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Deep learning is a powerful tool that may allow for improved outcome prediction. To determine if deep learning, specifically convolutional neural network (CNN) analysis, could detect and stage chronic obstructive pulmonary disease (COPD) and predict acute respiratory disease (ARD) events and mortality in smokers. A CNN was trained using computed tomography scans from 7,983 COPDGene participants and evaluated using 1,000 nonoverlapping COPDGene participants and 1,672 ECLIPSE participants. Logistic regression (C statistic and the Hosmer-Lemeshow test) was used to assess COPD diagnosis and ARD prediction. Cox regression (C index and the Greenwood-Nam-D'Agnostino test) was used to assess mortality. In COPDGene, the C statistic for the detection of COPD was 0.856. A total of 51.1% of participants in COPDGene were accurately staged and 74.95% were within one stage. In ECLIPSE, 29.4% were accurately staged and 74.6% were within one stage. In COPDGene and ECLIPSE, the C statistics for ARD events were 0.64 and 0.55, respectively, and the Hosmer-Lemeshow P values were 0.502 and 0.380, respectively, suggesting no evidence of poor calibration. In COPDGene and ECLIPSE, CNN predicted mortality with fair discrimination (C indices, 0.72 and 0.60, respectively), and without evidence of poor calibration (Greenwood-Nam-D'Agnostino P values, 0.307 and 0.331, respectively). A deep-learning approach that uses only computed tomography imaging data can identify those smokers who have COPD and predict who are most likely to have ARD events and those with the highest mortality. At a population level CNN analysis may be a powerful tool for risk assessment.

The Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) study, which began in 2007, is an ongoing multicenter observational cohort study of more than 10,000 current and former smokers. The study is aimed at understanding the etiology, progression, and heterogeneity of chronic obstructive pulmonary disease (COPD). In addition to genetic analysis, the participants have been extensively characterized by clinical questionnaires, spirometry, volumetric inspiratory and expiratory computed tomography, and longitudinal follow-up, including follow-up computed tomography at 5 years after enrollment. The purpose of this state-of-the-art review is to summarize the major advances in our understanding of COPD resulting from the imaging findings in the COPDGene study. Imaging features that are associated with adverse clinical outcomes include early interstitial lung abnormalities, visual presence and pattern of emphysema, the ratio of pulmonary artery to ascending aortic diameter, quantitative evaluation of emphysema, airway wall thickness, and expiratory gas trapping. COPD is characterized by the early involvement of the small conducting airways, and the addition of expiratory scans has enabled measurement of small airway disease. Computational advances have enabled indirect measurement of nonemphysematous gas trapping. These metrics have provided insights into the pathogenesis and prognosis of COPD and have aided early identification of disease. Important quantifiable extrapulmonary findings include coronary artery calcification, cardiac morphology, intrathoracic and extrathoracic fat, and osteoporosis. Current active research includes identification of novel quantitative measures for emphysema and airway disease, evaluation of dose reduction techniques, and use of deep learning for phenotyping COPD.

The relationship between the development and/or progression of interstitial lung abnormalities (ILA) and clinical outcomes has not been previously investigated. To determine the risk factors for, and the clinical consequences of, having ILA progression in participants from the Framingham Heart Study. ILA were assessed in 1,867 participants who had serial chest computed tomography (CT) scans approximately 6 years apart. Mixed effect regression (and Cox) models were used to assess the association between ILA progression and pulmonary function decline (and mortality). During the follow-up period 660 (35%) participants did not have ILA on either CT scan, 37 (2%) had stable to improving ILA, and 118 (6%) had ILA with progression (the remaining participants without ILA were noted to be indeterminate on at least one CT scan). Increasing age and increasing copies of the MUC5B promoter polymorphism were associated with ILA progression. After adjustment for covariates, ILA progression was associated with a greater FVC decline when compared with participants without ILA (20 ml; SE, ±6 ml; P = 0.0005) and with those with ILA without progression (25 ml; SE, ±11 ml; P = 0.03). Over a median follow-up time of approximately 4 years, after adjustment, ILA progression was associated with an increase in the risk of death (hazard ratio, 3.9; 95% confidence interval, 1.3-10.9; P = 0.01) when compared with those without ILA. These findings demonstrate that ILA progression in the Framingham Heart Study is associated with an increased rate of pulmonary function decline and increased risk of death.

