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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: 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]

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]

Objective: To determine whether intrathoracic fat volumes are associated with presence and chronicity of atrial fibrillation (AF) and radiofrequency ablation (RFA) treatment outcome.Methods: IRB approval was obtained and patient consent was waived for this HIPAA-compliant retrospective study. 169 patients with AF (75 non-paroxysmal and 94 paroxysmal) and 62 control patients underwent cardiac CT examination. Extrapericardial (EPFV) and epicardial fat volumes (EFV) were measured on CT, the sum of which is the total intrathoracic fat volume. Associations between these three fat volumes and presence and chronicity of AF, and outcome after RFA, were evaluated using logistic regression analysis.Results: EFV was significantly associated with presence [OR 1.01 (95 % CI 1.003-1.03), p = 0.01], chronicity of AF [1.008 (1.001-1.020), p = 0.03] and AF recurrence after RFA [1.009 (1.001-1.01), p = 0.02] after adjustment for age, gender and BMI. Patients with a larger EFV had a shorter time to AF recurrence (p = 0.017) and a higher rate of recurrence (54 % vs 46 %) (p = 0.002) after RFA. EPFV had no significant associations.Conclusion: Increased epicardial fat is associated with the presence and chronicity of AF, a higher probability of AF recurrence after RFA and a shorter AF-free interval.Key Points: • Increased epicardial fat is associated with presence and chronicity of atrial fibrillation • Extensive epicardial fat is associated with earlier recurrences of AF after ablation • Extensive epicardial fat may reduce transmurality of ablation by affecting current dynamics. [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]

Objectives: To evaluate selection criteria and duration of benefit for patients undergoing lung volume reduction surgery (LVRS). Methods: Eighty-nine consecutive patients with severe emphysema who underwent bilateral LVRS were prospectively followed up for up to 3 years. Patients underwent preoperative pulmonary function testing, 6-min walk, chest CT, and answered a baseline dyspnea questionnaire. CT scans in 65 patients were analyzed for emphysema extent and distribution using the percentage of emphysema in the lung, percentage of normal lower lung and the CT emphysema ratio (CFR, an index of the craniocaudal distribution of emphysena). All patients underwent at least 6 weeks of pulmonary rehabilitation prior to surgery. Outcome measures were FEV[sub 1], 6-min walk distance, and transitional dyspnea index (TDI). Results: Compared to baseline, FEV[sub 1] was significantly increased at 3, 6, 12, 18, 24, and 36 months after surgery (p ≤ 0.008). The 6-min walk distance increased from 871 feet (baseline) to 1,110 feet (3 months), 1,214 feet (6 months), 1,326 feet (12 months), 1,342 feet (18 months), 1,371 feet (24 months), and 1,390 feet (36 months) after surgery. Despite a decline in FEV[sub 1] over time, 6-min walk distance was preserved. Dyspnea as measured by TDI improved at 3, 6, 12, 18, 24, and 36 months after surgery. A high CTR was the best predictor of a 12% increase over baseline and an absolute increase of 200 mL in FEV[sub 1], although with a low area under the receiver operating characteristic curve. In addition, the sensitivity and negative predictive value of the CFR were limited. No radiographic or physiologic predictor was able to consistently predict a successful increase in walk distance or TDI. Conclusion: LVRS improves pulmonary function, decreases dyspnea, and enhances exercise capacity in many patients with severe emphysema, although improvement wanes 36 months after surgery. [ABSTRACT FROM AUTHOR]

Ventilation/perfusion scans with single-photon emission tomography (SPET) were reviewed to determine their usefulness in the evaluation of lung volume reduction surgery (LVRS) candidates, and as a predictor of outcome after surgery. Fifty consecutive planar ventilation ([sup 99m] Tc-DTPA aerosol) and perfusion ([sup 99m] Tc-MAA) scans with perfusion SPET of patients evaluated for LVRS were retrospectively reviewed. Technical quality and the severity and extent of radiotracer defects in the upper and lower halves of the lungs were scored from visual inspection of planar scans and SPET data separately. An emphysema index (EI) (extent x severity) for the upper and lower halves of the lung, and an EI ratio for upper to lower lung were calculated for both planar and SPET scans. The ratios were compared with post-LVRS outcomes, 3, 6 and 12 months after surgery. All perfusion and SPET images were technically adequate. Forty-six percent of ventilation scans were not technically adequate due to central airway tracer deposition. Severity, extent, EI scores and EI ratios between perfusion and SPET were in good agreement (r = 0.52-0.68). The mean perfusion EI ratio was significantly different between the 30 patients undergoing biapical LVRS and the 17 patients excluded from LVRS (3.3±1.8 versus 1.2±0.7; P<0.0001), in keeping with the anatomic distribution of emphysema by which patients were selected for surgery by computed tomography (CT). The perfusion EI ratio correlated moderately with the change in FEV[sub 1] at 3 months (r = 0.37, P = 0.04), 6 months (r = 0.36, P = 0.05), and 12 months (r = 0.42, P = 0.03), and the transition dyspnea index at 6 months (r = 0.48, P = 0.014) after LVRS. It is concluded that patients selected to undergo LVRS have more severe and extensive apical perfusion deficits than patients not selected for LVRS, based on CT determination. SPE... [ABSTRACT FROM AUTHOR]

