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Quantitative assessment of the extent of radiological alterations in interstitial lung diseases is a promising field of application that goes beyond the limitations of qualitative scoring. Analysis of density histograms, i.e., skewness, kurtosis, and mean lung attenuation, is among the most studied approaches. We recently proposed their integration in a single parameter, the computerized integrated index (CII), to reduce their redundancy. The CII has proven effective in detecting subclinical lung involvement, correlates with lung function/disease activity, and predicts mortality in systemic sclerosis patients. Seventy-three newly diagnosed and therapy-naive IPF patients (M = 50; median age: 70.2 years) were prospectively enrolled from January 2014 to December 2022, and followed till December 2023. At baseline, all underwent lung function testing and volumetric high resolution chest CT. Density histograms were analyzed with an open-source automatic platform (Slicer 3D) and CII derived by means of Principal Component Analysis, as previously described. During a median follow-up of 5.8 years, 39 (53.4%) subjects died. Median overall survival (OS) was 4.9 years (95% CI 3.7 years- not estimable ). The CII was significantly associated with OS (HR 0.49; 95% CI 0.35–0.68; P  < 0.001) and correlated with lung function (r = 0.41; 95% CI 0.19 to 0.60; P  < 0.001 for FVC, and r = 0.62; 95% CI 0.44 to 0.75; P  < 0.001 for DLCO sb ). Patients stratification according to CII tertile, showed a consistent reduction in the hazard of death. After adjusting for body mass index, smoking, GAP stage, and anti-fibrotic therapy, the CII preserved a significant association with the hazard of death (HR 0.35; 95% CI 0.2–0.63; P  < 0.001). CII is a proxy marker of IPF severity worthy of use for prognostication purposes in daily practice.

Background Progression of the disease in idiopathic pulmonary fibrosis (IPF) is difficult to predict, due to its variable and heterogenous course. The relationship between radiographic progression and functional decline in IPF is unclear. We sought to confirm that a simple HRCT fibrosis visual score is a reliable predictor of mortality in IPF, when longitudinally followed; and to ascertain which pulmonary functional variables best reflect clinically significant radiographic progression. Methods One-hundred-twenty-three consecutive patients with IPF from 2 centers were followed for an average of 3 years. Longitudinal changes of HRCT fibrosis scores, forced vital capacity (FVC), total lung capacity and diffusing lung capacity for carbon monoxide were considered. HRCTs were scored by 2 chest radiologists. The primary outcome was lung transplant (LTx)-free survival after the follow-up HRCT. Results During the follow-up period, 43 deaths and 11 LTx occurred. On average, the HRCT fibrosis score increased significantly, and a longitudinal increase > 7% predicted LTx-free survival significantly, with good specificity, but limited sensitivity. The correlation between radiographic and functional progression was moderately significant . HRCT progression and FVC decline predicted LTx-free survival independently and significantly, with better sensitivity, but worse specificity for a ≥ 5% decline of FVC. However, the area under the curve towards LTx-survival were only 0.61 and 0.62, respectively. Conclusions The HRCT fibrosis visual score is a reliable and responsive tool to detect clinically meaningful disease progression. Although no individual pulmonary function test closely reflects radiographic progression, a longitudinal FVC decline improves sensitivity in the detection of clinically significant disease progression. However, the accuracy of these methods remains limited, and better prognostication models need to be found.

