Radiographic and CT Features of Viral Pneumonia

Imaging of pulmonary infections encountered in the emergency department in post-COVID 19 era- common, rare and exotic. Bacterial and viral

Pulmonary infections contribute substantially to emergency department (ED) visits, posing a considerable health burden. Lower respiratory tract infections are prevalent, particularly among the elderly, constituting a significant percentage of infectious disease-related ED visits. Timely recognition and treatment are crucial to mitigate morbidity and mortality. Imaging studies, primarily chest radiographs and less frequently CT chests, play a pivotal role in diagnosis. This article aims to elucidate the imaging patterns of both common and rare pulmonary infections (bacterial and viral) in the post COVID-19 era, emphasizing the importance of recognizing distinct radiological manifestations. The integration of clinical and microbiological evidence aids in achieving accurate diagnoses, and guiding optimal therapeutic interventions. Despite potential overlapping manifestations, a nuanced understanding of radiological patterns, coupled with comprehensive clinical and microbiological information, enhances diagnostic precision in majority cases.

A Literature Review on the Relative Diagnostic Accuracy of Chest CT Scans versus RT-PCR Testing for COVID-19 Diagnosis

BACKGROUND

Reverse transcription polymerase chain reaction (RT-PCR) is the main technique used to identify COVID-19 from respiratory samples. It has been suggested in several articles that chest CTs could offer a possible alternate diagnostic tool for COVID-19; however, no professional medical body recommends using chest CTs as an early COVID-19 detection modality. This literature review examines the use of CT scans as a diagnostic tool for COVID-19.

METHOD

A comprehensive search of research works published in peer-reviewed journals was carried out utilizing precisely stated criteria. The search was limited to English-language publications, and studies of COVID-19-positive patients diagnosed using both chest CT scans and RT-PCR tests were sought. For this review, four databases were consulted: these were the Cochrane and ScienceDirect catalogs, and the CINAHL and Medline databases made available by EBSCOhost.

FINDINGS

In total, 285 possibly pertinent studies were found during an initial search. After applying inclusion and exclusion criteria, six studies remained for analysis. According to the included studies, chest CT scans were shown to have a 44 to 98% sensitivity and 25 to 96% specificity in terms of COVID-19 diagnosis. However, methodological limitations were identified in all studies included in this review.

CONCLUSION

RT-PCR is still the suggested first-line diagnostic technique for COVID-19; while chest CT is adequate for use in symptomatic patients, it is not a sufficiently robust diagnostic tool for the primary screening of COVID-19.

Pulmonary Infection

Pneumonia is a significant cause of morbidity and mortality in the community and hospital settings. Bacterial, viral, mycobacterial, and fungal pathogens are all potential causative agents of pulmonary infection. Chest radiographs and computed tomography are frequently utilized in the assessment of pneumonia. Learning the imaging patterns of different potential organisms allows the radiologist to formulate an appropriate differential diagnosis. An organism-based approach is used to discuss the imaging findings of different etiologies of pulmonary infection.

Mutations at the conserved N-Terminal of the human Rhinovirus capsid gene VP4, and their impact on the immune response

Rhinoviruses (RV) are the major cause of chronic obstructive pulmonary disease and are associated with exacerbation development as well as community-acquired pneumonia in children, leading to substantial morbidity, mortality, and hospital admission. Here we have examined how changes at the amino terminal of the conserved VP4 epitope of different RV serotypes may affect pulmonary cytokine and chemokine responses and disease severity. Samples positive for rhinovirus were used for genetic characterization, followed by profiling gene expression of pulmonary Th1 and Th2 cytokines/chemokines by RT-PCR arrays. Genetic sequencing and homology 3D modeling revealed changes at the amino terminal of the conserved viral protein 4 (VP4) epitope in the RV-A101 serotype, especially serine at several positions that are important for interactive binding with the host immune cells. We found dysregulation of pulmonary gene expression of Th1- and Th2-related cytokines and chemokines in RV-A 101 and RV-C 8 pneumonia patients. These findings might contribute to a better understanding of RV immunity and the potential mechanisms underlying the pathogenesis of severe RV infections, but further functional studies are needed to confirm the causal relationship.

Uncommon Causes of Interlobular Septal Thickening on CT Images and Their Distinguishing Features

Interlobular septa thickening (ILST) is a common and easily recognized feature on computed tomography (CT) images in many lung disorders. ILST thickening can be smooth (most common), nodular, or irregular. Smooth ILST can be seen in pulmonary edema, pulmonary alveolar proteinosis, and lymphangitic spread of tumors. Nodular ILST can be seen in the lymphangitic spread of tumors, sarcoidosis, and silicosis. Irregular ILST is a finding suggestive of interstitial fibrosis, which is a common finding in fibrotic lung diseases, including sarcoidosis and usual interstitial pneumonia. Pulmonary edema and lymphangitic spread of tumors are the commonly encountered causes of ILST. It is important to narrow down the differential diagnosis as much as possible by assessing the appearance and distribution of ILST, as well as other pulmonary and extrapulmonary findings. This review will focus on the CT characterization of the secondary pulmonary lobule and ILST. Various uncommon causes of ILST will be discussed, including infections, interstitial pneumonia, depositional/infiltrative conditions, inhalational disorders, malignancies, congenital/inherited conditions, and iatrogenic causes. Awareness of the imaging appearance and various causes of ILST allows for a systematic approach, which is important for a timely diagnosis. This study highlights the importance of a structured approach to CT scan analysis that considers ILST characteristics, associated findings, and differential diagnostic considerations to facilitate accurate diagnoses.

Lung ultrasound and procalcitonin, improving antibiotic management and avoiding radiation exposure in pediatric critical patients with bacterial pneumonia: a randomized clinical trial

BACKGROUND

Pneumonia is a major public health problem with an impact on morbidity and mortality. Its management still represents a challenge. The aim was to determine whether a new diagnostic algorithm combining lung ultrasound (LUS) and procalcitonin (PCT) improved pneumonia management regarding antibiotic use, radiation exposure, and associated costs, in critically ill pediatric patients with suspected bacterial pneumonia (BP).

METHODS

Randomized, blinded, comparative effectiveness clinical trial. Children < 18y with suspected BP admitted to the PICU from September 2017 to December 2019, were included. PCT was determined at admission. Patients were randomized into the experimental group (EG) and control group (CG) if LUS or chest X-ray (CXR) were done as the first image test, respectively. Patients were classified: 1.LUS/CXR not suggestive of BP and PCT < 1 ng/mL, no antibiotics were recommended; 2.LUS/CXR suggestive of BP, regardless of the PCT value, antibiotics were recommended; 3.LUS/CXR not suggestive of BP and PCT > 1 ng/mL, antibiotics were recommended.

RESULTS

194 children were enrolled, 113 (58.2%) females, median age of 134 (IQR 39-554) days. 96 randomized into EG and 98 into CG. 1. In 75/194 patients the image test was not suggestive of BP with PCT < 1 ng/ml; 29/52 in the EG and 11/23 in the CG did not receive antibiotics. 2. In 101 patients, the image was suggestive of BP; 34/34 in the EG and 57/67 in the CG received antibiotics. Statistically significant differences between groups were observed when PCT resulted < 1 ng/ml (p = 0.01). 3. In 18 patients the image test was not suggestive of BP but PCT resulted > 1 ng/ml, all of them received antibiotics. A total of 0.035 mSv radiation/patient was eluded. A reduction of 77% CXR/patient was observed. LUS did not significantly increase costs.

CONCLUSIONS

Combination of LUS and PCT showed no risk of mistreating BP, avoided radiation and did not increase costs. The algorithm could be a reliable tool for improving pneumonia management.

CLINICAL TRIAL REGISTRATION

NCT04217980.

Radiographic findings of adenoviral pneumonia in children

OBJECTIVE

Adenovirus pneumonia is a common cause of community-acquired pneumonia in children and can mimic bacterial pneumonia, but there are few publications on its radiographic features. This study has evaluated the chest radiography findings of community-acquired adenovirus pneumonia in children. The frequency of radiological findings mimicking bacterial pneumonia was investigated. The clinical features of patients with adenovirus pneumonia possessing radiological findings mimicking bacterial pneumonia were also evaluated.

MATERIALS AND METHODS

The chest radiographs of patients diagnosed with adenovirus pneumonia were retrospectively reviewed. The chest radiographs were interpreted independently by a pediatric infectious disease specialist and a pediatric radiologist. Chest radiography findings mimicking bacterial pneumonia (bacterial-like) were specified as consolidation +/- pleural effusion. Other findings on chest radiography or a completely normal chest X-ray were specified as findings that were compatible with "typical viral pneumonia".

