RATIONALE:Air pollution contributes additively to COPD morbidity beyond tobacco exposure. Limited current data focuses on the impact of fine particulate matter ≤ 2.5 µm in aerodynamic diameter (PM2.5) on COPD pathogenesis and CT-detected lung abnormalities. OBJECTIVE:To test the association between long term PM2.5 and CT measures of lung disease. METHODS:We studied participants enrolled in the SPIROMICS cohort (age 40-80 years; > = 20 pack-years). As part of the SPIROMICS Air study, 10-year mean outdoor concentrations of PM2.5 prior to enrollment at each participant's home were estimated using a hierarchical high-resolution spatiotemporal model. Using baseline chest CT scans completed at study enrollment, functional small airway disease and emphysema were quantified as percentages of total lung volume. We tested the association between PM2.5 and measures of emphysema (PRMEMPH), functional small airway disease (PRMfsad), and large airway disease (Pi10) using mixed effect regression models. Additional mixed effect regression models tested the association of PM2.5 with FEV1 and symptom scores. We stratified all analyses by baseline severity of airflow obstruction on spirometry: none (GOLD 0), mild-moderate (GOLD 1-2) and severe (GOLD 3-4), and non-tobacco exposed, healthy control participants. RESULTS:Among the 2,355 participants aged 40-80 years old included in this analysis, 176 were non-tobacco exposed control participants, 754 were GOLD 0, 982 GOLD 1-2 and 443 GOLD 3-4. Median 10-year average outdoor PM2.5 concentration for the entire cohort was 10.73 μg/m3 with an interquartile range of 3.52 μg/m3. In GOLD 0 participants, prior 10-year PM2.5 exposure was associated with greater baseline emphysema (1.33, 95% CI [1.12,1.59] per one IQR increase) and functional small airway disease (1.22, [95% CI 1.06, 1.40]); and lower FEV1 -68.8 ml [-126.3, -11.3]. Interaction analysis revealed an association between 10-year PM2.5 exposures and greater baseline symptom scores limited to women younger than 65 in the GOLD 0 and control group. No significant associations were found in participants with COPD. CONCLUSION:PM2.5 exposure was associated with CT-detected emphysema and small airway disease as well as lower FEV1 among those with smoking history and no airflow obstruction. Additionally, PM2.5 exposure was associated with greater symptom scores in women younger than 65 years.

Abstract Objective Coronary artery calcification (CAC) assessment for cardiovascular risk stratification is traditionally achieved using ECG-gated computed tomography (CT). Automated deep-learning (DL) algorithms may streamline opportunistic CAC detection and scoring, particularly on non-gated CT scans. This study evaluated the performance of a fully automated DL-based CAC scoring algorithm (“DL-CAC”) against expert human scoring. Methods The algorithm was trained on 1,260 chest CT scans from multiple databases to automatically identify coronary calcium, calculate Agatston scores, and assign a cardiovascular disease (CVD) risk classification. Performance was assessed on a holdout dataset (n=500) comprising ECG-gated calcium scoring CT scans and lung cancer screening non-gated chest CTs as well as in an external, independent CT dataset (n=129) from liver transplant candidates. Agreement with expert scoring was assessed using intraclass correlation coefficient (ICC) for Agatston scores and Cohen’s κ for CVD risk classification. Results The algorithm demonstrated high agreement with expert scoring in the pooled calcium scoring and lung cancer screening cohorts, with an ICC of 0.947 for Agatston scores and κ of 0.936 for CVD risk classification. For liver transplant candidates, the algorithm exhibited substantial agreement with expert scoring of non-gated CT scans (κ=0.79) and a sensitivity of 90.4% and specificity of 96.4% in high-risk cases. Conclusion These findings suggest that DL-based CAC scoring on non-gated CT scans may be a feasible alternative to traditional methods and could support opportunistic cardiovascular risk assessment in routine imaging. Further validation is warranted to assess clinical integration in broader practice settings.

