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

Discovery of a new long COVID mouse model via systemic histopathological comparison of SARS-CoV-2 intranasal and inhalation infection

Intranasal infection is commonly used to establish a SARS-CoV-2 mouse model due to its non-invasive procedures and a minimal effect from the operation itself. However, mice intranasally infected with SARS-CoV-2 have a high mortality rate, which limits the utility of this model for exploring therapeutic strategies and the sequelae of non-fatal COVID-19 cases. To resolve these limitations, an aerosolised viral administration method has been suggested. However, an in-depth pathological analysis comparing the two models is lacking. Here, we show that inhalation and intranasal SARS-CoV-2 (106 PFU) infection models established in K18-hACE2 mice develop unique pathological features in both the respiratory and central nervous systems, which could be directly attributed to the infection method. While the inhalation-infection model exhibited relatively milder pathological parameters, it closely mimicked the prevalent chest CT pattern observed in COVID-19 patients with focal, peripheral lesions and fibrotic scarring in the recuperating lung. We also found the evidence of direct neuron-invasion from the olfactory receptor neurons to the olfactory bulb in the intranasal model and showed the trigeminal nerve as an alternative route of transmission to the brain in inhalation infected mice. Even after viral clearance confirmed at 14 days post-infection, mild lesions were still found in the brain of inhalation-infected mice. These findings suggest that the inhalation-infection model has advantages over the intranasal-infection model in closely mimicking the pathological features of non-fatal symptoms of COVID-19, demonstrating its potential to study the sequelae and possible interventions for long COVID.

Emergency Presentations of Coronavirus Disease (COVID-19): A Review of the Literature and Radiologic Perspective

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the debilitating global pandemic known as Coronavirus disease (COVID-19). In this paper, we highlight the widespread manifestations and complications across disease systems. In addition, we present their relevant imaging findings to inform appropriate investigations and management in patients presenting to the Emergency Department with COVID-19 and its respective sequalae. Of note, we outline considerations for diagnosis of long COVID, an important medium to long term sequalae in patients with previous COVID-19 infections.

SARS-CoV-2 infection unevenly impacts metabolism in the coronal periphery of the lungs

COVID-19 significantly decreases amino acids, fatty acids, and most eicosanoidsSARS-CoV-2 preferentially localizes to central lung tissueMetabolic disturbance is highest in peripheral tissue, not central like viral loadSpatial metabolomics allows detection of metabolites not altered overallSARS-CoV-2, the virus responsible for COVID-19, is a highly contagious virus that can lead to hospitalization and death. COVID-19 is characterized by its involvement in the lungs, particularly the lower lobes. To improve patient outcomes and treatment options, a better understanding of how SARS-CoV-2 impacts the body, particularly the lower respiratory system, is required. In this study, we sought to understand the spatial impact of COVID-19 on the lungs of mice infected with mouse-adapted SARS2-N501Y MA30 . Overall, infection caused a decrease in fatty acids, amino acids, and most eicosanoids. When analyzed by segment, viral loads were highest in central lung tissue, while metabolic disturbance was highest in peripheral tissue. Infected peripheral lung tissue was characterized by lower levels of fatty acids and amino acids when compared to central lung tissue. This study highlights the spatial impacts of SARS-CoV-2 and helps explain why peripheral lung tissue is most damaged by COVID-19.

Pericardial Effusion Predicts Clinical Outcomes in Patients with COVID-19: A Nationwide Multicenter Study

RATIONALE AND OBJECTIVES

The prognostic role of pericardial effusion (PE) in Covid 19 is unclear. The aim of the present study was to estimate the prognostic role of PE in patients with Covid 19 in a large multicentre setting.

MATERIALS AND METHODS

This retrospective study is a part of the German multicenter project RACOON (Radiological Cooperative Network of the Covid 19 pandemic). The acquired sample comprises 1197 patients, 363 (30.3%) women and 834 (69.7%) men. In every case, chest computed tomography was analyzed for PE. Data about 30-day mortality, need for mechanical ventilation and need for intensive care unit (ICU) admission were collected. Data were evaluated by means of descriptive statistics. Group differences were calculated with Mann-Whitney test and Fisher exact test. Uni-and multivariable regression analyses were performed.

RESULTS

Overall, 46.4% of the patients were admitted to ICU, mechanical lung ventilation was performed in 26.6% and 30-day mortality was 24%. PE was identified in 159 patients (13.3%). The presence of PE was associated with 30-day mortality: HR= 1.54, CI 95% (1.05; 2.23), p = 0.02 (univariable analysis), and HR= 1.60, CI 95% (1.03; 2.48), p = 0.03 (multivariable analysis). Furthermore, density of PE was associated with the need for intubation (OR=1.02, CI 95% (1.003; 1.05), p = 0.03) and the need for ICU admission (OR=1.03, CI 95% (1.005; 1.05), p = 0.01) in univariable regression analysis. The presence of PE was associated with 30-day mortality in male patients, HR= 1.56, CI 95%(1.01-2.43), p = 0.04 (multivariable analysis). In female patients, none of PE values predicted clinical outcomes.

CONCLUSION

The prevalence of PE in Covid 19 is 13.3%. PE is an independent predictor of 30-day mortality in male patients with Covid 19. In female patients, PE plays no predictive role.

Lung abnormalities on computed tomography of Vietnamese patients with COVID-19 and the association with medical variables

Objectives

Patients with COVID-19 may experience a lung injury without presenting clinical symptoms. Early detection of lung injury in patients with COVID-19 is required to enhance prediction and prevent severe progression.

Methods

Lung lesions in patients with COVID-19 were defined using the Fleischner Society terminology. Chest computed tomography lesions and their correlation with demographic characteristics and medical variables were identified.

Results

Patients with mild and moderate COVID-19 had up to 45% lung injuries, whereas critical patients had 55%. However, patients with mild and moderate COVID-19 typically had low-level lung injuries. Ground-glass (68.1%), consolidation (48.8%), opacity (36.3%), and nodular (6.9%) lung lesions were the most prevalent in patients with COVID-19. Patients with COVID-19 infected with the Delta variant had worse lung injury than those infected with the Alpha and Omicron. People vaccinated with ≥2 doses showed a lower risk of lung injury than those vaccinated with <1 dose. Patients <18 years old were less likely to have a lung injury than patients >18 years old. The treatment outcomes were unaffected by the severity of the lung injury.

Conclusion

Patients with mild COVID-19 had a similar risk of lung injury as patients with severe COVID-19. Thus, using chest computed tomography to detect lung injury can enhance the treatment outcomes and reduce the patient's risk of pulmonary complications.

ERS International Congress 2023: highlights from the Thoracic Oncology Assembly

Lung cancer is the leading cause of cancer mortality in the world. It greatly affects the patients' quality of life, and is thus a challenge for the daily practice in respiratory medicine. Advances in the genetic knowledge of thoracic tumours' mutational landscape, and the development of targeted therapies and immune checkpoint inhibitors, have led to a paradigm shift in the treatment of lung cancer and pleural mesothelioma. During the 2023 European Respiratory Society Congress in Milan, Italy, experts from all over the world presented their high-quality research and reviewed best clinical practices. Lung cancer screening, management of early stages of lung cancer, application of artificial intelligence and biomarkers were discussed and they will be summarised here.

COVID-19 progression in hospitalized patients using follow-up in vivo CT and ex vivo microCT

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease-19 (COVID-19) which can lead to acute respiratory distress syndrome (ARDS) and evolve to pulmonary fibrosis. Computed tomography (CT) is used to study disease progression and describe radiological patterns in COVID-19 patients. This study aimed to assess disease progression regarding lung volume and density over time on follow-up in vivo chest CT and give a unique look at parenchymal and morphological airway changes in "end-stage" COVID-19 lungs using ex vivo microCT.

METHODS

Volumes and densities of the lung/lobes of three COVID-19 patients were assessed using follow-up in vivo CT and ex vivo whole lung microCT scans. Airways were quantified by airway segmentations on whole lung microCT and small-partition microCT. As controls, three discarded healthy donor lungs were used. Histology was performed in differently affected regions in the COVID-19 lungs.