RESULTS

A total of 1407 patients were positive for adenovirus with respiratory multiplex PCR. The 219 patients who met the study criteria were included in the study. Chest radiographs were normal in 58 (26.5 %) patients. The chest radiograph findings mimicked bacterial pneumonia in 41 (18.7 %) patients.

CONCLUSION

Adenovirus pneumonia occurs predominantly in children aged five years and younger, as with other viral pneumonias. The radiographic findings in adenovirus pneumonia are predominantly those seen in viral pneumonia. Increasing age and positivity for only adenovirus without other viruses on respiratory multiplex PCR were associated with the chest radiograph being more likely to be "bacterial-like". Adenovirus may lead to lobar/segmental consolidation at a rate that is not very rare.

Adenovirus pneumonia mimicking osimertinib-induced pneumonitis in a patient with advanced NSCLC with EGFR mutation: A case report

Drug-related pneumonitis (DRP) caused by epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) is a fatal adverse event in patients with EGFR-mutant non-small cell lung cancer (NSCLC). The diagnosis of DRP is based on radiological findings, the temporal association of presentation with the initiation of a systemic therapeutic agent, and the exclusion of other likely causes. Here we report a case in which severe adenoviral pneumonia mimicking DRP occurred during treatment with osimertinib, and osimertinib was successfully resumed after recovery from adenoviral pneumonia.

Viral Pneumonias

Viral pneumonia is usually community acquired and caused by influenza, parainfluenza, respiratory syncytial virus, human metapneumovirus, and adenovirus. Many of these infections are airway centric and chest imaging demonstrates bronchiolitis and bronchopneumonia, With the exception of adenovirus infections, the presence of lobar consolidation usually suggests bacterial coinfection. Community-acquired viral pathogens can cause more severe pneumonia in immunocompromised hosts, who are also susceptible to CMV and varicella infection. These latter 2 pathogens are less likely to manifest the striking airway-centric pattern. Airway-centric pattern is distinctly uncommon in Hantavirus pulmonary syndrome, a rare environmentally acquired infection with high mortality.

Radiological pulmonary sequelae after COVID-19 and correlation with clinical and functional pulmonary evaluation: results of a prospective cohort

OBJECTIVES

Whether COVID-19 leads to long-term pulmonary sequelae or not remains unknown. The aim of this study was to assess the prevalence of persisting radiological pulmonary fibrotic lesions in patients hospitalized for COVID-19.

MATERIALS AND METHODS

We conducted a prospective single-center study among patients hospitalized for COVID-19 between March and May 2020. Patients with residual symptoms or admitted into intensive care units were investigated 4 months after discharge by a chest CT (CCT) and pulmonary function tests (PFTs). The primary endpoint was the rate of persistent radiological fibrotic lesions after 4 months. Secondary endpoints included further CCT evaluation at 9 and 16 months, correlation of fibrotic lesions with clinical and PFT evaluation, and assessment of predictive factors.

RESULTS

Among the 1151 patients hospitalized for COVID-19, 169 patients performed a CCT at 4 months. CCTs showed pulmonary fibrotic lesions in 19% of the patients (32/169). These lesions were persistent at 9 months and 16 months in 97% (29/30) and 95% of patients (18/19) respectively. There was no significant clinical difference based on dyspnea scale in patients with pulmonary fibrosis. However, PFT evaluation showed significantly decreased diffusing lung capacity for carbon monoxide (p < 0.001) and total lung capacity (p < 0.001) in patients with radiological lesions. In multivariate analysis, the predictive factors of radiological pulmonary fibrotic lesions were pulmonary embolism (OR = 9.0), high-flow oxygen (OR = 6.37), and mechanical ventilation (OR = 3.49).

CONCLUSION

At 4 months, 19% of patients investigated after hospitalization for COVID-19 had radiological pulmonary fibrotic lesions; they persisted up to 16 months.

CLINICAL RELEVANCE STATEMENT

Whether COVID-19 leads to long-term pulmonary sequelae or not remains unknown. The aim of this study was to assess the prevalence of persisting radiological pulmonary fibrotic lesions in patients hospitalized for COVID-19. The prevalence of persisting lesions after COVID-19 remains unclear. We assessed this prevalence and predictive factors leading to fibrotic lesions in a large cohort. The respiratory clinical impact of these lesions was also assessed.

KEY POINTS

• Nineteen percent of patients hospitalized for COVID-19 had radiological fibrotic lesions at 4 months, remaining stable at 16 months. • COVID-19 fibrotic lesions did not match any infiltrative lung disease pattern. • COVID-19 fibrotic lesions were associated with pulmonary function test abnormalities but did not lead to clinical respiratory manifestation.

Prediction of viral pneumonia based on machine learning models analyzing pulmonary inflammation index scores

OBJECT

To obtain Pulmonary Inflammation Index scores from imaging chest CT and combine it with clinical correlates of viral pneumonia to predict the risk and severity of viral pneumonia using a computer learning model.

METHODS

All patients with suspected viral pneumonia on CT examination admitted to The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University from December 2022 to March 2023 were retrospectively selected. The respiratory viruses were monitored by RT-PCR and categorized into patients with viral pneumonia and those with non-viral pneumonia. The extent of lung inflammation was quantified according to the Pulmonary Inflammation Index score (PII). Information on patient demographics, comorbidities, laboratory tests, pathogenetic testing, and radiological data were collected. Five machine learning models containing Random Forest(RF), Radial Basis Function Neural Network (RBFNN), Support Vector Machine (SVM), K Nearest Neighbour Algorithm (KNN), and Kernel Ridge Regression (KRR) were used to predict the risk of onset and severity of viral pneumonia based on the clinically relevant factors or PII.

RESULTS

Among the five models, the SVM model performed best in ACC (76.75 %), SN (73.99 %), and F1 (72.42 %) and achieved a better area under the receiver operating characteristic curve (ROC) (0.8409) when predicting the risk of developing viral pneumonia. RF had the best overall classification accuracy in predicting the severity of viral pneumonia, especially in predicting pneumonia with a PII classification of grade I, the RF model achieved an accuracy of 98.89%.

CONCLUSION

Machine learning models are valuable in assessing the risk of viral pneumonia. Meanwhile, machine learning models confirm the importance in predicting the severity of viral pneumonia through PII. The establishment of machine learning models for predicting the risk and severity of viral pneumonia promotes the further development of machine learning in the medical field.

COVID-19 Severity Prediction from Chest X-ray Images Using an Anatomy-Aware Deep Learning Model

The COVID-19 pandemic has been adversely affecting the patient management systems in hospitals around the world. Radiological imaging, especially chest x-ray and lung Computed Tomography (CT) scans, plays a vital role in the severity analysis of hospitalized COVID-19 patients. However, with an increasing number of patients and a lack of skilled radiologists, automated assessment of COVID-19 severity using medical image analysis has become increasingly important. Chest x-ray (CXR) imaging plays a significant role in assessing the severity of pneumonia, especially in low-resource hospitals, and is the most frequently used diagnostic imaging in the world. Previous methods that automatically predict the severity of COVID-19 pneumonia mainly focus on feature pooling from pre-trained CXR models without explicitly considering the underlying human anatomical attributes. This paper proposes an anatomy-aware (AA) deep learning model that learns the generic features from x-ray images considering the underlying anatomical information. Utilizing a pre-trained model and lung segmentation masks, the model generates a feature vector including disease-level features and lung involvement scores. We have used four different open-source datasets, along with an in-house annotated test set for training and evaluation of the proposed method. The proposed method improves the geographical extent score by 11% in terms of mean squared error (MSE) while preserving the benchmark result in lung opacity score. The results demonstrate the effectiveness of the proposed AA model in COVID-19 severity prediction from chest X-ray images. The algorithm can be used in low-resource setting hospitals for COVID-19 severity prediction, especially where there is a lack of skilled radiologists.

Imaging in Lung Transplantation: Surgical Techniques and Complications

Lung transplant is an established treatment for patients with end-stage lung disease. As a result, there is increased demand for transplants. Despite improvements in pretransplant evaluation, surgical techniques, and postsurgical care, the average posttransplant life expectancy is only around 6.5 years. Early recognition of complications on imaging and treatment can improve survival. Knowledge of surgical techniques and imaging findings of surgical and nonsurgical complications is essential. This review covers surgical techniques and imaging appearance of postsurgical and nonsurgical complications, including allograft dysfunction, infections, neoplasms, and recurrence of primary lung disease.

