Lung Involvement In COVID-19 Research Articles

Efficacy of a short course of lung ultrasound for primary care physicians in the assessment of COVID-19-positive patients.

IntroductionLung ultrasound (LUS) has become the first diagnostic imaging approach to assess lung involvement in COVID-19. While LUS proved to be safe, reliable, and accurate, not many primary care physicians (PCP) are capable to employ this instrument in the first evaluation of COVID-19 outpatients. The aim of this study was to determine the effectiveness of a brief training program in LUS for PCP.MethodsItalian local authorities promoted a training program in LUS for PCP engaged in COVID-19 outpatients’ evaluation. The course took place in a COVID-19 unit and included a hands-on practice on real COVID-19 patients. We conducted a qualitative and quantitative analysis of the results of the training program.ResultsA total of 32 PCP completed the training. About 100% of participants reported an increase in competence and confidence in the use of LUS after the training. Self-reported confidence in detecting major COVID-19 LUS abnormalities was high (B-lines 8/10, pleural abnormalities 6.5/10). B-lines were accurately identified with a reliability of 81%, with a sensitivity of 96%, and a negative predictive value of 98%. Trainees were some less accurate in detecting pleural abnormalities (reliability 63%) but with a high specificity (99%).ConclusionsThis study showed that a short training program, but comprising a hands-on practice, is capable to bring even almost novices to achieve a high overall accuracy and reliability in detecting lung involvement in COVID-19. This may result in a significant improvement of the performances of PCP involved in the first evaluation of COVID-19 cases in primary care facilities.

Intravesical Bacillus Calmette-Guérin Reduces Lung Involvement in COVID-19 in Patients with Bladder Cancer

Intravesical Bacillus Calmette-Guérin Reduces Lung Involvement in COVID-19 in Patients with Bladder Cancer

CT-severity score in COVID-19 patients: for whom is it applicable best?

Background:lung involvement in COVID-19 can be quantified by chest CT scan. We evaluated the triage and prognostication performance of seven proposed CT-severity score (CTSS) systems in two age groups of ≥65 and <65 years old.Methods: Confirmed COVID-19 patients by reverse transcriptase polymerase chain reaction (RT-PCR) admitted from February 20th, 2020 to July 22nd were included in a retrospective single center study. Clinical disease severity at presentation and at peak disease severity were recorded. CT images were scored according to seven different scoring systems (CTSS1-CTSS7). The cohort was divided into two age groups of ≥65 and <65 years old. Receiver operator characteristic (ROC) curves for each age group for diagnosis of severe/critical disease on admission (for triage) were plotted. Such curves were also plotted for predicting severe/critical disease at peak disease severity (for prognostication), and critical disease at peak severity (for prognostication). Areas under the curve (AUCs), best thresholds, and corresponding sensitivities (Sens.) and specificities (Spec.) were calculated.Results:96 patients were included with a mean age of 63.6±17.4 years. All CTSSs in 65-year-old or more group (N=55) showed excellent performance (AUC=0.80-0.83, Sens.+Spec.= 155-162%) in triage and excellent or outstanding performance (AUC=0.81-0.92, Sens.+Spec.= 153-177%) in prognostication. In the younger group (N=44), all CTSSs were unsatisfactory for triage (AUC=0.49-0.57) and predicting severe/critical disease (AUC=0.67-0.70), but were acceptable for predicting critical disease (AUC=0.70-0.73, Sens.+Spec.= 132-151%).Conclusion:CTSS is an excellent tool in triage and prognostication in patients with COVID-19 ≥65 years old, but is of limited value in younger patients.

Incidentally Detected COVID-19 Lung Changes during Oncologic Fluorodeoxyglucose Positron Emission Tomography-Computerized Tomography Studies: Experience from Tertiary Care Cancer Hospital.