RESULTS

In vivo, COVID-19 lung volumes decreased while density increased over time, mainly in lower lobes as previously shown. Ex vivo COVID-19 lung volumes decreased by 60% and all lobes were smaller compared to controls. Airways were more visible on ex vivo microCT in COVID-19, probably due to fibrosis and increased airway diameter. In addition, small-partition microCT showed more deformation of (small) airway morphology and fibrotic organization in severely affected regions with heterogeneous distributions within the same lung which was confirmed by histology.

CONCLUSIONS

COVID-19-ARDS and subsequent pulmonary fibrosis alters lung architecture and airway morphology which is described using in vivo CT, ex vivo microCT, and histology.

COVID-19 Pneumonia with Migratory Pattern in Agammaglobulinemic Patients: A Report of Two Cases and Review of Literature

X-linked agammaglobulinemia (XLA) is a primary immunodeficiency characterized by marked reduction in serum immunoglobulins and early-onset infections. Coronavirus Disease-2019 (COVID-19) pneumonia in immunocompromised patients presents clinical and radiological peculiarities which have not yet been completely understood. Very few cases of agammaglobulinemic patients with COVID-19 have been reported since the beginning of the pandemic in February 2020. We report two cases of migrant COVID-19 pneumonia in XLA patients.

COVID-19: Findings in nuclear medicine from head to toe

COVID-19 is a multisystemic disease, and hence its potential manifestations on nuclear medicine imaging can extend beyond the lung. Therefore, it is important for the nuclear medicine physician to recognize these manifestations in the clinic. While FDG-PET/CT is not indicated routinely in COVID-19 evaluation, its unique capability to provide a functional and anatomical assessment of the entire body means that it can be a powerful tool to monitor acute, subacute, and long-term effects of COVID-19. Single-photon scintigraphy is routinely used to assess conditions such as pulmonary embolism, cardiac ischemia, and thyroiditis, and COVID-19 may present in these studies. The most common nuclear imaging finding of COVID-19 vaccination to date is hypermetabolic axillary lymphadenopathy. This may pose important diagnostic and management dilemmas in oncologic patients, particularly those with malignancies where the axilla constitutes a lymphatic drainage area. This article aims to summarize the relevant literature published since the beginning of the pandemic on the intersection between COVID-19 and nuclear medicine.

Hidden sources of bias in diagnostic studies: the example of visceral leishmaniasis in east Africa

A clear understanding of the accuracy of diagnostic tests is essential for the management of infections such as visceral leishmaniasis in endemic areas. Using both published and unpublished datasets from field diagnostic trials of visceral leishmaniasis in the past ten years, we show the potential effects of unrecognised sources of bias including work-up bias, referral bias, and reference test bias on the results. We outline why these biases, which can occur in diagnostic studies of any disease, can go unrecognised despite adherence to current STARD and QUADAS-2 guidelines. Using these examples and referring to others seen in studies of bacterial and viral infections, we make specific recommendations on how these biases might be avoided through specific steps in study design, study reporting, and the locations where studies are conducted.

New, fast, and precise method of COVID-19 detection in nasopharyngeal and tracheal aspirate samples combining optical spectroscopy and machine learning

Fast, precise, and low-cost diagnostic testing to identify persons infected with SARS-CoV-2 virus is pivotal to control the global pandemic of COVID-19 that began in late 2019. The gold standard method of diagnostic recommended is the RT-qPCR test. However, this method is not universally available, and is time-consuming and requires specialized personnel, as well as sophisticated laboratories. Currently, machine learning is a useful predictive tool for biomedical applications, being able to classify data from diverse nature. Relying on the artificial intelligence learning process, spectroscopic data from nasopharyngeal swab and tracheal aspirate samples can be used to leverage characteristic patterns and nuances in healthy and infected body fluids, which allows to identify infection regardless of symptoms or any other clinical or laboratorial tests. Hence, when new measurements are performed on samples of unknown status and the corresponding data is submitted to such an algorithm, it will be possible to predict whether the source individual is infected or not. This work presents a new methodology for rapid and precise label-free diagnosing of SARS-CoV-2 infection in clinical samples, which combines spectroscopic data acquisition and analysis via artificial intelligence algorithms. Our results show an accuracy of 85% for detection of SARS-CoV-2 in nasopharyngeal swab samples collected from asymptomatic patients or with mild symptoms, as well as an accuracy of 97% in tracheal aspirate samples collected from critically ill COVID-19 patients under mechanical ventilation. Moreover, the acquisition and processing of the information is fast, simple, and cheaper than traditional approaches, suggesting this methodology as a promising tool for biomedical diagnosis vis-à-vis the emerging and re-emerging viral SARS-CoV-2 variant threats in the future.

Research on the Application of Artificial Intelligence in Public Health Management: Leveraging Artificial Intelligence to Improve COVID-19 CT Image Diagnosis

Since the start of 2020, the outbreak of the Coronavirus disease (COVID-19) has been a global public health emergency, and it has caused unprecedented economic and social disaster. In order to improve the diagnosis efficiency of COVID-19 patients, a number of researchers have conducted extensive studies on applying artificial intelligence techniques to the analysis of COVID-19-related medical images. The automatic segmentation of lesions from computed tomography (CT) images using deep learning provides an important basis for the quantification and diagnosis of COVID-19 cases. For a deep learning-based CT diagnostic method, a few of accurate pixel-level labels are essential for the training process of a model. However, the translucent ground-glass area of the lesion usually leads to mislabeling while performing the manual labeling operation, which weakens the accuracy of the model. In this work, we propose a method for correcting rough labels; that is, to hierarchize these rough labels into precise ones by performing an analysis on the pixel distribution of the infected and normal areas in the lung. The proposed method corrects the incorrectly labeled pixels and enables the deep learning model to learn the infected degree of each infected pixel, with which an aiding system (named DLShelper) for COVID-19 CT image diagnosis using the hierarchical labels is also proposed. The DLShelper targets lesion segmentation from CT images, as well as the severity grading. The DLShelper assists medical staff in efficient diagnosis by providing rich auxiliary diagnostic information (including the severity grade, the proportions of the lesion and the visualization of the lesion area). A comprehensive experiment based on a public COVID-19 CT image dataset is also conducted, and the experimental results show that the DLShelper significantly improves the accuracy of segmentation for the lesion areas and also achieves a promising accuracy for the severity grading task.

Multi-center Integrating Radiomics, Structured Reports, and Machine Learning Algorithms for Assisted Classification of COVID-19 in Lung Computed Tomography

Purpose

To evaluate the classification performance of structured report features, radiomics, and machine learning (ML) models to differentiate between Coronavirus Disease 2019 (COVID-19) and other types of pneumonia using chest computed tomography (CT) scans.

Methods

Sixty-four COVID-19 subjects and 64 subjects with non-COVID-19 pneumonia were selected. The data was split into two independent cohorts: one for the structured report, radiomic feature selection and model building (n = 73), and another for model validation (n = 55). Physicians performed readings with and without machine learning support. The model's sensitivity and specificity were calculated, and inter-rater reliability was assessed using Cohen's Kappa agreement coefficient.

Results

Physicians performed with mean sensitivity and specificity of 83.4 and 64.3%, respectively. When assisted with machine learning, the mean sensitivity and specificity increased to 87.1 and 91.1%, respectively. In addition, machine learning improved the inter-rater reliability from moderate to substantial.

Conclusion

Integrating structured reports and radiomics promises assisted classification of COVID-19 in CT chest scans.

Cavitary lung lesion as sequelae of mild COVID-19 in a patient with HIV

Cavitary lung lesions involve a large spectrum of acute to chronic infections, chronic systemic diseases, and malignancies. During the COVID-19 pandemic, we have seen an increase in lung cavitation, mainly secondary to bacterial infection however, there have been limited reports of these lesions with mild COVID-19 disease. There has been an association between severe COVID-19 infection and secondary bacterial/fungal infections and cavitary lung lesions. We report the first case of a 32-year-old man with well-controlled HIV who presented with cough and fever from what appeared to be a cavitary lesion as a sequela of his recent COVID-19 infection.

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.