Introduction Interactions have been demonstrated between cigarette smoking (CS) and occupational exposures to several particles. This study tested the postulate that CS interacts with coal mine dust exposure to impact and change radiological and histological endpoints of coal mine dust lung disease. Methods A retrospective evaluation of coalminers with a high-resolution computed tomography (HRCT) of the chest was conducted at West Virginia University Hospital (2015- 2022). There was a consensus review of both radiology and histology findings and their comparative analysis with a non-miner surgical resection cohort collected from thoracic oncology clinic. Results The study cohort (n=556) was divided into groups: coal-/smoking- (8.3%), coal-/smoking+ (26.6%), coal+/smoking- (22.3%), and coal+/smoking+ (42.8%). Miners were older males with a median duration of coal mine work (CMW) of 30-years. Ever-smokers (66% of miner cohort and 76% of non-miner cohort) smoked 35 and 28 composite pack years (CPY) respectively, where miners had greater intensity of smoking (22 vs 18 cigarettes/day) compared to non-miners. On HRCT, 1/3 rd and 1/5 th of miners had simple and complicated coal workers’ pneumoconiosis (sCWP and cCWP), respectively. 35% of ever-smoking miners had radiologic patterns for probable usual interstitial pneumonitis, nonspecific interstitial pneumonitis, desquamative interstitial pneumonitis, and combined pulmonary fibrosis and emphysema. Radiologically, both coal-/smoking+ and coal+/smoking+ showed excessive emphysema (70-80%). Histologically, miners had more fibrosis (47% and 50% in coal+/smoking- and coal+/smoking+ vs. 11% and 28% in coal-/smoking- and coal-/smoking+). Never-smoking miners demonstrated more histological evidence of CWP than ever-smokers (60% and 27%); in addition, they had radiologic and histologic emphysema (30%), radiologic interstitial lung disease (ILD) (14.5%) and histologic evidence of fibrosis (47%). Ever-smokers demonstrated histologic emphysema more frequently (33% and 67% in coal+/smoking- and coal+/smoking+ vs. 24% and 72% in coal-/smoking- and coal-/smoking+). Logistic regression modeling showed the following associations: radiologic and histologic emphysema with CPY; histologic fibrosis, any ILD (not including RB-ILD), CPFE and anthracosis with both CPY and CMW; radiologic RB-ILD inclusive of small-opacities, cCWP with both CMW and silica; and sCWP and pulmonary artery dilation with CMW. Interestingly, CPY≥30 negatively correlated with radiologic cCWP and histologic CWP. Mortality was increased in smokers (14% and 29% in coal+/smoking- and coal+/smoking+ vs. 4% and 20% in coal-/smoking- and coal-/smoking+) with predictors being radiologic ILD, histologic CWP, and related co-morbid diseases including COPD, chronic kidney disease, and gastroesophageal reflux. Conclusion CS demonstrated a major impact on miners’ health including changing radiologic and histologic endpoints of interstitial lung diseases and emphysema.

Aim Idiopathic Pulmonary Fibrosis (IPF) has long been considered a disease primarily involving the lung interstitium, with relative sparing of the airways. This study aimed to investigate the presence and characteristics of small airway abnormalities in patients with IPF. Methods We analyzed 137 patients with IPF and 84 controls from a prospective, multicenter cohort (trial registration: NCT03666234). IPF patients were stratified by fibrosis severity on CT and clinical disease severity scores. Small airway function was assessed using predicted maximal expiratory flow at 25% (MEF25%), 50% (MEF50%), and 25–75% of forced vital capacity (MEF25–75%). Airway segmentation and quantitative analysis of airway number, length, and volume were performed using FACT-Digital Lung™ software. Group differences were analyzed using SPSS (version 26). Results The IPF group comprised 114 men and 23 women, with a mean age of 64.5 ± 10.7 years. MEF50%, MEF25%, and MEF25–75% were significantly lower in IPF patients than in controls across all severity stages. Conversely, the total number, length, and volume of segmented bronchi were higher in IPF patients, with the most pronounced differences observed in bronchi at generations 9–14. Small airway abnormalities were evident even in patients with mild fibrosis. Conclusion Small airway abnormalities, including increased airway counts and functional impairment, are detectable in early-stage IPF and persist throughout disease progression, suggesting that small airway involvement is a fundamental feature of IPF.

