Radiological approaches to COVID-19 pneumonia

Feature-based deep neural network approach for predicting mortality risk in patients with COVID-19

In this study, we integrate deep neural network (DNN) with hybrid approaches (feature selection and instance clustering) to build prediction models for predicting mortality risk in patients with COVID-19. Besides, we use cross-validation methods to evaluate the performance of these prediction models, including feature based DNN, cluster-based DNN, DNN, and neural network (multi-layer perceptron). The COVID-19 dataset with 12,020 instances and 10 cross-validation methods are used to evaluate the prediction models. The experimental results showed that the proposed feature based DNN model, holding Recall (98.62%), F1-score (91.99%), Accuracy (91.41%), and False Negative Rate (1.38%), outperforms than original prediction model (neural network) in the prediction performance. Furthermore, the proposed approach uses the Top 5 features to build a DNN prediction model with high prediction performance, exhibiting the well prediction as the model built by all features (57 features). The novelty of this study is that we integrate feature selection, instance clustering, and DNN techniques to improve prediction performance. Moreover, the proposed approach which is built with fewer features performs much better than the original prediction models in many metrics and can still remain high prediction performance.

Which sequence should be used in the thorax magnetic resonance imaging of COVID-19: a comparative study

Background and aim

To evaluate and compare magnetic resonance imaging (MRI) sequences that could potentially be used in the diagnosis of coronavirus disease 2019 (COVID-19).

Materials and methods

Included in the study were 42 patient who underwent thorax computed tomography (CT) for COVID-19 pneumonia and thorax MRI for any reason within 24 h after CT. The T2-weighted fast spin echo periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) (T2W-FSE-P), fast imaging employing steady-state acquisition, T2 fat-saturated FSE, axial T1 liver acquisition with volume acceleration (LAVA) and single-shot FSE images were compared in terms of their ability to show COVID-19 findings.

Results

The mean age of the patients was 47.2 ± 24 years. Of the patients, 22 were male (52.4%) and 20 (47.6%) were female. The interobserver intraclass coefficient (ICC) for the image quality score was the highest in the T2W-FSE-P sequence and lowest in the T1 LAVA sequence. All of the lesion-based evaluations of the interobserver agreement were statistically significant, with the kappa value varying between 0.798 and 0.998.

Conclusion

All 5 sequences evaluated in the study were successful in showing the parenchymal findings of COVID-19. Since the T2W-FSE-P sequence had the best scores in both interobserver agreement and ICC for the image quality score, it was considered that it can be included in thorax MRI examinations to assist the diagnosis of COVID-19.

Dysglycemias in patients admitted to ICUs with severe acute respiratory syndrome due to COVID-19 versus other causes - a cohort study

BACKGROUND

Dysglycemias have been associated with worse prognosis in critically ill patients with COVID-19, but data on the association of dysglycemia with COVID-19 in comparison with other forms of severe acute respiratory syndrome are lacking. This study aimed to compare the occurrence of different glycemic abnormalities in patients with severe acute respiratory syndrome and COVID-19 admitted to intensive care units versus glycemic abnormalities in patients with severe acute respiratory syndrome from other causes, to evaluate the adjusted attributable risk associated with COVID-19 and dysglycemia and to assess the influence of these dysglycemias on mortality.

METHODS

We conducted a retrospective cohort of consecutive patients with severe acute respiratory syndrome and suspected COVID-19 hospitalized in intensive care units between March 11 and September 13, 2020, across eight hospitals in Curitiba-Brazil. The primary outcome was the influence of COVID-19 on the variation of the following parameters of dysglycemia: highest glucose level at admission, mean and highest glucose levels during ICU stay, mean glucose variability, percentage of days with hyperglycemia, and hypoglycemia during ICU stay. The secondary outcome was the influence of COVID-19 and each of the six parameters of dysglycemia on hospital mortality within 30 days from ICU admission.

RESULTS

The sample consisted of 841 patients, of whom 703 with and 138 without COVID-19. Comparing patients with and without COVID-19, those with COVID-19 had significantly higher glucose peaks at admission (165 mg/dL vs. 146 mg/dL; p = 0.002) and during ICU stay (242 mg/dL vs. 187md/dL; p < 0.001); higher mean daily glucose (149.7 mg/dL vs. 132.6 mg/dL; p < 0.001); higher percentage of days with hyperglycemia during ICU stay (42.9% vs. 11.1%; p < 0.001); and greater mean glucose variability (28.1 mg/dL vs. 25.0 mg/dL; p = 0.013). However, these associations were no longer statistically significant after adjustment for Acute Physiology and Chronic Health Evaluation II scores, Sequential Organ Failure Assessment scores, and C-reactive protein level, corticosteroid use and nosocomial infection. Dysglycemia and COVID-19 were each independent risk factors for mortality. The occurrence of hypoglycemia (< 70 mg/dL) during ICU stay was not associated with COVID-19.

