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Abstract Background Follow-up studies on coronavirus disease 2019(COVID-19) were mainly focused on short-term sequelae in patients with comorbid diseases. The aim of this study was to investigate the pulmonary and extrapulmonary sequelae of moderate to severe COVID-19 in the midterm follow-up of healthy young adults. Methods In this prospective cohort study, we used the hospital information system (HIS) to identify patients who had recovered from moderate to severe COVID-19 without comorbidity. All eligible patients were invited to participate in the study. Participants were asked to fill out a set of questionnaires to evaluate fatigue, anxiety, and post-traumatic stress disorder (PTSD). They also underwent chest computed tomography (CT) scan, pulmonary function test (PFT), and tissue doppler imaging (TDI) echocardiography. A blood sample and a 12-lead electrocardiogram (ECG) were obtained. Results A total of 50 recovered patients and 12 healthy controls were enrolled in the study. Fifteen out of 50 patients received intensive care. Patients had significantly higher fatigue and anxiety scores than controls. PTSD criteria were met in 29 out of 50 patients. Ground glass opacities, nodules, and subpleural lines were the most frequent abnormalities in chest CT scans of patients. Patients had significantly lower left ventricular end-diastolic diameter (LVEDD) and left ventricular end-systolic diameter (LVESD) than controls (P value 0.019 and

Background and Objective: There is not enough information about the long-term pulmonary complications of patients with COVID-19. This prospective study aimed to compare the findings of lung CT scan upon admission and three months after the discharge of patients with COVID-19. Materials and Methods: This prospective study was conducted on 60 patients with COVID-19 hospitalized in Shahid Beheshti and Sina hospitals in Hamadan, Iran, in 2020-2021. The lung CT scan results of the patients upon admission and three months later were assessed and compared by an experienced radiologist quantitatively and qualitatively after obtaining and recording the demographic characteristics, risk factors, and clinical findings. Results: The mean score of lung involvement upon hospitalization of the patients was 3.7±1.4, and three months after discharge, it was determined at 1.1±1.2. The most frequent findings in the initial CT scan of the patients were ground-glass opacification, mixed GGO, and bands with frequencies of 60%, 36.7%, and 18.3%, respectively. After three months, CT scan results of 32 (53.3%) patients showed lung involvement and an abnormal finding in the lung, the most common of which were ground-glass opacification, mixed GGO, and bands with frequencies of 30%, 13.3%, and 10.0%, respectively. Patients who had pulmonary involvement three months after discharge had a higher average lung involvement score during hospitalization (4.4±1.5 vs. 2.9±0.7, P=0.001). Conclusion: Abnormal CT scan results of the lung three months after discharge is common in COVID-19 patients, among whom patients with more pulmonary involvement during hospitalization have more abnormal findings

Treatment of non‐small cell lung cancer depends on detecting the cancer stage. The oncologist decides the cancer stage based on the tumour‐node‐metastasis (TNM) staging suggested by the American Joint Committee on Cancer (AJCC). This study simplifies the complicated problem of classifying computed tomography (CT) images into TNM‐based classes using deep learning algorithms at various levels. In the first level, an optimised conditional generative adversarial network (c‐GAN) network is developed for automatic lung segmentation, including nodules within the lung and juxtapleural nodules. Earlier studies used time‐consuming manual identification of the region of interest patches from the lung CT image before applying the deep learning classification algorithm. At the next level, three different deep learning algorithms, along with three support vector machine classifiers, are used for the classification of Tumour, Node and Metastasis as per the AJCC staging nomenclature. The specially designed c‐GAN network's performance is maximised using the Taguchi approach, which helps automatically preprocess CT images by removing unwanted background noises. Further, three different pre‐trained Resnet50 networks are trained using transfer learning for extracting the deep features for finally applying to three different classifiers, resulting in three different classes. The comparative segmentation performance assessment in the form of the average dice similarity coefficient and Jaccard index indicates that the proposed c‐GAN gives the best segmentation performance of the lung without losing the nodule compared to other segmentation algorithms. The proposed approach gives the classification performance for the Tumour as 91.94%–97.32%, the Nodule as 91.99%–100%, and the Metastasis as 99.25%–100.00%. [ABSTRACT FROM AUTHOR]

