns were present with either risk of or actual referral bias (13 studies), patient spectrum bias (eight studies), disease progression bias (26 studies), observer variability bias (27 studies), and test review bias (14 studies). Pooled prevalence was 10.6% for normal chest CT imaging findings. Pooled prevalences were 90.0% for posterior predilection, 81.0% for ground-glass opacity, 75.8% for bilateral abnormalities, 73.1% for left lower lobe involvement, 72.9% for vascular thickening, and 72.2% for right lower lobe involvement. Pooled prevalences were 5.2% for pleural effusion, 5.1% for lymphadenopathy, 4.1% for airway secretions/tree-in-bud sign, 3.6% for central lesion distribution, 2.7% for pericardial effusion, and 0.7% for cavitation/cystic changes. Pooled prevalences of other CT imaging findings ranged between 10.5% and 63.2%.

INTERPRETATION

Studies on chest CT imaging findings in COVID-19 suffer from methodologic quality concerns. More high-quality research is necessary to establish diagnostic CT criteria for COVID-19. Based on the available evidence that requires cautious interpretation, several chest CT imaging findings appear to be suggestive of COVID-19, but normal chest CT imaging findings do not exclude COVID-19, not even in symptomatic patients.

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.

A Comprehensive Overview of the COVID-19 Literature: Machine Learning–Based Bibliometric Analysis (Preprint).. [DOI: 10.2196/preprints.23703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

BACKGROUND

Shortly after the emergence of COVID-19, researchers rapidly mobilized to study numerous aspects of the disease such as its evolution, clinical manifestations, effects, treatments, and vaccinations. This led to a rapid increase in the number of COVID-19–related publications. Identifying trends and areas of interest using traditional review methods (eg, scoping and systematic reviews) for such a large domain area is challenging.

OBJECTIVE

We aimed to conduct an extensive bibliometric analysis to provide a comprehensive overview of the COVID-19 literature.

METHODS

We used the COVID-19 Open Research Dataset (CORD-19) that consists of a large number of research articles related to all coronaviruses. We used a machine learning–based method to analyze the most relevant COVID-19–related articles and extracted the most prominent topics. Specifically, we used a clustering algorithm to group published articles based on the similarity of their abstracts to identify research hotspots and current research directions. We have made our software accessible to the community via GitHub.

RESULTS

Of the 196,630 publications retrieved from the database, we included 28,904 in our analysis. The mean number of weekly publications was 990 (SD 789.3). The country that published the highest number of COVID-19–related articles was China (2950/17,270, 17.08%). The highest number of articles were published in bioRxiv. Lei Liu affiliated with the Southern University of Science and Technology in China published the highest number of articles (n=46). Based on titles and abstracts alone, we were able to identify 1515 surveys, 733 systematic reviews, 512 cohort studies, 480 meta-analyses, and 362 randomized control trials. We identified 19 different topics covered among the publications reviewed. The most dominant topic was public health response, followed by clinical care practices during the COVID-19 pandemic, clinical characteristics and risk factors, and epidemic models for its spread.

CONCLUSIONS

We provide an overview of the COVID-19 literature and have identified current hotspots and research directions. Our findings can be useful for the research community to help prioritize research needs and recognize leading COVID-19 researchers, institutes, countries, and publishers. Our study shows that an AI-based bibliometric analysis has the potential to rapidly explore a large corpus of academic publications during a public health crisis. We believe that this work can be used to analyze other eHealth-related literature to help clinicians, administrators, and policy makers to obtain a holistic view of the literature and be able to categorize different topics of the existing research for further analyses. It can be further scaled (for instance, in time) to clinical summary documentation. Publishers should avoid noise in the data by developing a way to trace the evolution of individual publications and unique authors.

Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets

Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations.

Influenza A virus H1N1 associated pneumonia - acute and late aspects evaluated with high resolution tomography in hospitalized patients

Background

Influenza A (H1N1) virus often compromises the respiratory tract, leading to pneumonia, which is the principal cause of death in these patients. The purpose of this study was to review the acute and late phase pulmonary findings in influenza A(H1N1) associated pneumonia using high resolution computed tomography (HRCT), and to determine the importance of performing end expiration series.

Methods

Between July and August 2009, 140 patients presented with influenza A (H1N1) confirmed by real-timepolymerase chain reaction. Out of these, 27 patients underwent HRCT in the acute and late phases of pneumonia, allowing for a comparative study. Late phase exams were performed due to clinical worsening and up to 120 days later in patients with persistent complaints of dyspnea.

Results

Ground glass opacities, consolidations, and the combination of both were associated with the acute phase, whereas persistence or worsening of the lesions, lesion improvement, and air trapping in the end expiration series (as seen using HRCT, n=6) were observed in the late phase.

Conclusions

In the HRCT end expiration series, air trapping was found in the late phase of H1N1 associated pneumonia. Generally, these exams are not evaluated in research articles, and air trapping has not previously been studied using the end expiration series. Our study brings more scientific knowledge about aspects of pulmonary involvement by influenza A (H1N1), through evaluation with end expiration series, which makes the CT exam dynamic, translating the respiratory movement, and showing bronchial alteration.