BACKGROUND:Current quantitative chest CT techniques improve chronic obstructive pulmonary disease (COPD) phenotyping but do not capture spatial variability and potentially reversible disease in local lung parenchyma. METHODS:Applying elastic principal graphing to CT scans from Genetic Epidemiology of COPD study participants (age 45-80 years; ≥10 pack-years), we developed elastic parametric response mapping (ePRM), a tiered scoring system (tiers 0-3 and tier Op, ie, lung opacities) that classifies lung subvolumes based on their relative composition of normal lung, emphysema, small airways disease and parenchymal disease. For 3631 participants with longitudinal data, we evaluated how relative tier assignment and mean tier position of subvolumes changed over 5 years and how they associated with forced expiratory volume in 1 s (FEV1) change. We stratified analyses by baseline spirometry: no airflow obstruction, Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1-2 and GOLD 3-4. RESULTS:The proportion of tier 0 subvolumes decreased with worsening airflow obstruction, while tier 2 and 3 proportions increased. Tier 1 proportions were similar in GOLD 1-2 (25.7%) and GOLD 3-4 (28.1%), with over half of subvolumes remaining in tier 1 or reverting to tier 0 at year 5. In contrast to tiers 0 and 2, baseline mean tier 1 position was strongly predictive of reassignment to more advanced tiers at year 5 in participants without airflow obstruction, GOLD 1-2 and GOLD 3-4 (area under the curves (95% CIs) 0.86 (0.85 to 0.87), 0.90 (0.89 to 0.91) and 0.92 (0.90 to 0.93), respectively). A higher per cent volume of lung retained in tier 1 was associated with less FEV1 decline in all groups. CONCLUSION:CT ePRM categorises local lung tissue into distinct and potentially reversible tiers of disease severity.

RATIONALE –:Airway mucus plugging is a clinically relevant manifestation of airway pathology in chronic obstructive pulmonary disease (COPD) and is associated with increased mortality even in early disease; however, visual computed tomography (CT) assessment is subjective and labor intensive. OBJECTIVES –:To develop an AI-based quantitative CT method for automated detection of airway mucus plugging and evaluate associations with physiologic impairment and clinical outcomes. METHODS –:Inspiratory CT scans from 8,971 COPDGene Phase 1 (GOLD 0-4 and PRISm) participants were analyzed. An AI-based framework combining 3D airway segmentation discontinuities and convolutional neural network classification identified mucus plug obstructions, yielding mucus plug burden (total plug count). Associations with outcomes were evaluated using covariate-adjusted models. MEASUREMENTS AND MAIN RESULTS –:Higher mucus plug burden was associated with lower post-bronchodilator FEV1 % predicted (ρ = -0.41; P < 0.001), greater air trapping (LAA < -856 HU; ρ = 0.33; P < 0.001), worse health status (SGRQ; ρ = 0.31; P < 0.001), and shorter 6-minute walk distance (ρ = -0.26; P < 0.001). Among GOLD 1-4 participants, mucus plug presence was independently associated with increased all-cause mortality (adjusted hazard ratio, 1.28; P < 0.005) and exacerbation frequency (adjusted incidence rate ratio, 1.32; P < 0.005). Plug presence was also associated with increased respiratory mortality across GOLD categories and cardiovascular mortality in GOLD 1-2. CONCLUSIONS –:AI-based quantitative CT assessment of airway mucus plugging provides a scalable, reproducible measure associated with physiologic impairment and adverse outcomes in COPD, supporting its role in risk stratification and future therapeutic studies.

Accurate airway segmentation from chest computed tomography (CT) scans is essential for quantitative lung analysis, yet manual annotation is impractical and many automated U-Net-based methods yield disconnected components that hinder reliable biomarker extraction. We present RepAir, a three-stage framework for robust 3D airway segmentation that combines an nnU-Net-based network with anatomically informed topology correction. The segmentation network produces an initial airway mask, after which a skeleton-based algorithm identifies potential discontinuities and proposes reconnections. A 1D convolutional classifier then determines which candidate links correspond to true anatomical branches versus false or obstructed paths. We evaluate RepAir on two distinct datasets: ATM'22, comprising annotated CT scans from predominantly healthy subjects and AeroPath, encompassing annotated scans with severe airway pathology. Across both datasets, RepAir outperforms existing 3D U-Net-based approaches such as Bronchinet and NaviAirway on both voxel-level and topological metrics, and produces more complete and anatomically consistent airway trees while maintaining high segmentation accuracy.

