Analysis of High-Resolution CT Images of COVID-19 Patients

This research work is carried out to quantify the COVID-19 disease and to explore whether the quantitative can be used to analyze the survivability of the patient during admission. In this method, a novel percentage split distribution (PSD), thresholding-based image segmentation method is proposed to quantify normal and lesion regions by analyzing the benign GGOs. The method segments the lung-CT image based on pixel distribution. The segmented regions are quantified as a fraction of region of interest with total number of pixels. The study is also extended to analyze the left and right lungs separately with some common findings on lesion distribution involved with COVID-19 disease. The performance of PSD method has been compared with two traditional image segmentation-based methods. From the results, it has been observed that the segments created by the PSD method are better than experimental methods and clearly identify the margins of lesion and normal regions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Weakly-supervised lesion analysis with a CNN-based framework for COVID-19.

Objective.Lesions of COVID-19 can be clearly visualized using chest CT images, and hence provide valuable evidence for clinicians when making a diagnosis. However, due to the variety of COVID-19 lesions and the complexity of the manual delineation procedure, automatic analysis of lesions with unknown and diverse types from a CT image remains a challenging task. In this paper we propose a weakly-supervised framework for this task requiring only a series of normal and abnormal CT images without the need for annotations of the specific locations and types of lesions.Approach.A deep learning-based diagnosis branch is employed for classification of the CT image and then a lesion identification branch is leveraged to capture multiple types of lesions.Main Results.Our framework is verified on publicly available datasets and CT data collected from 13 patients of the First Affiliated Hospital of Shantou University Medical College, China. The results show that the proposed framework can achieve state-of-the-art diagnosis prediction, and the extracted lesion features are capable of distinguishing between lesions showing ground glass opacity and consolidation.Significance.The proposed approach integrates COVID-19 positive diagnosis and lesion analysis into a unified framework without extra pixel-wise supervision. Further exploration also demonstrates that this framework has the potential to discover lesion types that have not been reported and can potentially be generalized to lesion detection of other chest-based diseases.

Severe COVID-19 pneumonia leads to post-COVID-19 lung abnormalities on follow-up CT scans.

Purpose: To investigate the association of the maximal severity of pneumonia on CT scans obtained within 6-week of diagnosis with the subsequent development of post-COVID-19 lung abnormalities (Co-LA). Methods: COVID-19 patients diagnosed at our hospital between March 2020 and September 2021 were studied retrospectively. The patients were included if they had (1) at least one chest CT scan available within 6-week of diagnosis; and (2) at least one follow-up chest CT scan available ≥ 6 months after diagnosis, which were evaluated by two independent radiologists. Pneumonia Severity Categories were assigned on CT at diagnosis according to the CT patterns of pneumonia and extent as: 1) no pneumonia (Estimated Extent, 0%); 2) non-extensive pneumonia (GGO and OP, <40%); and 3) extensive pneumonia (extensive OP and DAD, >40%). Co-LA on follow-up CT scans, categorized using a 3-point Co-LA Score (0, No Co-LA; 1, Indeterminate Co-LA; and 2, Co-LA). Results: Out of 132 patients, 42 patients (32%) developed Co-LA on their follow-up CT scans 6-24 months post diagnosis. The severity of COVID-19 pneumonia was associated with Co-LA: In 47 patients with extensive pneumonia, 33 patients (70%) developed Co-LA, of whom 18 (55%) developed fibrotic Co-LA. In 52 with non-extensive pneumonia, 9 (17%) developed Co-LA: In 33 with no pneumonia, none (0%) developed Co-LA. Conclusions: Higher severity of pneumonia at diagnosis was associated with the increased risk of development of Co-LA after 6-24 months of SARS-CoV-2 infection.

COVID-19 CT ground-glass opacity segmentation based on attention mechanism threshold.