Recent advances in bronchoscopic lung volume reduction (BLVR) offer new therapeutic alternatives for patients with emphysema and hyperinflation. Endobronchial valves and coils are 2 potential BLVR techniques which have been shown to improve pulmonary function and the quality of life in patients with emphysema. Current patient selection for LVR procedures relies on 3 main inclusion criteria: low attenuation area (in %), also known as emphysema score, heterogeneity score, and fissure integrity score. Volumetric analysis in combination with densitometric analysis of the affected lung lobe or segment with quantitative CT to determine emphysema severity play an important role in treatment planning and post-operative assessment. Due to the variations in lung anatomy, manual corrections are often required to ensure successful and accurate lobe segmentation for pathological and post-treatment CT scan analysis. The advanced development and utilisation of quantitative CT do not simply represent regional changes in pulmonary function but aids in analysis for better patient selection with severe emphysema who are most likely to benefit from BLVR.

Evidence exists that HIV-seropositive individuals may be at increased risk for the development of precocious pulmonary emphysema. HIV infection is also associated with antioxidant deficiency in both the serum and lungs, and it is therefore possible that increased oxidant stress may contribute to parenchymal lung injury occurring in the setting of HIV. We sought to determine the regional distribution of emphysema and regional distribution of glutathione (GSH) concentrations among HIV-seropositive subjects with emphysema. Cross-sectional evaluation of a prospective, longitudinal study. University teaching hospital. HIV-seropositive subjects without AIDS-related pulmonary complications participating in a descriptive study of lung biology in HIV-seropositive individuals. Emphysema scoring and evaluation of emphysema lobar distribution was performed among 40 subjects with emphysema. Eleven subjects underwent BAL of the right middle lobe (RML) and right upper lobe (RUL) with measurement of epithelial lining fluid (ELF) GSH in each lobe. We found that the mean emphysema scores were much higher in the upper lobes compared to the rest of the lung. Mean GSH levels were significantly greater in the RUL compared to the RML. The regional differences were present in both smokers and nonsmokers. We conclude that in the setting of HIV, emphysema is more prominent and lung GSH concentrations are higher in the upper lobes. We hypothesize that the increased GSH may represent a compensatory response to increased oxidant stress in the upper lobes.

Objectives: Lyophyllum aggregatum (LA) is called Shimeji in Japanese and is eaten commonly as a mushroom. Shimeji mushrooms are cultivated in an indoor environment all year round. This study aimed to clarify the clinical features of hypersensitivity pneumonitis (HP) induced by LA. Ten patients showed mild respiratory symptoms including dry cough, sputum, and low-grade fever. We tried to characterize the clinical features and the findings using chest high-resolution CT (HRCT), pulmonary function tests (PFTs), and BAL fluid (BALF) tests in patients with HP induced by LA. HP was diagnosed from clinical features, HRCT findings, BALF findings, lung histology, and lymphocyte stimulation tests (LSTs) for LA. Laboratory findings showed mean (± SD) elevated levels of C-reactive protein (0.78 ± 1.3 mg/dL), erythrocyte sedimentation rate (48 ± 23 mm/h), and γ-globulin (26.9 ± 7.6%). PFTs revealed a slight decrease in the percentage diffusing capacity of the lung for carbon monoxide, possibly due to the presence of epithelial granulomas in the alveoli. Although 4 of 10 patients showed normal findings on the chest radiograph (CXR), chest HRCT findings of all patients showed centrilobular small nodules and diffuse ground-glass opacities. The BALF testing revealed an increase in total cell counts, showing predominantly activated T lymphocytes. The CD4/CD8 cell ratio was significantly decreased (0.5 ± 0.3). The results of the LSTs were positive in seven of seven cases. Since patients with HP induced by LA typically have mild respiratory symptoms and sometimes normal CXR findings, their conditions might remain undiagnosed. However, the chest HRCT images showed the typical subacute phase of HP.

