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The article addresses the global impact of chronic obstructive pulmonary disease (COPD), noting its high prevalence and the significant burden of undiagnosed cases. Topics include the limitations of current screening practices and the U.S. Preventive Services Task Force's stance against routine spirometry for asymptomatic individuals, despite evidence suggesting that at-risk populations could benefit from early detection.

Simple Summary: Detecting lung cancer early and initiating treatment promptly can greatly enhance patient outcomes. While low-dose computed tomography (LDCT) screening aids in identifying lung cancer at an early stage, there is a risk of diagnostic delays as patients await follow-up scans. To mitigate this challenge, we developed a deep predictive model leveraging generative AI methods to forecast nodule growth patterns in follow-up LDCT scans based on baseline LDCT scans. Our findings illustrated that utilizing the predicted follow-up nodule images generated by our model during baseline screening improved diagnostic accuracy compared to using baseline nodules alone and achieved comparable performance with using real follow-up nodules. This demonstrated the potential of employing deep generative models to forecast nodule appearance in follow-up imaging from baseline LDCT scans, thereby enhancing risk assessment during initial screening. Early diagnosis of lung cancer can significantly improve patient outcomes. We developed a Growth Predictive model based on the Wasserstein Generative Adversarial Network framework (GP-WGAN) to predict the nodule growth patterns in the follow-up LDCT scans. The GP-WGAN was trained with a training set (N = 776) containing 1121 pairs of nodule images with about 1-year intervals and deployed to an independent test set of 450 nodules on baseline LDCT scans to predict nodule images (GP-nodules) in their 1-year follow-up scans. The 450 GP-nodules were finally classified as malignant or benign by a lung cancer risk prediction (LCRP) model, achieving a test AUC of 0.827 ± 0.028, which was comparable to the AUC of 0.862 ± 0.028 achieved by the same LCRP model classifying real follow-up nodule images (p = 0.071). The net reclassification index yielded consistent outcomes (NRI = 0.04; p = 0.62). Other baseline methods, including Lung-RADS and the Brock model, achieved significantly lower performance (p < 0.05). The results demonstrated that the GP-nodules predicted by our GP-WGAN model achieved comparable performance with the nodules in the real follow-up scans for lung cancer diagnosis, indicating the potential to detect lung cancer earlier when coupled with accelerated clinical management versus the current approach of waiting until the next screening exam. [ABSTRACT FROM AUTHOR]

Background: Idiopathic pulmonary fibrosis (IPF) carries significant mortality and unpredictable progression, with limited therapeutic options. Designing trials with patient-meaningful endpoints, enhancing the reliability and interpretability of results, and streamlining the regulatory approval process are of critical importance to advancing clinical care in IPF. Methods: A landmark in-person symposium in June 2023 assembled 43 participants from the US and internationally, including patients with IPF, investigators, and regulatory representatives, to discuss the immediate future of IPF clinical trial endpoints. Patient advocates were central to discussions, which evaluated endpoints according to regulatory standards and the FDA's 'feels, functions, survives' criteria. Results: Three themes emerged: 1) consensus on endpoints mirroring the lived experiences of patients with IPF; 2) consideration of replacing forced vital capacity (FVC) as the primary endpoint, potentially by composite endpoints that include 'feels, functions, survives' measures or FVC as components; 3) support for simplified, user-friendly patient-reported outcomes (PROs) as either components of primary composite endpoints or key secondary endpoints, supplemented by functional tests as secondary endpoints and novel biomarkers as supportive measures (FDA Guidance for Industry (Multiple Endpoints in Clinical Trials) available at: ). Conclusions: This report, detailing the proceedings of this pivotal symposium, suggests a potential turning point in designing future IPF clinical trials more attuned to outcomes meaningful to patients, and documents the collective agreement across multidisciplinary stakeholders on the importance of anchoring IPF trial endpoints on real patient experiences—namely, how they feel, function, and survive. There is considerable optimism that clinical care in IPF will progress through trials focused on patient-centric insights, ultimately guiding transformative treatment strategies to enhance patients' quality of life and survival. [ABSTRACT FROM AUTHOR]

The American College of Radiology created the Lung CT Screening Reporting and Data System (Lung-RADS) in 2014 to standardize the reporting and management of screen-detected pulmonary nodules. Lung-RADS was updated to version 1.1 in 2019 and revised size thresholds for nonsolid nodules, added classification criteria for perifissural nodules, and allowed for short-interval follow-up of rapidly enlarging nodules that may be infectious in etiology. Lung-RADS v2022, released in November 2022, provides several updates including guidance on the classification and management of atypical pulmonary cysts, juxtapleural nodules, airway-centered nodules, and potentially infectious findings. This new release also provides clarification for determining nodule growth and introduces stepped management for nodules that are stable or decreasing in size. This article summarizes the current evidence and expert consensus supporting Lung-RADS v2022. [ABSTRACT FROM AUTHOR]

Background: Small airways disease (SAD) is a major cause of airflow obstruction in COPD patients and has been identified as a precursor to emphysema. Although the amount of SAD in the lungs can be quantified using our Parametric Response Mapping (PRM) approach, the full breadth of this readout as a measure of emphysema and COPD progression has yet to be explored. We evaluated topological features of PRM-derived normal parenchyma and SAD as surrogates of emphysema and predictors of spirometric decline. Methods: PRM metrics of normal lung (PRMNorm) and functional SAD (PRMfSAD) were generated from CT scans collected as part of the COPDGene study (n = 8956). Volume density (V) and Euler-Poincaré Characteristic (χ) image maps, measures of the extent and coalescence of pocket formations (i.e., topologies), respectively, were determined for both PRMNorm and PRMfSAD. Association with COPD severity, emphysema, and spirometric measures were assessed via multivariable regression models. Readouts were evaluated as inputs for predicting FEV1 decline using a machine learning model. Results: Multivariable cross-sectional analysis of COPD subjects showed that V and χ measures for PRMfSAD and PRMNorm were independently associated with the amount of emphysema. Readouts χfSAD (β of 0.106, p < 0.001) and VfSAD (β of 0.065, p = 0.004) were also independently associated with FEV1% predicted. The machine learning model using PRM topologies as inputs predicted FEV1 decline over five years with an AUC of 0.69. Conclusions: We demonstrated that V and χ of fSAD and Norm have independent value when associated with lung function and emphysema. In addition, we demonstrated that these readouts are predictive of spirometric decline when used as inputs in a ML model. Our topological PRM approach using PRMfSAD and PRMNorm may show promise as an early indicator of emphysema onset and COPD progression. [ABSTRACT FROM AUTHOR]