Conditional generative adversarial network (c-GAN) is one of the best-performing convolutional neural networks (CNN) for the segmentation of lung computed tomography (CT). However, lung segmentation from CT images becomes complicated in the presence of various dense abnormalities. The performance in the presence of dense abnormalities can be improved by tuning the c-GAN architecture and parameters of the network. This study focuses on maximizing lung segmentation performance of a c-GAN segmentation algorithm by configuring and tuning the network using the Taguchi optimization method. We have considered the benchmark interstitial lung disease (ILD) dataset for evaluating the performance of the proposed approach. The comparative performance analysis of the proposed algorithm shows that the proposed algorithms outperform the existing state-of-the-art methods, even in the presence of dense abnormalities in lung CT scans. Furthermore, the proposed approach has been demonstrated for lung segmentation in the presence of large nodules. [ABSTRACT FROM AUTHOR]

Abstract Background The ARDS is characterized by different degrees of impairment in oxygenation and distribution of the lung disease. Two radiological patterns have been described: a focal and a diffuse one. These two patterns could present significant differences both in gas exchange and in the response to a recruitment maneuver. At the present time, it is not known if the focal and the diffuse pattern could be characterized by a difference in the lung and chest wall mechanical characteristics. Our aims were to investigate, at two levels of PEEP, if focal vs. diffuse ARDS patterns could be characterized by different lung CT characteristics, partitioned respiratory mechanics and lung recruitability. Methods CT patterns were analyzed by two radiologists and were classified as focal or diffuse. The changes from 5 to 15 cmH2O in blood gas analysis and partitioned respiratory mechanics were analyzed. Lung CT scan was performed at 5 and 45 cmH2O of PEEP to evaluate lung recruitability. Results One-hundred and ten patients showed a diffuse pattern, while 58 showed a focal pattern. At 5 cmH2O of PEEP, the driving pressure and the elastance, both the respiratory system and of the lung, were significantly higher in the diffuse pattern compared to the focal (14 [11–16] vs 11 [9–15 cmH2O; 28 [23–34] vs 21 [17–27] cmH2O/L; 22 [17–28] vs 14 [12–19] cmH2O/L). By increasing PEEP, the driving pressure and the respiratory system elastance significantly decreased in diffuse pattern, while they increased or did not change in the focal pattern (Δ 15-5: − 1 [− 2 to 1] vs 0 [− 1 to 2]; − 1 [− 4 to 2] vs 1 [− 2 to 5]). At 5 cmH2O of PEEP, the diffuse pattern had a lower lung gas (743 [537–984] vs 1222 [918–1974] mL) and higher lung weight (1618 [1388–2001] vs 1222 [1059–1394] g) compared to focal pattern. The lung recruitability was significantly higher in diffuse compared to focal pattern 21% [13–29] vs 11% [6–16]. Considering the median of lung recruitability of the whole population (16.1%), the recruiters were 65% and 22% in the diffuse and focal pattern, respectively. Conclusions An early identification of lung morphology can be useful to choose the ventilatory setting. A diffuse pattern has a better response to the increase of PEEP and to the recruitment maneuver.

Due to the outbreak of the pandemic COVID-19 or 'novel corona virus disease', the world is facing a global emergency. In case of severe infection, lungs are affected by COVID-19 significantly, whichmay lead to the death of the patient. In this paper, an automated image-assisted system based on artificial intelligence is proposed to extract infected sections from lung CT scan images that are caused due to COVID-19. Multilevel thresholding is a typical example of maximization problem of optimization to identify the threshold(s) levels for image segmentation. In this paper, Elephant Swarm Water Search Algorithm (ESWSA) has been used for multilevel thresholding based on Otsu's and Kapur's method and further analysis of lungs images. It has been observed from the obtained simulated results that ESWSA performs better than other state-of-the-art optimization techniques for multilevel thresholding. Moreover, location of infection and severity of infection has also been extracted from the pixel ratio between the infection and lung sections from the lung CT scan images. It is expected that the proposed methodology will support the doctors as it will reduce diagnostic burden and respective treatment process can be planned faster as the severity of infection by COVID-19 is found by this automated methodology. [ABSTRACT FROM AUTHOR]