CONCLUSION

Patients with severe acute respiratory syndrome due to COVID-19 had higher mortality and more frequent dysglycemia than patients with severe acute respiratory syndrome due to other causes. However, this association did not seem to be directly related to the SARS-CoV-2 infection.

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.

Evaluation of risk factors for COVID-19 severity or death and their relationship to metabolic pathways

Background

Since the state of alarm was declared due to the COVID-19 pandemic, hospitals have been the main ones in charge of registering the therapeutic follow-up of affected people. The analysis of these data has allowed those different biochemical markers have been identified as predictors of the severity of the disease, but most of the published studies tend to be eminently descriptive and do not propose a biochemical hypothesis to explain the alteration of the results they are showing. Our objective is to recognize the main metabolic processes that are occurring in COVID-19 patients, as well as the identification of clinical parameters that are decisive to predict the severity of the disease.

Methods

A multivariate analysis was carried out from the clinical parameters collected in the database of the HM hospitals in Madrid, to determine the most relevant variables to predict the severity of the disease. Chemometric methods allow these variables to be obtained by applying a classification strategy with PLS-LDA.

Findings and interpretation

The variables that most contribute to separation are age in men and, in both sexes, the concentration of lactate dehydrogenase, urea and C-reactive protein.Oxygen deficiency in the tissues, due to the loss of functionality of the lungs, could be affecting the muscle tissue with special severity. Inflammation and tissue damage is related to increased LDH and CRP. The loss of muscle mass and the increase in the concentration of urea and LDH is explained by the adaptation of muscle metabolism to this oxygen deficiency.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profits sectors.

Post COVID-19 pulmonary mass: Case report and review of literatures

To present an unusual and a rare pulmonary affection by coronavirus disease-19 (COVID-19), in which only one lung is affected. Coronavirus disease-19 attacks the lungs and interferes seriously with their functions. The attack is usually bilaterally, while a uni lateral pulmonary affection is unusual. The presentation, both clinical and radiological findings, bronchoscopy appearance, the strange operative findings of the resected mass, the uneventful post-operative course, in addition to the histopathological report, will be presented.In conclusion, unilateral lung affection is unusual and post-viral pneumonia COVID-19 should be considered as a possible aftermath, which may not be uncommon in Iraq.

The comparison of chest X-ray and CT visibility according to size and lesion types in the patients with COVID-19

Introduction: Chest X-ray (CXR) is one of the routinely used radiological examinations in COVID-19. However, the lesion detectability level of CXR is low. To date, to the best of our knowledge, the visualization quality of X-ray in COVID-19 has not been specifically evaluated in different lesions. Our study aims to determine the visualization quality of CXR in COVID-19 patients according to elementary lesions. Material and Method: 52 COVID-positive patients (26 Males and 26 Females); 69,6346±15,14250 (32-89) years [mean±SD age (range)] were included in the study. 98 different elementary lesions of lung detected on CT were evaluated in six different groups (consolidation, indeterminate ground-glass opacity (IGGO), dense GGO (DGGO), reversed halo, parenchymal band and curvilinear band). Lesions were compared with CXR taken on the same day. The detectability rates of the lesions on CXR were evaluated. Results: The mean sizes of CXR negative and CXR positive lesions for every group (consolidations, IGGO, DGGO, reversed halo sign, parenchymal band, curvilinear band) were respectively 1.36 cm -5.75 cm, 3.44 cm -5.50 cm, 2.25 cm -5.06 cm, 2.5cm -4.09 cm, N/A -3.14 cm and 1 cm -4.5 cm. According to Mann-Whitney U analysis, p values were found as (respectively in consolidations, IGGO, DGGO, reversed halo sign, and curvilinear band) 0.0001p, 0.145, 0.0001 p, 0.143 and 0.286. Given consolidation and DGGO groups, there was a statistically significant difference between non-visualized and visualized groups. According to ROC analysis, cut-off values were respectively 3 cm and 3.5 cm for consolidation and DGGO. Conclusion: Our study showed that consolidations smaller than 3 cm and DGGO smaller than 3.5 cm are difficult to visualize with CXR. Although there is no definite cut-off value in other elementary lesions, the visualization ratio of parenchymal bands and curvilinear bants on chest X-rays is quite high. IGGOs may not be detected even at higher dimensions. Reversed halos less than 3 cm can rarely be detected on CXR.