High-Resolution Computed Tomography of Cystic Lung Disease

The cystic lung diseases (CLD) are characterized by the presence of multiple, thin-walled, air-filled spaces in the pulmonary parenchyma. Cyst formation may occur with congenital, autoimmune, inflammatory, infectious, or neoplastic processes. Recognition of cyst mimics such as emphysema and bronchiectasis is important to prevent diagnostic confusion and unnecessary evaluation. Chest CT can be diagnostic or may guide the workup based on cyst number, distribution, morphology, and associated lung, and extrapulmonary findings. Diffuse CLD (DCLDs) are often considered those presenting with 10 or more cysts. The more commonly encountered DCLDs include lymphangioleiomyomatosis, pulmonary Langerhans' cell histiocytosis, lymphoid interstitial pneumonia, Birt-Hogg-Dubé syndrome, and amyloidosis/light chain deposition disease.

Pneumonia Frequency and Severity in Patients With Symptomatic COVID-19: Impact of mRNA and Adenovirus Vector Vaccines

BACKGROUND. Additional evidence of the role of COVID-19 vaccination in reducing pneumonia frequency and severity in the setting of breakthrough infection could help combat ongoing vaccine hesitancy. OBJECTIVE. The purpose of this article was to compare the frequency and severity of pneumonia on chest CT in patients with confirmed COVID-19 between patients who are unvaccinated and those who are fully vaccinated by messenger RNA (mRNA) or adenovirus vector vaccines. METHODS. This retrospective single-center study included 467 patients (250 men, 217 women; mean age, 65 ± 17 [SD] years) who underwent chest CT between December 15, 2021, and February 18, 2022, during hospitalization for symptomatic COVID-19, confirmed by reverse transcriptase-polymerase chain reaction assay. A total of 216 patients were unvaccinated, and 167 and 84 patients were fully vaccinated (defined as receipt of the second dose at least 14 days before COVID-19 diagnosis) by the BNT162b2 mRNA vaccine or the ChAdOx1-S adenovirus vector vaccine, respectively. Semiquantitative CT severity scores (CT-SS; 0-25 scale) were determined; CT-SS of 0 indicated absence of pneumonia. Presence of bilateral involvement was assessed in patients with pneumonia. Associations were explored between vaccination status and CT findings. RESULTS. The frequency of the absence of pneumonia was 15% (32/216) in unvaccinated patients, 29% (24/84) in patients fully vaccinated with ChAdOx1-S vaccine, and 51% (85/167) in patients fully vaccinated with BNT162b2 vaccine (unvaccinated and ChAdOx1-S vs BNT162b2: p < .001; unvaccinated vs ChAdOx1-S: p = .08). Mean CT-SS was significantly higher in unvaccinated patients (9.7 ± 6.1) than in patients fully vaccinated with BNT162b2 (5.2 ± 6.1) or ChAdOx1-S (6.2 ± 5.9) vaccine (both p < .001). Full vaccination was significantly associated with CT-SS independent of patient age and sex (estimate = -4.46; p < .001). Frequency of bilateral lung involvement was significantly higher in unvaccinated patients (158/184, 86%) and in patients fully vaccinated with ChAdOx1-S vaccine (54/60, 90%) than in patients fully vaccinated with BNT162b2 vaccine (47/82, 57%) (both p < .001). CONCLUSION. Pneumonia frequency and severity were lower in patients with full vaccination by mRNA and adenovirus vector vaccines who experienced breakthrough infections in comparison with unvaccinated patients. CLINICAL IMPACT. The visual observation by radiologic imaging of the protective effect of vaccination on lung injury in patients with breakthrough infections provides additional evidence supporting the clinical benefit of vaccination.

Sarcoidosis and COVID-19: At the Cross-Road between Immunopathology and Clinical Manifestation

Coronavirus disease 2019 (COVID-19) has been associated with dysregulation of the immune system featuring inappropriate immune responses, exacerbation of inflammatory responses, and multiple organ dysfunction syndrome in patients with severe disease. Sarcoidosis, also known as Besnier-Boeck-Schaumann disease, is an idiopathic granulomatous multisystem disease characterized by dense epithelioid non-necrotizing lesions with varying degrees of lymphocytic inflammation. These two diseases have similar clinical manifestations and may influence each other at multiple levels, eventually affecting their clinical courses and prognosis. Notably, sarcoidosis patients are at high risk of severe COVID-19 pneumonia because of the underlying lung disease and chronic immunosuppressive treatment. In this narrative review, we will discuss interactions between sarcoidosis and COVID-19 in terms of clinical manifestations, treatment, and pathogenesis, including the role of the dysregulated renin-angiotensin system, altered immune responses involving increased cytokine levels and immune system hyperactivation, and cellular death pathways.

Factors Affecting the Risk of Interstitial Lung Disease Development in Hospitalized Patients With COVID-19 Pneumonia

BACKGROUND

Coronavirus disease 2019 (COVID-19) related chronic lung changes secondary to severe disease have become well known. The aim of this study was to determine the risk factors that affect the development of interstitial lung disease in subjects with COVID-19 pneumonia who were hospitalized.

METHODS

Patients hospitalized with COVID-19 pneumonia between June 2020 and March 2021 were retrospectively analyzed. Smoking histories, comorbidities, reverse transcriptase polymerase chain reaction test results, laboratory parameters at the time of the diagnosis, oxygen support, the use of corticosteroids with dosage and duration data, the need for ICU care were recorded. High-resolution computed tomographies (HRCT) were obtained for study population in their 3-6 months follow-up visit. The subjects were classified as having residual parenchymal lung disease if a follow-up HRCT revealed parenchymal abnormalities except pure ground-glass opacities (the residual disease group). The control group consisted of the subjects with normal chest radiograph or HRCT in their follow-up visit or the presence of pure ground-glass opacities. Two groups were compared for their demographic and clinical abnormalities, laboratory parameters, treatment regimens, and the need for ICU care.

RESULTS

The study included 446 subjects. The mean ± SD age was 58.4 ± 13.87 years, with 257 men (57.6%). Although 55 subjects had normal HRCT features on their follow-up HRCT, 157 had abnormal lung parenchymal findings. Univariate logistic regression analysis revealed statistically significant results for age, sex, corticosteroid treatment, and the need for ICU care for predicting interstitial lung disease development (P < .001, P = .003, P < .001, and P < .001, respectively). Also, the residual disease group had significantly higher leukocyte and neutrophil counts and lower lymphocyte counts (P < .001, P < .001, P = .004, respectively). Correlated with these findings, neutrophil-to-lymphocyte ratios and platelet-to-lymphocyte ratios were significantly higher in the residual disease group (P < .001 and P = .008, respectively).

CONCLUSIONS

Residual parenchymal disease was observed 3-6 months after discharge in one third of the subjects hospitalized with COVID-19 pneumonia. It was observed that interstitial lung disease developed more frequently in older men and in those subjects with more-severe disease parameters.

Biofilms possibly harbor occult SARS-CoV-2 may explain lung cavity, re-positive and long-term positive results

During the COVID-19 pandemic, there have been an increasing number of COVID-19 patients with cavitary or cystic lung lesions, re-positive or long-term positive nucleic acid tests, but the mechanism is still unclear. Lung cavities may appear at long time interval from initial onset of coronavirus infection, generally during the absorption phase of the disease. The main histopathological characteristic is diffuse alveolar damage and may have more severe symptoms after initial recovery from COVID-19 and an increased mortality rate. There are many possible etiologies of pulmonary cavities in COVID-19 patients and we hypothesize that occult SARS-CoV-2, in the form of biofilm, is harbored in the airway lacuna with other pathogenic microorganisms, which may be the cause of pulmonary cavities and repeated and long-term positive nucleic acid tests.