Loss of the peripheral pulmonary vasculature, termed vascular pruning, is associated with disease severity in patients with chronic obstructive pulmonary disease. To determine if pulmonary vascular pruning is associated with asthma severity and exacerbations. We measured the total pulmonary blood vessel volume (TBV) and the blood vessel volume of vessels less than 5 mm in cross-sectional area (BV5) and of vessels less than 10 mm (BV10) in cross-sectional area on noncontrast computed tomographic scans of participants from the Severe Asthma Research Program. Lower values of the BV5 to TBV ratio (BV5/TBV) and the BV10 to TBV ratio (BV10/TBV) represented vascular pruning (loss of the peripheral pulmonary vasculature). Compared with healthy control subjects, patients with severe asthma had more pulmonary vascular pruning. Among those with asthma, those with poor asthma control had more pruning than those with well-controlled disease. Pruning of the pulmonary vasculature was also associated with lower percent predicted FEV and FVC, greater peripheral and sputum eosinophilia, and higher BAL serum amyloid A/lipoxin A ratio but not with low-attenuation area or with sputum neutrophilia. Compared with individuals with less pruning, individuals with the most vascular pruning had 150% greater odds of reporting an asthma exacerbation (odds ratio, 2.50; confidence interval, 1.05-5.98; P = 0.039 for BV10/TBV) and reported 45% more asthma exacerbations during follow-up (incidence rate ratio, 1.45; confidence interval, 1.02-2.06; P = 0.036 for BV10/TBV). Pruning of the peripheral pulmonary vasculature is associated with asthma severity, control, and exacerbations, and with lung function and eosinophilia.

The scarcity of data for training deep learning models in pediatrics has prompted questions about the feasibility of employing CNNs trained with adult images for pediatric populations. In this work, a pneumonia classification CNN was used as an exploratory example to showcase the adaptability and efficacy of such models in pediatric healthcare settings despite the inherent data constraints. To develop a curated training dataset with reduced biases, 46,947 chest X-ray images from various adult datasets were meticulously selected. Two preprocessing approaches were tried to assess the impact of thoracic segmentation on model attention outside the thoracic area. Evaluation of our approach was carried out on a dataset containing 5,856 chest X-rays of children from 1 to 5 years old. An analysis of attention maps indicated that networks trained with thorax segmentation placed less attention on regions outside the thorax, thus eliminating potential bias. The ensuing network exhibited impressive performance when evaluated on an adult dataset, achieving a pneumonia discrimination AUC of 0.95. When tested on a pediatric dataset, the pneumonia discrimination AUC reached 0.82. The results of this study show that adult-trained CNNs can be effectively applied to pediatric populations. This could potentially shift focus towards validating adult models over pediatric population instead of training new CNNs with limited pediatric data. To ensure the generalizability of deep learning models, it is important to implement techniques aimed at minimizing biases, such as image segmentation or low-quality image exclusion.

Chronic obstructive pulmonary disease (COPD) is characterized by airway remodeling. Characterization of airway changes on computed tomography has been challenging due to the complexity of the recurring branching patterns, and this can be better measured using fractal dimensions. We analyzed segmented airway trees of 8,135 participants enrolled in the COPDGene cohort. The fractal complexity of the segmented airway tree was measured by the Airway Fractal Dimension (AFD) using the Minkowski-Bougliand box-counting dimension. We examined associations between AFD and lung function and respiratory morbidity using multivariable regression analyses. We further estimated the extent of peribronchial emphysema (%) within 5 mm of the airway tree, as this is likely to affect AFD. We classified participants into 4 groups based on median AFD, percentage of peribronchial emphysema, and estimated survival. AFD was significantly associated with forced expiratory volume in one second (FEV1; P < 0.001) and FEV1/forced vital capacity (FEV1/FVC; P < 0.001) after adjusting for age, race, sex, smoking status, pack-years of smoking, BMI, CT emphysema, air trapping, airway thickness, and CT scanner type. On multivariable analysis, AFD was also associated with respiratory quality of life and 6-minute walk distance, as well as exacerbations, lung function decline, and mortality on longitudinal follow-up. We identified a subset of participants with AFD below the median and peribronchial emphysema above the median who had worse survival compared with participants with high AFD and low peribronchial emphysema (adjusted hazards ratio [HR]: 2.72; 95% CI: 2.20-3.35; P < 0.001), a substantial number of whom were not identified by traditional spirometry severity grades. Airway fractal dimension as a measure of airway branching complexity and remodeling in smokers is associated with respiratory morbidity and lung function change, offers prognostic information additional to traditional CT measures of airway wall thickness, and can be used to estimate mortality risk. ClinicalTrials.gov identifier: NCT00608764. This study was supported by NIH K23 HL133438 (SPB) and the COPDGene study (NIH Grant Numbers R01 HL089897 and R01 HL089856). The COPDGene project is also supported by the COPD Foundation through contributions made to an Industry Advisory Board comprised of AstraZeneca, Boehringer Ingelheim, Novartis, Pfizer, Siemens, Sunovion and GlaxoSmithKline.