Objective:The objective is to evaluate incidental detection of COVID-19 lung involvement in asymptomatic individuals who undergo fluorodeoxyglucose (FDG) positron emission tomography-computerized tomography (PET/CT) scans for oncologic indications.Patients and Methods:The study was conducted in a tertiary care oncology hospital and included patients who were asymptomatic for COVID-19 infection and underwent FDG PET/CT scans for standard oncologic indications between April 15, 2020, and September 30, 2020. Patients who showed CO-RADS category 4/5 changes (high level of suspicion) on the CT chest component of the PET/CT study were considered for analysis. CT severity score, presence of FDG uptake, and maximum standardized uptake value of FDG avid lung involvement were noted and correlated with reverse transcriptase-polymerase chain reaction (RT-PCR) test.Results:1982 PET/CT scans were performed, 78 (3.9%) patients showed lung changes with high degree of suspicion of COVID-19 pneumonia (CO-RADS 4/5). Hematolymphoid and head-neck cancer were the most common tumor types (23%), and restaging/response evaluation was the most common PET/CT indication. Of the patients who underwent RT-PCR testing, 70% showed a positive result. The mean CT severity score was 6 (standard deviation 5.9) with no significant difference seen between the RT-PCR positive and negative groups. FDG avidity in lung lesions was noted in 41 out 57 (72%) patients. A significant correlation was seen between the RT-PCR positivity and FDG uptake in lung lesions.Conclusion:A small but significant proportion of patients undergoing routine oncologic PET/CT scans showed incidental COVID-19 lung involvement. Lung involvement in these asymptomatic patients showed a low CT severity score in all patients and FDG avidity in majority. Timely detection of such incidental cases can initiate further confirmatory RT-PCR testing and isolation measures that not only influence patient's cancer treatment protocols but also have a larger community impact of limiting the spread of infection.

Diagnostic accuracy and inter-observer agreement with the CO-RADS lexicon for CT chest reporting in COVID-19

PurposeTo measure the diagnostic accuracy and inter-observer agreement with the use of COVID-19 Reporting and Data System (CO-RADS) for detection of COVID-19 on CT chest imaging.MethodsThis retrospective study included 164 consecutive patients with clinical suspicion of COVID-19 in whom a CT chest examination was performed at a single institution between April 2020 and July 2020. Of them, 101 patients was RT-PCR positive for COVID-19. Six readers with varying radiological experience (two each of chest radiologists, general radiologists, and radiologists in training) independently assigned a CO-RADS assessment category for each CT chest study. The Fleiss’ K was used to quantify inter-observer agreement. The inter-observer agreement was also assessed based on the duration of onset of symptoms to CT scan. ROC curve analysis was used to determine the diagnostic accuracy of CO-RADS. The area under curve was calculated to determine the reader accuracy for detection of COVID-19 lung involvement with RT-PCR as reference standards. The data sets were plotted in ROC space, and Youden’s J statistic was calculated to determine the threshold cut-off CO-RADS category for COVID-19 positivity.ResultsThere was overall moderate inter-observer agreement between all readers (Fleiss’ K 0.54 [95% CI 0.54, 0.54]), with substantial agreement among chest radiologists (Fleiss’ K 0.68 [95% CI 0.67, 0.68]), general radiologists (Fleiss’ K 0.61 [95% CI 0.61, 0.61]), and moderate agreement among radiologists-in-training (Fleiss’ K 0.56 [95% CI 0.56, 0.56]). There was overall moderate inter-observer agreement in early disease (stages 1 and 2), with cumulative Fleiss’ K 0.45 [95% CI 0.45, 0.45]). The overall AUC for CO-RADS lexicon scheme to accurately diagnose COVID-19 yielded 0.92 (95% CI 0.91, 0.94) with strong concordance within and between groups, of chests radiologists with AUC of 0.91 (95% CI 0.88, 0.94), general radiologists with AUC 0.96 (95% CI 0.94, 0.98), and radiologists in training with AUC of 0.90 (95% CI 0.87, 0.94). For detecting COVID-19, ROC curve analysis yielded CO-RADS > 3 as the cut-off threshold with sensitivity 90% (95% CI 0.88, 0.93), and specificity of 87% (95% CI 0.83, 0.91).ConclusionReaders across different levels of experience could accurately identify COVID-19 positive patients using the CO-RADS lexicon with moderate inter-observer agreement and high diagnostic accuracy.

Evaluation of the chest computed tomography and hemogram data in patients with COVID-19: the importance of thymus

Background/aimTo investigate the relationship between imaging findings and peripheral blood cell counts of COVID-19 patients and the degree of thymus fat involution of these patients.Materials and methodsComputed tomography (CT) images of 87 patients with COVID-19 positive through RT-PCR testing were evaluated retrospectively by two radiologists. Ground glass densities and other signs of viral pneumonia were recorded, lung involvement was scored quantitatively. The patients thymus fat involution was graded on CT. Neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratios (PLR), lymphocyte and platelet counts were calculated. Imaging findings and degrees of thymus fat involution were compared with laboratory data. ResultsQuantitative scoring of lung involvement was calculated at mean 6.63 ± 4.70 (1–23) for observer 1 and mean 6.55 ± 4.65 (1–23) for observer 2 ( K = 0.824–1.000). Statistical significance was determined between the increase in age and the increase in scores of lung findings ( p = 0.003). Lymphocyte count ( p = 0.0001) and PLR ( p = 0.001) were significantly lower in patients with severe CT involvement. A statistically significantcorrelation was found between increased thymus fat component and presence of COVID-19 lung involvement in CT ( r = 0.461).ConclusionThe severity of imaging findings for COVID-19 patients significantly correlates with the degree of fat involution in patients’ thymus tissue.