Development and validation of a hybrid deep learning-machine learning approach for severity assessment of COVID-19 and other pneumonias

The Coronavirus Disease 2019 (COVID-19) is transitioning into the endemic phase. Nonetheless, it is crucial to remain mindful that pandemics related to infectious respiratory diseases (IRDs) can emerge unpredictably. Therefore, we aimed to develop and validate a severity assessment model for IRDs, including COVID-19, influenza, and novel influenza, using CT images on a multi-centre data set. Of the 805 COVID-19 patients collected from a single centre, 649 were used for training and 156 were used for internal validation (D1). Additionally, three external validation sets were obtained from 7 cohorts: 1138 patients with COVID-19 (D2), and 233 patients with influenza and novel influenza (D3). A hybrid model, referred to as Hybrid-DDM, was constructed by combining two deep learning models and a machine learning model. Across datasets D1, D2, and D3, the Hybrid-DDM exhibited significantly improved performance compared to the baseline model. The areas under the receiver operating curves (AUCs) were 0.830 versus 0.767 (p = 0.036) in D1, 0.801 versus 0.753 (p < 0.001) in D2, and 0.774 versus 0.668 (p < 0.001) in D3. This study indicates that the Hybrid-DDM model, trained using COVID-19 patient data, is effective and can also be applicable to patients with other types of viral pneumonia.

Milani A, Madani Tonekaboni SA, Rabbani H, Hekmatnia A, Kafieh R. Biased Deep Learning Methods in Detection of COVID-19 Using CT Images: A Challenge Mounted by Subject-Wise-Split ISFCT Dataset

Accurate detection of respiratory system damage including COVID-19 is considered one of the crucial applications of deep learning (DL) models using CT images. However, the main shortcoming of the published works has been unreliable reported accuracy and the lack of repeatability with new datasets, mainly due to slice-wise splits of the data, creating dependency between training and test sets due to shared data across the sets. We introduce a new dataset of CT images (ISFCT Dataset) with labels indicating the subject-wise split to train and test our DL algorithms in an unbiased manner. We also use this dataset to validate the real performance of the published works in a subject-wise data split. Another key feature provides more specific labels (eight characteristic lung features) rather than being limited to COVID-19 and healthy labels. We show that the reported high accuracy of the existing models on current slice-wise splits is not repeatable for subject-wise splits, and distribution differences between data splits are demonstrated using t-distribution stochastic neighbor embedding. We indicate that, by examining subject-wise data splitting, less complicated models show competitive results compared to the exiting complicated models, demonstrating that complex models do not necessarily generate accurate and repeatable results.

Improve the performance of CT-based pneumonia classification via source data reweighting

Pneumonia is a life-threatening disease. Computer tomography (CT) imaging is broadly used for diagnosing pneumonia. To assist radiologists in accurately and efficiently detecting pneumonia from CT scans, many deep learning methods have been developed. These methods require large amounts of annotated CT scans, which are difficult to obtain due to privacy concerns and high annotation costs. To address this problem, we develop a three-level optimization based method which leverages CT data from a source domain to mitigate the lack of labeled CT scans in a target domain. Our method automatically identifies and downweights low-quality source CT data examples which are noisy or have large domain discrepancy with target data, by minimizing the validation loss of a target model trained on reweighted source data. On a target dataset with 2218 CT scans and a source dataset with 349 CT images, our method achieves an F1 score of 91.8% in detecting pneumonia and an F1 score of 92.4% in detecting other types of pneumonia, which are significantly better than those achieved by state-of-the-art baseline methods.

A comprehensive review of COVID-19 detection with machine learning and deep learning techniques

Purpose

The first transmission of coronavirus to humans started in Wuhan city of China, took the shape of a pandemic called Corona Virus Disease 2019 (COVID-19), and posed a principal threat to the entire world. The researchers are trying to inculcate artificial intelligence (Machine learning or deep learning models) for the efficient detection of COVID-19. This research explores all the existing machine learning (ML) or deep learning (DL) models, used for COVID-19 detection which may help the researcher to explore in different directions. The main purpose of this review article is to present a compact overview of the application of artificial intelligence to the research experts, helping them to explore the future scopes of improvement.

Methods

The researchers have used various machine learning, deep learning, and a combination of machine and deep learning models for extracting significant features and classifying various health conditions in COVID-19 patients. For this purpose, the researchers have utilized different image modalities such as CT-Scan, X-Ray, etc. This study has collected over 200 research papers from various repositories like Google Scholar, PubMed, Web of Science, etc. These research papers were passed through various levels of scrutiny and finally, 50 research articles were selected.

Results

In those listed articles, the ML / DL models showed an accuracy of 99% and above while performing the classification of COVID-19. This study has also presented various clinical applications of various research. This study specifies the importance of various machine and deep learning models in the field of medical diagnosis and research.

Conclusion

In conclusion, it is evident that ML/DL models have made significant progress in recent years, but there are still limitations that need to be addressed. Overfitting is one such limitation that can lead to incorrect predictions and overburdening of the models. The research community must continue to work towards finding ways to overcome these limitations and make machine and deep learning models even more effective and efficient. Through this ongoing research and development, we can expect even greater advances in the future.

The Global Burden of Pleural Diseases

Pleural diseases include a spectrum of disorders broadly categorized into pneumothorax and pleural effusion. They often cause pain, breathlessness, cough, and reduced quality of life. The global burden of diseases reflects regional differences in conditions and exposures associated with pleural disease, such as smoking, pneumonia, tuberculosis, asbestos, cancer, and organ failure. Disease burden in high-income countries is overrepresented given the availability of data and disease burden in lower-income countries is likely underestimated. In the United States, in 2016, there were 42,215 treat-and-discharge visits to the emergency room for pleural diseases and an additional 361,270 hospitalizations, resulting in a national cost of $10.1 billion.

Epstein-Barr virus (EBV) induced pneumonitis in a patient with breast cancer receiving neoadjuvant chemotherapy: A case report

Background

Epstein-Barr virus (EBV) usually leads to latent infection and is reported mostly in infectious mononucleosis, lymphoma, and cancer in adolescents and adults, but pneumonitis due to EBV infection in adults is rare.

Case presentation

We hereby reported a case of a 52-year-old woman with breast cancer who developed acute pneumonia during neoadjuvant chemotherapy. Her serologic workup revealed a low CD4⁺ count and positive anti-EBV antibodies. Chest computed tomography (CT) shows multiple patchy ground-glass shadows in the bilateral lung. Microscopic examination of stained sputum and bronchoalveolar lavage fluid (BALF) smear specimens did not find any pathogens. Metagenomic next-generation sequencing (mNGS) of BALF indicated a large number of EBV reads, allowing to confirm the diagnosis of EBV induced pneumonitis. The patient was then treated with ganciclovir with subsequent dramatic clinical and radiological improvement.

Conclusions

This case highlights the combined application of mNGS and traditional tests in the clinical diagnosis of invasive pulmonary infection. In the meanwhile, clinicians should be aware neoadjuvant chemotherapy for breast cancer carries a risk of EBV induced pneumonitis, so that EBV induced pneumonitis could be considered in differential diagnosis while similar patients present, to orchestrate improvements in diagnosis, treatment, and prognosis.

Cross-platform privacy-preserving CT image COVID-19 diagnosis based on source-free domain adaptation

In the wake of the Coronavirus disease (COVID-19) pandemic, chest computed tomography (CT) has become an invaluable component in the rapid and accurate detection of COVID-19. CT scans traditionally require manual inspections from medical professionals, which is expensive and tedious. With advancements in machine learning, deep neural networks have been applied to classify CT scans for efficient diagnosis. However, three challenges hinder this application of deep learning: (1) Domain shift across CT platforms and human subjects impedes the performance of neural networks in different hospitals. (2) Unsupervised Domain Adaptation (UDA), the traditional method to overcome domain shift, typically requires access to both source and target data. This is not realistic in COVID-19 diagnosis due to the sensitivity of medical data. The privacy of patients must be protected. (3) Data imbalance may exist between easy/hard samples and between data classes which can overwhelm the training of deep networks, causing degenerate models. To overcome these challenges, we propose a Cross-Platform Privacy-Preserving COVID-19 diagnosis network (CP 3 Net) that integrates domain adaptation, self-supervised learning, imbalanced label learning, and rotation classifier training into one synergistic framework. We also create a new CT benchmark by combining real-world datasets from multiple medical platforms to facilitate the cross-domain evaluation of our method. Through extensive experiments, we demonstrate that CP 3 Net outperforms many popular UDA methods and achieves state-of-the-art results in diagnosing COVID-19 using CT scans.

Diagnostic accuracy of a set of clinical and radiological criteria for screening of COVID-19 using RT-PCR as the reference standard

BACKGROUND

The gold-standard method for establishing a microbiological diagnosis of COVID-19 is reverse-transcriptase polymerase chain reaction (RT-PCR). This study aimed to evaluate the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of a set of clinical-radiological criteria for COVID-19 screening in patients with severe acute respiratory failure (SARF) admitted to intensive care units (ICUs), using reverse-transcriptase polymerase chain reaction (RT-PCR) as the reference standard.

