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Aim:The aim of our study was to compare clinical severity scores and classic spirometry with impulse oscillometry (IOS) results and thoracic high resolution computed tomography (HRCT) scores in children with cystic fibrosis (CF) in order to determine the utility of the latter approach in patient follow-up.Materials and Methods:CF patients over 6 years of age were included. Shwachman-Kulczycki score, underclassical spirometry and IOS were performed when not in acute exacerbation. Thoracic HRCT images obtained within the previous 6 months were evaluated using the Bhalla scoring system.Results:The mean age of the children studied (n=30) was 12.1±4.2 years and 40% were female. Pseudomonas aeruginosa (P. aeroginosa) was isolated from sputum cultures of 40% of the patients. Patients with forced expiratory volume in one second (FEV1) below 80% exhibited significantly higher (resistance) R5, R10 values and significantly lower (reactance) X5 values on IOS (p=0.03, 0.027, 0.006, respectively). Patients with P. aeruginosa had significantly lower FEV1, forced vital capacity, and forced expiratory flow (25-75) values in classic spirometry when compared with patients without P. aeruginosa (p=0.002, p=0.002, and p=0.005, respectively). P. aeruginosa-positive patients showed significantly higher R5 and lower X5 values (p=0.047, 0.046, respectively). Bhalla scoring, bronchiectasis weight, peribronchial thickening, mucous plaques, saccularization, bronchial division, mosaic pattern parameters in groups with P. aeroginosa growth and/or FEV1 <80%; was found to be significantly more serious than the non-reproductive group (p<0.005, respectively). Again, in the group with P. aeroginosa growth, Shwachman-Kulczycki score was found to be significantly lower (p=0.001). No significant correlation was found between thoracic score data such as bronchiectasis weight and mosaic pattern presence and IOS values. In addition, in the group with high clinical score of Shwachman Kulczycki, resistance values such as R5 R10 R15 which are IOS parameters, and FEV1 were found above 80% (p=0.016, p=0.037, p=0.042, 0.004, respectively).Conclusion:IOS and tomographic scoring can be used safely in early detection of impairment in lung function. Further studies are needed to evaluate the utility of IOS in the clinical monitoring of children with CF who are not compliant with spirometry maneuvers.

It has been recently hypothesized that infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may lead to fibrotic sequelae in patients recovering from coronavirus disease 2019 (COVID-19). In this observational study, hospitalized patients with COVID-19 had a HRCT of the chest performed to detect the extension of fibrotic abnormalities via Hounsfield Units (HU). At follow-up, the lung density significantly improved in both lungs and in each lobe of all patients, being in the normal range (− 950 to − 700 HU). This study provides preliminary evidence that hospitalized patients with mild-to-moderate forms of COVID-19 are not at risk of developing pulmonary fibrosis.

The aim of this study was to explore the high-resolution computed tomography (HRCT) imaging characteristics of various malignant pure ground-glass nodules (pGGNs) with a diameter 10 mm and their correlation with the clinical diagnosis of invasive adenocarcinomas (IAs). A total of 381 patients were enrolled. No significant differences were observed in age, sex, vacuole sign, vascular penetration, tumor-pleura distance, or normal lung tissue density among the three groups (all P > 0.05). Tumor diameter (P = 0.001) and relative density (P = 0.001) were independent risk factors for distinguishing PGLs from MIAs, while relative density (P = 0.001) was the only independent risk factor for distinguishing MIAs from IAs. ROC curve analysis showed that a relative density > 320.5 HU could differentiate IAs from MIAs. For malignant pGGNs 10 mm, MIAs can be distinguished from PGLs when the diameter exceeds 6.015 mm and relative density exceeds 209.5 HU, IAs can be distinguished from MIAs when relative density exceeds 320.5 HU. These findings provide a reference for the pathological diagnosis and surgical strategy selection of small malignant pGGNs.

Coronavirus disease 2019 (COVID-19) emerged in early December 2019 in China, as an acute lower respiratory tract infection and spread rapidly worldwide being declared a pandemic in March 2020. Chest-computed tomography (CT) has been utilized in different clinical settings of COVID-19 patients; however, COVID-19 imaging appearance is highly variable and nonspecific. Indeed, many pulmonary infections and non-infectious diseases can show similar CT findings and mimic COVID-19 pneumonia. In this review, we discuss clinical conditions that share a similar imaging appearance with COVID-19 pneumonia, in order to identify imaging and clinical characteristics useful in the differential diagnosis.