The ground glass opacity (GGO) of the lung is one of the essential features of COVID-19. The GGO in computed tomography (CT) images has various features and low-intensity contrast between the GGO and edge structures. These problems pose significant challenges for segmenting the GGO. To tackle these problems, we propose a new threshold method for accurate segmentation of GGO. Specifically, we offer a framework for adjusting the threshold parameters according to the image contrast. Three functions include Attention mechanism threshold, Contour equalization, and Lung segmentation (ACL). The lung is divided into three areas using the attention mechanism threshold. Further, the segmentation parameters of the attention mechanism thresholds of the three parts are adaptively adjusted according to the image contrast. Only the segmentation regions restricted by the lung segmentation results are retained. Extensive experiments on four COVID datasets show that ACL can segment GGO images at low contrast well. Compared with the state-of-the-art methods, the similarity Dice of the ACL segmentation results is improved by 8.9%, the average symmetry surface distance ASD is reduced by 23%, and the required computational power F L O P s are only 0.09% of those of deep learning models. For GGO segmentation, ACL is more lightweight, and the accuracy is higher. Code will be released at https://github.com/Lqs-github/ACL.

COVID-19 Vaccination reduced pneumonia severity.

Purpose: To investigate the effect of vaccinations and boosters on the severity of COVID-19 pneumonia on CT scans during the period of Delta and Omicron variants. Methods: Retrospectively studied were 303 patients diagnosed with COVID-19 between July 2021 and February 2022, who had obtained at least one CT scan within 6 weeks around the COVID-19 diagnosis (-2 to +4 weeks). The severity of pneumonia was evaluated with a 6-point scale Pneumonia Score. The association between demographic and clinical data and vaccination status (booster/additional vaccination, complete vaccination and un-vaccination) and the difference between Pneumonia Scores by vaccination status were investigated. Results: Of 303 patients (59.4 ± 16.3 years; 178 females), 62 (20 %) were in the booster/additional vaccination group, 117 (39 %) in the complete vaccination group, and 124 (41 %) in the unvaccinated group. Interobserver agreement of the Pneumonia Score was high (weighted kappa score = 0.875). Patients in the booster/additionally vaccinated group tended to be older (P = 0.0085) and have more underlying comorbidities (P < 0.0001), and the Pneumonia Scores were lower in the booster/additionally vaccinated [median 2 (IQR 0-4)] and completely vaccinated groups [median 3 (IQR 1-4)] than those in the unvaccinated group [median 4 (IQR 2-4)], respectively (P < 0.0001 and P < 0.0001, respectively). A multivariable linear analysis adjusted for confounding factors confirmed the difference. Conclusion: Vaccinated patients, with or without booster/additional vaccination, had milder COVID-19 pneumonia on CT scans than unvaccinated patients during the period of Delta and Omicron variants. This study supports the efficacy of the vaccine against COVID-19 from a radiological perspective.

Pattern of Lung Involvement in Predicting Severity and Sequelae in Patients With COVID-19.

Background During the COVID pandemic, high-resolution CT scan has played a pivotal role in detecting lung involvement and severity based on the segments of the lung involved. The pattern of involvement was not considered, and our aim is to observe the pattern of lung involvement in predicting severity and guiding management protocol in patients with COVID-19. Methodology It was a prospective observational study conducted with 151 patients admitted with COVID-19 with a positive reverse transcriptase polymerase chain reaction test (RT-PCR) in a single tertiary care hospital in south India. Patients with pre-existing lung pathologies were excluded from the study. Eligible patients were then divided into mild, moderate, and severe categories based on Indian Council of Medical Research (ICMR) guidelines, and high-resolution computed tomography (HRCT) chest was done, findings of which were then categorized based on lung involvement; into ground glass opacities (GGO), interstitial involvement and mixture of both. These were then analyzed to determine their importance with respect to the duration of stay and severity of the disease. Results The data collected was analyzed by IBM SPSS software version 23.0 (IBM Corp., Armonk, NY, USA). The study population included 114 males (75.5%) and 37 females (24.5%). HRCT chest was done which showed 62.3% of patients had GGO, 14.6% had interstitial lung involvement, 18.5% had a mixture of both and 4.6% had normal lung findings. These findings, when compared to clinical categories of severity, showed a significant co-relation between pattern of involvement of the lung and the severity of the disease. It also showed significant co-relation with the duration of stay. Conclusion HRCT chest has proven to be useful in the determination of patient's severity and can guide with management. We suggest earlier initiation of steroids and anticoagulants in patients with interstitial involvement even for the patients not on oxygen therapy yet. It can be used as a triage modality for screening due to the advantage of presenting with immediate results as opposed to RT-PCR which might take hours and can delay treatment which can prevent worsening.