Purpose:To develop an interpretable feature-based Deep Parametric Response Mapping (PRMD) method that combines wavelet scattering convolution networks and machine learning to spatially detect and quantify functional small airways disease (fSAD) and emphysema on paired inspiratory-expiratory CT scans, with enhanced noise robustness. Materials and Methods:In this retrospective analysis of prospectively acquired data (2007-2017), we developed and validated a deep learning-based PRM approach using paired CT scans from 8,972 tobacco-exposed COPDGene participants (≥10 pack-years; mean age 60.1 ± 8.8 years; 46.5% women), including controls with normal spirometry (n = 3,872; controls), PRISm (n = 1,089), GOLD 1-4 COPD (n = 4,011). Data were stratified into training, validation, and testing sets (24:6:70). PRMD extracts translation-invariant image features using a wavelet scattering network and applies a subspace learning classifier to classify voxels as emphysema or non-emphysematous air trapping (fSAD). PRMD was compared with conventional density-based PRM for voxel-wise agreement, correlation with pulmonary function, robustness to noise, and sensitivity to misregistration using Pearson correlation, Bland-Altman analysis, and paired t tests. Results:PRMD achieved 95% voxel-wise agreement with standard PRM (r = 0.98) while demonstrating significantly greater robustness under noise. PRMD showed stronger correlations with FEV (emphysema: r = -0.54; fSAD: r = -0.51; P < 0.0001) than standard PRM (r = -0.42 for both; P < 0.0001). Under simulated high-noise conditions, standard PRM overestimated disease by ~15%, whereas PRMD limited error to < 5% (P < 0.001). Conclusion:PRMD provides an interpretable, feature-driven and noise-resilient alternative to traditional PRM for emphysema and fSAD classification, enhancing the reliability of CT-based COPD phenotyping for multi-center studies and low-dose imaging applications.

Rationale: Coronary artery disease (CAD) is a major cause of morbidity and mortality. Elevated coronary artery calcium (CAC) levels are common among individuals eligible for CT lung screening (CTLS), Agatston scores >400 indicate high CAD risk. Identifying CAC on CTLS offers an opportunity to detect high-risk individuals who may benefit from closer monitoring and targeted interventions. This study explored associations between Agatston scores >400, adverse outcomes and modifiable risk factors. Methods: This retrospective, multi-center CTLS cohort study included 4,673 patients from Lahey Hospital and Medical Center (LHMC) and 1,271 scans from Mount Auburn Hospital (MAH). Baseline scans (2012-2017 at LHMC; 2015-2019 at MAH) utilized the 4DMedical CAC algorithm to calculate Agatston scores. Follow-up extended through 2019 at LHMC and 2020 at MAH. A subset of 1,384 LHMC and 419 MAH patients with Agatston scores >400 and primary care within the health system were analyzed for risk factor modification and gender disparities. Chi-square tests examined group differences, and Cox proportional hazards models assessed associations with mortality, lung cancer incidence, and hospital admissions (all-cause, myocardial infarction, and congestive heart failure), with significance at p < 0.05. Results: Of the scans, 4,637 (99.2%) at LHMC and 1,251 (98.4%) at MAH successfully generated Agatston scores. A score >400 was associated with increased lung cancer risk at both LHMC (HR 2.11, CI 1.19-3.73, p = 0.01) and MAH (HR 1.75, CI 1.03-2.96, p = 0.037), and correlated with all-cause hospitalization at LHMC (HR 3.16, CI 1.43-6.99, p = 0.004) and MAH (HR 1.70, CI 1.17-2.48, p = 0.005). High scores were also linked to greater myocardial infarction events at LHMC (HR 2.8, CI 1.47-5.27, p = 0.002) and MAH (HR 2.8, CI 1.2-6.7, p = 0.017), as well as congestive heart failure admissions at LHMC (HR 1.99, CI 2.17-3.39, p = 0.011) and MAH (HR 7.87, CI 2.84-21.8, p < 0.001). Among those with Agatston scores >400, tobacco use was prevalent in 49% at LHMC and 58% at MAH. Elevated LDL (>100 mg/dL) was more common in women than men at both LHMC (55.2% vs. 40.25%, p < 0.001) and MAH (38.5% vs. 27.8%, p = 0.028). Conclusion: An Agatston score >400 on CTLS exams reliably indicates elevated risks for adverse outcomes. Targeted interventions, including smoking cessation and LDL management, are essential, with gender disparities in LDL among women warranting focused attention to improve outcomes in this high-risk group.