METHODS

Diagnostic accuracy study including a historical cohort of 1009 patients consecutively admitted to ICUs across six hospitals in Curitiba (Brazil) from March to September, 2020. The sample was stratified into groups by the strength of suspicion for COVID-19 (strong versus weak) using parameters based on three clinical and radiological (chest computed tomography) criteria. The diagnosis of COVID-19 was confirmed by RT-PCR (referent).

RESULTS

With respect to RT-PCR, the proposed criteria had 98.5% (95% confidence interval [95% CI] 97.5-99.5%) sensitivity, 70% (95% CI 65.8-74.2%) specificity, 85.5% (95% CI 83.4-87.7%) accuracy, PPV of 79.7% (95% CI 76.6-82.7%) and NPV of 97.6% (95% CI 95.9-99.2%). Similar performance was observed when evaluated in the subgroups of patients admitted with mild/moderate respiratory disfunction, and severe respiratory disfunction.

CONCLUSION

The proposed set of clinical-radiological criteria were accurate in identifying patients with strong versus weak suspicion for COVID-19 and had high sensitivity and considerable specificity with respect to RT-PCR. These criteria may be useful for screening COVID-19 in patients presenting with SARF.

Application Potential of Luteolin in the Treatment of Viral Pneumonia

Aim of the Review. This study aims to summarize the therapeutic effect of luteolin on the pathogenesis of viral pneumonia, explore its absorption and metabolism in the human body, evaluate the possibility of luteolin as a drug to treat viral pneumonia, and provide a reference for future research. Materials and Methods. We searched MEDLINE/PubMed, Web of Science, China National Knowledge Infrastructure, and Google Scholar and collected research on luteolin in the treatment of viral pneumonia and related diseases since 2003. Then, we summarized the efficacy and potential of luteolin in directly inhibiting viral activity, limiting inflammatory storms, reducing pulmonary inflammation, and treating pneumonia complications. Results and Conclusion. Luteolin has the potential to treat viral pneumonia in multiple ways. Luteolin has a direct inhibitory effect on coronavirus, influenza virus, and respiratory syncytial virus. Luteolin can alleviate the inflammatory factor storm induced by multiple factors by inhibiting the function of macrophages or mast cells. Luteolin can reduce pulmonary inflammation, pulmonary edema, or pulmonary fibrosis induced by multiple factors. In addition, viral pneumonia may cause multisystem complications, while luteolin has extensive protective effects on the gastrointestinal system, cardiovascular system, and nervous system. However, due to the first-pass metabolism mediated by phase II enzymes, the bioavailability of oral luteolin is low. The bioavailability of luteolin can be improved, and its potential value can be further developed by changing the dosage form or route of administration.

Ventilator-Associated Pneumonia in Immunosuppressed Patients

Immunocompromised patients-including patients with cancer, hematological malignancies, solid organ transplants and individuals receiving immunosuppressive therapies for autoimmune diseases-account for an increasing proportion of critically-ill patients. While their prognosis has improved markedly in the last decades, they remain at increased risk of healthcare- and intensive care unit (ICU)-acquired infections. The most frequent of these are ventilator-associated lower respiratory tract infections (VA-LTRI), which include ventilator-associated pneumonia (VAP) and tracheobronchitis (VAT). Recent studies have shed light on some of the specific features of VAP and VAT in immunocompromised patients, which is the subject of this narrative review. Contrary to previous belief, the incidence of VAP and VAT might actually be lower in immunocompromised than non-immunocompromised patients. Further, the relationship between immunosuppression and the incidence of VAP and VAT related to multidrug-resistant (MDR) bacteria has also been challenged recently. Etiological diagnosis is essential to select the most appropriate treatment, and the role of invasive sampling, specifically bronchoscopy with bronchoalveolar lavage, as well as new molecular syndromic diagnostic tools will be discussed. While bacteria-especially gram negative bacteria-are the most commonly isolated pathogens in VAP and VAT, several opportunistic pathogens are a special concern among immunocompromised patients, and must be included in the diagnostic workup. Finally, the impact of immunosuppression on VAP and VAT outcomes will be examined in view of recent papers using improved statistical methodologies and treatment options-more specifically empirical antibiotic regimens-will be discussed in light of recent findings on the epidemiology of MDR bacteria in this population.

DTLCx: An Improved ResNet Architecture to Classify Normal and Conventional Pneumonia Cases from COVID-19 Instances with Grad-CAM-Based Superimposed Visualization Utilizing Chest X-ray Images

COVID-19 is a severe respiratory contagious disease that has now spread all over the world. COVID-19 has terribly impacted public health, daily lives and the global economy. Although some developed countries have advanced well in detecting and bearing this coronavirus, most developing countries are having difficulty in detecting COVID-19 cases for the mass population. In many countries, there is a scarcity of COVID-19 testing kits and other resources due to the increasing rate of COVID-19 infections. Therefore, this deficit of testing resources and the increasing figure of daily cases encouraged us to improve a deep learning model to aid clinicians, radiologists and provide timely assistance to patients. In this article, an efficient deep learning-based model to detect COVID-19 cases that utilizes a chest X-ray images dataset has been proposed and investigated. The proposed model is developed based on ResNet50V2 architecture. The base architecture of ResNet50V2 is concatenated with six extra layers to make the model more robust and efficient. Finally, a Grad-CAM-based discriminative localization is used to readily interpret the detection of radiological images. Two datasets were gathered from different sources that are publicly available with class labels: normal, confirmed COVID-19, bacterial pneumonia and viral pneumonia cases. Our proposed model obtained a comprehensive accuracy of 99.51% for four-class cases (COVID-19/normal/bacterial pneumonia/viral pneumonia) on Dataset-2, 96.52% for the cases with three classes (normal/ COVID-19/bacterial pneumonia) and 99.13% for the cases with two classes (COVID-19/normal) on Dataset-1. The accuracy level of the proposed model might motivate radiologists to rapidly detect and diagnose COVID-19 cases.

Bilateral adaptive graph convolutional network on CT based Covid-19 diagnosis with uncertainty-aware consensus-assisted multiple instance learning

Coronavirus disease (COVID-19) has caused a worldwide pandemic, putting millions of people's health and lives in jeopardy. Detecting infected patients early on chest computed tomography (CT) is critical in combating COVID-19. Harnessing uncertainty-aware consensus-assisted multiple instance learning (UC-MIL), we propose to diagnose COVID-19 using a new bilateral adaptive graph-based (BA-GCN) model that can use both 2D and 3D discriminative information in 3D CT volumes with arbitrary number of slices. Given the importance of lung segmentation for this task, we have created the largest manual annotation dataset so far with 7,768 slices from COVID-19 patients, and have used it to train a 2D segmentation model to segment the lungs from individual slices and mask the lungs as the regions of interest for the subsequent analyses. We then used the UC-MIL model to estimate the uncertainty of each prediction and the consensus between multiple predictions on each CT slice to automatically select a fixed number of CT slices with reliable predictions for the subsequent model reasoning. Finally, we adaptively constructed a BA-GCN with vertices from different granularity levels (2D and 3D) to aggregate multi-level features for the final diagnosis with the benefits of the graph convolution network's superiority to tackle cross-granularity relationships. Experimental results on three largest COVID-19 CT datasets demonstrated that our model can produce reliable and accurate COVID-19 predictions using CT volumes with any number of slices, which outperforms existing approaches in terms of learning and generalisation ability. To promote reproducible research, we have made the datasets, including the manual annotations and cleaned CT dataset, as well as the implementation code, available at https://doi.org/10.5281/zenodo.6361963.

Pneumonia in Children During the 2019 Outbreak in Xiamen, China

BACKGROUND

To understand the regional epidemiology and clinical characteristics of adenovirus pneumonia in hospitalized children during the 2019 outbreak of respiratory adenoviruses in China.

METHODS

We analyzed the epidemiologic trend of adenovirus in children hospitalized for acute lower respiratory tract infections in Xiamen in 2019. Adenovirus was identified using direct fluorescent antibody detection. During the peak seasons of adenovirus epidemic, 170 adenovirus-positive specimens were obtained for molecular typing, and the clinical data were collected.

RESULTS

Among the 9890 children hospitalized for acute lower respiratory tract infection, 609 (6.2%) were tested positive for adenovirus. The detection rate of adenovirus was significantly higher in boys than in grils (9.5% vs. 4.6%, P < 0.05). Adenovirus activity increased markedly between April and August with the prevalence of 7.3%-12.4%. During the outbreak season, type 7 accounted for 70.6%, followed by type 3 (28.8%) and type 4 (0.6%). Of the 155 cases of adenovirus pneumonia, the median age was 3.0 years (range: 4 month to 9 years), 153 (98.7%) had fever with a mean fever duration of 9.04 ± 5.52 days, 28 (16.5%) had wheezing, 93 (60%) showed segmental or lobar consolidation with atelectasis and 13 (8.4%) showed pleural effusion. Forty-six (29.6%) cases developed severe pneumonia, 7 (4.1%) required mechanical ventilation and 2 (1.2%) died. Younger age, longer duration of fever and higher fever spike were more frequently seen in severe cases (P < 0.05). Twenty-five (16.2%) had C-reactive protein ≥ 40 mg/L, and 91 (58.7%) had procalcitonin ≥ 0.25 mg/L.