Study of Ct Severity Index in Covid-19 Patients in Tertiary Care Hospital of Maharashtra

Background: Covid-19 is an infection that has widely and rapidly spread all over the world and become a pandemic with significantly impacts upon the socio-political milieu and health care system. CT scan has high sensitivity in patients infected by covid-19 when other test like nasal pharyngeal swabs may be insensitive hence CT severity index has potential impact on clinical decision making for covid-19. Material(s) and Method(s): 50 patients aged between 18 to 50 years admitted at covid-19 ward were studied with CT chest scan images 120kvp automatic tube current modulation (30-70 mAS) pitch 0.99 - 1.22 mm slice thickness 10 mm, FoV=350 mmx 350 mm. All images were reconstructed with a slice interval of 0.625 to 1.250 mm opacity, (GGO), consolidation GGO with consolidation, linear opacities, and crazy paving halo sign reverse halo sign. Result(s): CT chest findings had 4 (8%) has 1 lobe affected, 6 (12%) had 2 lobes affected, 5 (10%) had 3 lobes affected, 3 (6%) had 4 lobes affected, 1 (2%) had 5 lobes affected. 4 (8%) had GGO with consolidation, 14 (28%) had GGO, 1 (2%) had consolidation. Frequency of lobe, 6 (12%) had right middle lobe, 14 (28%0 had right lower lobe, 3 (6%) had left upper lobe, 4 (8%) left lingual lobe, 15 (30%) had left lower lobe. The distribution of opacification and pattern had rounded shape, 2 (4%0 had linear opacity, 4 (8%) crazy paving pattern, 1 (2%) reverse halo sign, 4 (8%) halo sign, 13 (26%) had peripheral distribution. CT severity score was 4 (8%) severe, 32 (64%) moderate, 14 (28%) mild. Conclusion(s): CT scan study has significant role in management of severity and possible outcome of covid-19. CT severity scores can be positively correlated with inflammatory laboratory markers. Copyright © 2022 Authors.

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.

The Relationship among Splenomegaly, Lung Involvement Patterns, and Severity Score in COVID-19 Pneumonia.

INTRODUCTION: The disease caused by the novel coronavirus (COVID -19) is a vital public health problem that has now affected approximately 68,037,473 people and caused 1,552,802 deaths around the world. We aimed to correlate the frequency of the lung involvement patterns, the segmental distribution of lung infiltration, and TLSS in COVID-19 pneumonia patients with and without splenomegaly. MATERIAL AND METHODS: This retrospective study included patients admitted to Yunus Emre State Hospital Emergency, Internal Medicine and Infectious Disease Departments between March 11, 2020, and June 10, 2020, and diagnosed with COVID-19 by PCR test with a throat and nasal swab. The thoracic and upper abdomen CTs and the clinical and demographic features of the patients were analyzed at the time of initial diagnosis. RESULTS: Consolidation (group 1 - 18 (47%), group 2 - 69 (28.2%); P = 0017), crazy pavement pattern (15 (39.5%), 42 (17.1%); p = 0.001), pleural band formations (24 (63.2%), 87 (35.5%); p = 0.001), interlobular septal thickening (23 (60.5%), 79 (32.2%); p = 0.001), and sequelae of secondary tuberculosis (4 (10.5%), 8 (3.3%); p = 0.039) were more frequent in the patient with splemomegaly. The total lung severity score was high in the group with splenomegaly (7.32 ± 6.15, 3.69 ± 5.16; p = 0.001). CONCLUSION: Consolidation, interlobular septal thickening, tuberculosis sequela, pleural band, and crazy pavement patterns were frequent in the COVID-19 pneumonia patients with splenomegaly. The most frequently affected segment was the superior segment of the right lower lobe. TLSS was higher in the COVID-19 pneumonia patients with splenomegaly.