The radiological findings of COVID-19

Background/aim

Available information on the radiological findings of the 2019 novel coronavirus disease (COVID-19) is constantly updated. Ground glass opacities (GGOs) and consolidation with bilateral and peripheral distribution have been reported as the most common CT findings, but less typical features can also be identified. According to the reported studies, SARS-CoV-2 infection is not limited to the respiratory system, and it can also affect other organs. Renal dysfunction, gastrointestinal complications, liver dysfunction, cardiac manifestations, and neurological abnormalities are among the reported extrapulmonary features. This review aims to provide updated information for radiologists and all clinicians to better understand the radiological manifestations of COVID-19.

Materials and methods

Radiological findings observed in SARS-CoV-2 virus infections were explored in detail in PubMed and Google Scholar databases.

Results

The typical pulmonary manifestations of COVID-19 pneumonia were determined as GGOs and accompanying consolidations that primarily involve the periphery of the bilateral lower lobes. The most common extrapulmonary findings were increased resistance to flow in the kidneys, thickening of vascular walls, fatty liver, pancreas, and heart inflammation findings. However, these findings were not specific and significantly overlapped those caused by other viral diseases, and therefore alternative diagnoses should be considered in patients with negative diagnostic tests.

Conclusion

Radiological imaging plays a supportive role in the care of patients with COVID-19. Both clinicians and radiologists need to know associated pulmonary and extrapulmonary findings and imaging features to help diagnose and manage the possible complications of the disease at an early stage. They should also be familiar with CT findings in patients with COVID-19 since the disease can be incidentally detected during imaging performed with other indications.

COVID-19-Related Lung Involvement at Different Time Intervals: Evaluation of Computed Tomography Images With Semiquantitative Scoring System and COVID-19 Reporting and Data System Scoring

INTRODUCTION

This study aimed to evaluate the frequency of typical and atypical thoracic CT findings in patient groups diagnosed during different periods of the pandemic, examine disease severity using radiological scoring methods, and determine the relationship between atypical CT findings and disease severity.

MATERIALS AND METHODS

One hundred fifty-one patients with positive reverse transcription polymerase chain reaction (RT-PCR) test and thoracic CT scan were included in the study. The patients were divided into two groups as group 1 (March to August 2020) diagnosed in the first six months of the pandemic and group 2 (September 2020 to February 2021) diagnosed in the second six months. CT images of the patients were analyzed for the frequency of typical and atypical findings. Evaluation was made in terms of disease suspicion and severity by scoring methods, and the relationship between atypical findings and disease severity was examined.

RESULTS

There was no statistically significant difference between the frequency and distribution patterns of typical CT findings observed in both groups. The most common atypical finding in both groups was nodular lesions. Central distribution, one of the atypical findings, was not seen in group 1, whereas it was present in nine patients in group 2 (p=0.001). The mean CT severity score was higher in group 2, and there was a statistically significant difference between the mean CT scores of both groups (p<0.001). In addition, six (7.2%) patients in group 1 and 34 (50%) patients in group 2 had CT scores above the cut-off value (p<0.001). There was no statistically significant relationship between atypical findings and severity score.

CONCLUSION

Other diseases and atypical findings that may accompany COVID-19 pneumonia may increase the rate of misdiagnosis. In the diagnosis of the disease, clinical signs and symptoms and radiological findings should be evaluated together, and it should be kept in mind that lung findings in thorax CT change over time.

Is endogenous carboxyhaemoglobin level a useful biomarker of clinical course and prognosis in COVID-19 patients?

OBJECTIVE

SARS-CoV-2 has caused nearly 4 million confirmed cases of COVID-19 worldwide in the approximately 4 months since it emerged in Wuhan, China in December 2019. Comorbidities increase morbidity and mortality in COVID-19, and many laboratory parameters have been associated with mortality. The aim of the present study was to identify the relationship between endogenous carboxyhaemoglobin (COHb) level and the clinical course and prognosis of COVID-19.