Key Points: Question: How is diagnostic accuracy impacted when clinicians are provided artificial intelligence (AI) models with image-based AI model explanations, and can explanations help clinicians when shown systematically biased AI models? Findings: In this multicenter randomized clinical vignette survey study, diagnostic accuracy significantly increased by 4.4% when clinicians reviewed a patient clinical vignette with standard AI model predictions and model explanations compared with baseline accuracy. However, accuracy significantly decreased by 11.3% when clinicians were shown systematically biased AI model predictions and model explanations did not mitigate the negative effects of such predictions. Meaning: AI model explanations did not help clinicians recognize systematically biased AI models. Importance: Artificial intelligence (AI) could support clinicians when diagnosing hospitalized patients; however, systematic bias in AI models could worsen clinician diagnostic accuracy. Recent regulatory guidance has called for AI models to include explanations to mitigate errors made by models, but the effectiveness of this strategy has not been established. Objectives: To evaluate the impact of systematically biased AI on clinician diagnostic accuracy and to determine if image-based AI model explanations can mitigate model errors. Design, Setting, and Participants: Randomized clinical vignette survey study administered between April 2022 and January 2023 across 13 US states involving hospitalist physicians, nurse practitioners, and physician assistants. Interventions: Clinicians were shown 9 clinical vignettes of patients hospitalized with acute respiratory failure, including their presenting symptoms, physical examination, laboratory results, and chest radiographs. Clinicians were then asked to determine the likelihood of pneumonia, heart failure, or chronic obstructive pulmonary disease as the underlying cause(s) of each patient's acute respiratory failure. To establish baseline diagnostic accuracy, clinicians were shown 2 vignettes without AI model input. Clinicians were then randomized to see 6 vignettes with AI model input with or without AI model explanations. Among these 6 vignettes, 3 vignettes included standard-model predictions, and 3 vignettes included systematically biased model predictions. Main Outcomes and Measures: Clinician diagnostic accuracy for pneumonia, heart failure, and chronic obstructive pulmonary disease. Results: Median participant age was 34 years (IQR, 31-39) and 241 (57.7%) were female. Four hundred fifty-seven clinicians were randomized and completed at least 1 vignette, with 231 randomized to AI model predictions without explanations, and 226 randomized to AI model predictions with explanations. Clinicians' baseline diagnostic accuracy was 73.0% (95% CI, 68.3% to 77.8%) for the 3 diagnoses. When shown a standard AI model without explanations, clinician accuracy increased over baseline by 2.9 percentage points (95% CI, 0.5 to 5.2) and by 4.4 percentage points (95% CI, 2.0 to 6.9) when clinicians were also shown AI model explanations. Systematically biased AI model predictions decreased clinician accuracy by 11.3 percentage points (95% CI, 7.2 to 15.5) compared with baseline and providing biased AI model predictions with explanations decreased clinician accuracy by 9.1 percentage points (95% CI, 4.9 to 13.2) compared with baseline, representing a nonsignificant improvement of 2.3 percentage points (95% CI, −2.7 to 7.2) compared with the systematically biased AI model. Conclusions and Relevance: Although standard AI models improve diagnostic accuracy, systematically biased AI models reduced diagnostic accuracy, and commonly used image-based AI model explanations did not mitigate this harmful effect. Trial Registration: ClinicalTrials.gov Identifier: NCT06098950 This randomized clinical vignette study examines whether providing AI explanations to biased AI models enhances clinician diagnostic accuracy. [ABSTRACT FROM AUTHOR]

The article focuses on the challenges of achieving repeatable and accurate quantitative computed tomography (QCT) measurements in pulmonary medicine, particularly in chronic obstructive pulmonary disease (COPD) studies. It discusses the barriers to widespread adoption of QCT measures, the efforts to standardize QCT acquisition protocols, and the development of key repeatability data through the NIH-funded SPIROMICS cohort.

The article focuses on the repeatability of pulmonary quantitative CT measurements in a multi-center study called SPIROMICS. Topics include the acquisition of CT scans, follow-up scans, and the examination of both parenchymal density and airway measurements, including various parameters and adjustments for emphysema and air trapping indicators.

Rationale: Cigarette smoking contributes to the risk of death through different mechanisms. Objectives: To determine how causes of and clinical features associated with death vary in tobacco cigarette users by lung function impairment. Methods: We stratified current and former tobacco cigarette users enrolled in Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) into normal spirometry, PRISm (Preserved Ratio Impaired Spirometry), Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1-2 COPD, and GOLD 3-4 COPD. Deaths were identified via longitudinal follow-up and Social Security Death Index search. Causes of death were adjudicated after a review of death certificates, medical records, and next-of-kin interviews. We tested associations between baseline clinical variables and all-cause mortality using multivariable Cox proportional hazards models. Measurements and Main Results: Over a 10.1-year median follow-up, 2,200 deaths occurred among 10,132 participants (age 59.5 ± 9.0 yr; 46.6% women). Death from cardiovascular disease was most frequent in PRISm (31% of deaths). Lung cancer deaths were most frequent in GOLD 1-2 (18% of deaths vs. 9-11% in other groups). Respiratory deaths outpaced competing causes of death in GOLD 3-4, particularly when BODE index ⩾7. St. George's Respiratory Questionnaire score ⩾25 was associated with higher mortality in all groups: Hazard ratio (HR), 1.48 (1.20-1.84) normal spirometry; HR, 1.40 (1.05-1.87) PRISm; HR, 1.80 (1.49-2.17) GOLD 1-2; HR, 1.65 (1.26-2.17) GOLD 3-4. History of respiratory exacerbations was associated with higher mortality in GOLD 1-2 and GOLD 3-4, quantitative emphysema in GOLD 1-2, and airway wall thickness in PRISm and GOLD 3-4. Conclusions: Leading causes of death vary by lung function impairment in tobacco cigarette users. Worse respiratory-related quality of life is associated with all-cause mortality regardless of lung function. [ABSTRACT FROM AUTHOR]

Lung cancer screening (LCS) with low-dose CT (LDCT) imaging was recommended in 2013, making approximately 8 million Americans eligible for LCS. The demographic characteristics and outcomes of individuals screened in the United States have not been reported at the population level. What are the outcomes among people screened and entered in the American College of Radiology's Lung Cancer Screening Registry compared with those of trial participants? This was a cohort study of individuals undergoing baseline LDCT imaging for LCS between 2015 and 2019. Predictors of adherence to annual screening were computed. LDCT scan interpretations by Lung Imaging Reporting and Data System (Lung-RADS) score, cancer detection rates (CDRs), and stage at diagnosis were compared with National Lung Cancer Screening Trial data. Adherence was 22.3%, and predictors of poor adherence included current smoking status and Hispanic or Black race. On baseline screening, 83% of patients showed negative results and 17% showed positive screening results. The overall CDR was 0.56%. The percentage of people with cancer detected at baseline was higher in the positive Lung-RADS categories at 0.4% for Lung-RADS category 3, 2.6% for Lung-RADS category 4A, 11.1% for Lung-RADS category 4B, and 19.9% for Lung-RADS category 4X. The cancer stage distribution was similar to that observed in the National Lung Cancer Screening Trial, with 53.5% of patients receiving a diagnosis of stage I cancer and 14.3% with stage IV cancer. Underreporting into the registry may have occurred. This study revealed both the positive aspects of CT scan screening for lung cancer and the challenges that remain. Findings on CT imaging were correlated accurately with lung cancer detection using the Lung-RADS system. A significant stage shift toward early-stage lung cancer was present. Adherence to LCS was poor and likely contributes to the lower than expected cancer detection rate, all of which will impact the outcomes of patients undergoing screening for lung cancer. [Display omitted] [ABSTRACT FROM AUTHOR]