Role of CT scan in assessing post-acute COVID sequalae-a

A variety of chest imaging findings have been described in patients with COVID-19. Use of imaging could be useful for the diagnosis of patients with suspected COVID-19 and in patients diagnosed with COVID-19, to inform management. Our study aimed at assessing the significance of CT scan usage in determining the lung injury in patients as post-acute COVID sequalae having COVID 19 after a 3 month follow up and to study the various findings in post-acute COVID sequalae through CT scan. © 2022 International Journal of Health Sciences. All rights reserved.

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

Background: COVID-19 has still been expressed as a mysterious viral infection with dramatic pulmonary consequences. Objectives: This article aims to study the radiological pulmonary consequences of respiratory covid-19 infection at 6 months and their relevance to the clinical stage, laboratory markers, and management modalities. Methods: This study was implemented on two hundred and fifty (250) confirmed positive cases for COVID-19 infections. One hundred and ninety-seven cases (197) who completed the study displayed residual radiological lung shadowing (RRLS) on follow-up computed tomography (CT) of the chest. They were categorized by Simple clinical classification of COVID-19 into groups A, B and C. Results: GGO, as well as reticulations, were statistically significantly higher in group A than the other two groups;however, bronchiectasis changes, parenchymal scarring, nodules as well as pleural tractions were statistically significantly higher in group C than the other two groups. Conclusion: Respiratory covid-19 infection might be linked to residual radiological lung shadowing. Ground glass opacities GGO, reticulations pervaded in mild involvement with lower inflammatory markers level, unlike, severe changes that expressed scarring, nodules and bronchiectasis changes accompanied by increased levels of inflammatory markers. © 2022 Bentham Science Publishers.

Spectrum of High Resolution Computed tomography chest findings in PCR positive COVID-19 patients according to duration of infection and CT severity score assessment

Objective: To evaluate the spectrum of HRCT findings of COVID-19 in RT-PCR positive patients according to duration of infection and severity of disease. Methods: This retrospective study was conducted at Radiology department of Lahore General Hospital, Lahore from May to July 2020. Total 40 COVID-19 patients were reviewed for clinical features, HRCT chest findings based on time from symptom onset and CT conduction. Chi-square and fissure exact test were used for measuring association with severity of COVID-19, p value ≤0.05 was reported significant. Mean CT scores were calculated. ROC curve analysis showed threshold values of CT-SS for severe disease. Results: Of total 40 patients with age ranged from 22-83 years, 22(55%) were males and 18(45%) females. The hallmark of COVID-19 was combined GGO and consolidation, GGO alone and consolidation alone in bilateral, sub pleural and posterior distribution. Early stage had normal CT or GGO alone, intermediate and late stage had both GGO and consolidation. Septal lines/bands and crazy paving pattern were prevalent in late stage. Clinically, 24 (60%) were in severe group and 16(40%) in mild group. Severity of COVID-19 was associated with GGO alone (p=0.05), GGO and consolidation (p=0.01), crazy paving (p=0.01) and lung scores (p≤0.05). The threshold values of CT-SS for identifying severe disease by two radiologists were 18.50 and 20.50. Conclusion: HRCT manifestations along with CT-SS aids in predicting disease severity. Staging according to duration of infection is effective in understanding variation in pattern of chest findings in coronavirus disease.

Comparison of temporal evolution of computed tomography imaging features in COVID-19 and influenza infections in a multicenter cohort study.