FAM: focal attention module for lesion segmentation of COVID-19 CT images

The novel coronavirus pneumonia (COVID-19) is the world's most serious public health crisis, posing a serious threat to public health. In clinical practice, automatic segmentation of the lesion from computed tomography (CT) images using deep learning methods provides an promising tool for identifying and diagnosing COVID-19. To improve the accuracy of image segmentation, an attention mechanism is adopted to highlight important features. However, existing attention methods are of weak performance or negative impact to the accuracy of convolutional neural networks (CNNs) due to various reasons (e.g. low contrast of the boundary between the lesion and the surrounding, the image noise). To address this issue, we propose a novel focal attention module (FAM) for lesion segmentation of CT images. FAM contains a channel attention module and a spatial attention module. In the spatial attention module, it first generates rough spatial attention, a shape prior of the lesion region obtained from the CT image using median filtering and distance transformation. The rough spatial attention is then input into two 7 × 7 convolution layers for correction, achieving refined spatial attention on the lesion region. FAM is individually integrated with six state-of-the-art segmentation networks (e.g. UNet, DeepLabV3+, etc.), and then we validated these six combinations on the public dataset including COVID-19 CT images. The results show that FAM improve the Dice Similarity Coefficient (DSC) of CNNs by 2%, and reduced the number of false negatives (FN) and false positives (FP) up to 17.6%, which are significantly higher than that using other attention modules such as CBAM and SENet. Furthermore, FAM significantly improve the convergence speed of the model training and achieve better real-time performance. The codes are available at GitHub (https://github.com/RobotvisionLab/FAM.git).

Prediction of potential severe COVID-19 patients based on CT radiomics: a retrospective study

PURPOSE

Coronavirus disease 2019 (COVID-19) is a recently declared worldwide pandemic. Triaging of patients into severe and non-severe could further help in targeted management. "Potential severe patients" is a category of patients who did not have severe symptoms at their initial diagnosis, but eventually progressed to be severe patients and are easily overlooked in the early stage. This work aimed to develop and evaluate a CT-based radiomics signature for the prediction of these potential severe COVID-19 patients.

METHODS

150 COVID-19 patients were enrolled and randomly divided into cross-validation and independent test sets. First, Their clinical characteristics were screened using the univariate and multivariate logistic regression step by step. Then, radiomics features were extracted from the lesions on their chest CT images. Subsequently, the Inter- and intra-class correlation coefficients (ICC) analysis, minimum-redundancy maximum-relevance (mRMR) selection and the least absolute shrinkage and selection operator (LASSO) algorithm were used step by step for feature selection and construction of a radiomics signature. Finally, the screened clinical risk factors and constructed radiomics signature were combined for the Combined model and Radiomics+Clinics nomogram construction. The predictive performance of the Radiomics and Combined models were evaluated and compared using receiver operating characteristic curve (ROC) analysis, Hosmer-Lemeshow test and Delong test.

RESULTS

Clinical characteristics analysis resulted in the screening of five clinical risk factors. The combination of ICC, mRMR and LASSO methods resulted in the selection of ten radiomics features, which made up the radiomics signature. The differences in the radiomics signature between the potential severe and non-severe groups in cross-validation set and test sets were both p < 0.001. All Radiomics and Combined models showed a very good predictive performance with the accuracy and AUC of nearly or above 0.9. Additionally, we found no significant difference in the predictive performance between these two models.

CONCLUSIONS

A CT-based radiomics signature for the prediction of potential severe COVID-19 patients was constructed and evaluated. Constructed Radiomics and Combined model showed good feasibility and accuracy. The Radiomics+Clinical nomogram, acted as a useful tool, may assist clinicians to better identify potential severe cases to target their management in the COVID-19 pandemic prevention and control. This article is protected by copyright. All rights reserved.

Immune-based therapeutic approaches in COVID-19

Coronavirus disease 2019 (COVID-19) is a viral disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), a member of the Coronaviridae family. On March 11, 2020 the World Health Organization (WHO) has named the newly emerged rapidly-spreading epidemic as a pandemic. Besides the risk-reduction measures such as physical and social distancing and vaccination, a wide range of treatment modalities have been developed; aiming to fight the disease. The immune system is known as a double-edged sword in COVID-19 pathogenesis, with respect to its role in eliminating the pathogen and in inducing complications such as cytokine storm syndrome. Hence, immune-based therapeutic approaches have become an interesting field of COVID-19 research, including corticosteroids, intravenous immunoglobulins (IVIG), interferon therapy, and more COVID-19-specific approaches such as anti-SARS-CoV-2-monoclonal antibodies. Herein, we did a comprehensive review on immune-based therapeutic approaches for COVID-19. DATA AVAILABILITY STATEMENT: Not applicable.

Reduction in Chest CT Severity and Improved Hospital Outcomes in SARS-CoV-2 Omicron Compared with Delta Variant Infection

Background The SARS-Cov-2 Omicron variant demonstrates rapid spread but reduced disease severity. Studies evaluating lung imaging findings of Omicron infection versus non-Omicron infection remain lacking. Purpose To compare the Omicron variant with the SARS-CoV-2 Delta variant according to their chest CT radiologic pattern, biochemical parameters, clinical severity, and hospital outcomes after adjusting for vaccination status. Materials and Methods This retrospective study included hospitalized adult patients with reverse transcriptase-polymerase chain reaction test results positive for SARS-CoV-2, with CT pulmonary angiography performed within 7 days of admission between December 1, 2021, and January 14, 2022. Multiple readers performed blinded radiologic analyses that included RSNA CT classification, chest CT severity score (CTSS) (range, 0 [least severe] to 25 [most severe]), and CT imaging features, including bronchial wall thickening. Results A total of 106 patients (Delta group, n = 66; Omicron group, n = 40) were evaluated (overall mean age, 58 years ± 18 [SD]; 58 men). In the Omicron group, 37% of CT pulmonary angiograms (15 of 40 patients) were categorized as normal compared with 15% (10 of 66 patients) of angiograms in the Delta group (P = .016). A generalized linear model was used to control for confounding variables, including vaccination status, and Omicron infection was associated with a CTSS that was 7.2 points lower than that associated with Delta infection (β = -7.2; 95% CI: -9.9, -4.5; P < .001). Bronchial wall thickening was more common with Omicron infection than with Delta infection (odds ratio [OR], 2.4; 95% CI: 1.01, 5.92; P = .04). A booster shot was associated with a protective effect for chest infection (median CTSS, 5; IQR, 0-11) when compared with unvaccinated individuals (median CTSS, 11; IQR, 7.5-14.0) (P = .03). The Delta variant was associated with a higher OR of severe disease (OR, 4.6; 95% CI: 1.2, 26; P = .01) and admission to a critical care unit (OR, 7.0; 95% CI: 1.5, 66; P = .004) when compared with the Omicron variant. Conclusion The SARS-CoV-2 Omicron variant was associated with fewer and less severe changes on chest CT images compared with the Delta variant. Patients with Omicron infection had greater frequency of bronchial wall thickening but less severe disease and improved hospital outcomes when compared with patients with Delta infection. © RSNA, 2022 Online supplemental material is available for this article.

Changes in diagnostic usefulness of the JRS scoring system in COVID-19 pneumonia by SARS-CoV-2 vaccination

Introduction

The Japanese Respiratory Society (JRS) scoring system is a useful tool for the rapid presumptive diagnosis of atypical pneumonia in non-elderly (aged <60 years) patients. As SARS-CoV-2 vaccination progresses, COVID-19 in elderly people has markedly reduced. We investigated changes in diagnostic usefulness of the JRS scoring system in COVID-19 pneumonia between the Delta variant group (vaccination period) and non-Delta variant group (before the vaccination period).

Methods

This study was conducted at five institutions and assessed a total of 1121 patients with COVID-19 pneumonia (298 had the Delta variant). During the vaccination period, the Delta variant has spread and replaced the Alfa variant. We evaluated the vaccination period as the Delta variant group.

Results

Among the six parameters of the JRS scoring system, matching rates of two parameters were higher in the Delta variant group than the non-Delta variant group (pre-vaccination period): age <60 years (77.5% vs 42.2%, P < 0.0001) and no or minor comorbid illness (69.1% vs 57.8%, p = 0.0007). The sensitivity of the diagnosis of atypical pneumonia in patients with COVID-19 pneumonia was significantly higher in the Delta variant group compared with the non-Delta variant group (80.2% vs 58.3%, p < 0.0001). When the diagnostic sensitivity was analyzed for different ages, the diagnostic sensitivities for the Delta variant and non-Delta variant groups were 92.6% and 95.5% for non-elderly patients and 39.1% and 32.5% for elderly patients, respectively.