Objectives The aim of this study was to identify risk factors of percent predicted forced vital capacity (ppFVC) decline in patients with SSc-associated interstitial lung disease (SSc-ILD). Methods We identified 484 patients with SSc who had HRCT Chest, of which 312 with ILD. Those with serial pulmonary function tests were included in a longitudinal analysis (n  = 184). Linear mixed effect models were fitted to assess the decline in ppFVC over time, and to explore the effect of demographics and baseline characteristics on ppFVC decline. Results The majority of SSc-ILD patients were female (76.3%) and 51.3% had diffuse cutaneous subset. The mean (s. d.) age was 53.6 (12.7) years, median disease duration since first non-RP symptoms was 2.6 years, and 48.4% of the patients had ILD extent >20% on HRCT. In the univariate analysis, longer disease duration (>2.37 years), ILD extent >20%, and anti-topoisomerase I (ATA) positivity were significantly associated with ppFVC decline. In the multivariate analysis, the only statistically significant variable associated with ppFVC decline was ATA positivity. The overall group's mean decline in ppFVC was –0.28% (P -value 0.029), with –0.13% (n  = 163) in those who were alive and –8.28% (P -value 0.0002 for the change in ppFVC trajectory) in patients who died within 2 years. Conclusion Our study confirms that ppFVC is a marker of survival in SSc-ILD, supporting its use for risk stratification to identify patients who may benefit from earlier interventions and treatment. Our study also supports the role of ATA positivity as a predictive marker for ppFVC decline in this population. [ABSTRACT FROM AUTHOR]

Differently from computed tomography (CT), well-defined terminology for chest radiography (CXR) findings and standardized reporting in the setting of known or suspected COVID-19 are still lacking. We propose a revision of CXR major imaging findings in SARS-CoV-2 pneumonia derived from the comparison of CXR and CT, suggesting a precise and standardized terminology for CXR reporting. This description will consider asymptomatic patients, symptomatic patients, and patients with SARS-CoV-2-related pulmonary complications. We suggest using terms such as ground-glass opacities, consolidation, and reticular pattern for the most common findings, and characteristic chest radiographic pattern in presence of one or more of the above-mentioned findings with peripheral and mid-to-lower lung zone distribution. [ABSTRACT FROM AUTHOR]

Objectives To establish a framework by which experts define disease subsets in systemic sclerosis associated interstitial lung disease (SSc-ILD). Methods A conceptual framework for subclinical, clinical and progressive ILD was provided to 83 experts, asking them to use the framework and classify actual SSc-ILD patients. Each patient profile was designed to be classified by at least four experts in terms of severity and risk of progression at baseline; progression was based on 1-year follow-up data. A consensus was reached if ≥75% of experts agreed. Experts provided information on which items were important in determining classification. Results Forty-four experts (53%) completed the survey. Consensus was achieved on the dimensions of severity (75%, 60 of 80 profiles), risk of progression (71%, 57 of 80 profiles) and progressive ILD (60%, 24 of 40 profiles). For profiles achieving consensus, most were classified as clinical ILD (92%), low risk (54%) and stable (71%). Severity and disease progression overlapped in terms of framework items that were most influential in classifying patients (forced vital capacity, extent of lung involvement on high resolution chest CT [HRCT]); risk of progression was influenced primarily by disease duration. Conclusions Using our proposed conceptual framework, international experts were able to achieve a consensus on classifying SSc-ILD patients along the dimensions of disease severity, risk of progression and progression over time. Experts rely on similar items when classifying disease severity and progression: a combination of spirometry and gas exchange and quantitative HRCT. [ABSTRACT FROM AUTHOR]

Background: The diagnosis of constrictive bronchiolitis (CB) in previously deployed individuals, and evaluation of respiratory symptoms more broadly, presents considerable challenges, including using consistent histopathologic criteria and clinical assessments.Research Question: What are the recommended diagnostic workup and associated terminology of respiratory symptoms in previously deployed individuals?Study Design and Methods: Nineteen experts participated in a three-round modified Delphi study, ranking their level of agreement for each statement with an a priori definition of consensus. Additionally, rank-order voting on the recommended diagnostic approach and terminology was performed.Results: Twenty-five of 28 statements reached consensus, including the definition of CB as a histologic pattern of lung injury that occurs in some previously deployed individuals while recognizing the importance of considering alternative diagnoses. Consensus statements also identified a diagnostic approach for the previously deployed individual with respiratory symptoms, distinguishing assessments best performed at a local or specialty referral center. Also, deployment-related respiratory disease (DRRD) was proposed as a broad term to subsume a wide range of potential syndromes and conditions identified through noninvasive evaluation or when surgical lung biopsy reveals evidence of multicompartmental lung injury that may include CB.Interpretation: Using a modified Delphi technique, consensus statements provide a clinical approach to possible CB in previously deployed individuals. Use of DRRD provides a broad descriptor encompassing a range of postdeployment respiratory findings. Additional follow-up of individuals with DRRD is needed to assess disease progression and to define other features of its natural history, which could inform physicians better and lead to evolution in this nosology. [ABSTRACT FROM AUTHOR]

Lung cancer is a leading cause of cancer death in the United States and globally with the majority of lung cancer cases attributable to cigarette smoking. Given the high societal and personal cost of a diagnosis of lung cancer including that most cases of lung cancer when diagnosed are found at a late stage, work over the past 40 years has aimed to detect lung cancer earlier when curative treatment is possible. Screening trials using chest radiography and sputum failed to show a reduction in lung cancer mortality however multiple studies using low dose CT have shown the ability to detect lung cancer early and a survival benefit to those screened. This review will discuss the history of lung cancer screening, current recommendations and screening guidelines, and implementation and components of a lung cancer screening program. [ABSTRACT FROM AUTHOR]

Background: Lung cancer screening (LCS) with low-dose computed tomography (LDCT) was recommended by the U.S. Preventive Services Task Force (USPSTF) in 2013, making approximately 8 million Americans eligible for screening. The demographic characteristics and adherence of persons screened in the United States have not been reported at the population level.Objective: To define sociodemographic characteristics and adherence among persons screened and entered into the American College of Radiology's Lung Cancer Screening Registry (LCSR).Design: Cohort study.Setting: United States, 2015 to 2019.Participants: Persons receiving a baseline LDCT for LCS from 3625 facilities reporting to the LCSR.Measurements: Age, sex, and smoking status distributions (percentages) were computed among persons who were screened and among respondents in the 2015 National Health Interview Survey (NHIS) who were eligible for screening. The prevalence between the LCSR and the NHIS was compared with prevalence ratios (PRs) and 95% CIs. Adherence to annual screening was defined as having a follow-up test within 11 to 15 months of an initial LDCT.Results: Among 1 159 092 persons who were screened, 90.8% (n = 1 052 591) met the USPSTF eligibility criteria. Compared with adults from the NHIS who met the criteria (n = 1257), screening recipients in the LCSR were older (34.7% vs. 44.8% were aged 65 to 74 years; PR, 1.29 [95% CI, 1.20 to 1.39]), more likely to be female (41.8% vs. 48.1%; PR, 1.15 [CI, 1.08 to 1.23]), and more likely to currently smoke (52.3% vs. 61.4%; PR, 1.17 [CI, 1.11 to 1.23]). Only 22.3% had a repeated annual LDCT. If follow-up was extended to 24 months and more than 24 months, 34.3% and 40.3% were adherent, respectively.Limitations: Underreporting of LCS and missing data may skew demographic characteristics of persons reported to be screened. Underreporting of adherence may result in underestimates of follow-up.Conclusion: Approximately 91% of persons who had LCS met USPSTF eligibility criteria. In addition to continuing to target all eligible adults, men, those who formerly smoked, and younger eligible patients may be less likely to be screened. Adherence to annual follow-up screening was poor, potentially limiting screening effectiveness.Primary Funding Source: None. [ABSTRACT FROM AUTHOR]