CONCLUSIONS

Adenovirus types 7 and 3 caused the outbreak of adenovirus pneumonia in community children during late spring to summer in 2019 in Xiamen. The majority of adenovirus pneumonia resembles bacterial pneumonia. The incidence of severe pneumonia was high when type 7 predominantly prevailed. Adenovirus type 7 was more common in severe cases than in nonsevere cases.

The Role of Biomarkers in Influenza and COVID-19 Community-Acquired Pneumonia in Adults

Pneumonia is a growing problem worldwide and remains an important cause of morbidity, hospitalizations, intensive care unit admission and mortality. Viruses are the causative agents in almost a fourth of cases of community-acquired pneumonia (CAP) in adults, with an important representation of influenza virus and SARS-CoV-2 pneumonia. Moreover, mixed viral and bacterial pneumonia is common and a risk factor for severity of disease. It is critical for clinicians the early identification of the pathogen causing infection to avoid inappropriate antibiotics, as well as to predict clinical outcomes. It has been extensively reported that biomarkers could be useful for these purposes. This review describe current evidence and provide recommendations about the use of biomarkers in influenza and SARS-CoV-2 pneumonia, focusing mainly on procalcitonin (PCT) and C-reactive protein (CRP). Evidence was based on a qualitative analysis of the available scientific literature (meta-analyses, randomized controlled trials, observational studies and clinical guidelines). Both PCT and CRP levels provide valuable information about the prognosis of influenza and SARS-CoV-2 pneumonia. Additionally, PCT levels, considered along with other clinical, radiological and laboratory data, are useful for early diagnosis of mixed viral and bacterial CAP, allowing the proper management of the disease and adequate antibiotics prescription. The authors propose a practical PCT algorithm for clinical decision-making to guide antibiotic initiation in cases of influenza and SARS-CoV-2 pneumonia. Further well-design studies are needed to validate PCT algorithm among these patients and to confirm whether other biomarkers are indeed useful as diagnostic or prognostic tools in viral pneumonia.

Retrospective comparison of respiratory syncytial virus and metapneumovirus clinical presentation in hospitalized children

Respiratory syncytial virus (RSV) and Human metapneumovirus (hMPV), members of Pneumoviridae family are common causes of acute respiratory tract infections (ARTI) among children. Study material includes routine nasopharyngeal samples obtained during 8-year period for hMPV and one single season for RSV in children hospitalized for ARTI between 0 and 15 years at the Center Hospitalier Universitaire (CHU) Saint Pierre in Brussels. Positive samples for RSV or hMPV identified by viral culture, lateral flow chromatography test for RSV or direct fluorescent assay for hMPV were selected retrospectively. Characteristics of children hospitalized for RSV or hMPV infections were compared. Children hospitalized for RSV infection were significantly younger and requiring more respiratory support, longer hospital stay and transfers in Pediatric intensive Care Units than those hospitalized for hMPV infection. Pneumonia diagnostic and antibiotics therapies were more significantly associated with hMPV infections. In conclusion, despite their genetic similarities, RSV, and hMPV present epidemiological and clinical differences in pediatric infections. Our results should be confirmed prospectively.

Predicting the COVID-19 Patients Status Using Chest CT Scan Findings: A Risk Assessment Model Based on Decision Tree Analysis

BACKGROUND

The role of chest computed tomography (CT) to diagnose coronavirus disease 2019 (COVID-19) is still an open field to be explored. The aim of this study was to apply the decision tree (DT) model to predict critical or non-critical status of patients infected with COVID-19 based on available information on non-contrast CT scans.

METHODS

This retrospective study was performed on patients with COVID-19 who underwent chest CT scans. Medical records of 1078 patients with COVID-19 were evaluated. The classification and regression tree (CART) of decision tree model and k-fold cross-validation were used to predict the status of patients using sensitivity, specificity, and area under the curve (AUC) assessments.

RESULTS

The subjects comprised of 169 critical cases and 909 non-critical cases. The bilateral distribution and multifocal lung involvement were 165 (97.6%) and 766 (84.3%) in critical patients, respectively. According to the DT model, total opacity score, age, lesion types, and gender were statistically significant predictors for critical outcomes. Moreover, the results showed that the accuracy, sensitivity and specificity of the DT model were 93.3%, 72.8%, and 97.1%, respectively.

CONCLUSIONS

The presented algorithm demonstrates the factors affecting health conditions in COVID-19 disease patients. This model has the potential characteristics for clinical applications and can identify high-risk subpopulations that need specific prevention. Further developments including integration of blood biomarkers are underway to increase the performance of the model.

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.

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

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

Usefulness of High-Resolution Computed Tomography in Early Diagnosis of Patients with Suspected COVID-19

BACKGROUND

Diagnosis of coronavirus disease 2019 (COVID-19) is mainly based on molecular testing. General population studies have shown that chest Computed Tomography (CT) can also be useful.

OBJECTIVE

The study aims to examine the usefulness of high-resolution chest CT for early diagnosis of patients with suspected COVID-19.

DESIGN AND SETTING

This is a cross-sectional study from May 1, 2020, to August 31, 2021, at the COVID Hospital, Mexico City.

METHODS

This study examined the clinical, high-resolution chest CT imaging, and laboratory data of 160 patients who were suspected to have COVID-19. Patients with positive Reverse Transcription- Polymerase Chain Reaction (RT-PCR) testing and those with negative RT-PCR testing but clinical data compatible with COVID-19 and positive antibody testing were considered to have COVID-19 (positive). Sensitivity and specificity of CT for diagnosis of COVID-19 were calculated. p < 0.05 was considered significant.

RESULTS

Median age of 160 study patients was 58 years. The proportion of patients with groundglass pattern was significantly higher in patients with COVID-19 than in those without COVID (65.1% versus 0%; P = 0.005). COVID-19 was ruled out in sixteen (11.1%). Only four of the 132 patients diagnosed with COVID-19 (3.0%) did not show CT alterations (p < 0.001). Sensitivity and specificity of CT for COVID-19 diagnosis were 96.7% and 42.8%, respectively.

CONCLUSIONS

Chest CT can identify patients with COVID-19, as characteristic disease patterns are observed on CT in the early disease stage.

Diagnostic Performance in Differentiating COVID-19 from Other Viral Pneumonias on CT Imaging: Multi-Reader Analysis Compared with an Artificial Intelligence-Based Model

Growing evidence suggests that artificial intelligence tools could help radiologists in differentiating COVID-19 pneumonia from other types of viral (non-COVID-19) pneumonia. To test this hypothesis, an R-AI classifier capable of discriminating between COVID-19 and non-COVID-19 pneumonia was developed using CT chest scans of 1031 patients with positive swab for SARS-CoV-2 (n = 647) and other respiratory viruses (n = 384). The model was trained with 811 CT scans, while 220 CT scans (n = 151 COVID-19; n = 69 non-COVID-19) were used for independent validation. Four readers were enrolled to blindly evaluate the validation dataset using the CO-RADS score. A pandemic-like high suspicion scenario (CO-RADS 3 considered as COVID-19) and a low suspicion scenario (CO-RADS 3 considered as non-COVID-19) were simulated. Inter-reader agreement and performance metrics were calculated for human readers and R-AI classifier. The readers showed good agreement in assigning CO-RADS score (Gwet's AC2 = 0.71, p < 0.001). Considering human performance, accuracy = 78% and accuracy = 74% were obtained in the high and low suspicion scenarios, respectively, while the AI classifier achieved accuracy = 79% in distinguishing COVID-19 from non-COVID-19 pneumonia on the independent validation dataset. The R-AI classifier performance was equivalent or superior to human readers in all comparisons. Therefore, a R-AI classifier may support human readers in the difficult task of distinguishing COVID-19 from other types of viral pneumonia on CT imaging.

Challenges in the Differential Diagnosis of COVID-19 Pneumonia: A Pictorial Review

COVID-19 pneumonia represents a maximum medical challenge due to the virus's high contagiousness, morbidity, and mortality and the still limited possibilities of the health systems. The literature has primarily focused on the diagnosis, clinical-radiological aspects of COVID-19 pneumonia, and the most common possible differential diagnoses. Still, few studies have investigated the rare differential diagnoses of COVID-19 pneumonia or its overlap with other pre-existing lung pathologies. This article presents the main radiological features of COVID-19 pneumonia and the most common alternative diagnoses to establish the vital radiological criteria for a differential diagnosis between COVID-19 pneumonia and other lung pathologies with similar imaging appearance. The differential diagnosis of COVID-19 pneumonia is challenging because there may be standard radiologic features such as ground-glass opacities, crazy paving patterns, and consolidations. A multidisciplinary approach is crucial to define a correct final diagnosis, as an overlap of COVID-19 pneumonia with pre-existing lung diseases is often possible and suggests possible differential diagnoses. An optimal evaluation of HRTC can help limit the clinical evolution of the disease, promote therapy for patients and ensure an efficient allocation of human and economic resources.