METHODS

The study included 48 non-smokers or ex-smokers aged 18 years or older who presented to the emergency department, were diagnosed with COVID-19 by real-time PCR analysis of nasopharyngeal swab sample and were treated in the pulmonary diseases ward of the Atatürk University hospital after 24 March 2020 and 15 April 2020. The patients' laboratory parameters and demographic data were analysed retrospectively.

RESULTS

Prothrombin time and C-reactive protein (CRP), troponin-I, and D-dimer levels decreased in COVID-19 patients during follow-up (P = .024, P = .001, P = .001, P = .001), while PaO₂ /FiO₂ ratio and COHb increased (P = .002, P = .001). COHb level at admission was significantly lower in patients who developed macrophage activation syndrome (MAS), acute respiratory distress syndrome (ARDS), and those who died compared with the other patients (P = .002, P = .001). COHb level on day 5 of treatment was significantly higher in patients with ARDS and patients who died (P = .001, P = .001). Significant correlations were detected between COHb level and CRP (r=-0.425, P = .001), ferritin (r = -.395, P = .001) and PaO₂ /FiO₂ ratio (r = .431, P = .001).

CONCLUSIONS

COHb level may be an easily accessible biomarker that guides early follow-up and treatment planning to avoid ARDS, MAS and mortality in COVID-19.

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

AIM

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

METHOD

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

RESULTS

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

CONCLUSION

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

Relationship between ECG Findings and Serum Biomarkers in COVID-19 Patients

Background and aim: The aim of this study was to evaluate the relationship between ECG findings and blood parameters indicative of inflammation and myocardial injury in COVID-19 patients.

Methods: The study included 159 females and 194 males. Demographics, ECG findings (axis, rhythm, branch block, ST- and T-wave changes, premature ventricular contractions, early repolarization, S1Q3T3, fragmented QRS [fQRS], rate, PR, QRS, QT interval, QTc, P-wave dispersion) and albumin, D-dimer, ferritin, pro-BNP, procalcitonin, protein, troponin T, neutrophil-to-lymphocyte ratio (NLR), C-reactive protein/albumin ratio (CAR) were recorded.

Results: In the study, 45% of the cases were female and 55% were male. The mean age of the included patients was 45.7 ± 24.4 years. The most frequent comorbidities were chronic obstructive pulmonary disease (COPD) and hypertension (HT) in both groups. The incidence of fQRS on the 1st day was significantly higher in patients with negative COVID-19 test (23% for positive RT-PCR versus 35.6% for negative RT-PCR, p = 0.016). QTc values on the 3rd and 5th day were significantly higher in patients with negative RT-PCR (p = 0.045 and p = 0.042, respectively). Albumin and procalcitonin were significantly higher in patients with positive COVID-19 test results (p = 0.018 and p <0.001, respectively). Patients with fragmented QRS presented significantly lower serum albumin (40.62 ± 4.73 g/L vs. 42.92 ± 3.72 g/L, p = 0.01), and protein levels (p = 0.02), as well as lower lymphocyte count, and significantly higher levels of C-reactive protein (47.01 ± 65.01 mg/L vs. 24.55 ± 44.17 mg/L, p = 0.001), D-dimer (p = 0.009), neutrophil count, pro-BNP (p = 0.004), troponin T (p <0.001), NRL and CAR (1.28 ± 1.83 versus 0.6 ± 1.11, p <0.001).

Conclusion: Patients with COVID-19 infection presented significantly higher levels of C-reactive protein, D-dimer, neutrophil, pro-BNP, procalcitonin, troponin T, NLR, and CAR, and significantly lower levels of albumin, lymphocyte count, and serum proteins, indicating the level of inflammation and its relationship with myocardial injury. Further follow-up studies are required, on larger patient sets, for the development of risk prediction tools in COVID-19 patients.

The Role of Lung Ultrasound Before and During the COVID-19 Pandemic: A review article

Lung ultrasound [LUS] has evolved considerably over the last years. The aim of the current review is to conduct a systematic review reported from a number of studies to show the usefulness of [LUS] and point of care ultrasound for diagnosing COVID-19. A systematic search of electronic data was conducted including the national library of medicine, and the national institute of medicine, PubMed Central [PMC] to identify the articles depended on [LUS] to monitor COVID-19. This review highlights the ultrasound findings reported in articles before the pandemic [11], clinical articles before COVID-19 [14], review studies during the pandemic [27], clinical cases during the pandemic [5] and other varying aims articles. The reviewed studies revealed that ultrasound findings can be used to help in the detection and staging of the disease. The common patterns observed included irregular and thickened A-lines, multiple B-lines ranging from focal to diffuse interstitial consolidation, and pleural effusion. Sub-plural consolidation is found to be associated with the progression of the disease and its complications. Pneumothorax was not recorded for COVID-19 patients. Further improvement in the diagnostic performance of [LUS] for COVID-19 patients can be achieved by using elastography, contrast-enhanced ultrasound, and power Doppler imaging.