Objective: Accurate segmentation of the lung nodule in computed tomography images is a critical component of a computer‐assisted lung cancer detection/diagnosis system. However, lung nodule segmentation is a challenging task due to the heterogeneity of nodules. This study is to develop a hybrid deep learning (H‐DL) model for the segmentation of lung nodules with a wide variety of sizes, shapes, margins, and opacities. Materials and methods: A dataset collected from Lung Image Database Consortium image collection containing 847 cases with lung nodules manually annotated by at least two radiologists with nodule diameters greater than 7 mm and less than 45 mm was randomly split into 683 training/validation and 164 independent test cases. The 50% consensus consolidation of radiologists' annotation was used as the reference standard for each nodule. We designed a new H‐DL model combining two deep convolutional neural networks (DCNNs) with different structures as encoders to increase the learning capabilities for the segmentation of complex lung nodules. Leveraging the basic symmetric U‐shaped architecture of U‐Net, we redesigned two new U‐shaped deep learning (U‐DL) models that were expanded to six levels of convolutional layers. One U‐DL model used a shallow DCNN structure containing 16 convolutional layers adapted from the VGG‐19 as the encoder, and the other used a deep DCNN structure containing 200 layers adapted from DenseNet‐201 as the encoder, while the same decoder with only one convolutional layer at each level was used in both U‐DL models, and we referred to them as the shallow and deep U‐DL models. Finally, an ensemble layer was used to combine the two U‐DL models into the H‐DL model. We compared the effectiveness of the H‐DL, the shallow U‐DL and the deep U‐DL models by deploying them separately to the test set. The accuracy of volume segmentation for each nodule was evaluated by the 3D Dice coefficient and Jaccard index (JI) relative to the reference standard. For comparison, we calculated the median and minimum of the 3D Dice and JI over the individual radiologists who segmented each nodule, referred to as M‐Dice, min‐Dice, M‐JI, and min‐JI. Results: For the 164 test cases with 327 nodules, our H‐DL model achieved an average 3D Dice coefficient of 0.750 ± 0.135 and an average JI of 0.617 ± 0.159. The radiologists' average M‐Dice was 0.778 ± 0.102, and the average M‐JI was 0.651 ± 0.127; both were significantly higher than those achieved by the H‐DL model (p < 0.05). The radiologists' average min‐Dice (0.685 ± 0.139) and the average min‐JI (0.537 ± 0.153) were significantly lower than those achieved by the H‐DL model (p < 0.05). The results indicated that the H‐DL model approached the average performance of radiologists and was superior to the radiologist whose manual segmentation had the min‐Dice and min‐JI. Moreover, the average Dice and average JI achieved by the H‐DL model were significantly higher than those achieved by the individual shallow U‐DL model (Dice of 0.745 ± 0.139, JI of 0.611 ± 0.161; p < 0.05) or the individual deep U‐DL model alone (Dice of 0.739 ± 0.145, JI of 0.604 ± 0.163; p < 0.05). Conclusion: Our newly developed H‐DL model outperformed the individual shallow or deep U‐DL models. The H‐DL method combining multilevel features learned by both the shallow and deep DCNNs could achieve segmentation accuracy comparable to radiologists' segmentation for nodules with wide ranges of image characteristics. [ABSTRACT FROM AUTHOR]

Recent clinical practice guidelines have addressed the diagnosis of idiopathic pulmonary fibrosis (IPF) and fibrotic hypersensitivity pneumonitis (fHP). These disease-specific guidelines were developed independently, without clear direction on how to apply their respective recommendations concurrently within a single patient, where discrimination between these two fibrotic interstitial lung diseases represents a frequent diagnostic challenge. The objective of this review, created by an international group of experts, was to suggest a pragmatic approach on how to apply existing guidelines to distinguish IPF and fHP. Key clinical, radiologic, and pathologic features described in previous guidelines are integrated in a set of diagnostic algorithms, which then are placed in the broader context of multidisciplinary discussion to guide the generation of a consensus diagnosis. Although these algorithms necessarily reflect some uncertainty wherever strong evidence is lacking, they provide insight into the current approach favored by experts in the field based on currently available knowledge. The authors further identify priorities for future research to clarify ongoing uncertainties in the diagnosis of fibrotic interstitial lung diseases. [ABSTRACT FROM AUTHOR]

Comprehensive biomarker testing has become the standard of care for informing the choice of the most appropriate targeted therapy for many patients with advanced cancer. Despite evidence demonstrating the need for comprehensive biomarker testing to enable the selection of appropriate targeted therapies and immunotherapy, the incorporation of biomarker testing into clinical practice lags behind recommendations in National Comprehensive Cancer Network guidelines. Coverage policy differences across insurance health plans have limited the accessibility of comprehensive biomarker testing largely to patients whose insurance covers the recommended testing or those who can pay for the testing, and this has contributed to health disparities. Furthermore, even when insurance coverage exists for recommended biomarker testing, patients may incur burdensome out‐of‐pocket costs depending on their insurance plan benefits, which may also create barriers to testing. Prior authorization for biomarker testing for some patients can add an administrative burden and may delay testing and thus treatment if it is not done in a timely manner. Recently, three states (Illinois, Louisiana, and California) passed laws designed to improve access to biomarker testing at the state level. However, there is variability among these laws in terms of the population affected, the stage of cancer, and whether the coverage of testing is mandated, or the legislation addresses only prior authorization. Advocacy efforts by patient advocates, health care professionals, and professional societies are imperative at the state level to further improve coverage for and access to appropriate biomarker testing. [ABSTRACT FROM AUTHOR]

Objective: When patients develop acute respiratory failure (ARF), accurately identifying the underlying etiology is essential for determining the best treatment. However, differentiating between common medical diagnoses can be challenging in clinical practice. Machine learning models could improve medical diagnosis by aiding in the diagnostic evaluation of these patients.Materials and Methods: Machine learning models were trained to predict the common causes of ARF (pneumonia, heart failure, and/or chronic obstructive pulmonary disease [COPD]). Models were trained using chest radiographs and clinical data from the electronic health record (EHR) and applied to an internal and external cohort.Results: The internal cohort of 1618 patients included 508 (31%) with pneumonia, 363 (22%) with heart failure, and 137 (8%) with COPD based on physician chart review. A model combining chest radiographs and EHR data outperformed models based on each modality alone. Models had similar or better performance compared to a randomly selected physician reviewer. For pneumonia, the combined model area under the receiver operating characteristic curve (AUROC) was 0.79 (0.77-0.79), image model AUROC was 0.74 (0.72-0.75), and EHR model AUROC was 0.74 (0.70-0.76). For heart failure, combined: 0.83 (0.77-0.84), image: 0.80 (0.71-0.81), and EHR: 0.79 (0.75-0.82). For COPD, combined: AUROC = 0.88 (0.83-0.91), image: 0.83 (0.77-0.89), and EHR: 0.80 (0.76-0.84). In the external cohort, performance was consistent for heart failure and increased for COPD, but declined slightly for pneumonia.Conclusions: Machine learning models combining chest radiographs and EHR data can accurately differentiate between common causes of ARF. Further work is needed to determine how these models could act as a diagnostic aid to clinicians in clinical settings. [ABSTRACT FROM AUTHOR]