The purpose of this study was to analyze the imaging risk factors for the development of 2-3 cm ground-glass nodules (GGN) for invasive lung adenocarcinoma and to establish a nomogram prediction model to provide a reference for the pathological prediction of 2-3 cm GGN and the selection of surgical procedures. We reviewed the demographic, imaging, and pathological information of 596 adult patients who underwent 2-3 cm GGN resection, between 2018 and 2022, in the Department of Thoracic Surgery, Second Affiliated Hospital of the Air Force Medical University. Based on single factor analysis, the regression method was used to analyze multiple factors, and a nomogram prediction model for 2-3 cm GGN was established. (1) The risk factors for the development of 2-3 cm GGN during the invasion stage of the lung adenocarcinoma were pleural depression sign (OR = 1.687, 95%CI: 1.010-2.820), vacuole (OR = 2.334, 95%CI: 1.222-4.460), burr sign (OR = 2.617, 95%CI: 1.008-6.795), lobulated sign (OR = 3.006, 95%CI: 1.098-8.227), bronchial sign (OR = 3.134, 95%CI: 1.556-6.310), diameter of GGN (OR = 3.118, 95%CI: 1.151-8.445), and CTR (OR = 172.517, 95%CI: 48.023-619.745). (2) The 2-3 cm GGN risk prediction model was developed based on the risk factors with an AUC of 0.839; the calibration curve was close to the -line, and the decision curve was drawn in the range of 0.0-1.0. We analyzed the risk factors for the development of 2-3  cm GGN during the invasion stage of the lung adenocarcinoma. The predictive model developed based on the above factors had some clinical significance.

Background Generally, in retrospective studies, favourable short- and long-term outcomes for patients after lung lobectomy for early stage non-small cell lung cancer (NSCLC) using video-assisted thoracoscopic surgery (VATS) have been reported. However, the interpretation of lung lobectomy outcomes may be biased in retrospective settings. Methods We retrospectively reviewed patients who underwent lung lobectomy for cT1-2N0M0 NSCLC from 2001 to 2010. The outcomes of patients who underwent VATS lobectomy were compared to those who underwent open lobectomy before and after performing propensity score matching. Preoperative covariates were entered when developing the propensity score-matching model. Results This study reviewed the outcomes of 101 VATS patients and 184 open lobectomy patients. Before propensity score matching, the VATS group had a higher mean age ( p  < 0.0001), smaller solid tumour size ( p  = 0.0042), similar whole tumour size ( p  = 0.2082), and larger tumour-disappearance ratio ( p  = 0.0007). The VATS group had a shorter mean operation time ( p  = 0.0002), less blood loss ( p  < 0.0001), shorter chest tube duration ( p  = 0.0002), and shorter hospital stay ( p  < 0.0001). As for long-term outcomes, the VATS group had higher disease-free, disease-specific, and overall survival rates ( p values by log-rank test: 0.0049, 0.0154, and 0.032, respectively). After propensity score matching, all differences, except operation time, blood loss, chest tube duration, and hospital stay, were no longer significant. Conclusions VATS lobectomy is less invasive than open lobectomy, but in terms of survival outcomes, VATS lobectomy was oncologically equivalent to open lobectomy. The oncological benefit of VATS reported by retrospective studies might be overestimated.

Rationale: Birt–Hogg–Dubé (BHD) syndrome, a rare inherited autosomal genodermatosis first recognised in 1977, is characterised by fibrofolliculomas of the skin, an increased risk of renal tumours and multiple lung cysts with spontaneous pneumothorax. The BHD gene, a tumour suppressor gene located at chromosome 17p11.2, has recently been shown to be defective. Recent genetic studies revealed that clinical pictures of the disease may be variable and may not always present the full expression of the phenotypes. Objectives: We hypothesised that mutations of the BHD gene are responsible for patients who have multiple lung cysts of which the underlying causes have not yet been elucidated. Methods: We studied eight patients with lung cysts, without skin and renal disease; seven of these patients have a history of spontaneous pneumothorax and five have a family history of pneumothorax. The BHD gene was examined using PCR, denaturing high-performance liquid chromatography and direct sequencing. Main results: We found that five of the eight patients had a BHD germline mutation. All mutations were unique and four of them were novel, including three different deletions or insertions detected in exons 6, 12 and 13, respectively and one splice acceptor site mutation in intron 5 resulting in an in-frame deletion of exon 6. Conclusions: We found that germline mutations of the BHD gene are involved in some patients with multiple lung cysts and pneumothorax. Pulmonologists should be aware that BHD syndrome can occur as an isolated phenotype with pulmonary involvement.

