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Rationale: To study the relationship between emphysema and/or airflow obstruction and lung cancer in a high-risk population.Objective: We studied lung cancer related to radiographic emphysema and spirometric airflow obstruction in tobacco-exposed persons who were screened for lung cancer using chest computed tomography (CT).Methods: Subjects completed questionnaires, spirometry, and low-dose helical chest CT. CT scans were scored for emphysema based on National Emphysema Treatment Trial criteria. Multiple logistic regressions estimated the independent associations between various factors, including radiographic emphysema and airflow obstruction, and subsequent lung cancer diagnosis.Measurements and Main Results: Among 3,638 subjects, 57.5, 18.8, 14.6, and 9.1% had no, trace, mild, and moderate-severe emphysema, and 57.3, 13.6, 22.8, and 6.4% had no, mild (Global Initiative for Chronic Obstructive Lung Disease [GOLD] I), moderate (GOLD II), and severe (GOLD III-IV) airflow obstruction. Of 3,638 subjects, 99 (2.7%) received a lung cancer diagnosis. Adjusting for sex, age, years of cigarette smoking, and number of cigarettes smoked daily, logistic regression showed the expected lung cancer association with the presence of airflow obstruction (GOLD I-IV, odds ratio [OR], 2.09; 95% confidence interval [CI], 1.33-3.27). A second logistic regression showed lung cancer related to emphysema (OR, 3.56; 95% CI, 2.21-5.73). After additional adjustments for GOLD class, emphysema remained a strong and statistically significant factor related to lung cancer (OR, 3.14; 95% CI, 1.91-5.15).Conclusions: Emphysema on CT scan and airflow obstruction on spirometry are related to lung cancer in a high-risk population. Emphysema is independently related to lung cancer. Both radiographic emphysema and airflow obstruction should be considered when assessing lung cancer risk. [ABSTRACT FROM AUTHOR]

Purpose: Delayed chest closure (DCC) during lung transplantation (LTx) is a controversial surgical approach that lacks research in systemic sclerosis (SSc) patients. We investigated outcomes, clinical risk factors, and CT-based lung size-matching parameters associated with DCC in SSc recipients., Methods: This retrospective study included 92 SSc recipients (age 51 years ± 10, 56/92 (61.0%) females) who underwent bilateral LTx between 2007 and 2020. Of the recipients, 34.8% (32/92) underwent DCC. Recipient lung and chest cavity volumes were automatically computed from CT imaging using deep learning algorithms. Survival between groups was compared using Kaplan-Meier analysis. Multivariate logistic regression was used to identify risk factors and predict DCC occurrence using preoperative variables., Results: Recipients who underwent DCC had longer total vent duration (p = 0.001), more use of postoperative mechanical support (p = 0.001), longer ICU length of stay (p = 0.008), and lower incidence of pneumonia post-operation (p = 0.031). No significant difference in survival was observed between DCC and PCC recipients at 30 days (p = 0.713), 90 days (p = 0.267), 1 year (p = 0.941), and 5 years (p = 0.651). Clinical risk factors for DCC included BMI > 30 kg/m 2 (p = 0.009), tracheostomy (p = 0.002), atrial fibrillation (p = 0.012), decreased preoperative FEV1/FVC (p = 0.013), and previous chest operation (p = 0.020). Two CT-based measurements of lung matching were significantly associated with DCC occurrence (p = 0.021 and 0.050). The regression model achieved a mean AUC of 0.82 (0.70, 0.94) in retrospectively predicting DCC occurrence., Conclusion: SSc recipients undergoing DCC have similar survival rates but experience more complications than PCC recipients. Clinical risk factors and CT-based size matching can be leveraged to predict DCC pre-transplant., (© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Purpose: To investigate if body composition is a biomarker for assessing the risk of developing lung cancer., Materials and Methods: Low-dose computed tomography (LDCT) scans from the Pittsburgh Lung Screening Study (PLuSS) (n=3,635, 22 follow-up years) and NLST-ACRIN (n=16,435, 8 follow-up years) cohorts were used in the study. Artificial intelligence (AI) algorithms were developed to automatically segment and quantify subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), intramuscular adipose tissue (IMAT), skeletal muscle (SM), and bone. Cause-specific Cox proportional hazards models were used to evaluate the hazard ratios (HRs). Standard time-dependent receiver operating characteristic (ROC) analysis was used to evaluate the prognostic ability of different models over time., Results: The final composite models were formed by seven variables: age (HR=1.20), current smoking status (HR=1.59), bone volume (HR=1.79), SM density (HR=0.29), IMAT ratio (HR=0.33), IMAT density (HR=0.56), and SAT volume (HR=0.56). The models trained on the PLuSS cohort achieved a mean AUC of 0.76 (95% CI: 0.74-0.79) over 21 follow-up years and 0.70 (95% CI: 0.66-0.74) over the first 7 follow-up years for predicting lung cancer development within the PLuSS cohort. In contrast, models trained on the PLuSS cohort alone, as well as in combination with the NLST cohorts, achieved an AUC ranging from 0.61 to 0.68 in the NLST cohort over a 7-year follow-up period., Conclusion: Body composition assessed on LDCT is a significant predictor of lung cancer risk and could improve the effectiveness of LDCT lung cancer screening by optimizing the screening eligibility and frequency., Summary Statement: Body composition assessed on LDCT is a significant predictor of lung cancer risk and could improve the effectiveness of LDCT lung cancer screening by optimizing the screening eligibility and frequency., Key Points: This study unveils the significant associations between body tissues and lung cancer risk.The prediction models based on body composition alone, as well as the combination of demographics and body composition features can effectively identify patients at higher risk of developing lung cancer.

Objective: This study aims to investigate the association between the arteries and veins surrounding a pulmonary nodule and its malignancy., Methods: A dataset of 146 subjects from a LDCT lung cancer screening program was used in this study. AI algorithms were used to automatically segment and quantify nodules and their surrounding macro-vasculature. The macro-vasculature was differentiated into arteries and veins. Vessel branch count, volume, and tortuosity were quantified for arteries and veins at different distances from the nodule surface. Univariate and multivariate logistic regression (LR) analyses were performed, with a special emphasis on the nodules with diameters ranging from 8 to 20 mm. ROC-AUC was used to assess the performance based on the k-fold cross-validation method. Average feature importance was evaluated in several machine learning models., Results: The LR models using macro-vasculature features achieved an AUC of 0.78 (95% CI: 0.71-0.86) for all nodules and an AUC of 0.67 (95% CI: 0.54-0.80) for nodules between 8-20 mm. Models including macro-vasculature features, demographics, and CT-derived nodule features yielded an AUC of 0.91 (95% CI: 0.87-0.96) for all nodules and an AUC of 0.82 (95% CI: 0.71-0.92) for nodules between 8-20 mm. In terms of feature importance, arteries within 5.0 mm from the nodule surface were the highest-ranked among macro-vasculature features and retained their significance even with the inclusion of demographics and CT-derived nodule features., Conclusions: Arteries within 5.0 mm from the nodule surface emerged as a potential biomarker for effectively discriminating between malignant and benign nodules.

Background: Chest x-ray is widely utilized for the evaluation of pulmonary conditions due to its technical simplicity, cost-effectiveness, and portability. However, as a two-dimensional (2-D) imaging modality, chest x-ray images depict limited anatomical details and are challenging to interpret., Purpose: To validate the feasibility of reconstructing three-dimensional (3-D) lungs from a single 2-D chest x-ray image via Vision Transformer (ViT)., Methods: We created a cohort of 2525 paired chest x-ray images (scout images) and computed tomography (CT) acquired on different subjects and we randomly partitioned them as follows: (1) 1800 - training set, (2) 200 - validation set, and (3) 525 - testing set. The 3-D lung volumes segmented from the chest CT scans were used as the ground truth for supervised learning. We developed a novel model termed XRayWizard that employed ViT blocks to encode the 2-D chest x-ray image. The aim is to capture global information and establish long-range relationships, thereby improving the performance of 3-D reconstruction. Additionally, a pooling layer at the end of each transformer block was introduced to extract feature information. To produce smoother and more realistic 3-D models, a set of patch discriminators was incorporated. We also devised a novel method to incorporate subject demographics as an auxiliary input to further improve the accuracy of 3-D lung reconstruction. Dice coefficient and mean volume error were used as performance metrics as the agreement between the computerized results and the ground truth., Results: In the absence of subject demographics, the mean Dice coefficient for the generated 3-D lung volumes achieved a value of 0.738 ± 0.091. When subject demographics were included as an auxiliary input, the mean Dice coefficient significantly improved to 0.769 ± 0.089 (p < 0.001), and the volume prediction error was reduced from 23.5 ± 2.7%. to 15.7 ± 2.9%., Conclusion: Our experiment demonstrated the feasibility of reconstructing 3-D lung volumes from 2-D chest x-ray images, and the inclusion of subject demographics as additional inputs can significantly improve the accuracy of 3-D lung volume reconstruction., (© 2023 American Association of Physicists in Medicine.)