Background: Although recommended lung cancer screening with low‐dose computed tomography scanning (LDCT) reduces mortality among high‐risk adults, annual screening rates remain low. This study complements a previous nationwide assessment of access to lung cancer screening within 40 miles by evaluating differences in accessibility across rural and urban settings for the population aged 50 to 80 years and a subset eligible population based on the 2021 US Preventive Services Task Force LDCT lung screening recommendations. Methods: Distances from population centers to screening facilities (American College of Radiology Lung Cancer Screening Registry) were calculated, and the number of individuals who had access within graduating distances, including 10, 20, 40, 50, and 100 miles, were estimated. Census tract results were aggregated to counties, and both geographies were classified with rural‐urban schemas. Results: Approximately 5% of the eligible population did not have access to lung cancer screening facilities within 40 miles; however, different patterns of accessibility were observed at different distances, between regions, and across rural‐urban environments. Across all distances and geographies, there was a larger percentage of the population in rural geographies with no access. Although the rural population represented approximately 8% of the eligible population, the larger percentage of the rural population with no access was noteworthy and translated into a larger number of individuals with no access at longer distance thresholds (≥40 miles). Conclusions: Disparities in access should be examined as both percentages of the population and numbers of individuals with no access in order to tailor interventions to communities and increase access. Geospatial analysis at the census tract level is recommended to help to identify optimal focus areas and reach the most people. Lay Summary: As annual lung cancer screening rates remain low, this study examines access to lung cancer screening nationwide and across rural and urban settings.A geographic information system network analysis of census tract–level populations is used to estimate access at different distances, including 10, 20, 40, 50, and 100 miles, and the results are aggregated to counties.Approximately 5% of the eligible population does not have access to screening facilities within 40 miles; however, different patterns of accessibility are observed at different distances, between regions, and across rural‐urban environments.Across all distances and geographies, there is a larger percentage of the population in rural geographies with no access. This study uses geospatial analysis to examine access to lung cancer screening at graduating distances nationwide and across rural‐urban settings. Approximately 5% of the eligible population does not have access to lung cancer screening facilities within 40 miles; however, different patterns of accessibility are observed at different distances, between regions, and across rural‐urban environments. [ABSTRACT FROM AUTHOR]

Systemic sclerosis (SSc; scleroderma) has the highest individual mortality of all rheumatic diseases, and interstitial lung disease (ILD) is among the leading causes of SSc‐related death. Two drugs are now approved by the US Food and Drug Administration (FDA) and indicated for slowing the rate of decline in pulmonary function in patients with SSc‐associated ILD (SSc‐ILD): nintedanib (a tyrosine kinase inhibitor) and tocilizumab (the first biologic agent targeting the interleukin‐6 pathway in SSc). In addition, 2 generic drugs with cytotoxic and immunoregulatory activity, mycophenolate mofetil and cyclophosphamide, have shown comparable efficacy in a phase II trial but are not FDA‐approved for SSc‐ILD. In light of the heterogeneity of the disease, the optimal therapeutic strategy for the management of SSc‐ILD is still to be determined. The objectives of this review are 2‐fold: 1) review the body of research focused on the diagnosis and treatment of SSc‐ILD; and 2) propose a practical approach for diagnosis, stratification, management, and therapeutic decision‐making in this clinical context. This review presents a practical classification of SSc patients in terms of disease severity (subclinical versus clinical ILD) and associated risk of progression (low versus high risk). The pharmacologic and nonpharmacologic options for first‐ and second‐line therapy, as well as potential combination approaches, are discussed in light of the recent approval of tocilizumab for SSc‐ILD. [ABSTRACT FROM AUTHOR]

Background: Annual lung cancer screening (LCS) with low-dose chest computed tomography in older current and former smokers (ie, eligible adults) has been recommended since 2013. Uptake has been slow and variable across the United States. We estimated the LCS rate and growth at the national and state level between 2016 and 2018.Methods: The American College of Radiology's Lung Cancer Screening Registry was used to capture screening events. Population-based surveys, the US Census, and cancer registry data were used to estimate the number of eligible adults and lung cancer mortality (ie, burden). Lung cancer screening rates (SRs) in eligible adults and screening rate ratios with 95% confidence intervals (CI) were used to measure changes by state and year.Results: Nationally, the SR was steady between 2016 (3.3%, 95% CI = 3.3% to 3.7%) and 2017 (3.4%, 95% CI = 3.4% to 3.9%), increasing to 5.0% (95% CI = 5.0% to 5.7%) in 2018 (2018 vs 2016 SR ratio = 1.52, 95% CI = 1.51 to 1.62). In 2018, several southern states with a high lung-cancer burden (eg, Mississippi, West Virginia, and Arkansas) had relatively low SRs (<4%) among eligible adults, whereas several northeastern states with lower lung cancer burden (eg, Massachusetts, Vermont, and New Hampshire) had the highest SRs (12.8%-15.2%). The exception was Kentucky, which had the nation's highest lung cancer mortality rate and one of the highest SRs (13.7%).Conclusions: Fewer than 1 in 20 eligible adults received LCS nationally, and uptake varied widely across states. LCS rates were not aligned with lung cancer burden across states, except for Kentucky, which has supported comprehensive efforts to implement LCS. [ABSTRACT FROM AUTHOR]

Lung cancer screening with a low radiation dose chest CT scan is the standard of care for screening-eligible individuals. The net benefit of screening may be optimized by delivering high-quality care, capable of maximizing the benefit and minimizing the harms of screening. Valid, feasible, and relevant indicators of the quality of lung cancer screening may help programs to evaluate their current practice and to develop quality improvement plans. The purpose of this project was to develop quality indicators related to the processes and outcomes of screening. Potential quality indicators were explored through surveys of multidisciplinary lung cancer screening experts. Those that achieved predefined measures of consensus for each of the validity, feasibility, and relevance domains are proposed as quality indicators. Each of the proposed indicators is described in detail, with guidance on how to define, measure, and improve program performance within the indicator. [ABSTRACT FROM AUTHOR]

Background: Lung cancer risk prediction models do not routinely incorporate imaging metrics available on low-dose CT (LDCT) imaging of the chest ordered for lung cancer screening.Research Question: What is the association between quantitative emphysema measured on LDCT imaging and lung cancer incidence and mortality, all-cause mortality, and airflow obstruction in individuals who currently or formerly smoked and are undergoing lung cancer screening?Study Design and Methods: In 7,262 participants in the CT arm of the National Lung Screening Trial, percent low attenuation area (%LAA) was defined as the percentage of lung volume with voxels less than -950 Hounsfield units on the baseline examination. Multivariable Cox proportional hazards models, adjusting for competing risks where appropriate, were built to test for association between %LAA and lung cancer incidence, lung cancer mortality, and all-cause mortality with censoring at 6 years. In addition, multivariable logistic regression models were built to test the cross-sectional association between %LAA and airflow obstruction on spirometry, which was available in 2,700 participants.Results: The median %LAA was 0.8% (interquartile range, 0.2%-2.7%). Every 1% increase in %LAA was independently associated with higher hazards of lung cancer incidence (hazard ratio [HR], 1.02; 95% CI, 1.01-1.03; P = .004), lung cancer mortality (HR, 1.02; 95% CI, 1.00-1.05; P = .045), and all-cause mortality (HR, 1.01; 95% CI, 1.00-1.03; P = .042). Among participants with spirometry, 892 had airflow obstruction. The likelihood of airflow obstruction increased with every 1% increase in %LAA (odds ratio, 1.07; 95% CI, 1.06-1.09; P < .001). A %LAA cutoff of 1% had the best discriminative accuracy for airflow obstruction in participants aged > 65 years.Interpretation: Quantitative emphysema measured on LDCT imaging of the chest can be leveraged to improve lung cancer risk prediction and help diagnose COPD in individuals who currently or formerly smoked and are undergoing lung cancer screening. [ABSTRACT FROM AUTHOR]