Background: Interstitial lung disease (ILD) is a frequent and potentially progressive manifestation in patients with connective tissue diseases (CTDs). Accurate and reproducible quantification of parenchymal abnormalities on high-resolution computed tomography (HRCT) is essential for evaluating treatment response and monitoring disease progression, particularly in complex cases undergoing antifibrotic therapy. Artificial intelligence (AI)-based tools may improve consistency in visual assessment and assist less experienced radiologists in longitudinal follow-up. Methods: In this retrospective study, 48 patients with CTD-related ILD receiving antifibrotic treatment were included. Each patient underwent four HRCT scans, which were evaluated independently by two radiologists (one expert, one non-expert) using a semi-quantitative scoring system. Percentage estimates of lung involvement were assigned for four parenchymal patterns: hyperlucency, ground-glass opacity (GGO), reticulation, and honeycombing. AI-based analysis was performed using the Imbio Lung Texture Analysis platform, which generated continuous volumetric percentages for each pattern. Concordance between AI and human interpretation was assessed, along with mean absolute error (MAE) and inter-reader differences. Results: The AI-based system demonstrated high concordance with the expert radiologist, with an overall agreement of 81% across patterns. The MAE between AI and the expert ranged from 1.8% to 2.6%. In contrast, concordance between AI and the non-expert radiologist was significantly lower (60–70%), with higher MAE values (3.9% to 5.2%). McNemar’s and Wilcoxon tests confirmed that AI aligned more closely with the expert than the non-expert reader (p < 0.01). AI proved particularly effective in detecting subtle changes in parenchymal burden during follow-up, especially when visual interpretation was inconsistent. Conclusions: AI-driven quantitative imaging offers performance comparable to expert radiologists in assessing ILD patterns on HRCT and significantly outperforms less experienced readers. Its reproducibility and sensitivity to change support its role in standardizing follow-up evaluations and enhancing multidisciplinary decision-making in patients with CTD-related ILD, particularly in progressive fibrosing cases receiving antifibrotic therapy.

Background The assessment of Idiopathic Pulmonary Fibrosis (IPF) using HRCT requires great experience and is limited by a significant inter-observer variability, even between trained radiologists. The evaluation of HRCT through automated quantitative analysis may hopefully solve this problem. The accuracy of CT-histogram derived indexes in the assessment of survival in IPF patients has been poorly studied. Methods Forty-two patients with a diagnosis of IPF and a follow up time of 3 years were retrospectively collected; HRCT and Pulmonary Function Tests (PFTs) performed at diagnosis time were analysed; the extent of fibrotic disease was quantified on HRCT using kurtosis, skewness, Mean Lung Density (MLD), High attenuation areas (HAA%) and Fibrotic Areas (FA%). Univariate Cox regression was performed to assess hazard ratios for the explored variables and a multivariate model considering skewness, FVC, DL CO and age was created to test their prognostic value in assessing survival. Through ROC analysis, threshold values demonstrating the best sensitivity and specificity in predicting mortality were identified. They were used as cut-off points to graph Kaplan-Meier curves specific for the CT-indexes. Results Kurtosis, skewness, MLD, HAA% and FA% were good predictors of mortality (HR 0.44, 0.74, 1.01, 1.12, 1.06; p  = 0.03, p  = 0.01, p  = 0.02, p  = 0.02 and p  = 0.017 respectively). Skewness demonstrated the lowest Akaike’s information criterion value (55.52), proving to be the best CT variable for prediction of mortality. Significant survival differences considering proposed cut-off points were also demonstrated according to kurtosis ( p  = 0.02), skewness ( p  = 0.005), MLD ( p  = 0.003), HAA% ( p  = 0.009) and FA% ( p  = 0.02) – obtained from quantitative HRCT analysis at diagnosis time. Conclusions CT-histogram derived indexes may provide an accurate estimation of survival in IPF patients. They demonstrate a correlation with PFTs, highlighting their possible use in clinical practice.

Background Pulmonary involvement is common in several infectious and non-infectious diagnostic settings. Imaging findings consistently overlap and are therefore difficult to differentiate by chest-CT. The aim of this study was to evaluate the role of CT-textural features(CTTA) for discrimination between atypical viral (respiratory-syncitial-virus(RSV) and herpes-simplex-1-virus (HSV1)), fungal (pneumocystis-jirovecii-pneumonia(PJP)) interstitial pneumonias and alveolar hemorrhage. Methods By retrospective single-centre analysis we identified 46 consecutive patients (29 m) with RSV( n  = 5), HSV1( n  = 6), PJP( n  = 21) and lung hemorrhage( n  = 14) who underwent unenhanced chest CTs in early stages of the disease between 01/2016 and 02/2017. All cases were confirmed by microbiologic direct analysis of bronchial lavage. On chest-CT-scans, the presence of imaging features like ground-glass opacity(GGO), crazy-paving, air-space consolidation, reticulation, bronchial wall thickening and centrilobular nodules were described. A representative large area was chosen in both lungs and used for CTTA-parameters (included heterogeneity, intensity, average, deviation, skewness). Results Discriminatory CTTA-features were found between alveolar hemorrhage and PJP consisting of differences in mean heterogeneity( p  < 0.015) and uniformity of skewness( p  < 0.006). There was no difference between CT-textural features of diffuse alveolar hemorrhage and viral pneumonia or PJP and viral pneumonia. Visual HRCT-assessment yielded great overlap of imaging findings with predominance of GGO for PJP and airspace consolidation for pneumonia/alveolar hemorrhage. Significant correlations between HRCT-based imaging findings and CT-textural features were found for all three disease groups. Conclusion CT-textural features showed significant differences in mean heterogeneity and uniformity of skewness. HRCT-based imaging findings correlated with certain CT-textural features showing that the latter have the potential to characterize structural properties of lung parenchyma and related abnormalities.