Purpose: To compare temporal evolution of imaging features of coronavirus disease 2019 (COVID-19) and influenza in computed tomography and evaluate their predictive value for distinction. Methods: In this retrospective, multicenter study 179 CT examinations of 52 COVID-19 and 44 influenza critically ill patients were included. Lung involvement, main pattern (ground glass opacity, crazy paving, consolidation) and additional lung and chest findings were evaluated by two independent observers. Additional findings and clinical data were compared patient-wise. A decision tree analysis was performed to identify imaging features with predictive value in distinguishing both entities. Results: In contrast to influenza patients, lung involvement remains high in COVID-19 patients > 14 days after the diagnosis. The predominant pattern in COVID-19 evolves from ground glass at the beginning to consolidation in later disease. In influenza there is more consolidation at the beginning and overall less ground glass opacity (p = 0.002). Decision tree analysis yielded the following: Earlier in disease course, pleural effusion is a typical feature of influenza (p = 0.007) whereas ground glass opacities indicate COVID-19 (p = 0.04). In later disease, particularly more lung involvement (p < 0.001), but also less pleural (p = 0.005) and pericardial (p = 0.003) effusion favor COVID-19 over influenza. Regardless of time point, less lung involvement (p < 0.001), tree-in-bud (p = 0.002) and pericardial effusion (p = 0.01) make influenza more likely than COVID-19. Conclusions: This study identified differences in temporal evolution of imaging features between COVID-19 and influenza. These findings may help to distinguish both diseases in critically ill patients when laboratory findings are delayed or inconclusive.

Detection of Covid-19 Severity with CT Scan Images using Segmentation Techniques

The impact of COVID-19 is severe worldwide;detecting the Covid severity in a patient is a vital step. The further important actions such as isolating the patient from others and testing the people in frequent contact with the patient can only be done after the Covid-19 test results. Currently, different methods are used for detecting the Corona virus in a patient, they are Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) test, Rapid Diagnostic Test (RDT), and Computed Tomography (CT) scan for lungs. However, a CT scan is the most accurate way to detect covid compared to other tests. The CT scan can produce images of the lungs within 15 to 20 minutes. Whereas traditional methods such as RT-PCR will take at least six to eight hours to deliver results. This paper aims to determine the severity level of Covid from the Computed Tomography (CT) scan image of the lungs. © 2022 IEEE.

Combination of acute exacerbation of idiopathic nonspecific interstitial pneumonia and pulmonary embolism after booster anti-COVID-19 vaccination.

Coronavirus disease-2019 (COVID-19) is a systemic disorder with the lung and the vasculature being the preferred targets. Patients with interstitial lung diseases represent a category at high risk of progression in the case of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV)-2 infection, and as such deserve special attention. We first describe the combination of acute exacerbation and pulmonary embolism in an elderly ILD patient after booster anti-COVID-19 mRNA vaccination. Vaccines availability had significantly and safety impacted COVID-19 morbidity and mortality worldwide. Immunization against COVID-19 is indisputable but must not be separated from the awareness of potential adverse effects in fragile patients.

Pixel-based analysis of pulmonary changes on CT lung images due to COVID-19 pneumonia.

Objectives: Computed tomography (CT) plays a complementary role in the diagnosis of the pneumonia-burden of COVID-19 disease. However, the low contrast of areas of inflammation on CT images, areas of infection are difficult to identify. The purpose of this study is to develop a post-image-processing method for quantitative analysis of COVID-19 pneumonia-related changes in CT attenuation values using a pixel-based analysis rather than more commonly used clustered focal pneumonia volumes. The COVID-19 pneumonia burden is determined by experienced radiologists in the clinic. Previous AI software was developed for the measurement of COVID-19 lesions based on the extraction of local pneumonia features. In this respect, changes in the pixel levels beyond the clusters may be overlooked by deep learning algorithms. The proposed technique focuses on the quantitative measurement of COVID-19 related pneumonia over the entire lung in pixel-by-pixel fashion rather than only clustered focal pneumonia volumes. Material and Methods: Fifty COVID-19 and 50 age-matched negative control patients were analyzed using the proposed technique and commercially available artificial intelligence (AI) software. The %pneumonia was calculated using the relative volume of parenchymal pixels within an empirically defined CT density range, excluding pulmonary airways, vessels, and fissures. One-way ANOVA analysis was used to investigate the statistical difference between lobar and whole lung %pneumonia in the negative control and COVID-19 cohorts. Results: The threshold of high-and-low CT attenuation values related to pneumonia caused by COVID-19 were found to be between ₋642.4 HU and 143 HU. The %pneumonia of the whole lung, left upper, and lower lobes were 8.1 ± 4.4%, 6.1 ± 4.5, and 11.3 ± 7.3% for the COVID-19 cohort, respectively, and statistically different (P < 0.01). Additionally, the pixel-based methods correlate well with existing AI methods and are approximately four times more sensitive to pneumonia particularly at the upper lobes compared with commercial software in COVID-19 patients (P < 0.01). Conclusion: Pixel-by-pixel analysis can accurately assess pneumonia in COVID-19 patients with CT. Pixel-based techniques produce more sensitive results than AI techniques. Using the proposed novel technique, %pneumonia could be quantitatively calculated not only in the clusters but also in the whole lung with an improved sensitivity by a factor of four compared to AI-based analysis. More significantly, pixel-by-pixel analysis was more sensitive to the upper lobe pneumonia, while AI-based analysis overlooked the upper lung pneumonia region. In the future, this technique can be used to investigate the efficiency of vaccines and drugs and post COVID-19 effects.