Conclusions

Our results demonstrated that the JRS scoring system is a useful tool for distinguishing between COVID-19 pneumonia and bacterial pneumonia in the COVID-19 vaccination period, but not before the vaccination period.

Multiple small pneumatoceles as a complication of a SARS-CoV-2 infection in a child from Myanmar

During the COVID-19 pandemic, pneumatoceles caused by 2019-nCoV are easily neglected, particularly in children. Which may potentially increase the risk of pneumothorax and pulmonary infection, we need to pay special attention to the management of population migration, especially for children.

Multiorgan Involvement in SARS-CoV-2 Infection: The Role of the Radiologist from Head to Toe

Radiology plays a crucial role for the diagnosis and management of COVID-19 patients during the different stages of the disease, allowing for early detection of manifestations and complications of COVID-19 in the different organs. Lungs are the most common organs involved by SARS-CoV-2 and chest computed tomography (CT) represents a reliable imaging-based tool in acute, subacute, and chronic settings for diagnosis, prognosis, and management of lung disease and the evaluation of acute and chronic complications. Cardiac involvement can be evaluated by using cardiac computed tomography angiography (CCTA), considered as the best choice to solve the differential diagnosis between the most common cardiac conditions: acute coronary syndrome, myocarditis, and cardiac dysrhythmia. By using compressive ultrasound it's possible to study the peripheral arteries and veins and to exclude the deep vein thrombosis, directly linked to the onset of pulmonary embolism. Moreover, CT and especially MRI can help to evaluate the gastrointestinal involvement and assess hepatic function, pancreas involvement, and exclude causes of lymphocytopenia, thrombocytopenia, and leukopenia, typical of COVID-19 patients. Finally, radiology plays a crucial role in the early identification of renal damage in COVID-19 patients, by using both CT and US. This narrative review aims to provide a comprehensive radiological analysis of commonly involved organs in patients with COVID-19 disease.

Atypical unilateral SARS-CoV-2 pneumonia in a single lung re-transplanted patient: a case report

Background

Since December 2019, the SARS-CoV-2 pandemic significantly has impacted the medical community. When infected with SARS-CoV-2, most of the patients developed bilateral pneumonia. We have herein presented the atypical case of a patient who developed unilateral SARS-CoV-2 pneumonia, affecting only the second lung allograft re-transplanted (re-LTX).

Case Presentation

A SARS-CoV-2 infection occurred in a 2-dose vaccinated patient with LTx with a history of second unilateral lung transplantation performed after an end-stage bronchiolitis obliterans syndrome. The first symptoms started with a flu-like syndrome, and the patient's clinical condition worsened with nonsevere acute respiratory failure requiring conventional oxygen therapy. Treatment consisted in administrating specific anti-SARS-CoV-2 monoclonal antibodies along with probabilistic antibiotherapy, anticoagulation, and steroids. On day 7, the patient was discharged from hospital. We aimed to assess this atypical unilateral pneumonia based on different explorations. A ventilation scintigraphy showed a severe ventilation decrease owing to end-stage bronchiolitis obliterans syndrome within the left first allograft, which may be associated with asymmetrical virus diffusion between the 2 lungs. We did not identify any other relevant differences with respect to the 2 donors’ clinical characteristics. Using specific immunohistochemistry staining against angiotensin converting enzyme-2 receptor, the main known receptor for SARS-CoV-2 binding on airway epithelial cells, no staining difference was observed between the 2 lung biopsies that were collected at re-LTx from each lung.

Conclusions

With the present case report, we aimed to highlight how this kind of unusual presentation may be caused by the difference of ventilation between the 2 lungs.

Increased ACE2, sRAGE, and Immune Activation, but Lowered Calcium and Magnesium in COVID-19

BACKGROUND

The characterization of new biomarkers that could help externally validate the diagnosis of COVID-19 and optimize treatments is extremely important. Many studies have established changes in immune-inflammatory and antibody levels, but few studies measured the soluble receptor for the advanced glycation end product (sRAGE), angiotensin-converting enzyme 2 (ACE2), calcium, and magnesium in COVID-19.

OBJECTIVE

To evaluate serum advanced glycation end-product receptor (sRAGE) and angiotensin converting enzyme (ACE)2 and peripheral oxygen saturation (SpO2) and chest CT scan abnormalities (CCTA) in COVID-19.

METHODS

sRAGE, ACE2, interleukin (IL)-6, IL-10, C-reactive protein (CRP), calcium, magnesium, and albumin were measured in 60 COVID-19 patients and 30 healthy controls.

RESULTS

COVID-19 is characterized by significantly increased IL-6, CRP, IL-10, sRAGE, ACE2, and lowered SpO2, albumin, magnesium, and calcium. COVID-19 with CCTAs showed lower SpO₂ and albumin. SpO2 was significantly inversely correlated with IL-6, IL-10, CRP, sRAGE, and ACE2, and positively with albumin, magnesium, and calcium. Neural networks showed that a combination of calcium, IL-6, CRP, and sRAGE yielded an accuracy of 100% in detecting COVID-19 patients, with calcium being the most important predictor followed by IL-6 and CRP. Patients with positive IgG results showed a significant elevation in the serum level of IL-6, sRAGE, and ACE2 compared to the negatively IgG patient subgroup.

CONCLUSION

The results show that immune-inflammatory and RAGE pathways biomarkers may be used as an external validating criterion for the diagnosis of COVID-19. Those pathways coupled with lowered SpO2, calcium, and magnesium are drug targets that may help reduce the consequences of COVID-19.

Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review

Artificial intelligence (AI) and computer vision (CV) methods become reliable to extract features from radiological images, aiding COVID-19 diagnosis ahead of the pathogenic tests and saving critical time for disease management and control. Thus, this review article focuses on cascading numerous deep learning-based COVID-19 computerized tomography (CT) imaging diagnosis research, providing a baseline for future research. Compared to previous review articles on the topic, this study pigeon-holes the collected literature very differently (i.e., its multi-level arrangement). For this purpose, 71 relevant studies were found using a variety of trustworthy databases and search engines, including Google Scholar, IEEE Xplore, Web of Science, PubMed, Science Direct, and Scopus. We classify the selected literature in multi-level machine learning groups, such as supervised and weakly supervised learning. Our review article reveals that weak supervision has been adopted extensively for COVID-19 CT diagnosis compared to supervised learning. Weakly supervised (conventional transfer learning) techniques can be utilized effectively for real-time clinical practices by reusing the sophisticated features rather than over-parameterizing the standard models. Few-shot and self-supervised learning are the recent trends to address data scarcity and model efficacy. The deep learning (artificial intelligence) based models are mainly utilized for disease management and control. Therefore, it is more appropriate for readers to comprehend the related perceptive of deep learning approaches for the in-progress COVID-19 CT diagnosis research.

A comparative study of medical image enhancement algorithms and quality assessment metrics on COVID-19 CT images

Medical imaging can help doctors in better diagnosis of several conditions. During the present COVID-19 pandemic, timely detection of novel coronavirus is crucial, which can help in curing the disease at an early stage. Image enhancement techniques can improve the visual appearance of COVID-19 CT scans and speed-up the process of diagnosis. In this study, we analyze some state-of-the-art image enhancement techniques for their suitability in enhancing the CT scans of COVID-19 patients. Six quantitative metrics, Entropy, SSIM, AMBE, PSNR, EME, and EMEE, are used to evaluate the enhanced images. Two experienced radiologists were involved in the study to evaluate the performance of the enhancement techniques and the quantitative metrics used to assess them.