Rationale: Progressive pulmonary fibrosis (PPF) is common in patients with fibrotic interstitial lung disease (ILD) and leads to high mortality. Although PPF guideline criteria include computed tomography (CT)-based progression, these measures are qualitative and prone to interreader variability. Quantitative computed tomography (qCT) measurements have the potential to overcome this limitation. Objectives: The objectives of this study were to determine whether changes in qCT measures of pulmonary fibrosis are associated with transplant-free survival (TFS) in a diverse ILD cohort and establish a quantitative computed tomography measure of progressive pulmonary fibrosis (qctPPF). Methods: A retrospective cohort analysis was performed in individuals with fibrotic ILD, including idiopathic pulmonary fibrosis (n = 350), who underwent serial chest CT for clinical indications. Commercially available software was used to generate qCT measures of pulmonary fibrosis, which were tested for association with 2-year TFS using a multivariable Cox proportional hazards model. Iterative modeling was then performed to develop a composite qctPPF measure. Results were validated in an independent ILD cohort (n = 92). Measurements and Main Results: Increasing ground-glass opacity and decreasing lung volume showed consistent association with decreased TFS across cohorts when modeled continuously and dichotomously. qctPPF classification was associated with a greater than threefold increased hazard of death or transplant in the test (hazard ratio, 4.41; 95% confidence interval, 2.77-7.03) and validation (hazard ratio, 3.54; 95% confidence interval, 1.62-7.71) cohorts. Agreement between qctPPF and radiologist-determined PPF was poor (κ = 0.20), with qctPPF classification maintaining prognostic significance when discordant with radiologist interpretation. Conclusions: Changes in qCT measures are associated with clinically relevant outcomes and could improve PPF classification.

Chronic obstructive pulmonary disease (COPD) is complex, and its course is difficult to predict due to its diverse pathophysiology. Small airway disease (SAD), a key component of COPD and potential target for emerging therapeutics, may be reversible in mild COPD, but left unchecked, may worsen, leading to airway loss and emphysema. The dual nature of SAD complicates clinical management of COPD patients, necessitating more accurate monitoring methods. To meet this need, we developed elastic Parametric Response Mapping (ePRM), a tiered scoring system that classifies local lung volumes by the degree of PRM-derived SAD, normal, and emphysematous tissue. In individuals with or at risk for COPD, we demonstrate that chest CT ePRM can categorize local lung tissue into distinct tiers of disease severity that distinguish between tissue characterized by early reversible SAD and progressive destruction. This level of characterization is crucial to developing personalized treatment strategies for COPD.

Rationale Emphysema, quantified by the percentage of low attenuation area at -950 Hounsfield units (%LAA-950) on CT imaging, has a well-documented association with an elevated risk of lung cancer. Identifying patients at a low risk of lung cancer within two years could allow for less frequent screening. This study aimed to determine an optimal %LAA-950 threshold that could reliably identify patients with minimal lung cancer risk over two years, potentially supporting biennial screening intervals. Methods This retrospective, multi-center cohort study included data from 4,642 patients at Lahey Hospital and Medical Center (LHMC), with a validation cohort of 1,254 patients from Mount Auburn Hospital (MAH). Baseline lung cancer screening CT scans conducted at LHMC between 2012 and 2017 were followed through 2019 to assess lung cancer development within two years. We utilized 4D CT imaging to generate %LAA -950 scores. We analyzed %LAA-950 thresholds, focusing on identifying an optimal cut point below which patients exhibited a low lung cancer incidence. Cox Logistic regression and Chi-square tests were conducted to compare lung cancer outcomes based on %LAA-950 thresholds. Results During the study period, lung cancer was diagnosed in 190 patients (4.1%) at LHMC and in 60 patients (4.8%) at MAH. At LHMC, a %LAA-950 threshold of 0.07% was associated with a lower risk of lung cancer, with a hazard ratio of 0.56 (CI: 0.34-0.95, p=0.03), indicating a potentially protective effect. In contrast, patients with a %LAA-950 threshold of 5% showed increased lung cancer risk, with a hazard ratio of 1.45 (CI: 1.03-2.04, p=0.03). Further analysis revealed that among 577 patients at LHMC with %LAA-950 below 0.07%, 15 (2.6%) still developed lung cancer within two years, which challenges the assumption that low %LAA-950 alone can reliably identify patients for reduced screening frequency. Patients with %LAA-950 ≥0.07% had a significantly higher lung cancer incidence, with 191 cases (4.7%), supported by a statistically significant chi-square test (p=0.022). Additionally, among patients with Lung-RADS negative (1 or 2) scans and %LAA-950 below 0.07%, 8 cases (1.8%) developed lung cancer within two years, suggesting that %LAA-950 alone may not be sufficient to determine screening frequency. Conclusion Our findings indicate that some patients with low %LAA-950 scores still develop lung cancer within two years. Therefore, %LAA-950 thresholds alone may not be adequate to justify biennial screening. These results suggest that combining %LAA-950 thresholds with other risk factors, such as Lung-RADS assessment, may be necessary to optimize screening intervals safely.