Purpose: To clarify the causal relationship between factors contributing to the postoperative survival of patients with esophageal cancer., Methods: A cohort of 195 patients who underwent surgery for esophageal cancer between 2008 and 2021 was used in the study. All patients had preoperative chest computed tomography (CT) and positron emission tomography-CT (PET-CT) scans prior to receiving any treatment. From these images, high throughput and quantitative radiomic features, tumor features, and various body composition features were automatically extracted. Causal relationships among these image features, patient demographics, and other clinicopathological variables were analyzed and visualized using a novel score-based directed graph called "Grouped Greedy Equivalence Search" (GGES) while taking prior knowledge into consideration. After supplementing and screening the causal variables, the intervention do-calculus adjustment (IDA) scores were calculated to determine the degree of impact of each variable on survival. Based on this IDA score, a GGES prediction formula was generated. Ten-fold cross-validation was used to assess the performance of the models. The prediction results were evaluated using the R-Squared Score (R 2 score)., Results: The final causal graphical model was formed by two PET-based image variables, ten body composition variables, four pathological variables, four demographic variables, two tumor variables, and one radiological variable (Percentile 10). Intramuscular fat mass was found to have the most impact on overall survival month. Percentile 10 and overall TNM (T: tumor, N: nodes, M: metastasis) stage were identified as direct causes of overall survival (month). The GGES casual model outperformed GES in regression prediction (R 2  = 0.251) (p < 0.05) and was able to avoid unreasonable causality that may contradict common sense., Conclusion: The GGES causal model can provide a reliable and straightforward representation of the intricate causal relationships among the variables that impact the postoperative survival of patients with esophageal cancer., (© 2023 American Association of Physicists in Medicine.)

We present a novel computer algorithm to automatically detect and segment pulmonary embolisms (PEs) on computed tomography pulmonary angiography (CTPA). This algorithm is based on deep learning but does not require manual outlines of the PE regions. Given a CTPA scan, both intra- and extra-pulmonary arteries were firstly segmented. The arteries were then partitioned into several parts based on size (radius). Adaptive thresholding and constrained morphological operations were used to identify suspicious PE regions within each part. The confidence of a suspicious region to be PE was scored based on its contrast in the arteries. This approach was applied to the publicly available RSNA Pulmonary Embolism CT Dataset (RSNA-PE) to identify three-dimensional (3-D) PE negative and positive image patches, which were used to train a 3-D Recurrent Residual U-Net (R2-Unet) to automatically segment PE. The feasibility of this computer algorithm was validated on an independent test set consisting of 91 CTPA scans acquired from a different medical institute, where the PE regions were manually located and outlined by a thoracic radiologist (>18 years' experience). An R2-Unet model was also trained and validated on the manual outlines using a 5-fold cross-validation method. The CNN model trained on the high-confident PE regions showed a Dice coefficient of 0.676±0.168 and a false positive rate of 1.86 per CT scan, while the CNN model trained on the manual outlines demonstrated a Dice coefficient of 0.647±0.192 and a false positive rate of 4.20 per CT scan. The former model performed significantly better than the latter model (p<0.01). The promising performance of the developed PE detection and segmentation algorithm suggests the feasibility of training a deep learning network without dedicating significant efforts to manual annotations of the PE regions on CTPA scans., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Jiantao Pu reports financial support was provided by National Cancer Institute. Stephen Yu-Wah Chan reports financial support was provided by American Heart Association. Jiantao Pu reports financial support was provided by National Center for Complementary and Integrative Health. Dr. Stephen Yu-Wah Chan (SYC) has served as a consultant for Acceleron Pharma and United Therapeutics; SYC has held research grants from Actelion, Bayer, and Pfizer. SYC is a director, officer, and shareholder of Synhale Therapeutics. SYC has filed patents regarding metabolic dysregulation in pulmonary hypertension. The other authors declare no conflict of interest., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Introduction: To evaluate the ability of artificial intelligence (AI) software to quantify proptosis for identifying patients who need surgical drainage., Methods: We pursued a retrospective study including 56 subjects with a clinical diagnosis of subperiosteal orbital abscess (SPOA) secondary to sinusitis at a tertiary pediatric hospital from 2002 to 2016. AI computer software was developed to perform 3D visualization and quantitative assessment of proptosis from computed tomography (CT) images acquired at the time of hospital admission. The AI software automatically computed linear and volume metrics of proptosis to provide more practice-consistent and informative measures. Two experienced physicians independently measured proptosis using the interzygomatic line method on axial CT images. The AI software and physician proptosis assessments were evaluated for association with eventual treatment procedures as standalone markers and in combination with the standard predictors., Results: To treat the SPOA, 31 of 56 (55%) children underwent surgical intervention, including 18 early surgeries (performed within 24 h of admission), and 25 (45%) were managed medically. The physician measurements of proptosis were strongly correlated (Spearman r = 0.89, 95% CI 0.82-0.93) with 95% limits of agreement of ± 1.8 mm. The AI linear measurement was on average 1.2 mm larger (p = 0.007) and only moderately correlated with the average physicians' measurements (r = 0.53, 95% CI 0.31-0.69). Increased proptosis of both AI volumetric and linear measurements were moderately predictive of surgery (AUCs of 0.79, 95% CI 0.68-0.91, and 0.78, 95% CI 0.65-0.90, respectively) with the average physician measurement being poorly to fairly predictive (AUC of 0.70, 95% CI 0.56-0.84). The AI proptosis measures were also significantly greater in the early as compared to the late surgery groups (p = 0.02, and p = 0.04, respectively). The surgical and medical groups showed a substantial difference in the abscess volume (p < 0.001)., Conclusion: AI proptosis measures significantly differed from physician assessments and showed a good overall ability to predict the eventual treatment. The volumetric AI proptosis measurement significantly improved the ability to predict the likelihood of surgery compared to abscess volume alone. Further studies are needed to better characterize and incorporate the AI proptosis measurements for assisting in clinical decision-making., (© 2023. The Author(s).)

Nutrition knowledge, confidence, and skills are thought to be important elements in the role of healthcare professionals in obesity prevention and management. The Upstream Obesity Solutions curriculum goes upstream with a multidisciplinary approach to supplement nutrition education among health professional trainees. Educational strategies of didactics, teaching kitchens, and service-based learning were employed for medical, dental, and nursing students and resident physicians. Pre/post participation surveys assessed knowledge, attitude, and practices; lifestyle habits; and culinary skills among 75 trainees in this cross-sectional descriptive study. There was variability in statistically significant improvement in knowledge, attitudes, and practices about obesity management and nutrition education, lifestyle habits, and culinary skills among learner groups.

Purpose: Lung transplantation is the standard treatment for end-stage lung diseases. A crucial factor affecting its success is size matching between the donor's lungs and the recipient's thorax. Computed tomography (CT) scans can accurately determine recipient's lung size, but donor's lung size is often unknown due to the absence of medical images. We aim to predict donor's right/left/total lung volume, thoracic cavity, and heart volume from only subject demographics to improve the accuracy of size matching., Approach: A cohort of 4610 subjects with chest CT scans and basic demographics (i.e., age, gender, race, smoking status, smoking history, weight, and height) was used in this study. The right and left lungs, thoracic cavity, and heart depicted on chest CT scans were automatically segmented using U-Net, and their volumes were computed. Eight machine learning models [i.e., random forest, multivariate linear regression, support vector machine, extreme gradient boosting (XGBoost), multilayer perceptron (MLP), decision tree, k -nearest neighbors, and Bayesian regression) were developed and used to predict the volume measures from subject demographics. The 10-fold cross-validation method was used to evaluate the performances of the prediction models. R -squared ( R 2 ), mean absolute error (MAE), and mean absolute percentage error (MAPE) were used as performance metrics., Results: The MLP model demonstrated the best performance for predicting the thoracic cavity volume ( R 2 : 0.628, MAE: 0.736 L, MAPE: 10.9%), right lung volume ( R 2 : 0.501, MAE: 0.383 L, MAPE: 13.9%), and left lung volume ( R 2 : 0.507, MAE: 0.365 L, MAPE: 15.2%), and the XGBoost model demonstrated the best performance for predicting the total lung volume ( R 2 : 0.514, MAE: 0.728 L, MAPE: 14.0%) and heart volume ( R 2 : 0.430, MAE: 0.075 L, MAPE: 13.9%)., Conclusions: Our results demonstrate the feasibility of predicting lung, heart, and thoracic cavity volumes from subject demographics with superior performance compared with available studies in predicting lung volumes., (© 2023 Society of Photo-Optical Instrumentation Engineers (SPIE).)