A novel unsupervised approach based on the hidden features of Deep Denoising Autoencoders for COVID-19 disease detection

Chest imaging can represent a powerful tool for detecting the Coronavirus disease 2019 (COVID-19). Among the available technologies, the chest Computed Tomography (CT) scan is an effective approach for reliable and early detection of the disease. However, it could be difficult to rapidly identify by human inspection anomalous area in CT images belonging to the COVID-19 disease. Hence, it becomes necessary the exploitation of suitable automatic algorithms able to quick and precisely identify the disease, possibly by using few labeled input data, because large amounts of CT scans are not usually available for the COVID-19 disease. The method proposed in this paper is based on the exploitation of the compact and meaningful hidden representation provided by a Deep Denoising Convolutional Autoencoder (DDCAE). Specifically, the proposed DDCAE, trained on some target CT scans in an unsupervised way, is used to build up a robust statistical representation generating a target histogram. A suitable statistical distance measures how this target histogram is far from a companion histogram evaluated on an unknown test scan: if this distance is greater of a threshold, the test image is labeled as anomaly, i.e. the scan belongs to a patient affected by COVID-19 disease. Some experimental results and comparisons with other state-of-the-art methods show the effectiveness of the proposed approach reaching a top accuracy of 100% and similar high values for other metrics. In conclusion, by using a statistical representation of the hidden features provided by DDCAEs, the developed architecture is able to differentiate COVID-19 from normal and pneumonia scans with high reliability and at low computational cost.

Clinical update on COVID-19 for the emergency clinician: Presentation and evaluation

INTRODUCTION

Coronavirus disease of 2019 (COVID-19) has resulted in millions of cases worldwide. As the pandemic has progressed, the understanding of this disease has evolved.

OBJECTIVE

This first in a two-part series on COVID-19 updates provides a focused overview of the presentation and evaluation of COVID-19 for emergency clinicians.

DISCUSSION

COVID-19, caused by Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), has resulted in significant morbidity and mortality worldwide. Several variants exist, including a variant of concern known as Delta (B.1.617.2 lineage) and the Omicron variant (B.1.1.529 lineage). The Delta variant is associated with higher infectivity and poor patient outcomes, and the Omicron variant has resulted in a significant increase in infections. While over 80% of patients experience mild symptoms, a significant proportion can be critically ill, including those who are older and those with comorbidities. Upper respiratory symptoms, fever, and changes in taste/smell remain the most common presenting symptoms. Extrapulmonary complications are numerous and may be severe, including the cardiovascular, neurologic, gastrointestinal, and dermatologic systems. Emergency department evaluation includes focused testing for COVID-19 and assessment of end-organ injury. Imaging may include chest radiography, computed tomography, or ultrasound. Several risk scores may assist in prognostication, including the 4C (Coronavirus Clinical Characterisation Consortium) score, quick COVID Severity Index (qCSI), NEWS2, and the PRIEST score, but these should only supplement and not replace clinical judgment.

CONCLUSION

This review provides a focused update of the presentation and evaluation of COVID-19 for emergency clinicians.

Virus related acute pancreatitis and virus superinfection in the 'Dual disease' model of acute pancreatitis and SARS-Co-V2 infection: A multicentre prospective study

BACKGROUND

SARS-CoV-2 can cause acute pancreatitis (AP) and SARS-CoV-2 superinfection can occur in patients with AP during prolonged hospitalisation. Our objective was to characterize SARS-CoV-2 related AP and study the impact of SARS-CoV-2 superinfection on outcomes in AP.

METHODS

In this multicentre prospective study, all patients with AP and SARS-CoV-2 infection between August 2020 and February 2021 were divided into two groups: SARS-CoV-2-related AP and superadded SARS-CoV-2 infection in patients with AP. The two groups were compared with each other and the whole cohort was compared with a non-COVID AP cohort.

RESULTS

A total of 85 patients with SARS-CoV-2 and AP (SARS-CoV-2-related AP; n = 18 and AP with SARS-CoV-2 superadded infection; n = 67) were included during the study period. They had a higher mortality [28 (32.9%) vs. 44 (19.1%), aOR 2.8 (95% CI, 1.5-5.3)] than 230 propensity matched non-COVID AP patients. Mortality in SARS-CoV-2 and AP patients was due to critical COVID. SARS-CoV-2-related- AP (n = 18) had a higher but statistically insignificant mortality than SARS-CoV-2 superinfection in AP [8/18 (44.4%) vs 20/67 (29.8%), p = 0.24]. On multivariable analysis, infection with SARS-CoV-2 (aHR 2.3; 95% CI, 1.43.7) was a predictor of in-hospital mortality in addition to organ failure (OF) in patients with AP.

CONCLUSION

Patients with AP and SARS-CoV-2 infection had a higher mortality than matched non-COVID AP patients which was largely attributable to the severity of COVID-19. SARS-CoV-2 related AP had higher OF and in-hospital mortality.

Prognostic findings for ICU admission in patients with COVID-19 pneumonia: baseline and follow-up chest CT and the added value of artificial intelligence

Infection with SARS-CoV-2 has dominated discussion and caused global healthcare and economic crisis over the past 18 months. Coronavirus disease 19 (COVID-19) causes mild-to-moderate symptoms in most individuals. However, rapid deterioration to severe disease with or without acute respiratory distress syndrome (ARDS) can occur within 1-2 weeks from the onset of symptoms in a proportion of patients. Early identification by risk stratifying such patients who are at risk of severe complications of COVID-19 is of great clinical importance. Computed tomography (CT) is widely available and offers the potential for fast triage, robust, rapid, and minimally invasive diagnosis: Ground glass opacities (GGO), crazy-paving pattern (GGO with superimposed septal thickening), and consolidation are the most common chest CT findings in COVID pneumonia. There is growing interest in the prognostic value of baseline chest CT since an early risk stratification of patients with COVID-19 would allow for better resource allocation and could help improve outcomes. Recent studies have demonstrated the utility of baseline chest CT to predict intensive care unit (ICU) admission in patients with COVID-19. Furthermore, developments and progress integrating artificial intelligence (AI) with computer-aided design (CAD) software for diagnostic imaging allow for objective, unbiased, and rapid assessment of CT images.

Chest CT of Lung Injury 1 Year after COVID-19 Pneumonia: The CovILD Study

Background

The long-term pulmonary sequelae of COVID-19 is not well known.

Purpose

To characterize patterns and rates of improvement of chest CT abnormalities 1 year after COVID-19 pneumonia.

Materials and Methods

This was a secondary analysis of a prospective, multicenter observational cohort study conducted from April 29 to August 12, 2020, to assess pulmonary abnormalities at chest CT approximately 2, 3, and 6 months and 1 year after onset of COVID-19 symptoms. Pulmonary findings were graded for each lung lobe using a qualitative CT severity score (CTSS) ranging from 0 (normal) to 25 (all lobes involved). The association of demographic and clinical factors with CT abnormalities after 1 year was assessed with logistic regression. The rate of change of the CTSS at follow-up CT was investigated by using the Friedmann test.

Results

Of 142 enrolled participants, 91 underwent a 1-year follow-up CT examination and were included in the analysis (mean age, 59 years ± 13 [SD]; 35 women [38%]). In 49 of 91 (54%) participants, CT abnormalities were observed: 31 of 91 (34%) participants showed subtle subpleural reticulation, ground-glass opacities, or both, and 18 of 91 (20%) participants had extensive ground-glass opacities, reticulations, bronchial dilation, microcystic changes, or a combination thereof. At multivariable analysis, age of more than 60 years (odds ratio [OR], 5.8; 95% CI: 1.7, 24; P = .009), critical COVID-19 severity (OR, 29; 95% CI: 4.8, 280; P < .001), and male sex (OR, 8.9; 95% CI: 2.6, 36; P < .001) were associated with persistent CT abnormalities at 1-year follow-up. Reduction of CTSS was observed in participants at subsequent follow-up CT (P < .001); during the study period, 49% (69 of 142) of participants had complete resolution of CT abnormalities. Thirty-one of 49 (63%) participants with CT abnormalities showed no further improvement after 6 months.

Conclusion

Long-term CT abnormalities were common 1 year after COVID-19 pneumonia.

© RSNA, 2022

Online supplemental material is available for this article.

See also the editorial by Leung in this issue.