Background: Radiologic evidence of air trapping (AT) on expiratory computed tomography (CT) scans is associated with early pulmonary dysfunction in patients with cystic fibrosis (CF). However, standard techniques for quantitative assessment of AT are highly variable, resulting in limited efficacy for monitoring disease progression. Objective: To investigate the effectiveness of a convolutional neural network (CNN) model for quantifying and monitoring AT, and to compare it with other quantitative AT measures obtained from threshold-based techniques. Materials and methods: Paired volumetric whole lung inspiratory and expiratory CT scans were obtained at four time points (0, 3, 12 and 24 months) on 36 subjects with mild CF lung disease. A densely connected CNN (DN) was trained using AT segmentation maps generated from a personalized threshold-based method (PTM). Quantitative AT (QAT) values, presented as the relative volume of AT over the lungs, from the DN approach were compared to QAT values from the PTM method. Radiographic assessment, spirometric measures, and clinical scores were correlated to the DN QAT values using a linear mixed effects model. Results: QAT values from the DN were found to increase from 8.65% ± 1.38% to 21.38% ± 1.82%, respectively, over a two-year period. Comparison of CNN model results to intensity-based measures demonstrated a systematic drop in the Dice coefficient over time (decreased from 0.86 ± 0.03 to 0.45 ± 0.04). The trends observed in DN QAT values were consistent with clinical scores for AT, bronchiectasis, and mucus plugging. In addition, the DN approach was found to be less susceptible to variations in expiratory deflation levels than the threshold-based approach. Conclusion: The CNN model effectively delineated AT on expiratory CT scans, which provides an automated and objective approach for assessing and monitoring AT in CF patients. [ABSTRACT FROM AUTHOR]

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease and the traditional variables extracted from computed tomography (CT) images may not be sufficient to describe all the topological features of lung tissues in COPD patients. We employed an unsupervised three-dimensional (3D) convolutional autoencoder (CAE)-feature constructor (FC) deep learning network to learn from CT data and derive tissue pattern-clusters jointly. We then applied exploratory factor analysis (EFA) to discover the unobserved latent traits (factors) among pattern-clusters. CT images at total lung capacity (TLC) and residual volume (RV) of 541 former smokers and 59 healthy non-smokers from the cohort of the SubPopulations and Intermediate Outcome Measures in the COPD Study (SPIROMICS) were analyzed. TLC and RV images were registered to calculate the Jacobian (determinant) values for all the voxels in TLC images. 3D Regions of interest (ROIs) with two data channels of CT intensity and Jacobian value were randomly extracted from training images and were fed to the 3D CAE-FC model. 80 pattern-clusters and 7 factors were identified. Factor scores computed for individual subjects were able to predict spirometry-measured pulmonary functions. Two factors which correlated with various emphysema subtypes, parametric response mapping (PRM) metrics, airway variants, and airway tree to lung volume ratio were discriminants of patients across all severity stages. Our findings suggest the potential of developing factor-based surrogate markers for new COPD phenotypes. [ABSTRACT FROM AUTHOR]

Background: Screening current and former heavy smokers 55 to 80 years of age for lung cancer (LC) with low-dose chest CT scanning has been recommended by the United States Preventive Services Task Force since 2013. Although the number of screening facilities in the United States has increased, screening uptake has been slow.Research Question: To what extent is geographic access to screening facilities a barrier for screening uptake nationally?Study Design and Methods: Screening facilities were defined as American College of Radiology (ACR) Lung Cancer Screening Registry (LCSR) facilities. Analysis was performed at different geographic levels using a road network to calculate travel distances for the recommended age groups. Full access to screening was defined as the entire 55- to 79-year-old population being within 40 miles of an ACR LCSR facility. No access was defined as lack of access by the entire target population. Partial access was expressed in intervening quartiles. A geospatial approach then was used to integrate accessibility with smoking prevalence and LC mortality rates to identify potential focus areas visually.Results: Screening facilities addresses were geocoded to identify 3,592 unique locations. Analysis of census tracts and aggregation to counties revealed that among 3,142 counties, adults 55 to 79 years of age have full access to an LC screening registry facility in 1,988 (63%) counties, partial access in 587 (19%) counties, and no access in 567 (18%) counties. Overall, less than 6% of those 55 to 79 years of age do not have access to registry screening facilities. Variation in screening facility access was noted across the United States, between states, and within some states.Interpretation: It is recommended to calculate accessibility using subcounty geographies and to examine variation regionally and within states. A foundation geographic accessibility layer can be integrated with other variables to identify geographic disparities in access to screening and to focus on areas for interventions. Identifying areas of greatest need can inform state and local officials and healthcare organizations when planning and implementing LC screening programs. [ABSTRACT FROM AUTHOR]

Background: The risks from potential exposure to coronavirus disease 2019 (COVID-19), and resource reallocation that has occurred to combat the pandemic, have altered the balance of benefits and harms that informed current (pre-COVID-19) guideline recommendations for lung cancer screening and lung nodule evaluation. Consensus statements were developed to guide clinicians managing lung cancer screening programs and patients with lung nodules during the COVID-19 pandemic.Methods: An expert panel of 24 members, including pulmonologists (n = 17), thoracic radiologists (n = 5), and thoracic surgeons (n = 2), was formed. The panel was provided with an overview of current evidence, summarized by recent guidelines related to lung cancer screening and lung nodule evaluation. The panel was convened by video teleconference to discuss and then vote on statements related to 12 common clinical scenarios. A predefined threshold of 70% of panel members voting agree or strongly agree was used to determine if there was a consensus for each statement. Items that may influence decisions were listed as notes to be considered for each scenario.Results: Twelve statements related to baseline and annual lung cancer screening (n = 2), surveillance of a previously detected lung nodule (n = 5), evaluation of intermediate and high-risk lung nodules (n = 4), and management of clinical stage I non-small cell lung cancer (n = 1) were developed and modified. All 12 statements were confirmed as consensus statements according to the voting results. The consensus statements provide guidance about situations in which it was believed to be appropriate to delay screening, defer surveillance imaging of lung nodules, and minimize nonurgent interventions during the evaluation of lung nodules and stage I non-small cell lung cancer.Conclusions: There was consensus that during the COVID-19 pandemic, it is appropriate to defer enrollment in lung cancer screening and modify the evaluation of lung nodules due to the added risks from potential exposure and the need for resource reallocation. There are multiple local, regional, and patient-related factors that should be considered when applying these statements to individual patient care. [ABSTRACT FROM AUTHOR]

Annotating lesion locations by radiologists' manual marking is a key step to provide reference standard for the training and testing of a computer-aided detection system by supervised machine learning. Inter-reader variability is not uncommon in readings even by expert radiologists. This study evaluated the variability of the radiologist-identified pulmonary emboli (PEs) to demonstrate the importance of improving the reliability of the reference standard by a multi-step process for performance evaluation. In an initial reading of 40 CTPA PE cases, two experienced thoracic radiologists independently marked the PE locations. For markings from the two radiologists that did not agree, each radiologist re-read the cases independently to assess the discordant markings. Finally, for markings that still disagreed after the second reading, the two radiologists read together to reach a consensus. The variability of radiologists was evaluated by analyzing the agreement between two radiologists. For the 40 cases, 475 and 514 PEs were identified by radiologists R1 and R2 in the initial independent readings, respectively. For a total of 545 marks by the two radiologists, 81.5% (444/545) of the marks agreed but 101 marks in 36 cases differed. After consensus, 65 (64.4%) and 36 (35.6%) of the 101 marks were determined to be true PEs and false positives (FPs), respectively. Of these, 48 and 17 were false negatives (FNs) and 14 and 22 were FPs by R1 and R2, respectively. Our study demonstrated that there is substantial variability in reference standards provided by radiologists, which impacts the performance assessment of a lesion detection system. Combination of multiple radiologists' readings and consensus is needed to improve the reliability of a reference standard. [ABSTRACT FROM AUTHOR]