AI-Powered Semantic Segmentation and Fluid Volume Calculation of Lung CT Images in COVID-19 Patients

COVID-19 pandemic is a deadly disease spreading very fast. People with the confronted immune system are susceptible to many health conditions. A highly significant condition is pneumonia, which is found to be the cause of death in the majority of patients. The main purpose of this study is to find the volume of GGO and consolidation of a COVID-19 patient, so that the physicians can prioritize the patients. Here, we used transfer learning techniques for segmentation of lung CTs with the latest libraries and techniques which reduces training time and increases the accuracy of the AI Model. This system is trained with DeepLabV3 + network architecture and model ResNet50 with ImageNet weights. We used different augmentation techniques like Gaussian noise, horizontal shift, color variation, etc., to get to the result. Intersection over Union (IoU) is used as the performance metrics. The IoU of lung masks is predicted as 99.78% and that of infected masks is as 89.01%. Our work effectively measures the volume of infected region by calculating the volume of infected and lung mask region of the patients. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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

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

Pseudocavitation sign at chest CT scan due to COVID-19 pneumonia: A report of 5 cases and literature review.

Coronavirus disease 2019 (COVID-19) pneumonia computed tomography imaging features have been described in detail in many studies. The pseudocavitation sign has not been described in the previous COVID-19 studies. We present chest computed tomography scans of 5 reverse transcriptase-polymerase chain reaction positive patients with COVID-19 pneumonia who has bare areas among pulmonary infiltrates. All 5 also had previous scans with similarly sized low attenuated areas in the same location prior to the addition of pulmonary infiltrates. The pre-existing cystic changes had become remarkable due to the contrast around them after the pulmonary infiltrates added. Therefore, they should be termed as "pseodocavity" according to Fleischner Society glossary. Small air-containing spaces between pulmonary infiltrates have been termed in previous COVID-19 studies as a new sign called "round cystic changes/air bubble sign/vacuolar sign." We would like to draw attention that the vacuolar sign and the synonyms may be the pseudocavity sign that is due to pre-existing changes rather than a new defined sign.

Lung Function, Radiological Findings and Biomarkers of Fibrogenesis in a Cohort of COVID-19 Patients Six Months After Hospital Discharge.