An Imaging Overview of COVID-19 ARDS in ICU Patients and Its Complications: A Pictorial Review

A significant proportion of patients with COVID-19 pneumonia could develop acute respiratory distress syndrome (ARDS), thus requiring mechanical ventilation, and resulting in a high rate of intensive care unit (ICU) admission. Several complications can arise during an ICU stay, from both COVID-19 infection and the respiratory supporting system, including barotraumas (pneumothorax and pneumomediastinum), superimposed pneumonia, coagulation disorders (pulmonary embolism, venous thromboembolism, hemorrhages and acute ischemic stroke), abdominal involvement (acute mesenteric ischemia, pancreatitis and acute kidney injury) and sarcopenia. Imaging plays a pivotal role in the detection and monitoring of ICU complications and is expanding even to prognosis prediction. The present pictorial review describes the clinicopathological and radiological findings of COVID-19 ARDS in ICU patients and discusses the imaging features of complications related to invasive ventilation support, as well as those of COVID-19 itself in this particularly fragile population. Radiologists need to be familiar with COVID-19's possible extra-pulmonary complications and, through reliable and constant monitoring, guide therapeutic decisions. Moreover, as more research is pursued and the pathophysiology of COVID-19 is increasingly understood, the role of imaging must evolve accordingly, expanding from the diagnosis and subsequent management of patients to prognosis prediction.

Performances of clinical characteristics and radiological findings in identifying COVID-19 from suspected cases

BACKGROUND

To identify effective factors and establish a model to distinguish COVID-19 patients from suspected cases.

METHODS

The clinical characteristics, laboratory results and initial chest CT findings of suspected COVID-19 patients in 3 institutions were retrospectively reviewed. Univariate and multivariate logistic regression were performed to identify significant features. A nomogram was constructed, with calibration validated internally and externally.

RESULTS

239 patients from 2 institutions were enrolled in the primary cohort including 157 COVID-19 and 82 non-COVID-19 patients. 11 features were selected by LASSO selection, and 8 features were found significant using multivariate logistic regression analysis. We found that the COVID-19 group are more likely to have fever (OR 4.22), contact history (OR 284.73), lower WBC count (OR 0.63), left lower lobe involvement (OR 9.42), multifocal lesions (OR 8.98), pleural thickening (OR 5.59), peripheral distribution (OR 0.09), and less mediastinal lymphadenopathy (OR 0.037). The nomogram developed accordingly for clinical practice showed satisfactory internal and external validation.

CONCLUSIONS

In conclusion, fever, contact history, decreased WBC count, left lower lobe involvement, pleural thickening, multifocal lesions, peripheral distribution, and absence of mediastinal lymphadenopathy are able to distinguish COVID-19 patients from other suspected patients. The corresponding nomogram is a useful tool in clinical practice.

Air trapping in COVID-19 patients following hospital discharge: retrospective evaluation with paired inspiratory/expiratory thin-section CT

Objectives

The study reports our experience with paired inspiration/expiration thin-section computed tomographic (CT) scans in the follow-up of COVID-19 patients with persistent respiratory symptoms.

Methods

From August 13, 2020, to May 31, 2021, 48 long-COVID patients with respiratory symptoms (27 men and 21 women; median age, 62.0 years; interquartile range: 54.0–69.0 years) underwent follow-up paired inspiration-expiration thin-section CT scans. Patient demographics, length of hospital stay, intensive care unit admission rate, and clinical and laboratory features of acute infection were also included. The scans were obtained on a median of 72.5 days after onset of symptoms (interquartile range: 58.5–86.5) and at least 30 days after hospital discharge. Thin-section CT findings included ground-glass opacity, mosaic attenuation pattern, consolidation, traction bronchiectasis, reticulation, parenchymal bands, bronchial wall thickening, and air trapping. We used a quantitative score to determine the degree of air trapping in the expiratory scans.

Results

Parenchymal abnormality was found in 50% (24/48) of patients and included air trapping (37/48, 77%), ground-glass opacities (19/48, 40%), reticulation (18/48, 38%), parenchymal bands (15/48, 31%), traction bronchiectasis (9/48, 19%), mosaic attenuation pattern (9/48, 19%), bronchial wall thickening (6/48, 13%), and consolidation (2/48, 4%). The absence of air trapping was observed in 11/48 (23%), mild air trapping in 20/48 (42%), moderate in 13/48 (27%), and severe in 4/48 (8%). Independent predictors of air trapping were, in decreasing order of importance, gender (p = 0.0085), and age (p = 0.0182).

Conclusions

Our results, in a limited number of patients, suggest that follow-up with paired inspiratory/expiratory CT in long-COVID patients with persistent respiratory symptoms commonly displays air trapping.

Key Points

• Our experience indicates that paired inspiratory/expiratory CT in long-COVID patients with persistent respiratory symptoms commonly displays air trapping.

• Iterative reconstruction and dose-reduction options are recommended for demonstrating air trapping in long-COVID patients.

COVID Tarnish Lung: Residual Radiological Lung Consequences of Infection with COVID-19

Background:

COVID-19 has still expressed as a mysterious viral infection with dramatic pulmonary consequences.

Objectives:

This article aims to study the radiological pulmonary consequences of respiratory covid-19 infection at 6 months and their relevance to the clinical stage, laboratory markers, and management modalities.

Patients and methods:

This study was implemented on two hundred and fifty (250) confirmed positive cases for COVID-19 infections. One hundred and ninety-seven cases (197) who completed the study displayed residual radiological lung shadowing (RRLS) on follow-up computed tomography (CT) of the chest. They were categorized by Simple clinical classification of COVID-19 into groups A, B and C.

Results:

GGO as well as reticulations were statistically significantly higher in group A than other two groups; however, bronchiectasis changes, parenchymal scarring, nodules as well as pleural tractions were statistically significantly higher in group C than the other two groups.

Conclusion:

Respiratory covid-19 infection might be linked to residual radiological lung shadowing. Ground glass opacities GGO, reticulations pervaded in mild involvement with lower inflammatory markers level, unlike, severe changes that expressed scarring, nodules and bronchiectasis changes accompanied by increased levels of inflammatory markers.

Clinical Differences between Community-Acquired Mycoplasma pneumoniae Pneumonia and COVID-19 Pneumonia

Mycoplasmapneumoniae is one of the major causative pathogens of community-acquired pneumonia (CAP). M. pneumoniae CAP is clinically and radiologically distinct from bacterial CAPs. One feature of the Japanese Respiratory Society (JRS) guidelines is a trial to be carried out to differentiate between M. pneumoniae pneumonia and bacterial pneumonia for the selection of antibiotics. The purpose of the present study was to clarify the clinical and radiological differences of the M. pneumoniae CAP and coronavirus disease 2019 (COVID-19) CAP. This study was conducted at 5 institutions and assessed a total of 210 patients with M. pneumoniae CAP and 956 patients with COVID-19 CAP. The median age was significantly younger in patients with M. pneumoniae CAP than COVID-19 CAP. Among the clinical symptoms, cough and sputum were observed more frequently in patients with M. pneumoniae CAP than those with COVID-19 CAP. However, the diagnostic specificity of these findings was low. In contrast, loss of taste and anosmia were observed in patients with COVID-19 CAP but not observed in those with M. pneumoniae CAP. Bronchial wall thickening and nodules (tree-in-bud and centrilobular), which are chest computed tomography (CT) features of M. pneumoniae CAP, were rarely observed in patients with COVID-19 CAP. Our results demonstrated that there were two specific differences between M. pneumoniae CAP and COVID-19 CAP: (1) the presence of loss of taste and/or anosmia and (2) chest CT findings.

A case-control evaluation of pulmonary and extrapulmonary findings of incidental asymptomatic COVID-19 infection on FDG PET-CT

OBJECTIVES

To describe the findings of incidental asymptomatic COVID-19 infection on FDG PET-CT using a case-control design.

METHODS

Incidental pulmonary findings suspicious of asymptomatic COVID-19 infection on FDG PET-CT were classified as a confirmed (positive RT-PCR test) or suspected case (no/negative RT-PCR test). Control cases were identified using a 4:1 control:case ratio. Pulmonary findings were re-categorised by two reporters using the BSTI classification. SUV metrics in ground glass opacification (GGO)/consolidation (where present), background lung, intrathoracic nodes, liver, spleen and bone marrow were measured.