First results of spatial reconstruction and quantification of COVID-19 chest CT infiltrates using lung CT analyzer and 3D slicer

ObjectiveLung CT scans are early diagnostic tests in evaluation of COVID-19 patients. Data are usually analyzed visually and the extent of infiltrations can only roughly be estimated. The aim of the present study was to create a software to spatially visualize and quantify infiltrated and collapsed areas in lung CT scans and set these volumes into relation with non-affected lung areas.MethodsA new software "Lung CT Analyzer" (LCTA, 1) was created from scratch in an international team-effort within the 3D medical imaging software 3D Slicer (2). LCTA consists of two components: "Lung CT Segmenter" implements an intuitive and semiautomatic workflow for the generation of lung masks. LCTA then uses masked thresholds of Hounsfield units to detect non-affected versus affected (emphysematous, infiltrated, and collapsed) areas of the lung. Intrapulmonary vessels are subtracted from the other volumes. Segment volumes are expressed in milliliters and displayed in 3D. COVID-Q was defined as affected divided by non-affected volume and can be calculated separately for both lungs.3D Slicer and LCTA are open source, freely available and maintained on Github.ResultsCT data of twelve patients with moderate to severe COVID-19 (9 m, 3 f) were selected for the present retrospective study. All scans were performed shortly after admission. Thresholds of Hounsfield units (HU) for areas of interest were defined prior to the study and processing was identical for all patients. The median time effort for 3D reconstruction was 8 minutes per patient. For more detailed results please see the enclosed table. A 3D Slicer demo data set (Control) has been included for comparison.ConclusionThe COVID-19 pandemic promoted fast-paced innovations such as LCTA in our hospital. LCTA was feasible, reproducible and easy to perform. COVID-Q correlated with COVID-19 lung involvement in all cases. All fatal cases showed COVID-Q values of > 2.0.LCTA enabled the serial 3D reconstruction of infiltrated and collapsed lung areas in lung CT scans. The procedure may be of great help in the future analysis of pulmonary infiltrates of any cause. In COVID-19 disease, volumetric lung CT reconstruction could result in the definition of new prognostic factors, identify patients “at-risk” in the ICU, and be useful for follow-up.(1) Lung CT Analyzer: https://github.com/rbumm/SlicerLungCTAnalyzer(2) 3D Slicer: http://slicer.org

Radiographic findings in COVID-19: Comparison between AI and radiologist.

Context:As the burden of COVID-19 enhances, the need of a fast and reliable screening method is imperative. Chest radiographs plays a pivotal role in rapidly triaging the patients. Unfortunately, in low-resource settings, there is a scarcity of trained radiologists.Aim:This study evaluates and compares the performance of an artificial intelligence (AI) system with a radiologist in detecting chest radiograph findings due to COVID-19.Subjects and Methods:The test set consisted of 457 CXR images of patients with suspected COVID-19 pneumonia over a period of three months. The radiographs were evaluated by a radiologist with experience of more than 13 years and by the AI system (NeuraCovid, a web application that pairs with the AI model COVID-NET). Performance of AI system and the radiologist were compared by calculating the sensitivity, specificity and generating a receiver operating characteristic curve. RT-PCR test results were used as the gold standard.Results:The radiologist obtained a sensitivity and specificity of 44.1% and 92.5%, respectively, whereas the AI had a sensitivity and specificity of 41.6% and 60%, respectively. The area under curve for correctly classifying CXR images as COVID-19 pneumonia was 0.48 for the AI system and 0.68 for the radiologist. The radiologist's prediction was found to be superior to that of the AI with a P VALUE of 0.005.Conclusion:The specificity and sensitivity of detecting lung involvement in COVID-19, by the radiologist, was found to be superior to that by the AI system.