α2,6-Sialylation Is Upregulated in Severe COVID-19, Implicating the Complement Cascade

Better understanding of the molecular mechanisms underlying COVID-19 severity is desperately needed in current times. Although hyper-inflammation drives severe COVID-19, precise mechanisms triggering this cascade and what role glycosylation might play therein are unknown. Here we report the first high-throughput glycomic analysis of COVID-19 plasma samples and autopsy tissues. We find that α2,6-sialylation is upregulated in the plasma of patients with severe COVID-19 and in autopsied lung tissue. This glycan motif is enriched on members of the complement cascade (e.g., C5, C9), which show higher levels of sialylation in severe COVID-19. In the lung tissue, we observe increased complement deposition, associated with elevated α2,6-sialylation levels, corresponding to elevated markers of poor prognosis (IL-6) and fibrotic response. We also observe upregulation of the α2,6-sialylation enzyme ST6GAL1 in patients who succumbed to COVID-19. Our work identifies a heretofore undescribed relationship between sialylation and complement in severe COVID-19, potentially informing future therapeutic development.

Development and validation of a simple tool composed of items on dyspnea, respiration rates, and C-reactive protein for pneumonia prediction among acute febrile respiratory illness patients in primary care settings

BACKGROUND

Acute febrile respiratory illness (AFRI) patients are susceptible to pneumonia and suffer from significant morbidity and mortality throughout the world. In primary care settings, the situation is worse. Limited by computerized tomography resources and physician experiences, AFRI patients in primary care settings may not be diagnosed appropriately, which would affect following treatment. In this study, we aimed to develop and validate a simple prediction model to help physicians quickly identify AFRI patients of pneumonia risk in primary care settings.

METHODS

A total of 1977 AFRI patients were enrolled at two fever clinics in Shanghai, China, and among them, 727 patients who underwent CT scans were included in the analysis. Acute alveolar or interstitial infiltrates found on CT images were diagnosed with pneumonia. Characteristics and blood parameters were compared between pneumonia and non-pneumonia patients. Then a multivariable model for pneumonia prediction was developed through logistic regression analysis. Its value for pneumonia prediction was prospectively assessed in an external multi-center population, which included 1299 AFRI patients in primary settings from 5 different provinces throughout China.

RESULTS

In the model development population, pneumonia patients (n = 227) had a longer duration of fever; higher frequencies of purulent sputum, dyspnea, and thoracic pain; and higher levels of respiration rates and C-reactive protein (CRP) than non-pneumonia patients (n = 500). Logistic regression analysis worked out a model composed of items on dyspnea, respiration rates > 20/min, and CRP > 20 mg/l (DRC) for pneumonia prediction with an area under curve (AUC) of 0.8506. In the external validation population, the predictive accuracy of the DRC model was the highest when choosing at least one positive item (1 score) as a cut-off point with a sensitivity of 87.0% and specificity of 80.5%. DRC scores increased with pneumonia severity and lung lobe involvement and showed good performance for both bacterial and viral pneumonia. For viral pneumonia, dyspnea plus respiration rates > 20/min had good predictive capacity regardless of CRP concentration.

CONCLUSIONS

DRC model is a simple tool that predicts pneumonia among AFRI patients, which would help physicians utilize medical resources rationally in primary care settings.

Space-time-regulated imaging analyzer for smart coagulation diagnosis

The development of intelligent blood coagulation diagnoses is awaited to meet the current need for large clinical time-sensitive caseloads due to its efficient and automated diagnoses. Herein, a method is reported and validated to realize it through artificial intelligence (AI)-assisted optical clotting biophysics (OCB) properties identification. The image differential calculation is used for precise acquisition of OCB properties with elimination of initial differences, and the strategy of space-time regulation allows on-demand space time OCB properties identification and enables diverse blood function diagnoses. The integrated applications of smartphones and cloud computing offer a user-friendly automated analysis for accurate and convenient diagnoses. The prospective assays of clinical cases (n = 41) show that the system realizes 97.6%, 95.1%, and 100% accuracy for coagulation factors, fibrinogen function, and comprehensive blood coagulation diagnoses, respectively. This method should enable more low-cost and convenient diagnoses and provide a path for potential diagnostic-markers finding.

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An ultraportable optofluidic analyzer empowers convenient coagulation diagnoses

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The system enables optical clotting biophysics (OCB) properties acquisition and process

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Coagulation function diagnoses uses intelligent OCB properties identification

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Space-time regulation of OCB properties endow it capability to diverse diagnoses

Comparison of pneumonia features in children caused by SARS-CoV-2 and other viral respiratory pathogens

BACKGROUND

Pneumonia is a frequent manifestation of coronavirus disease 2019 (COVID-19) in hospitalized children.

METHODS

The study involved 80 hospitals in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Spanish Pediatric National Cohort. Participants were children <18 years, hospitalized with SARS-CoV-2 community-acquired pneumonia (CAP). We compared the clinical and radiological characteristics of SARS-CoV-2-associated CAP with CAP due to other viral etiologies from ValsDance (retrospective) cohort.

RESULTS

In total, 151 children with SARS-CoV-2-associated CAP and 138 with other viral CAP were included. Main clinical features of SARS-CoV-2-associated CAP were cough, fever, or dyspnea. Lymphopenia was found in 43% patients and 15% required admission to the pediatric intensive care unit (PICU). Chest X-ray revealed condensation (42%) and other infiltrates (58%). Compared with CAP from other viral pathogens, COVID-19 patients were older, with lower C-reactive protein (CRP) levels, less wheezing, and greater need of mechanical ventilation (MV). There were no differences in the use of continuous positive airway pressure (CPAP) or HVF, or PICU admission between groups.

CONCLUSION

SARS-CoV-2-associated CAP in children presents differently to other virus-associated CAP: children are older and rarely have wheezing or high CRP levels; they need less oxygen but more CPAP or MV. However, several features overlap and differentiating the etiology may be difficult. The overall prognosis is good.

A novel chest CT severity score in COVID-19 and its correlation with severity and prognosis of the lung disease: A retrospective cohort study

Background

HRCT chest has a high sensitivity in the diagnosis of patients with COVID-19 infection. Through our study, we intend to evaluate the diagnostic accuracy and inter-reader variability of a semi-quantitative CT severity score, a novel parameter designed for risk stratification and prognostication of COVID-19 pneumonia with clinical staging of disease.

Methods

It was a single-center retrospective analysis performed on an original cohort of 4180 symptomatic patients with the suspicion of SARS-CoV-2 interstitial pneumonia. Out of 4180, a total of 4004 patients with COVID-19 were confirmed by an RT-PCR. We used an HRCT chest severity score (CT-SS) to evaluate the COVID-19 disease burden on the initial scan obtained at admission. The data were analyzed with IBM SPSS Statistics Version 22.0 Release 2013.

Results

Our study subjects demonstrated the most common clinical features fever, cough, dyspnea, and body aches. Raised CRP levels (CRP >0.5 mg/dL) were found in 81.86% and increased D-dimer levels (>500 ng/mL) were found in 92.3% of patients. The most common radiological findings of the disease included ground-glass opacities, observed in 98.8%. Our study has a sensitivity of 89.2%, a specificity of 94.8%, a positive predictive value (PPV) of 90.6%, and a negative predictive value (NPV) of 94%.

Conclusion

As per our findings, this novel CT scoring system might aid in the risk stratification and the short-term prognostication of patients suffering from COVID-19 pneumonia. This will eventually help in curtailing the extensive burden on the healthcare system amid the current pandemic.

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There is a correlation between the severity of lung disease in COVID-19 pneumonia and HRCT severity score.

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The most common radiological findings of the disease included ground-glass opacities, followed by septal thickening (crazy paving), and bronchial wall thickening.

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This novel HRCT scoring system can help us in classifying COVID-19 pneumonia and ultimately triaging the patients. .