RESULTS

7/9 confirmed and 11/15 suspected cases (COVID-19 group) were re-categorised as BSTI 1 (classic/probable COVID-19) or BSTI 2 (indeterminate COVID-19); 0/96 control cases were categorised as BSTI 1. Agreement between two reporters using the BSTI classification was almost perfect (weighted κ = 0.94). SUVmax GGO/consolidation (5.1 vs 2.2; p < 0.0001) and target-to-background ratio, normalised to liver SUVmean (2.4 vs 1.0; p < 0.0001) were higher in the BSTI 1 & 2 group vs BSTI 3 (non-COVID-19) cases. SUVmax GGO/consolidation discriminated between the BSTI 1 & 2 group vs BSTI 3 (non-COVID-19) cases with high accuracy (AUC = 0.93). SUV metrics were higher (p < 0.05) in the COVID-19 group vs control cases in the lungs, intrathoracic nodes and spleen.

CONCLUSION

Asymptomatic COVID-19 infection on FDG PET-CT is characterised by bilateral areas of FDG avid (intensity > x2 liver SUVmean) GGO/consolidation and can be identified with high interobserver agreement using the BSTI classification. There is generalised background inflammation within the lungs, intrathoracic nodes and spleen.

ADVANCES IN KNOWLEDGE

Incidental asymptomatic COVID-19 infection on FDG PET-CT, characterised by bilateral areas of ground glass opacification and consolidation, can be identified with high reproducibility using the BSTI classification. The intensity of associated FDG uptake (>x2 liver SUVmean) provides high discriminative ability in differentiating such cases from pulmonary findings in a non-COVID-19 pattern. Asymptomatic COVID-19 infection causes a generalised background inflammation within the mid-lower zones of the lungs, hilar and central mediastinal nodal stations, and spleen on FDG PET-CT.

Early identification of novel coronavirus (COVID-19) pneumonia using clinical and radiographic findings

Introduction

The Japanese Respiratory Society (JRS) scoring system is a useful tool for identifying Mycoplasma pneumoniae pneumonia. Most COVID-19 pneumonia in non-elderly patients (aged <60 years) are classified as atypical pneumonia using the JRS scoring system. We evaluated whether physicians could distinguish between COVID-19 pneumonia and M. pneumoniae pneumonia using chest computed tomography (CT) findings. In addition, we investigated chest CT findings if there is a difference between the variant and non-variant strain.

Methods

This study was conducted at five institutions and assessed a total of 823 patients with COVID-19 pneumonia (335 had lineage B.1.1.7.) and 100 patients with M. pneumoniae pneumonia.

Results

In COVID-19 pneumonia, at the first CT examination, peripheral, bilateral ground-glass opacity (GGO) with or without consolidation or crazy-paving pattern was observed frequently. GGO frequently had a round morphology (39.2%). No differences were observed in the radiological findings between the non-B.1.1.7 groups and B.1.1.7 groups. The frequency of pleural effusion, lymphadenopathy, bronchial wall thickening and nodules (tree-in-bud and centrilobular) was low. In contrast to COVID-19 pneumonia, bronchial wall thickening (84%) was observed most frequently, followed by nodules (81%) in M. pneumoniae pneumonia. These findings were significantly higher in M. pneumoniae pneumonia than COVID-19 pneumonia.

Conclusions

Our results demonstrated that a combination of the JRS scoring system and chest CT findings is useful for the rapid presumptive diagnosis of COVID-19 pneumonia in patients aged <60 years. However, this clinical and radiographic diagnosis is not adapted to elderly people.

Bilateral parapneumonic pleural effusion with pneumothorax in a patient with covid 19 pneumonia: case report

Recurrent pyogenic effusion combined with bilateral pneumothorax is a rare complication associated with the COVID−19 infection. Current article presents the case report of a 68-year-old male with the severe community-acquired bilateral polysegmental viral COVID−19 pneumonia. Chest radiography on the 15th day after admission to the hospital showed the presence of air and pleural effusion in the right pleural cavity with collapse of the right lung. Thoracentesis and thoracostomy in the sixth intercostal space on the mid-axillary line were performed. About 1400 ml of a yellowish opaque liquid were evacuated from the pleural cavity. Pleural fluid analysis confirmed an exudative lymphocytic-rich effusion with no growth of acid-fast bacteria (AFB). In the pleural fluid such gram-negative bacteria as Acinetobacter baumannii and Pseudomonas aeruginosa were cultured. Chest computed tomography obtained on the third day after thoracentesis showed radiological sings of bilateral hydropneumothorax. Needle thoracocentesis and new pleural drainage in the second intercostal space on the right midclavicular line were established. Five days later after the second drainage of the pleural space was initiated the patient was diagnosed with pleural empyema and transferred to the Surgical Clinic. This case report highlights that in patients with COVID-19 recurrent pyogenic effusion combined with bilateral pneumothorax may occur.

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

BACKGROUND

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

AIMS

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

METHOD

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

RESULTS

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

DISCUSSION

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

Liver Transplant Imaging prior to and during the COVID-19 Pandemic

BACKGROUND

The suspension of the surgical activity, the burden of the infection in immunosuppressed patients, and the comorbidities underlying end-stage organ disease have impacted transplant programs significantly, even life-saving procedures, such as liver transplantation.

METHODS

A review of the literature was conducted to explore the challenges faced by transplant programs and the adopted strategies to overcome them, with a focus on indications for imaging in liver transplant candidates.

RESULTS

Liver transplantation relies on an appropriate imaging method for its success. During the Coronavirus Disease 2019 (COVID-19) pandemic, chest CT showed an additional value to detect early signs of SARS-CoV-2 infection and other screening modalities are less accurate than radiology.

CONCLUSION

There is an emerging recognition of the chest CT value to recommend its use and help COVID-19 detection in patients. This examination appears highly sensitive for liver transplant candidates and recipients, who otherwise would have not undergone it, particularly when asymptomatic.

Impact of COPD on clinical and CT characteristics of COVID-19-associated pneumonia: single tertiary center experience

Background

The severe acute respiratory syndrome-related coronavirus 2 pandemic continues to this day worldwide. Individuals with COPD are at increased risk of contracting SARS-CoV-2. Most of the conducted studies are based on the clinical assessment of COVID-19 infection with different comorbidities. The specific contribution of COPD to the severity of the disease and outcome still remains the point of investigation. The main goals of our study are to assess COPD’s influence on the severity of clinical and CT characteristics of COVID-19 pneumonia and associated in-hospital mortality.

Results

This is a retrospective study on 281 patients with RT-PCR-confirmed COVID-19 infection and CT spectrum of COVID-19 pneumonia. Fifty patients have COPD based on CT criteria. No significant difference was observed in the mean hospital length of stay, arterial oxygen saturation on admission or in-hospital mortality between COPD and non-COPD groups. Patients with COPD were two times less likely to have fever less than 37.9 °C (RR = 2.037; 95% CI 1.114–3.724, p = 0.016), but higher absolute neutrophil count (p = 0.033) and median level of neutrophil/lymphocyte ratio (p = 0.029). The COPD group was presented with milder CT severity score (especially CT1, less than 25% of lung involvement) (p = 0.022), less likely to have bilateral (RR = 2.601; 95% CI: 1.341–5.044, p = 0.023) or central (RR = 1.754; 95% CI 1.024–3.003, p = 0.046) distribution of ground-glass opacities, right lower lobe (RR = 2.887; 95% CI 1.574–5.293, p = 0.008) or left lung (RR = 2.627; 95% CI 1.448–4.765, p = 0.009) involvement, and “crazy-paving” pattern (RR = 2.208; 95% CI 1.292–3.774, p = 0.003). Both moderate positive and negative relationship was observed between CT1, CT4, hypoxia and in-hospital mortality in the COPD group (r = − 0.383, p = 0.033; r = 0.486, p = 0.007; r = − 0,354, p = 0,022, respectively).

Conclusion

The presence of COPD by imaging criteria in the settings of COVID-19-associated pneumonia did not significantly influence the clinical or imaging performance of the patients, nor was it linked to the increased in-hospital mortality.