Objective The aim of this study was to identify the main CT features that may help in distinguishing a progression of interstitial lung disease (ILD) secondary to SSc from COVID-19 pneumonia. Methods This multicentric study included 22 international readers grouped into a radiologist group (RADs) and a non-radiologist group (nRADs). A total of 99 patients, 52 with COVID-19 and 47 with SSc-ILD, were included in the study. Results Fibrosis inside focal ground-glass opacities (GGOs) in the upper lobes; fibrosis in the lower lobe GGOs; reticulations in lower lobes (especially if bilateral and symmetrical or associated with signs of fibrosis) were the CT features most frequently associated with SSc-ILD. The CT features most frequently associated with COVID- 19 pneumonia were: consolidation (CONS) in the lower lobes, CONS with peripheral (both central/peripheral or patchy distributions), anterior and posterior CONS and rounded-shaped GGOs in the lower lobes. After multivariate analysis, the presence of CONs in the lower lobes (P < 0.0001) and signs of fibrosis in GGOs in the lower lobes (P < 0.0001) remained independently associated with COVID-19 pneumonia and SSc-ILD, respectively. A predictive score was created that was positively associated with COVID-19 diagnosis (96.1% sensitivity and 83.3% specificity). Conclusion CT diagnosis differentiating between COVID-19 pneumonia and SSc-ILD is possible through a combination of the proposed score and radiologic expertise. The presence of consolidation in the lower lobes may suggest COVID-19 pneumonia, while the presence of fibrosis inside GGOs may indicate SSc-ILD. [ABSTRACT FROM AUTHOR]

COVID‐19, a novel coronavirus, has spread quickly and produced a worldwide respiratory ailment outbreak. There is a need for large‐scale screening to prevent the spreading of the disease. When compared with the reverse transcription polymerase chain reaction (RT‐PCR) test, computed tomography (CT) is far more consistent, concrete, and precise in detecting COVID‐19 patients through clinical diagnosis. An architecture based on deep learning has been proposed by integrating a capsule network with different variants of convolution neural networks. DenseNet, ResNet, VGGNet, and MobileNet are utilized with CapsNet to detect COVID‐19 cases using lung computed tomography scans. It has found that all the four models are providing adequate accuracy, among which the VGGCapsNet, DenseCapsNet, and MobileCapsNet models have gained the highest accuracy of 99%. An Android‐based app can be deployed using MobileCapsNet model to detect COVID‐19 as it is a lightweight model and best suited for handheld devices like a mobile. [ABSTRACT FROM AUTHOR]

Background: Frequent waves of corona virus disease (COVID-19) and lack of specific drugs against that, warrant studies to reduce the morbidity and mortality of this pandemic disease. In this study, we investigated the association between influenza vaccination and the severity and outcome of COVID-19 disease in Iranian patients living in the North. Methods: This retrospective case-control study was performed on186 patients with COVID- 19 infection between March and April, 2020. Patients with positive PCR were divided into two groups of case and control; Patients with moderate to severe and normal to mild lung involvement, respectively. The lung opacities in all of the 5 lobes were evaluated on chest CT images using a CT severity scoring system. The history of influenza vaccination during the fall of 2019-2020 was determined by a phone call. Statistical analysis was done using the chi-square test, student's t-test, and logistic regression. The significance level was p<0.05. Results: The mean age of patients was 54.67±15.05years. Most patients had pulmonary manifestations including ground-glass opacity (57%), consolidation (80%) and pleural effusion (3.2%). Adjusting for age, gender, and history of underlying disease, vaccination is an effective factor in the severity of pulmonary involvement (AOR=0.39; 95%CI: (0.21, 0.73); P=0.003). Furthermore, the chance of ICU admission decreased via influenza vaccination (OR=0.21, P=0.001). Conclusion: The results showed that the severity of COVID-19 pulmonary involvement and outcome as ICU admission, and severe symptoms in patients with history of influenza vaccination were significantly lower than those without history of vaccination. This strategy can be used to prevent and reduce the complications of COVID-19. [ABSTRACT FROM AUTHOR]