Efforts to phenotype veterans that developed respiratory symptoms following deployments to the Southwest Asia Theater of Military Operation have been limited by the insensitivity of current non-invasive testing to objectively identify deployment-related constrictive bronchiolitis and other features of chronic lung injury. In this study, we derived a quantitative CT (QCT)-based radiographic phenotype of biopsy-proven deployment-related constrictive bronchiolitis (DRCB) and assessed its ability to assist in the phenotyping of non-biopsied formerly deployed symptomatic veterans. QCT analysis combined with demographic, physiologic, symptom, and exposure data was obtained from three cohorts: military personnel with biopsy-proven deployment-related constrictive bronchiolitis (DRCB, n = 37), formerly deployed symptomatic veterans (FDSV, n = 71), and asymptomatic civilians (Control, n = 98). Differences in unadjusted QCT metrics and demographic variables between cohorts were identified and further assessed by principal component analysis. Thereafter, adjusted data from the DRCB cohort was used to derive a QCT-based radiographic phenotype of DRCB expressed as a DRCB-Probability Index (DRCB-PI). Application of the DRCB-PI to the FDSV cohort was used to assess additional phenotypic metrics associated with the DRCB phenotype (DRCB-PI > 0.5). Individual unadjusted QCT metrics for functional small airways disease and high attenuation area were elevated in DRCB and FDSV cohorts (relative to Control). Primary component analysis revealed that DRCB and FDSV cohorts overlapped and were distinguished from the Control cohort. The FDSV subjects whose DRCB-PI was > 0.5 had greater evidence of small airways disease (assessed by oscillometry and QCT) and self-reported more intense immediate health effects to their exposures to military burn pit smoke, and sand and dust. Application of a QCT-derived radiographic phenotype of DRCB identified a subset of veterans with evidence of abnormal small airways and more severe self-reported health effects following inhalational exposures during military deployment. Future studies incorporating QCT may help establish non-invasive strategies to detect DRCB and other forms of chronic lung injury.

Rationale: Chronic obstructive pulmonary disease (COPD) exhibits considerable progression heterogeneity. We hypothesized that elastic principal graph analysis (EPGA) would identify distinct clinical phenotypes and their longitudinal relationships. Objectives: Our primary objective was to create a map of COPD phenotypes and their connectivity using EPGA. Secondarily, we used longitudinal and external data sets to test the validity and reproducibility of this map. Methods: Cross-sectional data from 8,972 tobacco-exposed COPDGene participants, with and without COPD, were used to train a model with EPGA, using thirty clinical, physiologic and CT features. 4,585 participants from COPDGene Phase 2 were used to test longitudinal trajectories. 2,652 participants from SPIROMICS tested external reproducibility. Measurements and Main Results: Our analysis used crosssectional data to create an elastic principal tree, where time is associated with distance on the tree. Six clinically distinct tree segments were identified that differed by lung function, symptoms, and CT features: Subclinical (SC); Parenchymal Abnormality (PA); Chronic Bronchitis (CB); Emphysema Male (EM); Emphysema Female (EF); and Severe Airways (SA) disease. 5-year data from COPDGene mapped longitudinal changes onto the tree, and longitudinal trajectories demonstrated a net flow of patients from SC towards EM and EF, including trajectories through airway disease predominant phenotypes, CB and SA. Cross-sectional SPIROMICS data projected onto the tree showed clinically similar patient groupings. Conclusions: This novel analytic methodology provides an approach to defining longitudinal phenotypic trajectories using cross sectional data. These insights are clinically relevant and could facilitate precision therapy and future trials to modify disease progression. Clinical trial registered with www.clinicaltrials.gov (NCT00608764 and NCT01969344).

Rationale: Incidental features of interstitial lung disease (ILD) are commonly observed on chest computed tomography (CT) scans and are independently associated with poor outcomes. Although most studies to date have relied on qualitative assessments of ILD, quantitative imaging algorithms have the potential to effectively detect ILD and assist in risk stratification for population-based cohorts. Objectives: To determine whether quantitative measures of ILD are associated with clinically relevant outcomes in the NLST (National Lung Screening Trial). Methods: Quantitative measures of ILD were generated using low-dose CT (LDCT) data collected as part of the NLST and processed with Computer-Aided Lung Informatics for Pathology Evaluation and Ratings (CALIPER) and deep learning-based usual interstitial pneumonia (DL-UIP) algorithms (Imbio Inc.). A multivariable Cox proportional hazard regression model was used to test the association between ILD measures (percentage ground-glass opacity, reticular opacity, and honeycombing of total lung volume and binary DL-UIP classification) and all-cause mortality. Secondary outcomes of incident lung cancer and lung cancer mortality were also explored. Results: Quantitative CT data were generated in 11,518 individuals. Mean age was 61.5 years, and 58.7% were male. An increased risk of all-cause mortality was observed for each percentage increase in CALIPER-derived ground-glass opacity (hazard ratio [HR], 1.02; 95% confidence interval [CI], 1.01-1.02), reticular opacity (HR, 1.18; 95% CI, 1.12-1.24), and honeycombing (HR, 6.23; 95% CI, 4.23-9.16). Individuals with a positive DL-UIP classification pattern had a 4.8-fold increased risk of all-cause mortality (HR, 4.75; 95% CI, 2.50-9.04). CALIPER-derived reticular opacity was also associated with increased lung cancer-specific mortality. No quantitative measures of ILD were associated with incident lung cancer. Conclusions: Quantitative measures of ILD on LDCT are associated with clinically relevant endpoints in a large at-risk population of individuals with tobacco use history.