Objectives: To evaluate the impact of body composition derived from computed tomography (CT) scans on postoperative lung cancer recurrence., Methods: We created a retrospective cohort of 363 lung cancer patients who underwent lung resections and had verified recurrence, death, or at least 5-year follow-up without either event. Five key body tissues and ten tumor features were automatically segmented and quantified based on preoperative whole-body CT scans (acquired as part of a PET-CT scan) and chest CT scans, respectively. Time-to-event analysis accounting for the competing event of death was performed to analyze the impact of body composition, tumor features, clinical information, and pathological features on lung cancer recurrence after surgery. The hazard ratio (HR) of normalized factors was used to assess individual significance univariately and in the combined models. The 5-fold cross-validated time-dependent receiver operating characteristics analysis, with an emphasis on the area under the 3-year ROC curve (AUC), was used to characterize the ability to predict lung cancer recurrence., Results: Body tissues that showed a standalone potential to predict lung cancer recurrence include visceral adipose tissue (VAT) volume (HR = 0.88, p = 0.047), subcutaneous adipose tissue (SAT) density (HR = 1.14, p = 0.034), inter-muscle adipose tissue (IMAT) volume (HR = 0.83, p = 0.002), muscle density (HR = 1.27, p < 0.001), and total fat volume (HR = 0.89, p = 0.050). The CT-derived muscular and tumor features significantly contributed to a model including clinicopathological factors, resulting in an AUC of 0.78 (95% CI: 0.75-0.83) to predict recurrence at 3 years., Conclusions: Body composition features (e.g., muscle density, or muscle and inter-muscle adipose tissue volumes) can improve the prediction of recurrence when combined with clinicopathological factors., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Introduction: Muscle loss is an important extrapulmonary manifestation of COPD. Dual energy X-ray absorptiometry (DXA) is the method of choice for body composition measurement but is not widely used for muscle mass evaluation. The pectoralis muscle area (PMA) is quantifiable by CT and predicts cross-sectional COPD-related morbidity. There are no studies that compare PMA with DXA measures or that evaluate longitudinal relationships between PMA and lung disease progression., Methods: Participants from our longitudinal tobacco-exposed cohort had baseline and 6-year chest CT (n=259) and DXA (n=164) data. Emphysema was quantified by CT density histogram parenchymal scoring using the 15th percentile technique. Fat-free mass index (FFMI) and appendicular skeletal mass index (ASMI) were calculated from DXA measurements. Linear regression model relationships were reported using standardised coefficient (β) with 95% CI., Results: PMA was more strongly associated with DXA measures than with body mass index (BMI) in both cross-sectional (FFMI: β=0.76 (95% CI 0.65 to 0.86), p<0.001; ASMI: β=0.76 (95% CI 0.66 to 0.86), p<0.001; BMI: β=0.36 (95% CI 0.25 to 0.47), p<0.001) and longitudinal (ΔFFMI: β=0.43 (95% CI 0.28 to 0.57), p<0.001; ΔASMI: β=0.42 (95% CI 0.27 to 0.57), p<0.001; ΔBMI: β=0.34 (95% CI 0.22 to 0.46), p<0.001) models. Six-year change in PMA was associated with 6-year change in emphysema (β=0.39 (95% CI 0.23 to 0.56), p<0.001) but not with 6-year change in airflow obstruction., Conclusions: PMA is an accessible measure of muscle mass and may serve as a useful clinical surrogate for assessing skeletal muscle loss in smokers. Decreased PMA correlated with emphysema progression but not lung function decline, suggesting a difference in the pathophysiology driving emphysema, airflow obstruction and comorbidity risk., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Body composition can be accurately quantified based on computed tomography (CT) and typically reflects an individual's overall health status. However, there is a dearth of research examining the relationship between body composition and survival following esophagectomy., Methods: We created a cohort consisting of 183 patients who underwent esophagectomy for esophageal cancer without neoadjuvant therapy. The cohort included preoperative PET-CT scans, along with pathologic and clinical data, which were collected prospectively. Radiomic, tumor, PET, and body composition features were automatically extracted from the images. Cox regression models were utilized to identify variables associated with survival. Logistic regression and machine learning models were developed to predict one-, three-, and five-year survival rates. Model performance was evaluated based on the area under the receiver operating characteristics curve (ROC/AUC). To test for the statistical significance of the impact of body composition on survival, body composition features were excluded for the best-performing models, and the DeLong test was used., Results: The one-year survival model contained 10 variables, including three body composition variables (bone mass, bone density, and visceral adipose tissue (VAT) density), and demonstrated an AUC of 0.817 (95% CI: 0.738-0.897). The three-year survival model incorporated 14 variables, including three body composition variables (intermuscular adipose tissue (IMAT) volume, IMAT mass, and bone mass), with an AUC of 0.693 (95% CI: 0.594-0.792). For the five-year survival model, 10 variables were included, of which two were body composition variables (intramuscular adipose tissue (IMAT) volume and visceral adipose tissue (VAT) mass), with an AUC of 0.861 (95% CI: 0.783-0.938). The one- and five-year survival models exhibited significantly inferior performance when body composition features were not incorporated., Conclusions: Body composition features derived from preoperative CT scans should be considered when predicting survival following esophagectomy.

Rationale: Extrapulmonary manifestations of asthma, including fatty infiltration in tissues, may reflect systemic inflammation and influence lung function and disease severity. Objectives: To determine if skeletal muscle adiposity predicts lung function trajectory in asthma. Methods: Adult SARP III (Severe Asthma Research Program III) participants with baseline computed tomography imaging and longitudinal postbronchodilator FEV 1 % predicted (median follow-up 5 years [1,132 person-years]) were evaluated. The mean of left and right paraspinous muscle density (PSMD) at the 12th thoracic vertebral body was calculated (Hounsfield units [HU]). Lower PSMD reflects higher muscle adiposity. We derived PSMD reference ranges from healthy control subjects without asthma. A linear multivariable mixed-effects model was constructed to evaluate associations of baseline PSMD and lung function trajectory stratified by sex. Measurements and Main Results: Participants included 219 with asthma (67% women; mean [SD] body mass index, 32.3 [8.8] kg/m 2 ) and 37 control subjects (51% women; mean [SD] body mass index, 26.3 [4.7] kg/m 2 ). Participants with asthma had lower adjusted PSMD than control subjects (42.2 vs. 55.8 HU; P  < 0.001). In adjusted models, PSMD predicted lung function trajectory in women with asthma (β = -0.47 Δ slope per 10-HU decrease; P  = 0.03) but not men (β = 0.11 Δ slope per 10-HU decrease; P  = 0.77). The highest PSMD tertile predicted a 2.9% improvement whereas the lowest tertile predicted a 1.8% decline in FEV 1 % predicted among women with asthma over 5 years. Conclusions: Participants with asthma have lower PSMD, reflecting greater muscle fat infiltration. Baseline PSMD predicted lung function decline among women with asthma but not men. These data support an important role of metabolic dysfunction in lung function decline.

Objective: To develop and validate a novel deep learning architecture to classify retinal vein occlusion (RVO) on color fundus photographs (CFPs) and reveal the image features contributing to the classification., Methods: The neural understanding network (NUN) is formed by two components: (1) convolutional neural network (CNN)-based feature extraction and (2) graph neural networks (GNN)-based feature understanding. The CNN-based image features were transformed into a graph representation to encode and visualize long-range feature interactions to identify the image regions that significantly contributed to the classification decision. A total of 7062 CFPs were classified into three categories: (1) no vein occlusion ("normal"), (2) central RVO, and (3) branch RVO. The area under the receiver operative characteristic (ROC) curve (AUC) was used as the metric to assess the performance of the trained classification models., Results: The AUC, accuracy, sensitivity, and specificity for NUN to classify CFPs as normal, central occlusion, or branch occlusion were 0.975 (± 0.003), 0.911 (± 0.007), 0.983 (± 0.010), and 0.803 (± 0.005), respectively, which outperformed available classical CNN models., Conclusion: The NUN architecture can provide a better classification performance and a straightforward visualization of the results compared to CNNs., (© 2022 American Association of Physicists in Medicine.)

Background: Estimating whole-body composition from limited region-computed tomography (CT) scans has many potential applications in clinical medicine; however, it is challenging., Purpose: To investigate if whole-body composition based on several tissue types (visceral adipose tissue [VAT], subcutaneous adipose tissue [SAT], intermuscular adipose tissue [IMAT], skeletal muscle [SM], and bone) can be reliably estimated from a chest CT scan only., Methods: A cohort of 97 lung cancer subjects who underwent both chest CT scans and whole-body positron emission tomography-CT scans at our institution were collected. We used our in-house software to automatically segment and quantify VAT, SAT, IMAT, SM, and bone on the CT images. The field-of-views of the chest CT scans and the whole-body CT scans were standardized, namely, from vertebra T1 to L1 and from C1 to the bottom of the pelvis, respectively. Multivariate linear regression was used to develop the computer models for estimating the volumes of whole-body tissues from chest CT scans. Subject demographics (e.g., gender and age) and lung volume were included in the modeling analysis. Ten-fold cross-validation was used to validate the performance of the prediction models. Mean absolute difference (MAD) and R-squared (R 2 ) were used as the performance metrics to assess the model performance., Results: The R 2 values when estimating volumes of whole-body SAT, VAT, IMAT, total fat, SM, and bone from the regular chest CT scans were 0.901, 0.929, 0.900, 0.933, 0.928, and 0.918, respectively. The corresponding MADs (percentage difference) were 1.44 ± 1.21 L (12.21% ± 11.70%), 0.63 ± 0.49 L (29.68% ± 61.99%), 0.12 ± 0.09 L (16.20% ± 18.42%), 1.65 ± 1.40 L (10.43% ± 10.79%), 0.71 ± 0.68 L (5.14% ± 4.75%), and 0.17 ± 0.15 L (4.32% ± 3.38%), respectively., Conclusion: Our algorithm shows promise in its ability to estimate whole-body compositions from chest CT scans. Body composition measures based on chest CT scans are more accurate than those based on vertebra third lumbar., (© 2022 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Objective: To develop and validate a novel convolutional neural network (CNN) termed "Super U-Net" for medical image segmentation., Methods: Super U-Net integrates a dynamic receptive field module and a fusion upsampling module into the classical U-Net architecture. The model was developed and tested to segment retinal vessels, gastrointestinal (GI) polyps, skin lesions on several image types (i.e., fundus images, endoscopic images, dermoscopic images). We also trained and tested the traditional U-Net architecture, seven U-Net variants, and two non-U-Net segmentation architectures. K-fold cross-validation was used to evaluate performance. The performance metrics included Dice similarity coefficient (DSC), accuracy, positive predictive value (PPV), and sensitivity., Results: Super U-Net achieved average DSCs of 0.808±0.0210, 0.752±0.019, 0.804±0.239, and 0.877±0.135 for segmenting retinal vessels, pediatric retinal vessels, GI polyps, and skin lesions, respectively. The Super U-net consistently outperformed U-Net, seven U-Net variants, and two non-U-Net segmentation architectures (p < 0.05)., Conclusion: Dynamic receptive fields and fusion upsampling can significantly improve image segmentation performance., Competing Interests: Declaration of Interests The authors have no conflicts of interest to declare.