A rapid outbreak of novel coronavirus, coronavirus disease‐2019 (COVID‐19), has made it a global pandemic. This study focused on the possible association between lymphopenia and computed tomography (CT) scan features and COVID‐19 patient mortality. The clinical data of 596 COVID‐19 patients were collected from February 2020 to September 2020. The patients' serological survey and CT scan features were retrospectively explored. The median age of the patients was 56.7 ± 16.4 years old. Lung involvement was more than 50% in 214 COVID‐19 patients (35.9%). The average blood lymphocyte percentage was 20.35 ± 10.16 (normal range, 20%–50%). Although the levels of C‐reactive protein (CRP) and erythrocyte sedimentation rate (ESR) were high in more than 80% of COVID‐19 patients; CRP, ESR, and platelet‐to‐lymphocyte ratio (PLR) may not indicate the in‐hospital mortality of COVID‐19. Patients with severe lung involvement and lymphopenia were found to be significantly associated with increased odds of death (odds ratio, 9.24; 95% confidence interval, 4.32–19.78). These results indicated that lymphopenia < 20% along with pulmonary involvement >50% impose a multiplicative effect on the risk of mortality. The in‐hospital mortality rate of this group was significantly higher than other COVID‐19 hospitalized cases. Furthermore, they meaningfully experienced a prolonged stay in the hospital (p =.00). Lymphocyte count less than 20% and chest CT scan findings with more than 50% involvement might be related to the patient's mortality. These could act as laboratory and clinical indicators of disease severity, mortality, and outcome. [ABSTRACT FROM AUTHOR]

Background: The ARDS is characterized by different degrees of impairment in oxygenation and distribution of the lung disease. Two radiological patterns have been described: a focal and a diffuse one. These two patterns could present significant differences both in gas exchange and in the response to a recruitment maneuver. At the present time, it is not known if the focal and the diffuse pattern could be characterized by a difference in the lung and chest wall mechanical characteristics. Our aims were to investigate, at two levels of PEEP, if focal vs. diffuse ARDS patterns could be characterized by different lung CT characteristics, partitioned respiratory mechanics and lung recruitability. Methods: CT patterns were analyzed by two radiologists and were classified as focal or diffuse. The changes from 5 to 15 cmH2O in blood gas analysis and partitioned respiratory mechanics were analyzed. Lung CT scan was performed at 5 and 45 cmH2O of PEEP to evaluate lung recruitability. Results: One-hundred and ten patients showed a diffuse pattern, while 58 showed a focal pattern. At 5 cmH2O of PEEP, the driving pressure and the elastance, both the respiratory system and of the lung, were significantly higher in the diffuse pattern compared to the focal (14 [11–16] vs 11 [9–15 cmH2O; 28 [23–34] vs 21 [17–27] cmH2O/L; 22 [17–28] vs 14 [12–19] cmH2O/L). By increasing PEEP, the driving pressure and the respiratory system elastance significantly decreased in diffuse pattern, while they increased or did not change in the focal pattern (Δ15-5: − 1 [− 2 to 1] vs 0 [− 1 to 2]; − 1 [− 4 to 2] vs 1 [− 2 to 5]). At 5 cmH2O of PEEP, the diffuse pattern had a lower lung gas (743 [537–984] vs 1222 [918–1974] mL) and higher lung weight (1618 [1388–2001] vs 1222 [1059–1394] g) compared to focal pattern. The lung recruitability was significantly higher in diffuse compared to focal pattern 21% [13–29] vs 11% [6–16]. Considering the median of lung recruitability of the whole population (16.1%), the recruiters were 65% and 22% in the diffuse and focal pattern, respectively. Conclusions: An early identification of lung morphology can be useful to choose the ventilatory setting. A diffuse pattern has a better response to the increase of PEEP and to the recruitment maneuver. [ABSTRACT FROM AUTHOR]