A NEW APPROACH TO MEDICAL DIAGNOSIS OF COVID-19 AND CONTROLLING THE EFFECTS OF COVID-19 ON ARTERIAL BLOOD GAS WORKING

Aim: The purpose of this study is to provide information about the effect of the defense mechanism formed by the lung mucosal surface against Covid-19 and the effect of Covid-19's lung involvement on Arterial Blood Gas Values, whose number of cases and mortality rate increases day by day in our country. In addition, by sharing a new method in the diagnosis of Covid-19, important issues in the monitoring of Covid-19 patients with a high symptom burden and high risk of complications associated with lung damage were highlighted. Method: The blood values of 4000 patients who read the consent form and gave consent were collected in the SPSS 26 program in our study conducted in various public hospitals within the city of Istanbul with the permission of Istanbul Provincial Health Directorate with the number 15916306-604.02 and dated 08.09.2020. With our quantitative research, it is possible to interpret the arterial blood gas values of patients who have been diagnosed with covid-19 positive, to determine the common output and to determine the treatment methods, drugs, etc. It is aimed to be a resource for the studies. Common values were searched in the formed control groups and analyzed in accordance with the Henderson-Hasselbalch equation and in accordance with the literature. Arterial and vein blood from the patients participating in the study were collected in anaerobic environment with sterile syringes containing 1.0 or 5.0 mL lyophilized heparin. During blood collection, necessary measures have been taken to prevent or minimize blood contact with air. Since arterial blood gas collection requires expertise, no results that could be interpreted as suspicious were included in the study. Analyzes were performed with pH, pO2, pCO2, HCO3, LDH and Hgb values of 4000 participants. Results: Our study includes 4000 people between the ages of 19-88, who have been diagnosed with Covid-19 and voluntarily agree to participate in our study. 58% of the patients who participated in the study by approving the consent form were male and 42% were female. In order for the blood values of the deceased patients to be used in our study, permission was also requested from their relatives. In this group, 68% of the participants are male and 32% are female participants. It was found that the pO2 and pCO2 ratio of 4000 patients participating in the study did not exceed 10% of the LDH and HCO3 ratio (1O X (LDH) / 100 X (HCO3)&gt; pO2 / pCO2). If the equation is correct in the equation [(LDH) X (pCO2)] / [(HCO3) X (pO2)] ≤ (Hgb), the covid-19 diagnostic status is positive. Conclusion: : It has been determined that the pO2 / pCO2 ratio does not exceed 10% of the LDH / HCO3 ratio in the critical cases examined within the scope of the research. As this rate decreased, the rate of survival of patients also decreased. Therefore, in positive cases, a reference equation that can detect covid-19 positivity has been found by looking at whether there is an anion gap and metabolic acidosis is observed and LDH level. This formula; [(LDH) X (pCO2)] / [(HCO3) X (pO2)] ≤ (Hgb). When the formula was applied, it was determined that the ratio of [(LDH) X (pCO2)] / [(HCO3) X (pO2)] was equal or less than the value of hemoglobin (Hgb).

Factors determining generalization in deep learning models for scoring COVID-CT images.

The COVID-19 pandemic has inspired unprecedented data collection and computer vision modelling efforts worldwide, focused on the diagnosis of COVID-19 from medical images. However, these models have found limited, if any, clinical application due in part to unproven generalization to data sets beyond their source training corpus. This study investigates the generalizability of deep learning models using publicly available COVID-19 Computed Tomography data through cross dataset validation. The predictive ability of these models for COVID-19 severity is assessed using an independent dataset that is stratified for COVID-19 lung involvement. Each inter-dataset study is performed using histogram equalization, and contrast limited adaptive histogram equalization with and without a learning Gabor filter. We show that under certain conditions, deep learning models can generalize well to an external dataset with F1 scores up to 86%. The best performing model shows predictive accuracy of between 75% and 96% for lung involvement scoring against an external expertly stratified dataset. From these results we identify key factors promoting deep learning generalization, being primarily the uniform acquisition of training images, and secondly diversity in CT slice position.

Lung Ultrasound in COVID-19 pandemic: Why? How? And When?

During COVID-19 pandemic Lung ultrasound has rapidly become a tool for diagnosis and monitoring of lung involvement and it’s severity. Accurate evaluation of lung pathologic entities at the bedside, especially in critically ill patients, and those on mechanical ventilation, remains problematic. CT should not be frequently repeated and is not available everywhere, especially for critically ill patients. Limitations of bedside chest X-ray have been well described and lead to poor-quality X-ray films with low sensitivity.The lung ultrasound has been shown to be a useful tool in intensive care patients with adult respiratory distress syndrome (ARDS) and can be used forassessing severity of lung involvement in COVID-19. In this paper the accuracy of bedside LUS, chest X-ray and computer tomography are compared based on clinical cases, typical for COVID-19 lung ultrasound appearance is evaluated. There have been shown that lung ultrasound can predict the deterioration of the patient's conditionand can be used for risk stratification and clinical decision making, reducethe use of both chest x‐rays and computer tomography, what is very important especially in limited resources settings.