Objectives: This study retrospectively analyses the screening CT examinations and outcomes of the National Lung Screening Trial (NLST) participants who had interval lung cancer diagnosed within 1 year after a negative CT screen and before the next annual screen.Methods: The screening CTs of all 44 participants diagnosed with interval lung cancer (cases) were matched with negative CT screens of participants who did not develop lung cancer (controls). A majority consensus process was used to classify each CT screen as positive or negative according to the NLST criteria and to estimate the likelihood that any abnormalities detected retrospectively were due to lung cancer.Results: By retrospective review, 40/44 cases (91%) and 17/44 controls (39%) met the NLST criteria for a positive screen (P < 0.001). Cases had higher estimated likelihood of lung cancer (P < 0.001). Abnormalities included pulmonary nodules ≥4 mm (n = 16), mediastinal (n = 8) and hilar (n = 6) masses, and bronchial lesions (n = 6). Cancers were stage III or IV at diagnosis in 32/44 cases (73%); 37/44 patients (84%) died of lung cancer, compared to 225/649 (35%) for all screen-detected cancers (P < 0.0001).Conclusion: Most cases met the NLST criteria for a positive screen. Awareness of missed abnormalities and interpretation errors may aid lung cancer identification in CT screening.Key Points: • Lung cancer within a year of a negative CT screen was rare. • Abnormalities likely due to lung cancer were identified retrospectively in most patients. • Awareness of error types may help identify lung cancer sooner. [ABSTRACT FROM AUTHOR]

Rationale: Evidence suggests damage to small airways is a key pathologic lesion in chronic obstructive pulmonary disease (COPD). Computed tomography densitometry has been demonstrated to identify emphysema, but no such studies have been performed linking an imaging metric to small airway abnormality.Objectives: To correlate ex vivo parametric response mapping (PRM) analysis to in vivo lung tissue measurements of patients with severe COPD treated by lung transplantation and control subjects.Methods: Resected lungs were inflated, frozen, and systematically sampled, generating 33 COPD (n = 11 subjects) and 22 control tissue samples (n = 3 subjects) for micro-computed tomography analysis of terminal bronchioles (TBs; last generation of conducting airways) and emphysema.Measurements and Main Results: PRM analysis was conducted to differentiate functional small airways disease (PRMfSAD) from emphysema (PRMEmph). In COPD lungs, TB numbers were reduced (P = 0.01); surviving TBs had increased wall area percentage (P < 0.001), decreased circularity (P < 0.001), reduced cross-sectional luminal area (P < 0.001), and greater airway obstruction (P = 0.008). COPD lungs had increased airspace size (P < 0.001) and decreased alveolar surface area (P < 0.001). Regression analyses demonstrated unique correlations between PRMfSAD and TBs, with decreased circularity (P < 0.001), decreased luminal area (P < 0.001), and complete obstruction (P = 0.008). PRMEmph correlated with increased airspace size (P < 0.001), decreased alveolar surface area (P = 0.003), and fewer alveolar attachments per TB (P = 0.01).Conclusions: PRMfSAD identifies areas of lung tissue with TB loss, luminal narrowing, and obstruction. This is the first confirmation that an imaging biomarker can identify terminal bronchial pathology in established COPD and provides a noninvasive imaging methodology to identify small airway damage in COPD. [ABSTRACT FROM AUTHOR]

Background: Hypersensitivity pneumonitis (HP) is an interstitial lung disease with a better prognosis, on average, than idiopathic pulmonary fibrosis (IPF). We compare survival time and pulmonary function trajectory in patients with HP and IPF by radiologic phenotype.Methods: HP (n = 117) was diagnosed if surgical/transbronchial lung biopsy, BAL, and exposure history results suggested this diagnosis. IPF (n = 152) was clinically and histopathologically diagnosed. All participants had a baseline high-resolution CT (HRCT) scan and FVC % predicted. Three thoracic radiologists documented radiologic features. Survival time is from HRCT scan to death or lung transplant. Cox proportional hazards models identify variables associated with survival time. Linear mixed models compare post-HRCT scan FVC % predicted trajectories.Results: Subjects were grouped by clinical diagnosis and three mutually exclusive radiologic phenotypes: honeycomb present, non-honeycomb fibrosis (traction bronchiectasis and reticulation) present, and nonfibrotic. Nonfibrotic HP had the longest event-free median survival (> 14.73 years) and improving FVC % predicted (1.92%; 95% CI, 0.49-3.35; P = .009). HP with non-honeycomb fibrosis had longer survival than IPF (> 7.95 vs 5.20 years), and both groups experienced a significant decline in FVC % predicted. Subjects with HP and IPF with honeycombing had poor survival (2.76 and 2.81 years, respectively) and significant decline in FVC % predicted.Conclusions: Three prognostically distinct, radiologically defined phenotypes are identified among patients with HP. The importance of pursuing a specific diagnosis (eg, HP vs IPF) among patients with non-honeycomb fibrosis is highlighted. When radiologic honeycombing is present, invasive diagnostic testing directed at determining the diagnosis may be of limited value given a uniformly poor prognosis. [ABSTRACT FROM AUTHOR]

Background: COPD is associated with cardiovascular disease (CVD), and coronary artery calcification (CAC) provides additional prognostic information. With increasing use of nongated CT scans in clinical practice, this study hypothesized that the visual Weston CAC score would perform as well as the Agatston score in predicting prevalent and incident coronary artery disease (CAD) and CVD in COPD.Methods: CAC was measured by using Agatston and Weston scores on baseline CT scans in 1,875 current and former smokers enrolled in the Genetic Epidemiology of COPD (COPDGene) study. Baseline cardiovascular disease and incident cardiac events on longitudinal follow-up were recorded. Accuracy of the CAC scores was measured by using receiver-operating characteristic analysis, and Cox proportional hazards analyses were used to estimate the risk of incident cardiac events.Results: CAD was reported by 133 (7.1%) subjects at baseline. A total of 413 (22.0%) and 241 (12.9%) patients had significant CAC according to the Weston (≥ 7) and Agatston (≥ 400) scores, respectively; the two methods were significantly correlated (r = 0.84; P < .001). Over 5 years of follow-up, 127 patients (6.8%) developed incident CVD. For predicting prevalent CAD, c-indices for the Weston and Agatston scores were 0.78 and 0.74 and for predicting incident CVD, they were 0.62 and 0.61. After adjustment for age, race, sex, smoking pack-years, FEV1, percent emphysema, and CT scanner type, a Weston score ≥ 7 was associated with time to first acute coronary event (hazard ratio, 2.16 [95% CI, 1.32 to 3.53]; P = .002), but a Agatston score ≥ 400 was not (hazard ratio, 1.75 [95% CI, 0.99-3.09]; P = .053).Conclusions: A simple visual score for CAC performed well in predicting incident CAD in smokers with and without COPD.Trial Registry: ClinicalTrials.gov; No.: NCT00608764; URL: www.clinicaltrials.gov. [ABSTRACT FROM AUTHOR]