Background Clustering key clinical characteristics of participants in the Severe Asthma Research Program (SARP), a large, multicenter prospective observational study of patients with asthma and healthy controls, has led to the identification of novel asthma phenotypes. Purpose To determine whether quantitative CT (qCT) could help distinguish between clinical asthma phenotypes. Materials and Methods A retrospective cross-sectional analysis was conducted with the use of qCT images (maximal bronchodilation at total lung capacity [TLC], or inspiration, and functional residual capacity [FRC], or expiration) from the cluster phenotypes of SARP participants (cluster 1: minimal disease; cluster 2: mild, reversible; cluster 3: obese asthma; cluster 4: severe, reversible; cluster 5: severe, irreversible) enrolled between September 2001 and December 2015. Airway morphometry was performed along standard paths (RB1, RB4, RB10, LB1, and LB10). Corresponding voxels from TLC and FRC images were mapped with use of deformable image registration to characterize disease probability maps (DPMs) of functional small airway disease (fSAD), voxel-level volume changes (Jacobian), and isotropy (anisotropic deformation index [ADI]). The association between cluster assignment and qCT measures was evaluated using linear mixed models. Results A total of 455 participants were evaluated with cluster assignments and CT (mean age ± SD, 42.1 years ± 14.7; 270 women). Airway morphometry had limited ability to help discern between clusters. DPM fSAD was highest in cluster 5 (cluster 1 in SARP III: 19.0% ± 20.6; cluster 2: 18.9% ± 13.3; cluster 3: 24.9% ± 13.1; cluster 4: 24.1% ± 8.4; cluster 5: 38.8% ± 14.4; P < .001). Lower whole-lung Jacobian and ADI values were associated with greater cluster severity. Compared to cluster 1, cluster 5 lung expansion was 31% smaller (Jacobian in SARP III cohort: 2.31 ± 0.6 vs 1.61 ± 0.3, respectively, P < .001) and 34% more isotropic (ADI in SARP III cohort: 0.40 ± 0.1 vs 0.61 ± 0.2, P < .001). Within-lung Jacobian and ADI SDs decreased as severity worsened (Jacobian SD in SARP III cohort: 0.90 ± 0.4 for cluster 1; 0.79 ± 0.3 for cluster 2; 0.62 ± 0.2 for cluster 3; 0.63 ± 0.2 for cluster 4; and 0.41 ± 0.2 for cluster 5; P < .001). Conclusion Quantitative CT assessments of the degree and intraindividual regional variability of lung expansion distinguished between well-established clinical phenotypes among participants with asthma from the Severe Asthma Research Program study. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Verschakelen in this issue.

We present a novel integrative computerized solution to automatically identify and differentiate pulmonary arteries and veins depicted on chest computed tomography (CT) without iodinated contrast agents. We first identified the central extrapulmonary arteries and veins using a convolutional neural network (CNN) model. Then, a computational differential geometry method was used to automatically identify the tubular-like structures in the lungs with high densities, which we believe are the intrapulmonary vessels. Beginning with the extrapulmonary arteries and veins, we progressively traced the intrapulmonary vessels by following their skeletons and differentiated them into arteries and veins. Instead of manually labeling the numerous arteries and veins in the lungs for machine learning, this integrative strategy limits the manual effort only to the large extrapulmonary vessels. We used a dataset consisting of 120 chest CT scans acquired on different subjects using various protocols to develop, train, and test the algorithms. Our experiments on an independent test set (n = 15) showed promising performance. The computer algorithm achieved a sensitivity of ∼98% in labeling the pulmonary artery and vein branches when compared with a human expert's results, demonstrating the feasibility of our computerized solution in pulmonary artery/vein labeling., Competing Interests: Declaration of Competing Interest None., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Background: Sarcoidosis is a multisystem granulomatous disease of unknown origin with a variable and often unpredictable course and pattern of organ involvement. In this study we sought to identify specific bronchoalveolar lavage (BAL) cell gene expression patterns indicative of distinct disease phenotypic traits., Methods: RNA sequencing by Ion Torrent Proton was performed on BAL cells obtained from 215 well-characterised patients with pulmonary sarcoidosis enrolled in the multicentre Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis (GRADS) study. Weighted gene co-expression network analysis and nonparametric statistics were used to analyse genome-wide BAL transcriptome. Validation of results was performed using a microarray expression dataset of an independent sarcoidosis cohort (Freiburg, Germany; n=50)., Results: Our supervised analysis found associations between distinct transcriptional programmes and major pulmonary phenotypic manifestations of sarcoidosis including T-helper type 1 (Th1) and Th17 pathways associated with hilar lymphadenopathy, transforming growth factor-β1 (TGFB1) and mechanistic target of rapamycin (MTOR) signalling with parenchymal involvement, and interleukin (IL)-7 and IL-2 with airway involvement. Our unsupervised analysis revealed gene modules that uncovered four potential sarcoidosis endotypes including hilar lymphadenopathy with increased acute T-cell immune response; extraocular organ involvement with PI3K activation pathways; chronic and multiorgan disease with increased immune response pathways; and multiorgan involvement, with increased IL-1 and IL-18 immune and inflammatory responses. We validated the occurrence of these endotypes using gene expression, pulmonary function tests and cell differentials from Freiburg., Conclusion: Taken together, our results identify BAL gene expression programmes that characterise major pulmonary sarcoidosis phenotypes and suggest the presence of distinct disease molecular endotypes., Competing Interests: Conflict of interest: M. Vukmirovic has nothing to disclose. Conflict of interest: X. Yan has nothing to disclose. Conflict of interest: K.F. Gibson has nothing to disclose. Conflict of interest: M. Gulati reports grants from NIH, during the conduct of the study; personal fees for advisory board work and other (PI/publication committee) from Boehringer Ingelheim, other (lectures) from France Foundation, other (PI/centre director) from Pulmonary Fibrosis Foundation, grants from NIH and Sarcoidosis Research Foundation, outside the submitted work. Conflict of interest: J.C. Schupp has nothing to disclose. Conflict of interest: G. DeIuliis has nothing to disclose. Conflict of interest: T.S. Adams has nothing to disclose. Conflict of interest: B. Hu has nothing to disclose. Conflict of interest: A. Mihaljinec has nothing to disclose. Conflict of interest: T.N. Woolard has nothing to disclose. Conflict of interest: H. Lynn has nothing to disclose. Conflict of interest: N. Emeagwali has nothing to disclose. Conflict of interest: E.L Herzog reports grants from NIH, Sanofi, Bristol Myers and Promedior, personal fees for consultancy from Boehringer Ingelheim and Pfizer, outside the submitted work. Conflict of interest: E.S. Chen has nothing to disclose. Conflict of interest: A. Morris reports grants from NIH, during the conduct of the study. Conflict of interest: J.K. Leader has nothing to disclose. Conflict of interest: Y. Zhang has nothing to disclose. Conflict of interest: J.G.N. Garcia has nothing to disclose. Conflict of interest: L.A. Maier grants from NIH (1U01 HL112695-01, U01 HL112707-03) and NIH/NCRR (UL1TRR002535), during the conduct of the study; grants from National Institutes of Health (1R01 HL127461-01A1, R01HL136681-01A1, 1R01 HL140357-01A1, R01HL136681-01A1), FSR, University of Cinncinati under a Mallinckrodt foundation, MNK14344100, ATYR1923-C-002, outside the submitted work; and is a member of the FSR scientific advisory board, for which no compensation is received. Conflict of interest: R.G. Collman reports grants from National Institutes of Health, during the conduct of the study. Conflict of interest: W.P. Drake has nothing to disclose. Conflict of interest: M.J. Becich reports grants from NCATS, NCI, PCORI, NHLBI and CDC NIOSH, other (startup) from SpIntellx, during the conduct of the study; other (startup) from SpIntellx, outside the submitted work; and has patents SpIntellx (multiple) pending. Conflict of interest: H. Hochheiser has nothing to disclose. Conflict of interest: S.R. Wisniewski has nothing to disclose. Conflict of interest: P.V. Benos has nothing to disclose. Conflict of interest: D.R. Moller reports grants from NHLBI (1U01HL112708), during the conduct of the study; personal fees for consultancy from Merck, aTYR and Roivant, personal fees for advisory board work from SarcoMed, personal fees for consultancy/witness from Legal Expert, other (royalties) from Hodder Education and Taylor & Francis Group, outside the submitted work; has patents number 9,683,999 B2 issued, and number 9,977,029 B2 issued; is Chairman and Chief Technical Officer of Sarcoidosis Diagnostic Testing, LLC (a company whose goal is to develop a diagnostic blood test for sarcoidosis) and has received funding including past salary support under the NHLBI STTR programme, grant R41 HL129728 more than 3 years ago; and is a former member of the Scientific Advisory Board of the Foundation for Sarcoidosis Research. Conflict of interest: A. Prasse reports personal fees for lectures and consultancy and non-financial support for meeting attendance from Boehringer Ingelheim and Roche, personal fees for lectures from Novartis and AstraZeneca, personal fees for consultancy from Amgen, Pliant and Nitto Denko, outside the submitted work. Conflict of interest: L.L. Koth has nothing to disclose. Conflict of interest: N. Kaminski reports personal fees for consultancy and/or advisory board work from Biogen Idec, Boehringer Ingelheim, Third Rock, Samumed, NuMedii, Indaloo, Theravance, LifeMax, Three Lake Partners, RohBar and Pliant, non-financial support from Miragen, equity with Pliant, a grant from Veracyte; all outside the submitted work; and has a patent New Therapies in Pulmonary Fibrosis and on Peripheral Blood Gene Expression that have been licensed to Biotech., (Copyright ©The authors 2021. For reproduction rights and permissions contact permissions@ersnet.org.)