Radiomics-based machine learning differentiates "ground-glass" opacities due to COVID-19 from acute non-COVID-19 lung disease

Ground-glass opacities (GGOs) are a non-specific high-resolution computed tomography (HRCT) finding tipically observed in early Coronavirus disesase 19 (COVID-19) pneumonia. However, GGOs are also seen in other acute lung diseases, thus making challenging the differential diagnosis. To this aim, we investigated the performance of a radiomics-based machine learning method to discriminate GGOs due to COVID-19 from those due to other acute lung diseases. Two sets of patients were included: a first set of 28 patients (COVID) diagnosed with COVID-19 infection confirmed by real-time polymerase chain reaction (RT-PCR) between March and April 2020 having (a) baseline HRCT at hospital admission and (b) predominant GGOs pattern on HRCT; a second set of 30 patients (nCOVID) showing (a) predominant GGOs pattern on HRCT performed between August 2019 and April 2020 and (b) availability of final diagnosis. Two readers independently segmented GGOs on HRCTs using a semi-automated approach, and radiomics features were extracted using a standard open source software (PyRadiomics). Partial least square (PLS) regression was used as the multivariate machine-learning algorithm. A leave-one-out nested cross-validation was implemented. PLS β-weights of radiomics features, including the 5% features with the largest β-weights in magnitude (top 5%), were obtained. The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. The Youden's test assessed sensitivity and specificity of the classification. A null hypothesis probability threshold of 5% was chosen (p < 0.05). The predictive model delivered an AUC of 0.868 (Youden's index = 0.68, sensitivity = 93%, specificity 75%, p = 4.2 × 10-7). Of the seven features included in the top 5% features, five were texture-related. A radiomics-based machine learning signature showed the potential to accurately differentiate GGOs due to COVID-19 pneumonia from those due to other acute lung diseases. Most of the discriminant radiomics features were texture-related. This approach may assist clinician to adopt the appropriate management early, while improving the triage of patients.

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

Background/aim

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

Materials and methods

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

Results

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

Conclusion

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

The Role of Th17 Response in COVID-19

COVID-19 is an acute infectious disease of the respiratory system caused by infection with the SARS-CoV-2 virus (Severe Acute Respiratory Syndrome Coronavirus 2). Transmission of SARS-CoV-2 infections occurs through droplets and contaminated objects. A rapid and well-coordinated immune system response is the first line of defense in a viral infection. However, a disturbed and over-activated immune response may be counterproductive, causing damage to the body. Severely ill patients hospitalised with COVID-19 exhibit increased levels of many cytokines, including Interleukin (IL)-1β, IL-2, IL-6, IL-7, IL-8, IL-10, IL-17, granulocyte colony stimulating factor (G-CSF), monocyte chemoattractant protein 1 (MCP-1) and tumor necrosis factor (TNF). Increasing evidence suggests that Th17 cells play an important role in the pathogenesis of COVID-19, not only by activating cytokine cascade but also by inducing Th2 responses, inhibiting Th1 differentiation and suppressing Treg cells. This review focuses on a Th17 pathway in the course of the immune response in COVID-19, and explores plausible targets for therapeutic intervention.

Could chest CT findings taken on admission in symptomatic patients with COVID-19 be related to the prognosis and clinical outcome of the disease?

AIM

Initial chest CT findings of patients were compared by grouping them according to the clinical outcome of the infection and those which could predict clinical outcome, prognosis and mortality were investigated.

BACKGROUND

Published studies on chest CT in COVID-19 infection do not go beyond describing the characteristics of the current period. Nevertheless, comparative analysis of chest CT findings on hospital admission among patients in different clinical outcomes is scarce.

OBJECTIVE

198 consecutive symptomatic patients with COVID-19 infection confirmed by positive polymerase chain reaction (PCR) and who had undergone chest CT were enrolled in this retrospective study.