INTRODUCTION: Impairment in pulmonary function tests and radiological abnormalities are a major concern in COVID-19 survivors. Our aim is to evaluate functional respiratory parameters, changes in chest CT, and correlation with peripheral blood biomarkers involved in lung fibrosis at two and six months after SARS-CoV-2 pneumonia. METHODS: COVID-FIBROTIC (clinicaltrials.gov NCT04409275) is a multicenter prospective observational cohort study aimed to evaluate discharged patients. Pulmonary function tests, circulating serum biomarkers, chest radiography and chest CT were performed at outpatient visits. RESULTS: In total, 313, aged 61.12 ± 12.26 years, out of 481 included patients were available. The proportion of patients with DLCO < 80% was 54.6% and 47% at 60 and 180 days. Associated factors with diffusion impairment at 6 months were female sex (OR: 2.97, 95%CI 1.74-5.06, p = 0.001), age (OR: 1.03, 95% CI: 1.01-1.05, p = 0.005), and peak RALE score (OR: 1.22, 95% CI 1.06-1.40, p = 0.005). Patients with altered lung diffusion showed higher levels of MMP-7 (11.54 ± 8.96 vs 6.71 ± 4.25, p = 0.001), and periostin (1.11 ± 0.07 vs 0.84 ± 0.40, p = 0.001). 226 patients underwent CT scan, of whom 149 (66%) had radiological sequelae of COVID-19. In severe patients, 68.35% had ground glass opacities and 38.46% had parenchymal bands. Early fibrotic changes were associated with higher levels of MMP7 (13.20 ± 9.20 vs 7.92 ± 6.32, p = 0.001), MMP1 (10.40 ± 8.21 vs 6.97 ± 8.89, p = 0.023), and periostin (1.36 ± 0.93 vs 0.87 ± 0.39, p = 0.001). CONCLUSION: Almost half of patients with moderate or severe COVID-19 pneumonia had impaired pulmonary diffusion six months after discharge. Severe patients showed fibrotic lesions in CT scan and elevated serum biomarkers involved in pulmonary fibrosis.

INTRODUCCIÓN: El deterioro de la función pulmonar en las pruebas correspondientes y las alteraciones radiológicas son las preocupaciones principales en los supervivientes de la COVID-19. Nuestro objetivo fue evaluar los parámetros de la función respiratoria, los cambios en la TC de tórax y la correlación con los biomarcadores en sangre periférica involucrados en la fibrosis pulmonar a los 2 y a los 6 meses tras la neumonía por SARS-CoV-2. MÉTODOS: El ensayo COVID-FIBROTIC (clinicaltrials.gov NCT04409275) es un estudio de cohortes multicéntrico, prospectivo y observacional cuyo objetivo fue evaluar los pacientes dados de alta. Se realizaron pruebas de función pulmonar, detección de biomarcadores en plasma circulante y radiografía y TC de tórax durante las visitas ambulatorias. RESULTADOS: En total 313 pacientes, de 61,12 ± 12,26 años, de los 481 incluidos estuvieron disponibles.La proporción de pacientes con DLCO < 80% fue del 54,6 y del 47% a los 60 y 180 días.Los factores que se asociaron a la alteración de la difusión a los 6 meses fueron el sexo femenino (OR: 2,97; IC del 95%: 1,74-5,06; p = 0,001), la edad (OR: 1,03; IC del 95%: 1,01-1,05; p = 0,005) y la puntuación RALE más alta (OR: 1,22; IC del 95%: 1,06-1,40; p = 0,005). Los pacientes con alteración de la difusión pulmonar mostraron niveles más altos de MMP-7 (11,54 ± 8,96 frente a 6,71 ± 4,25; p = 0,001) y periostina (1,11 ± 0.07 frente a 0,84 ± 0,40; p = 0,001). Se le realizó una TC a 226 pacientes de los cuales 149 (66%) presentaban secuelas radiológicas de la COVID-19. En los pacientes graves, el 68,35% mostraban opacidades en vidrio esmerilado y el 38,46%, bandas parenquimatosas. Los cambios fibróticos tempranos se asociaron a niveles más altos de MMP7 (13,20 ± 9,20 frente a 7,92 ± 6,32; p = 0,001), MMP1 (10,40 ± 8,21 frente a 6,97 ± 8,89; p = 0,023), y periostina (1,36 ± 0,93 frente a 0,87 ± 0,39; p = 0,001). CONCLUSIÓN: Casi la mitad de los pacientes con neumonía moderada o grave por COVID-19 presentaba alteración de la difusión pulmonar 6 meses después del alta. Los pacientes graves mostraban lesiones fibróticas en laTC y un aumento de los biomarcadores séricos relacionados con la fibrosis pulmonar.