The role of exhaled nitric oxide (FeNO) in the evaluation of lung parenchymal involvement in COVID-19 patients

The inflammatory balance is an important factor in the clinical course of COVID-19 (SARS-CoV-2) infection, which has affected over 300 million people globally since its appearance in December 2019. This study aimed to evaluate the correlation between exhaled nitric oxide (FeNO) level and parenchymal involvement in COVID-19. The study included 106 patients with the delta variant of COVID-19 identified by real-time PCR as well as 40 healthy control groups between October 2021 and March 2022. The patients were analyzed in three groups: moderate COVID-19 (group 1), severe COVID-19 without macrophage activation syndrome (MAS) (group 2), and severe COVID-19 with MAS (group 3). FeNO and CT scores were significantly higher in groups 2 and 3 at admission and discharge compared to group 1 (p = 0.001 for all). In addition, CT score at admission and CT score and FeNO level at discharge were higher in group 3 than in group 2 (p = 0.001 for all). It was found that the FeNO levels were higher in Groups 2 and 3 than in the control group (p = 0.001) during the admission. FeNO and CT scores showed strong positive correlation at admission and discharge (r = 0.917, p = 0.001; r = 0.790, p = 0.001). In receiver operating characteristic curve analysis for prediction of MAS, FeNO at a cut-off of 10.5 ppb had 66% sensitivity and 71% specificity. COVID-19 causes more severe lung involvement than other viral lower respiratory tract infections, leading to the frequent use of chest CT in these patients. FeNO assessment is a practical and noninvasive method that may be useful in evaluating for parenchymal infiltration in the diagnosis and follow-up of COVID-19 patients.

Radiological Findings in SARS-CoV-2 Viral Pneumonia Compared to Other Viral Pneumonias: A Single-Centre Study

BACKGROUND

Thorax computed tomography (CT) imaging is widely used as a diagnostic method in the diagnosis of coronavirus disease 2019 (COVID-19)-related pneumonia. Radiological differential diagnosis and isolation of other viral agents causing pneumonia in patients have gained importance, particularly during the pandemic.

AIMS

We aimed to investigate whether there is a difference between CT images from patients with COVID-19-associated pneumonia compared to CT images of patients with pneumonia due to other viral agents and which finding may be more effective in diagnosis. Study Design. The study included 249 adult patients with pneumonia identified by thorax CT examination and with a positive COVID-19 RT-PCR test compared to 94 patients diagnosed with non-COVID-19 pneumonia (viral PCR positive but no bacterial or fungal agents detected in other cultures) between 2015 and 2019. CT images were retrospectively analyzed using the PACS system. CT findings were evaluated by two radiologists with 5 and 20 years of experience, in a blinded fashion, and the outcome was decided by consensus.

METHODS

Demographic data (age, gender, and known chronic disease) and CT imaging findings (percentage of involvement, number of lesions, distribution preference, dominant pattern, ground-glass opacity distribution pattern, nodule, tree in bud sign, interstitial changes, crazy paving sign, reversed halo sign, vacuolar sign, halo sign, vascular enlargement, linear opacities, traction bronchiectasis, peribronchial wall thickness, air trapping, pleural retraction, pleural effusion, pericardial effusion, cavitation, mediastinal/hilar lymphadenopathy, dominant lesion size, consolidation, subpleural curvilinear opacities, air bronchogram, and pleural thickening) of the patients were evaluated. CT findings were also evaluated with the RSNA consensus guideline and the CORADS scoring system. Data were divided into two main groups-non-COVID-19 and COVID-19 pneumonia-and compared statistically with chi-squared tests and multiple regression analysis of independent variables.

RESULTS

RSNA and CORADS classifications of CT scan images were able to successfully differentiate between positive and negative COVID-19 pneumonia patients. Statistically significant differences were found between the two patient groups in various categories including the percentage of involvement, number of lesions, distribution preference, dominant pattern, nodule, tree in bud, interstitial changes, crazy paving, reverse halo vascular enlargement, peribronchial wall thickness, air trapping, pleural retraction, pleural/pericardial effusion, cavitation, and mediastinal/hilar lymphadenopathy (p < 0.01). Multiple linear regression analysis of independent variables found a significant effect in reverse halo sign (β = 0.097, p < 0.05) and pleural effusion (β = 10.631, p < 0.05) on COVID-19 pneumonia patients.

CONCLUSION

The presence of reverse halo and absence of pleural effusion was found to be characteristic of COVID-19 pneumonia and therefore a reliable diagnostic tool to differentiate it from non-COVID-19 pneumonia.

Relation of Pulmonary Diffusing Capacity Decline to HRCT and VQ SPECT/CT Findings at Early Follow-Up after COVID-19: A Prospective Cohort Study (The SECURe Study)

A large proportion of patients exhibit persistently reduced pulmonary diffusion capacity after COVID-19. It is unknown whether this is due to a post-COVID restrictive lung disease and/or pulmonary vascular disease. The aim of the current study was to investigate the association between initial COVID-19 severity and haemoglobin-corrected diffusion capacity to carbon monoxide (DLco) reduction at follow-up. Furthermore, to analyse if DLco reduction could be linked to pulmonary fibrosis (PF) and/or thromboembolic disease within the first months after the illness, a total of 67 patients diagnosed with COVID-19 from March to December 2020 were included across three severity groups: 12 not admitted to hospital (Group I), 40 admitted to hospital without intensive care unit (ICU) admission (Group II), and 15 admitted to hospital with ICU admission (Group III). At first follow-up, 5 months post SARS-CoV-2 positive testing/4 months after discharge, lung function testing, including DLco, high-resolution CT chest scan (HRCT) and ventilation-perfusion (VQ) single photon emission computed tomography (SPECT)/CT were conducted. DLco was reduced in 42% of the patients; the prevalence and extent depended on the clinical severity group and was typically observed as part of a restrictive pattern with reduced total lung capacity. Reduced DLco was associated with the extent of ground-glass opacification and signs of PF on HRCT, but not with mismatched perfusion defects on VQ SPECT/CT. The severity-dependent decline in DLco observed early after COVID-19 appears to be caused by restrictive and not pulmonary vascular disease.

Automatic diagnosis of severity of COVID-19 patients using an ensemble of transfer learning models with convolutional neural networks in CT images

Introduction: Quantification of lung involvement in COVID-19 using chest Computed tomography (CT) scan can help physicians to evaluate the progression of the disease or treatment response. This paper presents an automatic deep transfer learning ensemble based on pre-trained convolutional neural networks (CNNs) to determine the severity of COVID -19 as normal, mild, moderate, and severe based on the images of the lungs CT.

Material and methods: In this study, two different deep transfer learning strategies were used. In the first procedure, features were extracted from fifteen pre-trained CNNs architectures and then fed into a support vector machine (SVM) classifier. In the second procedure, the pre-trained CNNs were fine-tuned using the chest CT images, and then features were extracted for the purpose of classification by the softmax layer. Finally, an ensemble method was developed based on majority voting of the deep learning outputs to increase the performance of the recognition on each of the two strategies. A dataset of CT scans was collected and then labeled as normal (314), mild (262), moderate (72), and severe (35) for COVID-19 by the consensus of two highly qualified radiologists.

Results: The ensemble of five deep transfer learning outputs named EfficientNetB3, EfficientNetB4, InceptionV3, NasNetMobile, and ResNext50 in the second strategy has better results than the first strategy and also the individual deep transfer learning models in diagnosing the severity of COVID-19 with 85% accuracy.

Conclusions: Our proposed study is well suited for quantifying lung involvement of COVID-19 and can help physicians to monitor the progression of the disease.

Pneumonia Update for Emergency Clinicians

Purpose of Review

Many new concepts in diagnosis, management, and risk stratification of patients with pneumonia have been described recently. The COVID pandemic made importance of viruses as dangerous pathogens of pneumonia quite clear while several non-invasive measures for patients with respiratory failure gained a more wide-spread usage.

Recent Findings

Studies continue to examine feasibility of bedside ultrasound as a tool in accurate diagnosis of pneumonia in the emergency department, and several new antibiotics have been approved for treatment while others are in late-stage clinical trials. Additionally, the Infectious Diseases Society, American Thoracic Society, and their European counterparts published updated guidelines in recent years. For differences important to emergency medicine clinicians and new emphasis as compared to the prior guidelines, please see Table 1. Several new antibiotics have been approved recently but remain relatively unknown to emergency clinicians as their use is frequently restricted to infectious disease specialists.Table 1

Artificial intelligence model on chest imaging to diagnose COVID-19 and other pneumonias: A systematic review and meta-analysis

Objectives

When diagnosing Coronavirus disease 2019(COVID‐19), radiologists cannot make an accurate judgments because the image characteristics of COVID‐19 and other pneumonia are similar. As machine learning advances, artificial intelligence(AI) models show promise in diagnosing COVID-19 and other pneumonias. We performed a systematic review and meta-analysis to assess the diagnostic accuracy and methodological quality of the models.

Methods

We searched PubMed, Cochrane Library, Web of Science, and Embase, preprints from medRxiv and bioRxiv to locate studies published before December 2021, with no language restrictions. And a quality assessment (QUADAS-2), Radiomics Quality Score (RQS) tools and CLAIM checklist were used to assess the quality of each study. We used random-effects models to calculate pooled sensitivity and specificity, I² values to assess heterogeneity, and Deeks' test to assess publication bias.