Purpose: To investigate the relationship between macrovasculature features and the standardized uptake value (SUV) of positron emission tomography (PET), which is a surrogate for the metabolic activity of a lung tumor., Methods: We retrospectively analyzed a cohort of 90 lung cancer patients who had both chest CT and PET-CT examinations before receiving cancer treatment. The SUVs in the medical reports were used. We quantified three macrovasculature features depicted on CT images (i.e., vessel number, vessel volume, and vessel tortuosity) and several tumor features (i.e., volume, maximum diameter, mean diameter, surface area, and density). Tumor size (e.g., volume) was used as a covariate to adjust for possible confounding factors. Backward stepwise multiple regression analysis was performed to develop a model for predicting PET SUV from the relevant image features. The Bonferroni correction was used for multiple comparisons., Results: PET SUV was positively correlated with vessel volume (R = 0.44, p < 0.001) and vessel number (R = 0.44, p < 0.001) but not with vessel tortuosity (R = 0.124, p > 0.05). After adjusting for tumor size, PET SUV was significantly correlated with vessel tortuosity (R = 0.299, p = 0.004) and vessel number (R = 0.224, p = 0.035), but only marginally correlated with vessel volume (R = 0.187, p = 0.079). The multiple regression model showed a performance with an R-Squared of 0.391 and an adjusted R-Squared of 0.355 (p < 0.001)., Conclusions: Our investigations demonstrate the potential relationship between macrovasculature and PET SUV and suggest the possibility of inferring the metabolic activity of a lung tumor from chest CT images., (© 2021 American Association of Physicists in Medicine.)

Background: Smokers manifest varied phenotypes of pulmonary impairment., Research Question: Which pulmonary phenotypes are associated with coronary artery disease (CAD) in smokers?, Study Design and Methods: We analyzed data from the University of Pittsburgh COPD Specialized Center for Clinically Oriented Research (SCCOR) cohort (n = 481) and the Genetic Epidemiology of COPD (COPDGene) cohort (n = 2,580). Participants were current and former smokers with > 10 pack-years of tobacco exposure. Data from the two cohorts were analyzed separately because of methodologic differences. Lung hyperinflation was assessed by plethysmography in the SCCOR cohort and by inspiratory and expiratory CT scan lung volumes in the COPDGene cohort. Subclinical CAD was assessed as the coronary artery calcium score, whereas clinical CAD was defined as a self-reported history of CAD or myocardial infarction (MI). Analyses were performed in all smokers and then repeated in those with airflow obstruction (FEV 1 to FVC ratio, < 0.70)., Results: Pulmonary phenotypes, including airflow limitation, emphysema, lung hyperinflation, diffusion capacity, and radiographic measures of airway remodeling, showed weak to moderate correlations (r < 0.7) with each other. In multivariate models adjusted for pulmonary phenotypes and CAD risk factors, lung hyperinflation was the only phenotype associated with calcium score, history of clinical CAD, or history of MI (per 0.2 higher expiratory and inspiratory CT scan lung volume; coronary calcium: OR, 1.2; 95% CI, 1.1-1.5; P = .02; clinical CAD: OR, 1.6; 95% CI, 1.1-2.3; P = .01; and MI in COPDGene: OR, 1.7; 95% CI, 1.0-2.8; P = .05). FEV 1 and emphysema were associated with increased risk of CAD (P < .05) in models adjusted for CAD risk factors; however, these associations were attenuated on adjusting for lung hyperinflation. Results were the same in those with airflow obstruction and were present in both cohorts., Interpretation: Lung hyperinflation is associated strongly with clinical and subclinical CAD in smokers, including those with airflow obstruction. After lung hyperinflation was accounted for, FEV 1 and emphysema no longer were associated with CAD. Subsequent studies should consider measuring lung hyperinflation and examining its mechanistic role in CAD in current and former smokers., (Copyright © 2021. Published by Elsevier Inc.)

Purpose: The potential to compute volume metrics of emphysema from planar scout images was investigated in this study. The successful implementation of this concept will have a wide impact in different fields, and specifically, maximize the diagnostic potential of the planar medical images., Methods: We investigated our premise using a well-characterized chronic obstructive pulmonary disease (COPD) cohort. In this cohort, planar scout images from computed tomography (CT) scans were used to compute lung volume and percentage of emphysema. Lung volume and percentage of emphysema were quantified on the volumetric CT images and used as the "ground truth" for developing the models to compute the variables from the corresponding scout images. We trained two classical convolutional neural networks (CNNs), including VGG19 and InceptionV3, to compute lung volume and the percentage of emphysema from the scout images. The scout images (n = 1,446) were split into three subgroups: (1) training (n = 1,235), (2) internal validation (n = 99), and (3) independent test (n = 112) at the subject level in a ratio of 8:1:1. The mean absolute difference (MAD) and R-square (R2) were the performance metrics to evaluate the prediction performance of the developed models., Results: The lung volumes and percentages of emphysema computed from a single planar scout image were significantly linear correlated with the measures quantified using volumetric CT images (VGG19: R2 = 0.934 for lung volume and R2 = 0.751 for emphysema percentage, and InceptionV3: R2 = 0.977 for lung volume and R2 = 0.775 for emphysema percentage). The mean absolute differences (MADs) for lung volume and percentage of emphysema were 0.302 ± 0.247L and 2.89 ± 2.58%, respectively, for VGG19, and 0.366 ± 0.287L and 3.19 ± 2.14, respectively, for InceptionV3., Conclusions: Our promising results demonstrated the feasibility of inferring volume metrics from planar images using CNNs., (© 2021 American Association of Physicists in Medicine.)

Objectives: To develop and validate a deep learning algorithm to automatically detect and segment an orbital abscess depicted on computed tomography (CT)., Methods: We retrospectively collected orbital CT scans acquired on 67 pediatric subjects with a confirmed orbital abscess in the setting of infectious orbital cellulitis. A context-aware convolutional neural network (CA-CNN) was developed and trained to automatically segment orbital abscess. To reduce the requirement for a large dataset, transfer learning was used by leveraging a pre-trained model for CT-based lung segmentation. An ophthalmologist manually delineated orbital abscesses depicted on the CT images. The classical U-Net and the CA-CNN models with and without transfer learning were trained and tested on the collected dataset using the 10-fold cross-validation method. Dice coefficient, Jaccard index, and Hausdorff distance were used as performance metrics to assess the agreement between the computerized and manual segmentations., Results: The context-aware U-Net with transfer learning achieved an average Dice coefficient and Jaccard index of 0.78 ± 0.12 and 0.65 ± 0.13, which were consistently higher than the classical U-Net or the context-aware U-Net without transfer learning (P < 0.01). The average differences of the abscess between the computerized results and the experts in terms of volume and Hausdorff distance were 0.10 ± 0.11 mL and 1.94 ± 1.21 mm, respectively. The context-aware U-Net detected all orbital abscess without false positives., Conclusions: The deep learning solution demonstrated promising performance in detecting and segmenting orbital abscesses on CT images in strong agreement with a human observer., (© 2021 American Association of Physicists in Medicine.)

Introduction: Our objective was to assess the utility of using lesion-mapping software to calculate precise orbital volumes to predict patients who would benefit from early surgical intervention., Methods: We retrospectively reviewed patients diagnosed with subperiosteal orbital abscess (SPOA) secondary to sinusitis at a tertiary pediatric hospital from 2005 to 2016. Diagnoses were confirmed by CT scans. Lesion-mapping software was used to measure SPOA volume using initial CT images. Data collected included patient demographics, length of hospital stay, and subsequent medical or surgical treatment., Results: Thirty-three (52%) patients ultimately underwent surgical intervention, while 30 (48%) were managed medically. Between the surgical and medical groups, there were no differences in gender, age, or comorbidities. The surgical group had larger abscess volumes than the medically managed group (0.94 mL vs. 0.31 mL, p < 0.01). Overall, increased SPOA volume was associated with increased age (Pearson's coefficient = 0.374, p ≤ 0.01) and increased total days of intravenous (IV) antibiotic administration (Pearson's coefficient = 0.260, p = 0.039). Patients who underwent surgery on the day of admission had 25% shorter hospital stay than patients who had delayed surgery (p < 0.01). Our calculated sensitivity-optimized SPOA volume cutoff of 0.231 mL yielded sensitivity of 90.9% and specificity of 70.0%., Conclusions: This is the first study to use lesion-mapping software for precise calculation of SPOA volumes, which can help refine indications for early surgical intervention and help decrease length of hospital stay.