Rationale: Lung disease is a common but poorly understood complication among children and adolescents with HIV (AWH). Airways disease is emerging as a predominant phenotype. Quantitative CT scoring has shown promise in pediatrics for airways diseases in other pathophysiologic processes but has not been evaluated in HIV-associated lung disease. We examined quantitative CT airway metrics of structural lung disease in AWH and hypothesized that CT airway metrics are associated with FEV1 z-scores (zFEV1). Methods: This is a cross-sectional secondary analysis of a prospective cohort study of AWH aged 10-19 years in Nairobi, Kenya (BREATHE II). We performed non-contrast HRCT using a standardized protocol with inspiratory and expiratory views at one site and applied quantitative metrics using YACTA for airway measurements. We obtained standardized questionnaires, focused respiratory exam, spirometry, and serum CD4/CD8. We used linear regression models to determine associations of segmental and subsegmental airway metrics separately with zFEV1, adjusting for age, sex, height-for-age z-score, and CD4 count. Results: Of 165 enrolled AWH, 152 had high-quality CT scans that could be scored quantitatively. Median age was 15.7 (IQR 13.4-18.5) years and 44% were female. Median age of ART initiation was 7.0 (IQR 3.2-12.1) years, 94% acquired HIV perinatally, and all were taking ART during the study (Table). Median CD4 count was 576 (IQR 361-783) cells/. Forty-two (25%) AWH had zFEV1 below the lower limit of normal (LLN). Based on visual radiologist interpretation, 27% had mosaic attenuation and 12% had bronchiectasis, while groundglass opacities (6%), consolidation (5%), emphysema (5%), and fibrosis (1%) were rare (Table). Increasing segmental and subsegmental airway wall thickness by 1mm was significantly associated with -0.97 (95% CI: -1.46, -0.48) and -1.31 (95% CI: -1.86, -0.76) lower zFEV1 among AWH, respectively. Conclusions: Quantitative CT airway metrics were significantly associated with lower zFEV1 among AWH. Quantitative CT scoring is a promising tool to detect structural and functional lung disease among AWH. Future directions include evaluating associations between sociodemographic, clinical, environmental, and immune factors with CT airway metrics among AWH.

RATIONALE: Bronchoscopic lung volume reduction (BLVR) utilizing endobronchial valves (EBV) has proven to be effective in improving the quality of life for patients with advanced emphysema. The success of the procedure, however, diminishes in individuals with incomplete lobar fissure integrity, as collateral ventilation (CV) can cancel the occlusive effect of EBVs by hindering atelectasis. Existing non-invasive methods for evaluating CV rely on estimating fissure integrity from static CT scans, a proxy for CV that does not always match with in-situ CV measurements (i.e. Chartis system). X-ray Velocimetry (XV, 4DMedical Limited, Australia) captures 4D (3D+time) images of the lungs, allowing time-resolved measurement of regional lung ventilation. This study aims to evaluate the discriminative accuracy of XV in predicting CV non-invasively, potentially improving patient selection for BLVR. METHODS: In the prospective study conducted at Temple University Hospital, 39 participants (40 years of age or older) eligible for clinical evaluations for BLVR procedure were recruited. A Chartis assessment was conducted to identify participants with CV-positive status. The study utilized XV technology to analyze lung mechanics through voxel-wise ventilation measurements which included measuring the rates of inspiration and expiration in the target and adjacent lobes. Additionally, it assessed the quantitative CT-based emphysema scores and fissure integrity measurements. RESULTS: 11 participants were CV-positive as assessed by Chartis. The model incorporated three imaging biomarkers: (1) the target-to-ipsilateral lobes inspiration expiration rates ratio, (2) quantitative emphysema score within a 1-cm distance from the fissure, and (3) quantitative fissure integrity score. It predicted CV likelihood with a p-value of 0.032, an AUC of 0.840, and an accuracy of 0.767. Implementing a probability threshold of 0.5 resulted in a specificity of 0.900 (Figure 1). CONCLUSIONS: This study highlights the potential of using XV technology to evaluate lung mechanics for detecting CV. It presents several advantages over the conventional Chartis method, including a significantly reduced burden on patients, a lower radiation dose compared to traditional CT, and the elimination of the need for intraoperative assessment of CV. Declaration of Interest: Eikelis, Nilsen and Hatt are employees of 4DMedical. Key Words: Bronchoscopic lung volume reduction (BLVR), endobronchial valve (EBV), collateral ventilation (CV), regional ventilation, X-ray Velocimetry (XV) Figure 1. This figure demonstrates the predictive performance of a model used to evaluate the likelihood of collateral ventilation (CV). When a probability threshold of 0.5 is applied, the specificity increases to 0.900, demonstrating the model's strong ability to correctly identify true negatives.