Murine studies have linked TGF-β signaling to emphysema, and human genome-wide association studies (GWAS) studies of lung function and COPD have identified associated regions near genes in the TGF-β superfamily. However, the functional regulatory mechanisms at these loci have not been identified. We performed the largest GWAS of emphysema patterns to date, identifying 10 GWAS loci including an association peak spanning a 200 kb region downstream from TGFB2 . Integrative analysis of publicly available eQTL, DNaseI, and chromatin conformation data identified a putative functional variant, rs1690789, that may regulate TGFB2 expression in human fibroblasts. Using chromatin conformation capture, we confirmed that the region containing rs1690789 contacts the TGFB2 promoter in fibroblasts, and CRISPR/Cas-9 targeted deletion of a ~ 100 bp region containing rs1690789 resulted in decreased TGFB2 expression in primary human lung fibroblasts. These data provide novel mechanistic evidence linking genetic variation affecting the TGF-β pathway to emphysema in humans. It is well known that smoking is bad for the lungs. Not only can smoking cause lung cancer, it can also lead to conditions such as emphysema. This is the gradual damage to lung tissue that occurs when the walls of the tiny air-sacs in the lungs where the blood takes up oxygen, called the alveoli, weaken and break. Emphysema causes shortness of breath and difficulty pushing air out of the lungs, and it is part of chronic obstructive pulmonary disease (also known as COPD). Genetic differences mean that certain people are more likely to develop emphysema than others. As an example, if someone has genetic mutations that alter the activity of a gene called TGFB2 , their risk of developing emphysema increases. However, the specific genetic mutations that modify the activity of TGFB2 were previously unknown. Parker et al. analyzed the genetic sequences of TGFB2 from patients with emphysema and compared them to those from healthy individuals. This revealed that certain mutations near the TGFB2 gene were more common in patients with emphysema. Next, Parker et al. showed that, in healthy lung cells called fibroblasts, the stretch of DNA that was mutated in patients with emphysema touched the part of TGFB2 that controls when the gene is activated. Deleting that same stretch of DNA in the fibroblasts meant the cells could no longer activate the TGFB2 gene as efficiently. Together, these results reveal a genetic difference that increases the risk for emphysema. COPD affects approximately 175 million people worldwide, causing over three million deaths each year. The findings of Parker et al. suggest that developing drugs that safely and efficiently target TGFB2 may be a way to help patients with early signs of emphysema.

Variants in the gene encoding mucin 5B ( MUC5B ) have been associated with interstitial fibrosis. This study shows a relationship between the presence of the associated variants in MUC5B and interstitial lung abnormalities in participants in a Framingham study cohort. Subclinical interstitial lung abnormalities are relatively common findings on imaging studies in smokers and elderly persons. 1 Accumulating evidence suggests that these abnormalities may precede the development of clinically relevant pulmonary fibrosis. 1 – 7 However, it is not known whether there is a genetic association between interstitial lung abnormalities and pulmonary fibrosis in the general population. Recently, a single-nucleotide polymorphism (SNP) (rs35705950) in the promoter of the gene encoding mucin 5B ( MUC5B ) was shown to be associated with both familial interstitial pneumonia and sporadic idiopathic pulmonary fibrosis. 8 In addition, the MUC5B variant was associated with increased expression of MUC5B in . . .

Background Studies have demonstrated an inverse relationship between body mass index (BMI) and the risk of developing lung cancer. We conducted a retrospective cohort study evaluating baseline quantitative computed tomography (CT) measurements of body composition, specifically muscle and fat area in a large CT lung screening cohort (CTLS). We hypothesized that quantitative measurements of baseline body composition may aid in risk stratification for lung cancer. Methods Patients who underwent baseline CTLS between January 1st, 2012 and September 30th, 2014 and who had an in-network primary care physician were included. All patients met NCCN Guidelines eligibility criteria for CTLS. Quantitative measurements of pectoralis muscle area (PMA) and subcutaneous fat area (SFA) were performed on a single axial slice of the CT above the aortic arch with the Chest Imaging Platform Workstation software. Cox multivariable proportional hazards model for cancer was adjusted for variables with a univariate p  < 0.2. Data were dichotomized by sex and then combined to account for baseline differences between sexes. Results One thousand six hundred and ninety six patients were included in this study. A total of 79 (4.7%) patients developed lung cancer. There was an association between the 25th percentile of PMA and the development of lung cancer [HR 1.71 (1.07, 2.75), p  < 0.025] after adjusting for age, BMI, qualitative emphysema, qualitative coronary artery calcification, and baseline Lung-RADS® score. Conclusions Quantitative assessment of PMA on baseline CTLS was associated with the development of lung cancer. Quantitative PMA has the potential to be incorporated as a variable in future lung cancer risk models.

Changes in Airway Mucus Plugs in COPDAmong patients with COPD, those with persistent or newly formed mucus plugs at 5-year follow-up had a greater decrease in lung function than those with resolved or no mucus plugs.