Background: Pentraxin 3 (PTX3) influences innate immunity and inflammation, host defence, the complement cascade and angiogenesis. PTX3 expression in lung and blood of subjects with tobacco exposure, and its potential relationship with disease pattern and clinical outcome are poorly understood., Methods: Using independent platforms and cohorts, we identified associations of PTX3 gene expression in lung tissue and plasma from current and former tobacco smokers (with and without chronic obstructive pulmonary disease, COPD) to disease phenotypes including quantitative CT determined emphysema, lung function, symptoms and survival. Two putative regulatory variants of the PTX3 gene were examined for association with COPD manifestations. The relationship between plasma PTX3 and hyaluronic acid levels was further examined., Results: PTX3 gene expression in lung tissue was directly correlated with emphysema severity (p<0.0001). Circulating levels of PTX3 were inversely correlated with FEV 1 (p=0.006), and positively associated with emphysema severity (p=0.004) and mortality (p=0.008). Two PTX3 gene regulatory variants were associated with a lower risk for emphysema and expiratory airflow obstruction, and plasma levels of PTX3 and hyaluronic acid were related., Conclusions: These data show strong and overlapping associations of lung and blood PTX3 levels, and PTX3 regulatory gene variants, with the severity of airflow obstruction, emphysema and mortality among smokers. These findings have potential implications regarding the pathogenesis of smoking-related lung diseases and warrant further exploration for the use of PTX3 as a predictive biomarker., Competing Interests: Competing interests: NK reports personal fees from Biogen Idec, Boehringer Ingelheim, Third Rock, Pliant, Samumed, uMedii, Indaloo, Theravance, LifeMax, Three Lake Partners, RohBar; Non-financial support from Miragen and a research grant from Veracyte. NK has a patent New Therapies in Pulmonary Fibrosis and a patent on Peripheral Blood Gene Expression that is licensed to Biotech. All are outside this work., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Alpha-1 antitrypsin deficiency (AATD) is a genetic condition that causes early onset pulmonary emphysema and airways obstruction. The complete mechanisms via which AATD causes lung disease are not fully understood. To improve our understanding of the pathogenesis of AATD, we investigated gene expression profiles of bronchoalveolar lavage (BAL) and peripheral blood mononuclear cells (PBMCs) in AATD individuals., Methods: We performed RNA-Seq on RNA extracted from matched BAL and PBMC samples isolated from 89 subjects enrolled in the Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis (GRADS) study. Subjects were stratified by genotype and augmentation therapy. Supervised and unsupervised differential gene expression analyses were performed using Weighted Gene Co-expression Network Analysis (WGCNA) to identify gene profiles associated with subjects' clinical variables. The genes in the most significant WGCNA module were used to cluster AATD individuals. Gene validation was performed by NanoString nCounter Gene Expression Assay., Result: We observed modest effects of AATD genotype and augmentation therapy on gene expression. When WGCNA was applied to BAL transcriptome, one gene module, ME31 (2312 genes), correlated with the highest number of clinical variables and was functionally enriched with numerous immune T-lymphocyte related pathways. This gene module identified two distinct clusters of AATD individuals with different disease severity and distinct PBMC gene expression patterns., Conclusions: We successfully identified novel clusters of AATD individuals where severity correlated with increased immune response independent of individuals' genotype and augmentation therapy. These findings may suggest the presence of previously unrecognised disease endotypes in AATD that associate with T-lymphocyte immunity and disease severity., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: To develop and test computer software to detect, quantify, and monitor progression of pneumonia associated with COVID-19 using chest CT scans., Methods: One hundred twenty chest CT scans from subjects with lung infiltrates were used for training deep learning algorithms to segment lung regions and vessels. Seventy-two serial scans from 24 COVID-19 subjects were used to develop and test algorithms to detect and quantify the presence and progression of infiltrates associated with COVID-19. The algorithm included (1) automated lung boundary and vessel segmentation, (2) registration of the lung boundary between serial scans, (3) computerized identification of the pneumonitis regions, and (4) assessment of disease progression. Agreement between radiologist manually delineated regions and computer-detected regions was assessed using the Dice coefficient. Serial scans were registered and used to generate a heatmap visualizing the change between scans. Two radiologists, using a five-point Likert scale, subjectively rated heatmap accuracy in representing progression., Results: There was strong agreement between computer detection and the manual delineation of pneumonic regions with a Dice coefficient of 81% (CI 76-86%). In detecting large pneumonia regions (> 200 mm 3 ), the algorithm had a sensitivity of 95% (CI 94-97%) and specificity of 84% (CI 81-86%). Radiologists rated 95% (CI 72 to 99) of heatmaps at least "acceptable" for representing disease progression., Conclusion: The preliminary results suggested the feasibility of using computer software to detect and quantify pneumonic regions associated with COVID-19 and to generate heatmaps that can be used to visualize and assess progression., Key Points: • Both computer vision and deep learning technology were used to develop computer software to quantify the presence and progression of pneumonia associated with COVID-19 depicted on CT images. • The computer software was tested using both quantitative experiments and subjective assessment. • The computer software has the potential to assist in the detection of the pneumonic regions, monitor disease progression, and assess treatment efficacy related to COVID-19.

Introduction: Inverse associations of cardiovascular disease (CVD) and atherosclerosis with osteoporosis and bone mineral density (BMD) have been reported in post-menopausal women and elderly men. We aimed to investigate an association between vetebral bone density (VBD) and coronary artery cacification (CAC) in an international multi-ethnic cohort of middle-aged men in the EBCT and Risk Factor Assessment among Japanese and US Men in the Post-World-War-II birth cohort (ERA JUMP)., Methods: ERA JUMP examined 1134 men aged 40-49 (267 white, 84 black, and 242 Japanese Americans, 308 Japanese in Japan, and 233 Koreans in South Korea) free from CVD for CAC, and VBD, biomarkers of coronary atherosclerosis and BMD, respectively, with electron-beam computed tomography, and other risk factors. CAC was quantified with the Agatston method and VBD by computing the mean Hounsfield Unit (HU) value of the T12 to L3 vertebrae. To examine multivariable-adjusted associations of CAC with VBD, we used robust linear and logistic regressions., Results: The mean VBD and median CAC were 175.4 HU (standard deviation: 36.3) and 0 (interquartile range: (0, 4.5)), respectively. The frequency of CAC was 19.0%. There was no significant interaction by race. VBD had a significant inverse association with CAC score (β = -0.207, p-value = 0.005), while a 10-unit increase in VBD was significantly associated with the frequency of CAC (odds ratio (95% confidence interval) = 0.929 (0.890-0.969)). Both associations remained significant after adjusting for covariates., Conclusions: VBD had a significant inverse association with CAC in this international multi-ethnic cohort of men aged 40-49., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2020 Published by Elsevier B.V.)

Atherosclerosis prevalence is increased in chronic obstructive pulmonary disease (COPD) patients, independent of other risk factors. The etiology of the excess vascular disease in COPD is unknown, although it is presumably related to an underlying (if cryptic) systemic immune response. Autoantibodies with specificity for glucose-regulated protein 78 (GRP78), a multifunctional component of the unfolded protein response, are common in COPD patients and linked to comorbidities of this lung disease. We hypothesized anti-GRP78 autoreactivity might also be a risk factor for atherosclerosis in COPD patients. Carotid intima-medial thickness (cIMT) was measured in 144 current and former smokers by ultrasound. Concentrations of circulating IgG autoantibodies against full-length GRP78, determined by ELISA, were greater among subjects with abnormally increased cIMT ( p < 0.01). Plasma levels of autoantibodies against a singular GRP78 peptide segment, amino acids 246-260 (anti-GRP78 aa 246-260 ), were even more highly correlated with cIMT, especially among males with greater than or equal to moderate COPD ( r s = 0.62, p = 0.001). Anti-GRP78 aa 246-260 concentrations were independent of CRP, IL-6, and TNF-α levels. GRP78 autoantigen expression was upregulated among human aortic endothelial cells (HAECs) stressed by incubation with tunicamycin (an unfolded protein response inducer) or exposure to culture media flow disturbances. Autoantibodies against GRP78 aa 246-260 , isolated from patient plasma by immunoprecipitation, induced HAEC production of proatherosclerotic mediators, including IL-8. In conclusion, anti-GRP78 autoantibodies are highly associated with carotid atherosclerosis in COPD patients and exert atherogenic effects on HAECs. These data implicate Ag-specific autoimmunity in the pathogenesis of atherosclerosis among COPD patients and raise possibilities that directed autoantibody reduction might ameliorate vascular disease in this high-risk population., (Copyright © 2020 The Authors.)