• We observed that in the early stage of COVID-19, C-reactive protein levels can reflect the extent of lung lesions and disease severity. • It provides an important clinical evaluation index. • CRP levels can reflect disease changes, especially for patients who are not suitable to be referred to other facilities or patients in critical condition. COVID-19 is a new infectious disease, for which there is currently no treatment. It is therefore necessary to explore biomarkers to determine the extent of lung lesions and disease severity. We aimed to assess the usefulness of CRP levels in the early stage of COVID-19 and to correlate them with lung lesions and severe presentation. Confirmed cases of COVID-19 were selected at the Fever Unit in two regions of Guizhou, China. On admission CRP levels were collected, and the diameter of the largest lung lesion was measured in the most severe lung lesion by lung CT scan. Differences in the diameter and CRP levels were compared in the following groups of patients: mild group, moderate group, severe group, and critical group. CRP levels and the diameter of the largest lung lesion in the moderate group were higher than those in the mild group (Mann-Whitney test = −2.647, −2.171, P ˂ 0.05), those in the severe group were higher than those in the moderate group (Mann-Whitney test = 0.693, −2.177, P ˂ 0.05), and those in the critical group were higher than those in the severe group (Mann-Whitney test = −0.068, −1.549, P ˂ 0.05). The difference was statistically significant. CRP levels were positively correlated with the diameter of lung lesion and severe presentation (correlation coefficient = 0.873, 0.734, P ˂ 0.001). In the early stage of COVID-19 CRP levels were positively correlated with lung lesions and could reflect disease severity. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to investigate nodule features and patient-specific characteristics associated with improvement in predictive ability of lung cancer screening while maintaining the sensitivity of low-dose CT intact.Methods: All authors were approved to use data from the National Lung Screening Trial, a previously conducted randomized clinical trial, through submission of a proposal to the Cancer Data Access System. The National Lung Screening Trial had a multilevel design with nodules nested within rounds and rounds nested within individuals; hence, to incorporate nodule-level features, multilevel logistic regression was used. Both nodule-level features and patient characteristics were included for model construction. Model construction was based on improvement in predictive ability of the model, and there were no restrictions to any significance level on variable inclusion.Results: A total of 32,746 nodules for 9,728 patients were included in the analysis. With a sensitivity value equal to that of the National Lung Screening Trial (93.6%), positive predictive value was improved to 7.94%, which was more than twice that of the National Lung Screening Trial (3.6%). Area under receiver operating characteristic curve was 91.7% (95% confidence interval: 90.6-92.8).Conclusions: Increment in positive predictive value of lung cancer screening with sensitivity same as National Lung Screening Trial is feasible, and inclusion of other nodule size dimensions plus longest diameter to the model significantly improves the predictive ability of models. [ABSTRACT FROM AUTHOR]

Background: Previously, we and other investigators have described reversible loss of lung elastic recoil in patients with acute and persistent, moderate-to-severe, chronic, treated asthma who never smoked, and its adverse effect on maximal expiratory airflow. In four consecutive autopsies, we reported the pathophysiologic mechanism(s) has been unsuspected mild, diffuse, middle and upper lobe centrilobular emphysema.Methods: We performed prospective studies (5 to 22 years) in 25 patients (12 female) with chronic asthma, age 55 ± 15 years, who never smoked, with persistent moderate-to-severe expiratory obstruction. Studies included measuring blood eosinophils, IgE, total exhaled nitric oxide (NO), central airway NO flux, peripheral airway/alveolar NO concentration, impulse oscillometry, heliox curves, lung elastic recoil, and high-resolution thin-section (1 mm) lung CT imaging at full inspiration with voxel quantification.Results: In 25 patients with stable asthma with varying type 2 phenotype, after 270 μg of aerosolized albuterol sulfate had been administered with a metered dose inhaler with space chamber, FVC was 3.1 ± 1.0 L (83% ± 13% predicted) (mean ± SD), FEV1 was 1.8 ± 0.6 L (59% ± 11%), the FEV1/FVC ratio was 59% ± 10%, and the ratio of single-breath diffusing capacity of the lung for carbon monoxide to alveolar volume was 4.8 ± 1.1 mL/min/mm Hg/L (120% ± 26%). All 25 patients with asthma had loss of static lung elastic recoil pressure, which contributed equally to decreased intrinsic airway conductance in limiting expiratory airflow. Lung CT scanning detected none or mild emphysema. In all four autopsied asthmatic lungs previously reported and one unreported explanted lung, microscopy revealed unsuspected mild, diffuse centrilobular emphysema in the upper and middle lung fields, and asthma-related remodeling in airways. In eight cases, during asthma remission, there were increases in measured static lung elastic recoil pressure-calculated intrinsic airway conductance, and measured maximal expiratory airflow at effort-independent lung volumes.Conclusions: As documented now in five cases, unsuspected microscopic mild centrilobular emphysema is the sentinel cause of loss of lung elastic recoil. This contributes significantly to expiratory airflow obstruction in never-smoking patients with asthma, with normal diffusing capacity and near-normal lung CT scan results.Trial Registry: Protocol No. 20070934 and Study No. 1090472, Western Institutional Review Board, Olympia, WA; ClinicalTrials.gov; No. NCT00576069; URL: www.clinicaltrials.gov. [ABSTRACT FROM AUTHOR]