METHOD

According to their clinical outcomes, we divided them (n:98) into 3 groups. Group 1 (n: 62) involved patients discharged from the service, group 2 (n: 60) included patients hospitalized in the intensive care unit, and group 3 (n: 76) comprised patients who died despite any treatment.

RESULTS

Clinical characteristics involving age, dyspnea, hypertension, and chest CT findings of mediastinal lymphadenopathy, pleural effusion, and pericardial effusion, were determined as poor prognosis and mortality predictors, and halo sign in chest CT finding was a good prognosis predictor in multivariate analysis.

CONCLUSION

It was seen that some CT findings were significantly correlated to the patients' endpoints, such as discharge, hospitalization in the intensive care unit, and as a worst consequence, death. These findings support the role of CT imaging for potentially predicting the clinical outcomes of these patients with COVID-19.

An automated COVID-19 detection based on fused dynamic exemplar pyramid feature extraction and hybrid feature selection using deep learning

The new coronavirus disease known as COVID-19 is currently a pandemic that is spread out the whole world. Several methods have been presented to detect COVID-19 disease. Computer vision methods have been widely utilized to detect COVID-19 by using chest X-ray and computed tomography (CT) images. This work introduces a model for the automatic detection of COVID-19 using CT images. A novel handcrafted feature generation technique and a hybrid feature selector are used together to achieve better performance. The primary goal of the proposed framework is to achieve a higher classification accuracy than convolutional neural networks (CNN) using handcrafted features of the CT images. In the proposed framework, there are four fundamental phases, which are preprocessing, fused dynamic sized exemplars based pyramid feature generation, ReliefF, and iterative neighborhood component analysis based feature selection and deep neural network classifier. In the preprocessing phase, CT images are converted into 2D matrices and resized to 256 × 256 sized images. The proposed feature generation network uses dynamic-sized exemplars and pyramid structures together. Two basic feature generation functions are used to extract statistical and textural features. The selected most informative features are forwarded to artificial neural networks (ANN) and deep neural network (DNN) for classification. ANN and DNN models achieved 94.10% and 95.84% classification accuracies respectively. The proposed fused feature generator and iterative hybrid feature selector achieved the best success rate, according to the results obtained by using CT images.

Olfactory Cleft Opacification in COVID-19 Related Smell Loss: CT Findings and Correlation With Objective Testing

OBJECTIVES

Besides the common symptoms of the coronavirus disease 2019 (COVID-19) including fever, shortness of breath, and cough, a "sudden loss of smell" has recently been added as a diagnostic symptom. The relationship between paranasal sinus computed tomography (PNS CT) and sudden loss of smell in COVID-19 was examined.

MATERIALS AND METHODS

Two groups were selected for the study, the COVID-19 and the control groups. The control group consisted of 40 patients who applied to our clinic with headache and therefore underwent PNS CT. The other group consisted of 40 patients with COVID-19 who were diagnosed with sudden loss of smell with the Connecticut Chemosensory Clinical Research Center (CCCRC) olfactory test. Clinical and demographic characteristics, tomography results, and olfactory test scores of patients with COVID-19 loss of smell and control group patients were recorded. The relationship between CT changes in the olfactory cleft and the degree of loss of smell was evaluated. The "Opacification in the olfactory cleft" was accepted as a positive CT finding.

RESULTS

Comparison of patients with COVID-19 who had a loss of smell and the control group indicated that a significant difference was observed in terms of CT findings (P = .022). When we evaluated the paranasal CTs obtained from our patients with loss of smell, the CT of 13 patients showed pathological findings (P < .05). As the COVID-19 progressed (pneumonia and respiratory failure), the degree of loss of smell increased (P < .05). A statistically significant relationship was found between the CCCRC score and the presence of PNS CT findings (P = .0012).

CONCLUSION

The PNS CT findings are significant in patients with COVID-19 with a loss of smell and were significantly associated with the degree of loss of smell. In patients with olfactory loss due to COVID-19, PNS CT can help in diagnosis. However, for this imaging to be diagnostic, a larger patient series is needed.

Thorax Magnetic Resonance Imaging Findings in Patients with Coronavirus Disease (COVID-19)

Rationale and Objectives

The aim of this study was to compare the findings found in thorax computed tomography (CT), which is increasingly used in the diagnosis of the important public health problem of coronavirus disease (COVID-19), and the findings of magnetic resonance imaging (MRI) as an important diagnostic alternative.