In this paper, we compare five common classifier families in their ability to categorize six lung tissue patterns in high-resolution computed tomography (HRCT) images of patients affected with interstitial lung diseases (ILD) and with healthy tissue. The evaluated classifiers are naive Bayes, k -nearest neighbor, J48 decision trees, multilayer perceptron, and support vector machines (SVM). The dataset used contains 843 regions of interest (ROI) of healthy and five pathologic lung tissue patterns identified by two radiologists at the University Hospitals of Geneva. Correlation of the feature space composed of 39 texture attributes is studied. A grid search for optimal parameters is carried out for each classifier family. Two complementary metrics are used to characterize the performances of classification. These are based on McNemar’s statistical tests and global accuracy. SVM reached best values for each metric and allowed a mean correct prediction rate of 88.3% with high class-specific precision on testing sets of 423 ROIs.

Background Coal workers’ pneumoconiosis is a chronic occupational lung disease with considerable pulmonary complications, including irreversible lung diseases that are too complex to accurately identify via chest X-rays. The classification of clinical imaging features from high-resolution computed tomography might become a powerful clinical tool for diagnosing pneumoconiosis in the future. Methods All chest high-resolution computed tomography (HRCT) medical images presented in this work were obtained from 217 coal workers' pneumoconiosis (CWP) patients and dust-exposed workers. We segmented regions of interest according to the diagnostic results, which were evaluated by radiologists. These regions were then classified regions into four categories. We employed an efficient deep learning model and various image augmentation techniques (DenseNet-ECA). The classification performance of the different deep learning models was assessed, and receiver operating characteristic (ROC) curves and accuracy (ACC) were used to determine the optimal algorithm for classifying CWP clinical imaging features obtained from HRCT images. Results Four primary clinical imaging features in HRCT images, with a total of more than 1700 regions of interest (ROIs), were annotated, augmented, and used as a training set for tenfold cross-validation to generate the model. We selected DenseNet-Attention Net as the optimal model through assessing the performance of different classification algorithms, which yielded an average area under the ROC curve (AUC) of 0.98, and all clinical imaging features were classified with an AUC greater than 0.92. For the individual classifications, the AUCs were as follows: small miliary opacities, 0.99; nodular opacities, 1.0; interstitial changes, 0.92; and emphysema, 1.0. Conclusion We successfully applied a data augmentation strategy to develop a deep learning model by combining DenseNet with ECA-Net. We used our novel model to automatically classify CWP clinical imaging features from 2D HRCT images. This successful application of a deep learning-data augmentation algorithm can help clinical radiologists by providing reliable diagnostic information for classification. Trial registration : Chinese Clinical Trial Registry, ChiCTR2100050379. Registered on 27 August 2021, https://www.chictr.org.cn/bin/project/edit?pid=132619 .

Medical images such as CT and X-ray have been widely used for the detection of several chest infections and lung diseases. However, these images are susceptible to different types of noise, and it is hard to remove these noises due to their complex distribution. The presence of such noise significantly deteriorates the quality of the images and significantly affects the diagnosis performance. Hence, the design of an effective de-noising technique is highly essential to remove the noise from chest CT and X-ray images prior to further processing. Deep learning methods, mainly, CNN have shown tremendous progress on de-noising tasks. However, existing CNN based models estimate the noise from the final layers, which may not carry adequate details of the image. To tackle this issue, in this paper a deep multi-level semantic fusion network is proposed, called DMF-Net for the removal of noise from chest CT and X-ray images. The DMF-Net mainly comprises of a dilated convolutional feature extraction block, a cascaded feature learning block (CFLB) and a noise fusion block (NFB) followed by a prominent feature extraction block. The CFLB cascades the features from different levels (convolutional layers) which are later fed to NFB to attain correct noise prediction. Finally, the Prominent Feature Extraction Block(PFEB) produces the clean image. To validate the proposed de-noising technique, a separate and a mixed dataset containing high-resolution CT and X-ray images with specific and blind noise are used. Experimental results indicate the effectiveness of the DMF-Net compared to other state-of-the-art methods in the context of peak signal-to-noise ratio (PSNR) and structural similarity measurement (SSIM) while drastically cutting down on the processing power needed.