Results

We screened 32 studies from the 2001 retrieved articles for inclusion in the meta-analysis. We included 6737 participants in the test or validation group. The meta-analysis revealed that AI models based on chest imaging distinguishes COVID-19 from other pneumonias: pooled area under the curve (AUC) 0.96 (95 % CI, 0.94–0.98), sensitivity 0.92 (95 % CI, 0.88–0.94), pooled specificity 0.91 (95 % CI, 0.87–0.93). The average RQS score of 13 studies using radiomics was 7.8, accounting for 22 % of the total score. The 19 studies using deep learning methods had an average CLAIM score of 20, slightly less than half (48.24 %) the ideal score of 42.00.

Conclusions

The AI model for chest imaging could well diagnose COVID-19 and other pneumonias. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the quality of research methodology and further improve the generalizability of the developed predictive models.

Chest multidetector computed tomography imaging of COVID-19 pneumonia patients with hematologic malignancies

Background

Data on the association between coronavirus disease 2019 (COVID-19) and the epidemiology and outcomes of hematological malignancies are limited. Hence, the present study aimed to assess the imaging findings using chest multidetector computed tomography (MDCT) in patients with hematologic malignancies who developed COVID-19 pneumonia.

Methods

This retrospective study included two groups, the first group consisted of COVID-19 infected patients with hematologic malignancies (100 patients), while the second group consisted of COVID-19 infected patients without hematologic malignancies or other comorbidities (100 patients). The hematological malignancies included in this study were non-Hodgkin’s lymphoma (40 patients), acute myeloid leukemia (25 patients), chronic lymphocytic leukemia (15 patients), multiple myeloma (10 patients), Hodgkin’s lymphoma (8 patients), and myelodysplastic syndrome (2 patients). Chest multidetector CT imaging was performed in all patients to assess for ground-glass opacity, consolidation, pleural effusion, and airway abnormalities.

Results

More than one CT finding was reported in each patient. No significant difference was observed in the ground-glass opacities (P=0.0594), nodule formation (P=0.2278), or airway thickening/dilatation (P=0.0566) between the two groups; meanwhile, a significant difference was observed in the degree of consolidation, the number of lobes affected, and pleural effusion (P=0.0001) as well as in the total lung severity (P=0.0001); minimal, mild, and severe affection rates; and (P=0.0047) moderate affection rates.

Conclusion

Early and reliable diagnosis of lung disease in COVID-19-infected patients may be achieved through multidetector CT imaging. Patients with hematological malignancies are more likely to have severe COVID-19 pneumonia, and radiologists should recognize the CT characteristics of this infection.

Applicability of CO-RADS in an Anonymized Cohort Including Early and Advanced Stages of COVID-19 in Comparison to the Recommendations of the German Radiological Society and Radiological Society of North America

PURPOSE

Classifications were created to facilitate radiological evaluation of the novel coronavirus disease 2019 (COVID-19) on computed tomography (CT) images. The categorical CT assessment scheme (CO-RADS) categorizes lung parenchymal changes according to their likelihood of being caused by SARS-CoV-2 infection. This study investigates the diagnostic accuracy of diagnosing COVID-19 with CO-RADS compared to the Thoracic Imaging Section of the German Radiological Society (DRG) classification and Radiological Society of North America (RSNA) classification in an anonymized patient cohort. To mimic advanced disease stages, follow-up examinations were included as well.

METHOD

This study includes all patients undergoing chest CT in the case of a suspected SARS-CoV-2 infection or an already confirmed infection between March 13 and November 30, 2020. During the study period, two regional lockdowns occurred due to high incidence values, increasing the pre-test probability of COVID-19. Anonymized CT images were reviewed retrospectively and in consensus by two radiologists applying CO-RADS, DRG, and RSNA classification. Afterwards, CT findings were compared to results of sequential real-time reverse transcriptase polymerase chain reaction (qPCR) test performed during hospitalization to determine statistical analysis for diagnosing COVID-19.

RESULTS

536 CT examinations were included. CO-RADS, DRG and RSNA achieved an NPV of 96 %/94 %/95 % (CO-RADS/DRG/RSNA), PPV of 83 %/80 %/88 %, sensitivity of 86 %/76 %/80 %, and specificity of 96 %/95 %/97 %. The disease prevalence was 20 %.

CONCLUSION

All applied classifications can reliably exclude a SARS-CoV-2 infection even in an anonymous setting. Nevertheless, pre-test probability was high in our study setting and has a great influence on the classifications. Therefore, the applicability of the individual classifications will become apparent in the future with lower prevalence and incidence of COVID-19.

KEY POINTS

· CO-RADS, DRG, and RSNA classifications help to reliably detect infected patients in an anonymized setting. · Pre-test probability has a great influence on the individual classifications. · Difficulties in an anonymized study setting are severe pulmonary changes and residuals..

CITATION FORMAT

· Valentin B, Steuwe A, Wienemann T et al. Applicability of CO-RADS in an Anonymized Cohort Including Early and Advanced Stages of COVID-19 in Comparison to the Recommendations of the German Radiological Society and Radiological Society of North America. Fortschr Röntgenstr 2022; 194: 862 - 872.

Molecular imaging as a tool for evaluation of COVID-19 sequelae – A review of literature

Coronavirus disease 2019 (COVID-19) is caused by the novel viral pathogen, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 primarily involves the lungs. Nucleic acid testing based on reverse-transcription polymerase chain reaction of respiratory samples is the current gold standard for the diagnosis of SARS-CoV-2 infection. Imaging modalities have an established role in triaging, diagnosis, evaluation of disease severity, monitoring disease progression, extra-pulmonary involvement, and complications. As our understanding of the disease improves, there has been substantial evidence to highlight its potential for multi-systemic involvement and development of long-term sequelae. Molecular imaging techniques are highly sensitive, allowing non-invasive visualization of physiological or pathological processes at a cellular or molecular level with potential for detection of functional changes earlier than conventional radiological imaging. The purpose of this review article is to highlight the evolving role of molecular imaging in evaluation of COVID-19 sequelae. Though not ideal for diagnosis, the various modalities of molecular imaging play an important role in assessing pulmonary and extra-pulmonary sequelae of COVID-19. Perfusion imaging using single photon emission computed tomography fused with computed tomography (CT) can be utilized as a first-line imaging modality for COVID-19 related pulmonary embolism. ¹⁸F-fluorodeoxyglucose positron emission tomography (PET)/CT is a sensitive tool to detect multi-systemic inflammation, including myocardial and vascular inflammation. PET in conjunction with magnetic resonance imaging helps in better characterization of neurological sequelae of COVID-19. Despite the fact that the majority of published literature is retrospective in nature with limited sample sizes, it is clear that molecular imaging provides additional valuable information (complimentary to anatomical imaging) with semi-quantitative or quantitative parameters to define inflammatory burden and can be used to guide therapeutic strategies and assess response. However, widespread clinical applicability remains a challenge owing to longer image acquisition times and the need for adoption of infection control protocols.

Pulmonary artery enlargement: an independent risk factor for mortality in hospitalized COVID-19 patients

Objective

To assess whether baseline pulmonary artery diameter (PAD), obtained from Non-contrast non-gated computed tomography (NCCT), can be associated with COVID-19 outcomes.

Patients and Methods

This is a retrospective study of hospitalized COVID-19 patients admitted to Hôtel-Dieu de France university hospital (Beirut, Lebanon) between March 2020 and March 2021. PAD was measured on baseline NCCT. Various outcomes were assessed, including hospital length of stay, ICU admission, invasive mechanical ventilation, mortality, and post-covid functional scale (PCFS) status at discharge and at 2-month follow-up.

Results

465 patients had a baseline NCCT, including 315 males (67.7%) with a mean age of 63.7±16 years. Baseline PAD was higher in critically ill patients admitted to the ICU (mean difference 0.8 mm [95% CI 0.4-1.59 mm]) and those receiving invasive mechanical ventilation (mean difference 1.1 mm [95% CI 0.11-2.04 mm]). PAD at baseline correlated significantly with hospital length of stay (r = 0.130, p=0.005), discharge status (r=0.117, p=0.023) and with PCFS at 2-month follow-up (r=0.121, p=0.021). Moreover, multivariable logistic regression showed that a PAD ≥ 24.5 mm independently predicted in-hospital all-cause mortality remained unaffected in COVID-19 patients (OR 2.07 (95% CI 1.05 - 4.09)).

Conclusion

Baseline PAD measurement using NCCT can be a useful prognostic parameter. Its measurement can help identify early severe cases and adapt the initial management of hospitalized Covid-19 patients.