Objectives: Atelectasis develops in critically ill obese patients when undergoing mechanical ventilation due to increased pleural pressure. The current study aimed to determine the relationship between transpulmonary pressure, lung mechanics, and lung morphology and to quantify the benefits of a decremental positive end-expiratory pressure trial preceded by a recruitment maneuver.Design: Prospective, crossover, nonrandomized interventional study.Setting: Medical and Surgical Intensive Care Units at Massachusetts General Hospital (Boston, MA) and University Animal Research Laboratory (São Paulo, Brazil).Patients/subjects: Critically ill obese patients with acute respiratory failure and anesthetized swine.Interventions: Clinical data from 16 mechanically ventilated critically ill obese patients were analyzed. An animal model of obesity with reversible atelectasis was developed by placing fluid filled bags on the abdomen to describe changes of lung mechanics, lung morphology, and pulmonary hemodynamics in 10 swine.Measurements and Main Results: In obese patients (body mass index, 48 ± 11 kg/m), 21.7 ± 3.7 cm H2O of positive end-expiratory pressure resulted in the lowest elastance of the respiratory system (18.6 ± 6.1 cm H2O/L) after a recruitment maneuver and decremental positive end-expiratory pressure and corresponded to a positive (2.1 ± 2.2 cm H2O) end-expiratory transpulmonary pressure. Ventilation at lowest elastance positive end-expiratory pressure preceded by a recruitment maneuver restored end-expiratory lung volume (30.4 ± 9.1 mL/kg ideal body weight) and oxygenation (273.4 ± 72.1 mm Hg). In the swine model, lung collapse and intratidal recruitment/derecruitment occurred when the positive end-expiratory transpulmonary pressure decreased below 2-4 cm H2O. After the development of atelectasis, a decremental positive end-expiratory pressure trial preceded by lung recruitment identified the positive end-expiratory pressure level (17.4 ± 2.1 cm H2O) needed to restore poorly and nonaerated lung tissue, reestablishing lung elastance and oxygenation while avoiding increased pulmonary vascular resistance.Conclusions: In obesity, low-to-negative values of transpulmonary pressure predict lung collapse and intratidal recruitment/derecruitment. A decremental positive end-expiratory pressure trial preceded by a recruitment maneuver reverses atelectasis, improves lung mechanics, distribution of ventilation and oxygenation, and does not increase pulmonary vascular resistance. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to evaluate the possibility of early detection of pulmonary fungal infections by lung CT scan in chronic granulomatous disease (CGD). Methods: A retrospective study on 14 patients affected with CGD for a total of 18 infectious episodes was performed. Revision of clinical data and CT scan analysis before and after treatment was performed. Results: The presence of lung nodules <30 mm was evaluated in 18 infectious episodes in 14 patients. A total of 125 nodules in 18 CT scans were identified. Identification of the infectious agent through biopsy and in vitro culture resulted positive only in 3/18 cases. The remaining cases received clinical/radiologic diagnosis of suspected pulmonary fungal infection. In all cases, the introduction of empirical antifungal treatment resulted in reduction in size or complete resolution of the pulmonary lung nodules in all patients affected with CGD. Conclusions: Lung CT scan allows for early detection of pulmonary fungal infection in CGD. Pulmonary nodules (<30 mm), single or multiple, uni- or bilateral, with or without a halo sign may represent the first radiologic sign of pulmonary fungal infection in CGD. [ABSTRACT FROM AUTHOR]

Background. Radiofrequency thermal ablation (RFA) is a minimally invasive, image guided technique for destroying tumour cells without damage to adjacent healthy tissue. It is used for partial or complete ablation of non resectable lung cancers and cancers of metastases to lung, providing an effective, relatively safe option for patients ineligible for surgery. We describe our experience with it. Methods. In 2005 and 2006, we performed radiofrequency ablation of 7 lung lesions in 6 patients. RFA was done percutaneously under image guided CT scan in 5 patients and in one patient during thoracotomy when we found a radically unresectable tumor necessitating debulking. CT lung screening was performed after 6 months and PET/CT was done within 12 months. Results. In the course of the screening, we diagnosed regression in 2 patients, a stationary state in 2 cases and local tumor progression in 2 patients, using computed tomography within 6 months after RFA. Using PET/CT within 12 months, we diagnosed non-ablation and liver metastases (there were none before) in one of the two patients with a stationary state diagnosed by means of CT before and recurrence of primary tumor in another patient. In one case of diagnosed regression, we diagnosed tumor progression. The patients survived an average of 30 months (range 9 to 60 months). Conclusion. RFA of lung tumors is an easy method with little patient discomfort. It can be performed percutaneously using guided CT under general anaesthesia. RFA of lung tumors possibly alone or in combination with oncology treatment can prolong patient life. [ABSTRACT FROM AUTHOR]