Materials and Methods

Thirty-two patients diagnosed with COVID-19 who underwent thorax CT for COVID pneumonia and MRI for any reason within 24 hours after CT were included in the study. The number of lobes affected, number of lobes containing ground-glass opacities and consolidation, number of nodules, distribution of lesions (central, peripheral, or diffuse), lobes with centrilobular nodular pattern, and the presence of pleural effusion were recorded separately for both imaging methods.

Results

Seventeen of the patients were female (53%) and 15 were male (47%). The mean age of the patients was 60.5 (range, 20–85) years. A total of 31 patients (96%) had signs of pneumonia on CT. The most common finding in CT was ground-glass opacities in 29 patients (90.6%), followed by consolidation in 14 patients (43.75%). Both consolidation and ground-glass opacities were also observed in MRI in all of these patients. Nodules were detected in 12 patients (37.5%) on CT and 11 patients (34.4%) on MRI. The sensitivity and specificity of MRI in nodule detection were calculated as 91.67% and 100%, respectively.

Conclusion

Although thorax CT is widely used in the imaging of COVID-19 infection, due to its advantages, MRI can also be used as an alternative diagnostic tool.

COVID-19 infection can cause chemotherapy resistance development in patients with breast cancer and tamoxifen may cause susceptibility to COVID-19 infection

Breast cancer is the most common cancer in women and is the second most common cause of death in women. Estrogen plays an important role in breast tumor etiopathogenesis. Tamoxifen and other anti-estrogen drugs are used in breast cancer patients who have a positive estrogen receptor (ER). While angiotensin II plays a key role in breast cancer etiology and causes tamoxifen resistance, angiotensin 1-7 has been reported to may reduce the spread and invasion of breast cancer. During the COVID-19 infection, the virus blocks ACE2, and angiotensin 1-7 production discontinued. Angiotensin III production may increase as angiotensin II destruction is reduced. Thus, aminopeptidase upregulation may occur. Increased aminopeptidase may develop resistance to chemotherapy in breast cancer patients receiving chemotherapy. Estrogen can have a protective effect against COVID-19. Estrogen increase causes ER-α upregulation in T lymphocytes. Thus, estrogen increases the release of interferon I and III from T lymphocytes. Increasing interferon I and III alleviates COVID-19 infection. Tamoxifen treatment causes down-regulation, mutation, or loss in estrogen receptors. In the long-term use of tamoxifen, its effects on estrogen receptors can be permanent. Thus, since estrogen receptors are damaged or downregulated, estrogen may not act by binding to these receptors. Tamoxifen is a P-glycoprotein inhibitor, independent of its effect on estrogen receptors. It suppresses T cell functions and interferon release. We think tamoxifen may increase the COVID-19 risk due to its antiestrogen and P-glycoprotein inhibitory effects.

Early Computed Tomography Findings of Novel Coronavirus Disease 2019 (COVID-19) Pneumonia

Background: Novel coronavirus disease (COVID-19) pneumonia is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is highly contagious and threatens human health. Chest computed tomography (CT) is an accurate imaging modality for diagnosis and follow-up. Objectives: To investigate early CT features of COVID-19 pneumonia. Methods: Retrospective study. Sixty-three patients with COVID-19 pneumonia (28 men, 35 women; age range, 21 - 85 years; mean, 59 years) confirmed by reverse transcription-polymerase chain reaction (RT-PCR) were enrolled in this retrospective study and CT findings were evaluated. Patients were classified into three groups for estimating COVID-19 pneumonia, according to CT features. Results: Nineteen (30%) patients classified as out of COVID-19 pneumonia had normal CT findings. Twenty-eight (44%) patients, classified as highly suggestive for COVID-19 pneumonia, had typical findings for COVID-19 pneumonia. In highly suggestive for COVID-19 pneumonia group most common CT features were peripheral (64%), multilobar (96%), patchy, and round (54%) ground-glass opacity (GGO) (57%). Air bronchogram (93%), crazy-paving patterns (79%), and vascular thickening (61%) were the most common special findings. We only detected these rare findings such as halo sign, reverse halo sign, lymphadenopathy, pleural and pericardial effusion, pleural thickening, and fibrotic lines in patients with COVID-19 with chronic diseases. Conclusions: The early CT findings are peripheral, multilobar, patchy, and round ground glass opacities accompanied by vascular thickening, crazy-paving pattern, or air bronchogram sign. Rare findings are only seen in the presence of concomitant diseases.