Background: Persons living with HIV (PLWH) are at risk of developing different phenotypes of chronic lung disease, including chronic obstructive pulmonary disease. Mechanisms underlying these phenotypes are unclear., Objective: To identify clusters of peripheral inflammatory mediators associated with pulmonary function to determine inflammatory pathways and phenotypes of chronic obstructive pulmonary disease in PLWH and HIV-uninfected individuals., Methods: Study participants were PLWH and HIV-uninfected individuals enrolled in the Pittsburgh HIV Lung Cohort. Pulmonary function tests were performed for all participants. Chest computed tomographic scans were performed in a subset of PLWH. Plasma levels of 19 inflammatory mediators were measured by Luminex or ELISA. Clusters were identified based on the expression pattern of inflammatory mediators in PLWH and HIV-uninfected individuals, and the relationships among clinical parameters were evaluated within clusters by using cluster and network analyses., Results: In PLWH, we identified a distinct cluster with higher levels of Th1, Th2, and Th17 inflammatory mediators with increased complexity of these mediators and inferred presence of pathogenic Th17 cell types. Individuals in this cluster had worse airway obstruction and more radiographic emphysema. In HIV-uninfected individuals, a cluster with high-grade systemic inflammation also had worse diffusing capacity for carbon monoxide., Conclusions: Inflammatory pathways associated with pulmonary dysfunction in PLWH suggest multifaceted immune dysregulation involved in different phenotypes of pulmonary dysfunction with a potential specific contribution of the Th17 pathway to airway obstruction in PLWH. Identification of these associations may help in development of treatments that could alter the course of the disease.

Introduction: Low-dose CT (LDCT) is currently used in lung cancer screening of high-risk populations for early lung cancer diagnosis. However, 96% of individuals with detected nodules are false positives., Methods: In order to develop an efficient early lung cancer predictor from clinical, demographic and LDCT features, we studied a total of 218 subjects with lung cancer or benign nodules. Probabilistic graphical models (PGMs) were used to integrate demographics, clinical data and LDCT features from 92 subjects (training cohort) from the Pittsburgh Lung Screening Study cohort., Results: Learnt PGMs identified three variables directly (causally) linked to malignant nodules and the largest benign nodule and used them to build the Lung Cancer Causal Model (LCCM), which was validated in a separate cohort of 126 subjects. Nodule and vessel numbers and years since the subject quit smoking were sufficient to discriminate malignant from benign nodules. Comparison with existing predictors in the training and validation cohorts showed that (1) incorporating LDCT scan features greatly enhances predictive accuracy; and (2) LCCM improves cancer detection over existing methods, including the Brock parsimonious model (p < 0.001). Notably, the number of surrounding vessels, a feature not previously used in predictive models, significantly improves predictive efficiency. Based on the validation cohort results, LCCM is able to identify 30% of the benign nodules without risk of misclassifying cancer nodules., Discussion: LCCM shows promise as a lung cancer predictor as it is significantly improved over existing models. Validated in a larger, prospective study, it may help reduce unnecessary follow-up visits and procedures., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Elastin breakdown and the resultant loss of lung elastic recoil is a hallmark of pulmonary emphysema in susceptible individuals as a consequence of tobacco smoke exposure. Systemic alterations to the synthesis and degradation of elastin may be important to our understanding of disease phenotypes in chronic obstructive pulmonary disease. We investigated the association of skin elasticity with pulmonary emphysema, obstructive lung disease, and blood biomarkers of inflammation and tissue protease activity in tobacco-exposed individuals., Methods: Two hundred and thirty-six Caucasian individuals were recruited into a sub-study of the University of Pittsburgh Specialized Center for Clinically Orientated Research in chronic obstructive pulmonary disease, a prospective cohort study of current and former smokers. The skin viscoelastic modulus (VE), a determinant of skin elasticity, was recorded from the volar forearm and facial wrinkling severity was determined using the Daniell scoring system., Results: In a multiple regression analysis, reduced VE was significantly associated with cross-sectional measurement of airflow obstruction (FEV1/FVC) and emphysema quantified from computed tomography (CT) images, β = 0.26, p = 0.001 and β = 0.24, p = 0.001 respectively. In emphysema-susceptible individuals, elasticity-determined skin age was increased (median 4.6 years) compared to the chronological age of subjects without emphysema. Plasma biomarkers of inflammation (TNFR1, TNFR2, CRP, PTX3, and SAA) and matrix metalloproteinase activity (MMP1, TIMP1, TIMP2, and TIMP4) were inversely associated with skin elasticity., Conclusions: We report that an objective non-invasive determinant of skin elasticity is independently associated with measures of lung function, pulmonary emphysema, and biomarkers of inflammation and tissue proteolysis in tobacco-exposed individuals. Loss of skin elasticity is a novel observation that may link the common pathological processes that drive tissue elastolysis in the extracellular matrix of the skin and lung in emphysema-susceptible individuals.

Objectives: The idea of inferring the prognosis of lung tumor via its surrounding vasculature is novel, but not supported by available technology. In this study, we described and validated a computerized method to identify the vasculature surrounding a pulmonary nodule depicted on low-dose computed tomography (LDCT)., Materials and Methods: The proposed computerized scheme identified the vessels surrounding a lung nodule by using novel computational geometric solutions and quantified them by decomposing the vessels into independent vessel branches. We validated this scheme by testing it on a dataset consisting of 100 chest CT examinations, with 50-paired benign and malignant nodules. Two experienced pulmonologists were asked to measure the vessel branches associated with a nodule under the aid of a visualization tool. We used the Bland-Altman plots and the concordance correlation coefficient (CCC) to assess the agreement between the results of the computer algorithm and two experienced pulmonologists., Results: Bland-Altman different analysis demonstrated a mean bias of 0.61 ± 4.17 in terms of vessel branches between the computer results and the human experts, while the inter-rater mean bias was -0.61 ± 1.60. The CCC-based agreements between the computer and the two raters were 0.90 / 0.86, 0.79 / 0.83 for benign and malignant nodules, respectively., Conclusion: The small width of the limits of agreement between the computer algorithm and the human experts suggests that the results from the computer and the pulmonologist experts were relatively consistent, namely the computerized scheme is capable of reliably identifying the vasculature surrounding a nodule., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Rationale: Impaired lung function is a potent independent predictor of coronary artery disease (CAD) in individuals without human immunodeficiency virus (HIV) infection; however, the relationship between lung function and CAD in HIV remains undefined. Objectives: To examine the relationship between lung function, CAD, mortality, and circulating biomarkers in HIV. Methods: Spirometry, diffusing capacity of the lung for carbon monoxide (Dl CO ), emphysema, coronary artery calcium, mortality, cause of death, and biomarkers were examined in HIV-infected and uninfected individuals enrolled in a cohort study at the University of Pittsburgh. Results were then validated in the Multicenter AIDS Cohort Study (MACS) cohort. Results: We examined data on 234 participants in the Pittsburgh cohort. The mean ± standard deviation age was 49.5 ± 10.2 years old, 82.1% were male, and 67.5% were ever smokers. Among the 177 of 234 individuals with HIV infection, lower Dl CO (not forced expiratory volume in 1 second or emphysema) was independently associated with greater coronary artery calcium (odds ratio, 1.43 per 10% lower Dl CO ; 95% confidence interval, 1.14-1.81). HIV-infected individuals with both reduced Dl CO and coronary artery calcium had a much higher mortality than those with either low Dl CO or coronary calcium alone or with neither condition. Endothelin-1, a circulating biomarker of endothelial dysfunction, was associated with both lower Dl CO and greater coronary artery calcium in those with HIV infection. Results were reproducible in 144 individuals enrolled in the MACS cohort; intercellular adhesion molecule 1 was the biomarker of endothelial dysfunction assessed in the MACS cohort. Conclusions: Impaired Dl CO and CAD were associated with each other and with higher mortality in individuals with HIV infection.

Rationale: Osteoporosis is common in individuals with chronic obstructive pulmonary disease. Lung-specific factors, including radiographic emphysema, independently associate with low bone mineral density in cross-sectional smoking cohorts. However, factors associated with progressive bone loss in smokers are understudied and largely unknown., Objectives: To determine the relationship between radiographic emphysema, circulating bone metabolism markers, and pulmonary function and accelerated bone mineral density loss in smokers., Methods: Two hundred and forty male and female current and former smokers, 40 years of age or older, underwent baseline and 2-year assessments of pulmonary function, computed tomography-assessed emphysema, dual X-ray absorptiometry-measured bone mineral density, and circulating bone metabolism biomarker levels (type I collagen C-telopeptide [CTX], amino-terminal propeptide of type I procollagen [P1NP]). The association of radiographic emphysema, bone metabolism biomarker levels, and pulmonary function with accelerated hip bone mineral density loss, defined by the 75th percentile of annual hip bone mineral density decline, was determined by logistic regression modeling with adjustment for age, sex, inhaled and intermittent steroid use, active smoking, body mass index, and the presence of baseline low hip bone mineral density., Results: Of those participants with accelerated hip bone mineral density loss, 22% had moderate or severe visually assessed emphysema compared with 7.2% of smokers without accelerated bone mineral density decline. Moderate to severe visually assessed emphysema (odds ratio, 2.84; 95% confidence interval, 1.01-7.98 compared with trace/mild or no visually assessed emphysema) and the 75th percentile of CTX levels (odds ratio, 2.38; 95% confidence interval, 1.20-4.72 compared with CTX levels below the 75th percentile), a marker of bone resorption, were associated with accelerated hip bone mineral density decline after adjustment for covariates and the presence of baseline low hip bone mineral density. FEV 1 % predicted was not associated with accelerated bone mineral density decline after adjustment for covariates. Multivariate modeling showed moderate to severe visually assessed emphysema, and the 75th percentiles of CTX were independently associated with accelerated hip bone mineral density decline after adjustment for covariates., Conclusions: Emphysema and elevated markers of bone resorption are independently associated with progressive bone mineral density loss in smokers. These clinical markers may guide targeted bone mineral density screening and monitoring in smokers at highest risk.