Rationale: Adaptive multiple features method (AMFM) lung texture analysis software recognizes high-resolution computed tomography (HRCT) patterns.Objectives: To evaluate AMFM and visual quantification of HRCT patterns and their relationship with disease progression in idiopathic pulmonary fibrosis.Methods: Patients with idiopathic pulmonary fibrosis in a clinical trial of prednisone, azathioprine, and N-acetylcysteine underwent HRCT at study start and finish. Proportion of lung occupied by ground glass, ground glass-reticular (GGR), honeycombing, emphysema, and normal lung densities were measured by AMFM and three radiologists, documenting baseline disease extent and postbaseline change. Disease progression includes composite mortality, hospitalization, and 10% FVC decline.Measurements and Main Results: Agreement between visual and AMFM measurements was moderate for GGR (Pearson's correlation r = 0.60, P < 0.0001; mean difference = -0.03 with 95% limits of agreement of -0.19 to 0.14). Baseline extent of GGR was independently associated with disease progression when adjusting for baseline Gender-Age-Physiology stage and smoking status (hazard ratio per 10% visual GGR increase = 1.98, 95% confidence interval [CI] = 1.20-3.28, P = 0.008; and hazard ratio per 10% AMFM GGR increase = 1.36, 95% CI = 1.01-1.84, P = 0.04). Postbaseline visual and AMFM GGR trajectories were correlated with postbaseline FVC trajectory (r = -0.30, 95% CI = -0.46 to -0.11, P = 0.002; and r = -0.25, 95% CI = -0.42 to -0.06, P = 0.01, respectively).Conclusions: More extensive baseline visual and AMFM fibrosis (as measured by GGR densities) is independently associated with elevated hazard for disease progression. Postbaseline change in AMFM-measured and visually measured GGR densities are modestly correlated with change in FVC. AMFM-measured fibrosis is an automated adjunct to existing prognostic markers and may allow for study enrichment with subjects at increased disease progression risk. [ABSTRACT FROM AUTHOR]

Rationale: The predominant cause of chronic lung allograft failure is small airway obstruction arising from bronchiolitis obliterans. However, clinical methodologies for evaluating presence and degree of small airway disease are lacking.Objectives: To determine if parametric response mapping (PRM), a novel computed tomography voxel-wise methodology, can offer insight into chronic allograft failure phenotypes and provide prognostic information following spirometric decline.Methods: PRM-based computed tomography metrics quantifying functional small airways disease (PRMfSAD) and parenchymal disease (PRMPD) were compared between bilateral lung transplant recipients with irreversible spirometric decline and control subjects matched by time post-transplant (n = 22). PRMfSAD at spirometric decline was evaluated as a prognostic marker for mortality in a cohort study via multivariable restricted mean models (n = 52).Measurements and Main Results: Patients presenting with an isolated decline in FEV1 (FEV1 First) had significantly higher PRMfSAD than control subjects (28% vs. 15%; P = 0.005), whereas patients with concurrent decline in FEV1 and FVC had significantly higher PRMPD than control subjects (39% vs. 20%; P = 0.02). Over 8.3 years of follow-up, FEV1 First patients with PRMfSAD greater than or equal to 30% at spirometric decline lived on average 2.6 years less than those with PRMfSAD less than 30% (P = 0.004). In this group, PRMfSAD greater than or equal to 30% was the strongest predictor of survival in a multivariable model including bronchiolitis obliterans syndrome grade and baseline FEV1% predicted (P = 0.04).Conclusions: PRM is a novel imaging tool for lung transplant recipients presenting with spirometric decline. Quantifying underlying small airway obstruction via PRMfSAD helps further stratify the risk of death in patients with diverse spirometric decline patterns. [ABSTRACT FROM AUTHOR]

Background: Growth differentiation factor-15 (GDF-15), a cytokine associated with cardiovascular mortality, increases during chronic obstructive pulmonary disease (COPD) exacerbations, but any role in stable COPD is unknown. We tested associations between GDF-15 and subclinical coronary atherosclerosis, assessed by coronary artery calcium (CAC) score, in COPD subjects free of clinical cardiovascular disease (CVD).Methods: Cross-sectional analysis of COPD participants (GOLD stages 2-4) in the COPDGene cohort without CVD at enrollment, using baseline CAC (from non-EKG-gated chest computed tomography) and plasma GDF-15 (by custom ELISA). We used multinomial logistic modeling of GDF-15 associations with CAC, adjusting for demographics, baseline risk (calculated using the HEART: Personal Heart Early Assessment Risk Tool (Budoff et al. 114:1761-1791, 2006) score), smoking history, measures of airflow obstruction, emphysema and airway disease severity.Results: Among 694 participants with COPD (47% women, mean age 63.6 years) mean GDF-15 was 1,304 pg/mL, and mean CAC score was 198. Relative to the lower GDF-15 tertile, higher tertiles showed bivariate association with increasing CAC score (mid tertile odds ratio [OR] 1.80, 95% confidence interval [CI] 1.29, 2.51; higher tertile OR 2.86, CI 2.04, 4.02). This association was maintained after additionally adjusting for baseline CVD risk, for co-morbidities and descriptors of COPD severity and impact, markers of cardiac stress (N-terminal pro-B-type natriuretic peptide, troponin T) and of inflammation (Interleukin-6), and in subgroup analysis excluding men, diabetics, current smokers or those with limited ambulation.Conclusions: In ever-smokers with COPD free of clinical CVD, GDF-15 contributes independently to subclinical coronary atherosclerosis.Trial Registration: ClinicalTrials.gov, NCT00608764 . Registered 28 January 2008. [ABSTRACT FROM AUTHOR]

Rationale: Aging is associated with reduced FEV1 to FVC ratio (FEV1/FVC), hyperinflation, and alveolar enlargement, but little is known about how age affects small airways.Objectives: To determine if chest computed tomography (CT)-assessed functional small airway would increase with age, even among asymptomatic individuals.Methods: We used parametric response mapping analysis of paired inspiratory/expiratory CTs to identify functional small airway abnormality (PRMFSA) and emphysema (PRMEMPH) in the SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study) cohort. Using adjusted linear regression models, we analyzed associations between PRMFSA and age in subjects with or without airflow obstruction. We subdivided participants with normal spirometry based on respiratory-related impairment (6-minute-walk distance <350 m, modified Medical Research Council ≥2, chronic bronchitis, St. George's Respiratory Questionnaire >25, respiratory events requiring treatment [antibiotics and/or steroids or hospitalization] in the year before enrollment).Measurements and Main Results: Among 580 never- and ever-smokers without obstruction or respiratory impairment, PRMFSA increased 2.7% per decade, ranging from 3.6% (ages 40-50 yr) to 12.7% (ages 70-80 yr). PRMEMPH increased nonsignificantly (0.1% [ages 40-50 yr] to 0.4% [ages 70-80 yr]; P = 0.34). Associations were similar among nonobstructed individuals with respiratory-related impairment. Increasing PRMFSA in subjects without airflow obstruction was associated with increased FVC (P = 0.004) but unchanged FEV1 (P = 0.94), yielding lower FEV1/FVC ratios (P < 0.001). Although emphysema was also significantly associated with lower FEV1/FVC (P = 0.04), its contribution relative to PRMFSA in those without airflow obstruction was limited by its low burden.Conclusions: In never- and ever-smokers without airflow obstruction, aging is associated with increased FVC and CT-defined functional small airway abnormality regardless of respiratory symptoms. [ABSTRACT FROM AUTHOR]