Which role for chest x-ray score in predicting the outcome in COVID-19 pneumonia?

ObjectiveWe aim to demonstrate that a chest X-ray (CXR) scoring system for COVID-19 patients correlates with patient outcome and has a prognostic value.MethodsThis retrospective study included CXRs of COVID-19 patients that reported the Brixia score, a semi-quantitative scoring system rating lung involvement from 0 to 18. The highest (H) and lowest (L) values were registered along with scores on admission (A) and end of hospitalization (E). The Brixia score was correlated with the outcome (death or discharge).ResultsA total of 953 patients met inclusion criteria. In total, 677/953 were discharged and 276/953 died during hospitalization. A total of 524/953 had one CXR and 429/953 had more than one CXR. H-score was significantly higher in deceased (median, 12; IQR 9–14) compared to that in discharged patients (median, 8; IQR 5–11) (p < 0.0001). In 429/953 patients with multiple CXR, A-score, L-score, and E-score were higher in deceased than in discharged patients (A-score 9 vs 8; p = 0.039; L-score 7 vs 5; p < 0.0003; E-score 12 vs 7; p < 0.0001). In the entire cohort, logistic regression showed a significant predictive value for age (p < 0.0001, OR 1.13), H-score (p < 0.0001, OR 1.25), and gender (p = 0.01, male OR 1.67). AUC was 0.863. In patients with ≥ 2 CXR, A-, L-, and E-scores correlated significantly with the outcome. Cox proportional hazards regression indicated age (p < 0.0001, HR 4.17), H-score (< 9, HR 0.36, p = 0.0012), and worsening of H-score vs A score > 3 (HR 1.57, p = 0.0227) as associated with worse outcome.ConclusionsThe Brixia score correlates strongly with disease severity and outcome; it may support the clinical decision-making, particularly in patients with moderate-to-severe signs and symptoms. The Brixia score should be incorporated in a prognostic model, which would be desirable, particularly in resource-constraint scenarios.Key Points• To demonstrate the importance of the Brixia score in assessing and monitoring COVID-19 lung involvement.• The Brixia score strongly correlates with patient outcome and can be easily implemented in the routine reporting of CXR.

Prognostic Value and Reproducibility of AI-assisted Analysis of Lung Involvement in COVID-19 on Low-Dose Submillisievert Chest CT: Sample Size Implications for Clinical Trials.

PurposeTo compare the prognostic value and reproducibility of visual versus AI-assisted analysis of lung involvement on submillisievert low-dose chest CT in COVID-19 patients.Materials and MethodsThis was a HIPAA-compliant, institutional review board-approved retrospective study. From March 15 to June 1, 2020, 250 RT-PCR confirmed COVID-19 patients were studied with low-dose chest CT at admission. Visual and AI-assisted analysis of lung involvement was performed by using a semi-quantitative CT score and a quantitative percentage of lung involvement. Adverse outcome was defined as intensive care unit (ICU) admission or death. Cox regression analysis, Kaplan-Meier curves, and cross-validated receiver operating characteristic curve with area under the curve (AUROC) analysis was performed to compare model performance. Intraclass correlation coefficients (ICCs) and Bland- Altman analysis was used to assess intra- and interreader reproducibility.ResultsAdverse outcome occurred in 39 patients (11 deaths, 28 ICU admissions). AUC values from AI-assisted analysis were significantly higher than those from visual analysis for both semi-quantitative CT scores and percentages of lung involvement (all P<0.001). Intrareader and interreader agreement rates were significantly higher for AI-assisted analysis than visual analysis (all ICC ≥0.960 versus ≥0.885). AI-assisted variability for quantitative percentage of lung involvement was 17.2% (coefficient of variation) versus 34.7% for visual analysis. The sample size to detect a 5% change in lung involvement with 90% power and an α error of 0.05 was 250 patients with AI-assisted analysis and 1014 patients with visual analysis.ConclusionAI-assisted analysis of lung involvement on submillisievert low-dose chest CT outperformed conventional visual analysis in predicting outcome in COVID-19 patients while reducing CT variability. Lung involvement on chest CT could be used as a reliable metric in future clinical trials.