Purpose: To investigate whether the vessels surrounding a nodule depicted on non-contrast, low-dose computed tomography (LDCT) can discriminate benign and malignant screen detected nodules., Materials and Methods: We collected a dataset consisting of LDCT scans acquired on 100 subjects from the Pittsburgh Lung Screening study (PLuSS). Fifty subjects were diagnosed with lung cancer and 50 subjects had suspicious nodules later proven benign. For the lung cancer cases, the location of the malignant nodule in the LDCT scans was known; while for the benign cases, the largest nodule in the LDCT scan was used in the analysis. A computer algorithm was developed to identify surrounding vessels and quantify the number and volume of vessels that were connected or near the nodule. A nonparametric receiver operating characteristic (ROC) analysis was performed based on a single nodule per subject to assess the discriminability of the surrounding vessels to provide a lung cancer diagnosis. Odds ratio (OR) were computed to determine the probability of a nodule being lung cancer based on the vessel features., Results: The areas under the ROC curves (AUCs) for vessel count and vessel volume were 0.722 (95% CI=0.616-0.811, p<0.01) and 0.676 (95% CI=0.565-0.772), respectively. The number of vessels attached to a nodule was significantly higher in the lung cancer group 9.7 (±9.6) compared to the non-lung cancer group 4.0 (±4.3) CONCLUSION: Our preliminary results showed that malignant nodules are often surrounded by more vessels compared to benign nodules, suggesting that the surrounding vessel characteristics could serve as lung cancer biomarker for indeterminate nodules detected during LDCT lung cancer screening using only the information collected during the initial visit., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Rationale: Genetic association studies in chronic obstructive pulmonary disease have primarily tested for association with common variants, the results of which explain only a portion of disease heritability. Because rare variation is also likely to contribute to susceptibility, we used whole-genome sequencing of subjects with clinically extreme phenotypes to identify genomic regions enriched for rare variation contributing to chronic obstructive pulmonary disease susceptibility., Objectives: To identify regions of rare genetic variation contributing to emphysema with severe airflow obstruction., Methods: We identified heavy smokers that were resistant (n = 65) or susceptible (n = 64) to emphysema with severe airflow obstruction in the Pittsburgh Specialized Center of Clinically Oriented Research cohort. We filtered whole-genome sequencing results to include only rare variants and conducted single variant tests, region-based tests across the genome, gene-based tests, and exome-wide tests., Measurements and Main Results: We identified several suggestive associations with emphysema with severe airflow obstruction, including a suggestive association of all rare variation in a region within the gene ZNF816 (19q13.41; P = 4.5 × 10 -6 ), and a suggestive association of nonsynonymous coding rare variation in the gene PTPRO (P = 4.0 × 10 -5 ). Association of rs61754411, a rare nonsynonymous variant in PTPRO, with emphysema and obstruction was demonstrated in all non-Hispanic white individuals in the Pittsburgh Specialized Center of Clinically Oriented Research cohort. We found that cells containing this variant have decreased signaling in cellular pathways necessary for survival and proliferation., Conclusions: PTPRO is a novel candidate gene in emphysema with severe airflow obstruction, and rs61754411 is a previously unreported rare variant contributing to emphysema susceptibility. Other suggestive candidate genes, such as ZNF816, are of interest for future studies.

Rationale: Individuals with HIV are at increased risk for coronary artery disease (CAD). Early detection of subclinical CAD by assessment of coronary artery calcium (CAC) may help risk stratify and prevent CAD events in these individuals. However, the current standard to quantify CAC i.e. Agatston scoring requires EKG-gated cardiac CT imaging., Objective: To determine if the assessment of CAC using non-EKG-gated chest CT and the Weston scoring system is a useful surrogate for Agatston scores in HIV-infected and HIV-uninfected individuals., Methods and Measurements: CAC was assessed by both the Weston and Agatston score in 108 men enrolled in the Multicenter AIDS Cohort Study., Results: Participants were 55.2 (IQR 50.4; 59.9) years old and 62 (57.4%) were seropositive for HIV. Inter-observer agreement (rs = 0.94, κ = 90.0%, p<0.001, n = 21) and intra-observer agreement (rs = 0.95, κ = 95.2%, p<0.001, n = 97) for category of Weston score were excellent. Weston scores were associated with similar CAD risk factors as Agatston scores (age, race, HDL cholesterol level, all p<0.05) in our cohort. There was excellent correlation (rs = 0.92, p<0.001) and agreement (κw = 0.77, p<0.001) between Weston and Agatston scores., Conclusions: This study is the first to examine calcium scoring using chest CT in HIV-infected individuals and to independently validate the Weston score as a surrogate for the Agatston score. In clinical or research settings where EKG-gated cardiac CT is not feasible for the assessment of coronary calcium, Weston scoring by using chest CT should be considered.

Background: Age-related chronic diseases are prevalent in HIV-infected persons in the antiretroviral therapy (ART) era. Bone mineral density (BMD) loss and emphysema have separately been shown to occur at a younger age and with lesser risk exposure in HIV-infected compared to HIV-uninfected individuals. In non-HIV infected smokers, emphysema has been shown to independently predict low BMD. We hypothesized that emphysema would independently associate with thoracic vertebral bone attenuation, a surrogate for bone mineral density, in HIV-infected individuals., Methods: Clinical, pulmonary function, and radiographic data were analyzed for 164 individuals from the University of Pittsburgh's HIV Lung Research Center cohort. Chest CT scans were used to quantify emphysema and compute Hounsfield Unit (HU) attenuation of the 4th, 7th, and 10th thoracic vertebrae. The association between mean HU attenuation values across the three vertebrae and radiographic emphysema, age, sex, body mass index (BMI), steroid use, viral load, CD4 count, and forced expiratory volume in the first second (FEV1) was assessed by univariate and multivariate analyses., Results: In univariate analysis, mean HU attenuation decreased with increasing age (p<0.001), pack years (p = 0.047), and percent emphysema (p<0.001). In a multivariable model, including pack years, age, sex, ART and steroid use, greater emphysema was independently associated with this surrogate marker of BMD in HIV-infected individuals (p = 0.034)., Conclusions: The association of emphysema with thoracic bone attenuation in HIV-infected individuals is consistent with previous reports in non-HIV infected smokers. These findings suggest that emphysema should be considered a potential marker of osteoporosis risk in HIV-infected individuals.

Objectives: Chronic obstructive pulmonary disease (COPD) is more prevalent in HIV-infected individuals and is associated with persistent inflammation. Therapies unique to HIV are lacking. We performed a pilot study of the 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitor rosuvastatin to determine effects on lung function., Design: Randomized, placebo-controlled, triple-blinded trial., Methods: HIV-infected individuals with abnormal lung function were recruited from an ongoing lung function study. Participants were randomized to 24 weeks of placebo (n = 11) or rosuvastatin (n = 11) using an adaptive randomization based on change in peripheral C-reactive protein levels at 30 days of treatment. Forced expiratory volume in 1 s (FEV1) and diffusing capacity for carbon monoxide (DLco)%-predicted were compared to baseline at 24 weeks in the two groups using a Wilcoxon rank-sum test. The %-predicted change at 24 weeks in pulmonary function variables was compared between groups using simulated randomization tests., Results: The placebo group experienced a significant decline in FEV1%-predicted (P = 0.027), and no change in DLco%-predicted over 24 weeks. In contrast, FEV1%-predicted remained stable in the rosuvastatin group, and DLco%-predicted increased significantly (P = 0.027). There was no significant difference in absolute change in either measure between placebo and rosuvastatin groups., Conclusion: In a pilot study, the use of rosuvastatin for 24 weeks appeared to slow worsening of airflow obstruction and to improve DLco in HIV-infected individuals with abnormal lung function, although comparison of absolute changes between the groups did not reach significance. This study is the first to test a therapy for COPD in an HIV-infected population, and large-scale clinical trials are needed.

The histologic changes occurring in severe/therapy-resistant asthma (SA) as defined by the European Respiratory Society/American Thoracic Society guidelines, particularly at the level of the distal airways are unknown. This study describes the clinical, radiologic, and histologic characteristics of 29 SA patients who underwent video-assisted thoracoscopic surgery lung biopsy. Pathologic observations were correlated with clinical features, especially the presence of autoimmune disease (AID) (15/29, 51.7%). Ten biopsies (10/29, 34.5%) showed only small airway manifestations of asthma, whereas in 19 (65.5%) asthmatic granulomatosis, manifested by asthmatic bronchiolitis supplemented by an alveolar septal mononuclear infiltrates with non-necrotizing granulomas, was present. SA patients without asthmatic granulomatosis showed more striking small airway injury, subbasement membrane thickening, and neutrophilic infiltrates. Cases with concurrent AID had a tendency to more parenchymal eosinophilic inflammation, more bronchiolocentric granulomas, and a suggestion of increased responsivity to nonsteroidal immunosuppressive therapy. Histologic examination of video-assisted thoracoscopic surgery lung biopsies in SA demonstrates diverse pathologies including cases associated with granulomatous inflammation in addition to eosinophilic infiltrates. This spectrum of histologies may link to a high incidence of AID.