Background Most of our understanding regarding chronic obstructive pulmonary disease (COPD) comes from studies of older individuals, but we will never understand disease pathogenesis if we continue to examine mild disease in older subjects. Aims and objectives The goal of the MAP COPD cohort is to examine disease extent among at risk individuals and those with early COPD. Methods The MAP COPD study (n=200) is a partnership between the COPD Foundation, Taubman Institute and the University of Michigan to establish an early COPD cohort. The enrollment criteria includes individuals with ≥ 10 pack years smoking history, aged 30-55. All individuals underwent baseline spirometry, symptom questionnaires and high resolution CT scanning. CT metrics included % low attenuation area (LAA), parametric response mapping defined small airways disease (PRMfSAD) and pi10. Linear models to understand the relationship between CT variables and symptoms were additionally adjusted for age, sex, BMI and smoking pack-years. Results We recruited 200 individuals with data from n=197 available as part of this analysis. Mean age of the population was 48.6 years with mean pack-years of 26.3. Roughly 82% of the patients were GOLD 0 (n=161); 12% GOLD 1-2 (n=24) and 6% PRISm (n=12). Over half of the patient population was symptomatic as defined by the Chronic Airways Assessment Test (CAAT) ≥10 (54.8%). From a descriptive standpoint, the GOLD 1-2 group, however had the highest symptoms, highest percentage of individuals experiencing an exacerbation in the prior year, the thickest airway walls as measured by pi10 and the greatest PRMfSAD(Table 1). In a linear regression model to identify imaging factors associated with increased symptoms as defined by SGRQ total score, Pi10 was the strongest predictor of increased SGRQ score both among all participants (3.04 per SD; p=0.002) and among GOLD 0 individuals separately (3.04 per SD; p=0.02). Among GOLD 1-2 subjects, greater PRMnormal was associated with lower SGRQ score (-0.68 per percent; p=0.04). Conclusions In early COPD, symptom burden and evidence radiographic emphysema were common, even among individuals who did not meet spirometric criteria for COPD. Airway wall thickness was the strongest CT measure associated with symptoms among individuals without COPD. Among those with COPD, having greater percentage of healthy lung tissue was associated with lower symptoms.