The article discusses a study which examines the effects of pleural effusion in patients with acute respiratory distress syndrome or acute lung injury. The lung computed tomography (CT) scans of patients with acute lung injury was analyzed to determine pleural effusion volume. The study revealed that pleural effusion in acute lung injury patients causes chest wall expansion, but does not affect respiratory system mechanics or gas exchange.

Purpose: The computation of lung recruitability in acute respiratory distress syndrome (ARDS) is advocated to set positive end-expiratory pressure (PEEP) for preventing lung collapse. The quantitative lung CT scan, obtained by manual image processing, is the reference method but it is time consuming. The aim of this study was to evaluate the accuracy of a visual anatomical analysis compared with a quantitative lung CT scan analysis in assessing lung recruitability. Methods: Fifty sets of two complete lung CT scans of ALI/ARDS patients computing lung recruitment were analyzed. Lung recruitability computed at an airway pressure of 5 and 45 cmHO was defined as the percentage decrease in the collapsed/consolidated lung parenchyma assessed by two expert radiologists using a visual anatomical analysis and as the decrease in not aerated lung regions using a quantitative analysis computed by dedicated software. Results: Lung recruitability was 11.3 % (interquartile range 7.39-16.41) and 15.5 % (interquartile range 8.18-21.43) with the visual anatomical and quantitative analysis, respectively. In the Bland-Altman analysis, the bias and agreement bands between the visual anatomical and quantitative analysis were −2.9 % (−11.8 to +5.9 %). The ROC curve showed that the optimal cutoff values for the visual anatomical analysis in predicting high versus low lung recruitability was 8.9 % (area under the ROC curve 0.9248, 95 % CI 0.8550-0.9946). Considering this cutoff, the sensitivity, specificity, and diagnostic accuracy were 0.96, 0.76, and 0.86, respectively. Conclusions: Visual anatomical analysis can classify patients into those with high and low lung recruitability allowing more intensivists to get access to lung recruitability assessment. [ABSTRACT FROM AUTHOR]