Pulmones azules en pacientes COVID-19: un paso más allá del diagnóstico de tromboembolismo pulmonar mediante TCMD con mapa de yodo

ObjectiveTo evaluate the diagnostic capacity of pulmonary angiography with multidetector computed tomography (MDCT) and iodine mapping in the diagnosis of pulmonary thromboembolism (PTE) in patients with Covid-19 disease. MethodsRetrospective observational study of 81 consecutive patients admitted with Covid-19 respiratory infection who underwent MDCT for clinical suspicion of PTE (sudden dyspnea, chest pain, hemoptysis, severe respiratory failure (SRF) not corrected with high O2 flow) and/or raised D-dimer. ResultsOf the 81 patients studied [64 (79.01%) men], acute PTE was identified in 22 (27.16%), bilaterally in 13 (59.09%), and 13 (59,09%) showed areas of hypoperfusion. Of the 59 (72.83%) patients without PTE, hypoperfusion was observed in 41 (69.49%) (attributable in one case to pulmonary emphysema). In 18 (22.2%) of the total number of patients, neither PTE nor hypoperfusion were seen. A crazy paving pattern is a risk factor for developing PTE (OR 1.94; 95% CI 0.28-13.57), as are consolidations (OR 1.44; 95% CI 0.24-8.48) and septal thickening/bronchiectasis (OR 1.47; 95% CI 0.12-17.81).Patients with O2-refractory SRF showed a 6.36-fold higher risk for hypoperfusion on the iodine map. ConclusionBy adding the functional image to the anatomical image, pulmonary angiography with MDCT and iodine mapping can demonstrate not only PTE in main, lobar and segmental arteries, but also the presence of hypoperfusion in distal vessels. This makes it a highly useful tool for the accurate diagnosis and therapeutic orientation of patients with Covid-19 lung involvement.

CORTICOSTEROIDS IN THE TREATMENT OF SEVERE COVID-19 LUNG DISEASE: THE PULMONOLOGY PERSPECTIVE FROM THE FIRST UNITED STATES EPICENTER

The SARS-CoV-2 pandemic has introduced the medical community to a lung disease heretofore unknown to most clinicians. In much of the discourse about COVID-19 lung disease, the more familiar clinical entity of ARDS has been used as the guiding paradigm. Reflecting on studies in ARDS, particularly that due to influenza, and on data from the SARS-CoV and MERS epidemics, many authorities, including within the discipline of infectious diseases, were initially passionate in their opposition to the use of corticosteroids for lung involvement in COVID-19. The voice of the pulmonology community—the community of lung experts—has continued to be among the quietest in this conversation. Herein we offer our perspective as academic pulmonologists who encountered COVID-19 in its first United States epicenter of New York City. We encourage a conceptual separation between early COVID-19 lung involvement and ARDS. We draw on history with other immune cell-mediated lung diseases, on insights from the SARS-CoV experience, and on frontline observations in an attempt to allay the skepticism towards corticosteroids in COVID-19 lung disease that is likely to persist even as favorable study results emerge.

COVID-19 severity scoring systems in radiological imaging - a review.

The current reference standard to make a definitive diagnosis of SARS-CoV-2 infection is the reverse transcription- polymerase chain reaction assay (rt-PCR). However, radiological imaging plays a crucial role in evaluating the course of COVID-19 and in choosing proper management of infected patients. Chest X-ray (CXR) is generally considered not to be sensitive for the detection of pulmonary abnormalities in the early stage of the disease. However, in the emergency setting CXR can be a useful diagnostic tool for monitoring the rapid progression of lung involvement in COVID-19, especially in patients admitted to intensive care units. The rapid course of SARS-CoV-2 infection and the severity and progression of lung aberrations require a method of radiological evaluation to implement and manage the appropriate treatment for infected patients. Computed tomography (CT) imaging is considered to be the most effective method for the detection of lung abnormalities, especially in the early stage of the disease. Moreover, serial chest CT imaging with different time intervals is also effective in estimating the evolution of the disease from initial diagnosis to discharge from hospital. Despite having low specificity in distinguishing abnormalities in viral infections, the high sensitivity of CT makes this method ideal for assessing the severity of the disease in patients with confirmed COVID-19.In this review, we present and discuss currently available scales that can be used to assess the severity of lung involvement in COVID-19 patients in everyday work, both for CXR and CT imaging.