Rationale: The widespread adoption of low-dose-CT (LDCT) for lung cancer screening has created opportunities to identify undiagnosed interstitial lung disease (ILD) through finding interstitial lung abnormalities (ILA). ILA are frequently present, but often under-recognized, in these high-risk populations. ILA are associated with adverse clinical outcomes, including mortality and progression to ILD, underscoring the need for timely diagnosis. We investigated the association between a deep learning (DL)-based IQ-UIP classifier and Lung Texture Analysis (LTA) (4DMedical, Los Angeles) quantitative measurements for ILA and key outcomes: mortality; lung cancer incidence; and all-cause and pneumonia-related hospitalizations. Methods: This multicenter, retrospective cohort study included patients undergoing LDCT at Lahey Hospital and Medical Center (LHMC) (2012-2017) and Mt. Auburn Hospital (MAH) (2015-2017) per NCCN high-risk criteria for lung cancer screening. IQ-UIP and LTAwere utilized to generateIQUIP-high-riskand IQUIP-moderate-risk scores andhoneycombing and reticulation extent on LDCT. Follow-up through 2019 (LHMC) and 2020 (MAH) tracked key clinical outcomes. Cox proportional hazards models assessed associations between IQ-UIP and LTA for each outcome, adjusting for age, sex, BMI, smoking status/pack-years, with significance set at p<0.05 for associations that replicated in the MAH cohort. Results: Of 4673 scans at LHMC and 1271 at MAH, 4644 (99.4%) and 1253 (98.6%) were processed for IQ-UIP; and 3951 (84.5%) and 1253 (98.6%) for LTA, respectively. Mean age was 62.4 years (54.4% male) at LHMC and 64.3 years (49.5% male) at MAH. There were 11 and 5 IQ-high-risk; and 29 and 7 IQ-moderate-risk scans at LHMC and MAH, respectively. Hazard ratios (HRs) for mortality in LHMC and MAH cohorts were 11.0 and 12.54 for IQ-high-risk; 3.99 and 8.45 for IQ-moderate-risk; 4.37 and 1.73 for honeycombing, respectively. In LHMC cohort, IQ-high-risk (HR 6.72), IQ-moderate-risk (HR 3.61) and honeycombing (HR 5.30) were associated with lung cancer (MAH without association). Association with all-cause hospitalization was highest among LHMC IQ-high-risk (HR 5.90) and MAH IQ-moderate-risk patients (HR 4.32). Pneumonia-related hospitalizations showed the strongest associations: HRs in LHMC and MAH cohorts of 13.20 and 8.79 for IQ-high-risk; 4.99 and 6.99 for IQ-moderate-risk; 5.20 and 1.85 for honeycombing (Table#1). Conclusion: This study highlights the strong association between ILA identified by quantitative algorithms and pneumonia-related hospitalizations, suggesting that ILA are often misclassified as pneumonia, contributing to diagnostic delays in ILD. These results support the use of DL-based tools within lung screening programs to detect and classify ILA patterns, potentially enabling early diagnosis and appropriate intervention to reduce the clinical burden of undiagnosed ILD.

Rationale: Chronic obstructive pulmonary disease (COPD) and lung cancer are major causes of morbidity and mortality. The low attenuation area at -950 Hounsfield units (%LAA-950) on CT scans quantifies emphysema severity, with levels between 1-5% linked to higher mortality and cancer risk. However, its potential in predicting COPD-specific hospital admissions and guiding preventive measures remains understudied. This study aimed to identify a %LAA threshold corresponding to a two-fold hazard ratio (HR) for COPD hospitalizations, proposing that this could inform preventive care through accessible strategies, such as smoking cessation, vaccinations, and COPD screening. Methods: This retrospective, multi-center cohort study included patients from Lahey Hospital and Medical Center (LHMC) and Mount Auburn Hospital (MAH), eligible for CT lung screening (CTLS) per NCCN high-risk criteria. Baseline CTLS scans (2012-2017 at LHMC; 2015-2019 at MAH) were analyzed using the 4DMedical Lung Density Analysis (LDA) algorithm to generate global %LAA-950 scores. Follow-up through 2019 (LHMC) and 2020 (MAH) included assessments of mortality, lung cancer incidence, COPD hospitalizations, and clinical opportunities. Cox proportional hazards models evaluated associations between %LAA-950 thresholds and COPD admissions, adjusted for age, sex, BMI, smoking status, and pack-years (p<0.05 for significance). Results: At LHMC, 4,642 of 4,673 scans (99.3%) and, at MAH, 1,254 of 1,271 scans (98.7%) were successfully processed. The LHMC cohort had a mean age of 62.4 years (54.4% male, 94.3% white), and the MAH cohort averaged 64.3 years (49% male, 90% white). A 2% LAA threshold corresponded to a two-fold HR for COPD admissions: HR 2.03 (CI: 1.45-2.83, p < 0.001) at LHMC and HR 3.47 (CI: 1.93-6.24, p < 0.001) at MAH. Preventive care opportunities for these high-risk patients included: smoking cessation for 389 (45.0%) at LHMC and 44 (44%) at MAH; PCV13 vaccination review for 350 (41%) at LHMC and 27 (27%) at MAH; PCV23 review for 182 (21%) at LHMC and 42 (42%) at MAH; and PFT screening for 516 (60%) at LHMC and 30 (30%) at MAH. Table 1 details primary care and risk factor modification opportunities. Conclusion: The 2% LAA threshold is an effective marker for identifying patients at high risk of COPD admissions, presenting significant opportunities for preventive interventions. By incorporating this threshold into lung cancer screening, providers can target high-risk individuals for interventions that could reduce hospitalizations, healthcare costs, and disease burden. This study supports the 2% LAA threshold as a practical tool for early identification and management of at-risk patients.