Purpose: To determine the diagnostic capabilities of dynamic high-resolution electron-beam (HREB) CT scanning for diagnosing bronchiolitis obliterans syndrome (BOS) in lung transplant recipients. Materials and methods: At the time of follow-up examinations after lung transplantation, 52 patients were examined by dynamic HREB CT scan. Visual signs of small airway disease were assessed and compared with lung function. For numerical analysis, the mean lung attenuation and its SD were determined and compared with the course of lung function tests. Results: On visual analysis, significant parenchymal attenuation inhomogeneities were present in eight of nine patients with manifest BOS, and in two of four patients who developed BOS during follow-up. Thirteen of 20 patients with persistent normal lung function displayed homogeneous lung attenuation. On numerical analysis, mean lung attenuation was significantly lower in patients who developed BOS during follow-up than in patients with persistent normal lung function (both in expiration and inspiration, p <0.0001). With an optimal threshold, the sensitivity was 100% (4 of 4 patients) and the specificity was 90% (19 of 20 patients). In patients with BOS at the time of the CT scan examination, parenchymal attenuation was less homogeneous than in patients with persistent normal lung function (p < 0.0001). With an optimal threshold, the sensitivity was 78% (7 of 9 patients) and the specificity was 85% (17 of 20 patients). Conclusions: Dynamic HREB CT of lung transplant recipients correlates well with lung function criteria of BOS at the time of the CT examination and with the subsequent progression to BOS. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to identify the main CT features that may help in distinguishing a progression of interstitial lung disease (ILD) secondary to SSc from COVID-19 pneumonia., Methods: This multicentric study included 22 international readers grouped into a radiologist group (RADs) and a non-radiologist group (nRADs). A total of 99 patients, 52 with COVID-19 and 47 with SSc-ILD, were included in the study., Results: Fibrosis inside focal ground-glass opacities (GGOs) in the upper lobes; fibrosis in the lower lobe GGOs; reticulations in lower lobes (especially if bilateral and symmetrical or associated with signs of fibrosis) were the CT features most frequently associated with SSc-ILD. The CT features most frequently associated with COVID- 19 pneumonia were: consolidation (CONS) in the lower lobes, CONS with peripheral (both central/peripheral or patchy distributions), anterior and posterior CONS and rounded-shaped GGOs in the lower lobes. After multivariate analysis, the presence of CONs in the lower lobes (P < 0.0001) and signs of fibrosis in GGOs in the lower lobes (P < 0.0001) remained independently associated with COVID-19 pneumonia and SSc-ILD, respectively. A predictive score was created that was positively associated with COVID-19 diagnosis (96.1% sensitivity and 83.3% specificity)., Conclusion: CT diagnosis differentiating between COVID-19 pneumonia and SSc-ILD is possible through a combination of the proposed score and radiologic expertise. The presence of consolidation in the lower lobes may suggest COVID-19 pneumonia, while the presence of fibrosis inside GGOs may indicate SSc-ILD., (© The Author(s) 2021. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Automatic lung segmentation is an essential step towards the Computer-Aided Diagnosis of the lung CT scan. However, in presence of dense abnormalities, existing methods fail in accurate lung segmentation. In this paper, a generative adversarial networks-based approach is proposed for improving the accuracy of lung segmentation. The proposed network effectively segments the lung region from the surrounding chest region hence, named as LungSeg-Net. In the proposed LungSeg-Net, the input lung CT slices are processed through the trail of encoders which encode these slices into a set of feature maps. Further, a multi-scale dense-feature extraction (MSDFE) module is designed for extraction of multi-scale features from the set of encoded feature maps. Finally, the decoders are employed to obtain the lung segmentation map from the multi-scale features. The MSDFE makes the network to learn the relevant features of dense abnormalities whereas the iterative down-sampling followed by the up-sampling makes it invariant to the size of the dense abnormality. The publicly available benchmark ILD dataset is used for the experimental analysis. The qualitative and quantitative analysis has been carried out to compare the performance of the proposed network with the existing state-of-the-art methods for lung segmentation. The experimental analysis show that the performance of the proposed LungSeg-Net is invariant to the presence of dense abnormalities in lung CT scan. • The proposed end-to-end conditional generative adversarial network for accurate lung segmentation is named as LungSeg-Net which processes the input lung CT slice and gives lung segmentation map without any post-processing. • The proposed LungSeg-Net consists of multi-scale dense feature extraction module which extracts the robust multi-scale features. • The proposed LungSeg-Net is effective for lung segmentation and its performance is invariant to the presence of dense abnormalities (due to ILD patterns) in lung CT scan. [ABSTRACT FROM AUTHOR]

Rationale: Pediatric COVID-19 studies have been mostly restricted to case reports and small case series, which have prevented the identification of specific pediatric lung disease patterns in COVID-19. The overarching goal of this systematic review and meta-analysis is to provide the first comprehensive summary of the findings of published studies thus far describing COVID-19 lung imaging data in the pediatric population., Methods: A systematic literature search of PubMed was performed to identify studies assessing lung-imaging features of COVID-19 pediatric patients (0-18 years). A single-arm meta-analysis was conducted to obtain the pooled prevalence and 95% confidence interval (95% CI)., Results: A total of 29 articles (n = 1026 children) based on chest computerized tomography (CT) images were included. The main results of this comprehensive analysis are as follows: (1) Over a third of pediatric patients with COVID-19 (35.7%, 95% CI: 27.5%-44%) had normal chest CT scans and only 27.7% (95% CI: 19.9%-35.6%) had bilateral lesions. (2) The most typical pediatric chest CT findings of COVID-19 were ground-glass opacities (GGO) (37.2%, 95% CI: 29.3%-45%) and the presence of consolidations or pneumonic infiltrates (22.3%, 95% CI: 17.8%-26.9%). (3) The lung imaging findings in children with COVID-19 were overall less frequent and less severe than in adult patients. (4) Typical lung imaging features of viral respiratory infections in the pediatric population such as increased perihilar markings and hyperinflation were not reported in children with COVID-19., Conclusion: Chest CT manifestations in children with COVID-19 could potentially be used for early identification and prompt intervention in the pediatric population., (© 2020 Wiley Periodicals LLC.)