Presents a study to determine if a mandatory radiology consultation service can decrease radiology resource use on inpatient internal medicine services. Design and setting; Patients and other participants; Intervention; Main outcome measure; Results; Conclusions.

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]

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]

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]

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]

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]

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]

A letter to the editor is presented discussing the cost-effectiveness of computed-tomography (CT) scanning for lung cancer.

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: 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]

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 screening with CT remains controversial. Lung cancer is the leading cause of cancer death. To date, no screening test has been demonstrated to reduce mortality. Given the large population of adult cigarette smokers and former smokers worldwide, there is a large population at risk for lung cancer. While a lot has been learned from prospective single-arm cohort studies about the feasibility of performing annual CT to screen for lung cancer, many questions have also been raised. While we know that screening for lung cancer with CT detects many small nodules, with up to half the subjects having a positive baseline screen, and up to 75% of subjects having a positive screen at least once if screened annually for 5 years, the great majority of these nodules exhibit benign biologic behavior. The innumerable small nodules detected with screening CT, and diagnostic chest CT in general, present a daily clinical challenge, and result in extensive medical resource utilization and additional radiation exposure. Algorithms for how and when to follow small nodules detected on CT are in evolution. Ongoing studies are designed to determine if lung cancer screening with CT reduces lung cancer mortality. [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]

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]

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]

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]

Lung cancer screening with low dose computed tomography (CT) is the only method ever proven to reduce lung cancer--specific mortality in high-risk current and former cigarette smokers. Radiation exposure from annual screening CT examinations and subsequent CT and nuclear medicine testing to further evaluate positive screening CTs is sometimes raised as a reason to avoid screening and is often misunderstood. With all testing, there are potential benefits and risks. As we sit on the brink of widespread adoption of lung cancer screening CT, we aim to explain why the risks associated with radiation exposure from lung cancer screening are very low and should not be used to avoid screening or dissuade individuals who qualify for screening CT to participate in a lung cancer screening program. [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]

The purpose of this study was to evaluate agreement between radiologists and semiautomated software measurements of pulmonary vein ostial diameters and distance to the first bifurcation. Computed tomography (CT) examinations of the thorax were retrospectively reviewed in 50 consecutive patients. The pulmonary vein ostial diameter and distance to the first bifurcation of the four major, and the middle lobe pulmonary veins, were measured independently by two experienced cardiothoracic radiologists, and using CardEP semiautomated software. The two measurement methods were compared. Analyses were performed using a Bland-Altman test. There is no significant variation between readers or between readers and the software in pulmonary vein ostial diameter measurements for the four major pulmonary veins. There is significant variation between the first reader ( P = 0.03) and between the second reader and the software ( P = 0.01) in vein diameter measurements for the middle lobe vein. And, also between the second reader and the first reader for the right inferior ( P = 0.02) and left inferior ( P = 0.02) pulmonary vein distance to first bifurcation, and between the second reader and the software for the right inferior ( P = 0.01) and left inferior vein ( P = 0.02) distance to first bifurcation. There is good interobserver agreement when measuring the major pulmonary vein diameters. Measurements of distances to first bifurcation were less reliable across readers and the readers and the semiautomated software. [ABSTRACT FROM AUTHOR]

More than 150,00 patients a year present to their physicians with the diagnostic dilemma of a solitary pulmonary nodule (SPN) found either on chest radiography or chest CT. A thoughtful and timely workup of this finding is essential if lung cancer is to be recognized early and the chance for cure optimized. Based on the literature to date, recommendations are made for appropriate imaging modalities and diagnostic testing, as well as indications for obtaining preoperative tissue diagnosis for the patient with an SPN. [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]

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: 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]

Purpose This study aimed to investigate the breast dose reduction potential of a breast-positioning ( BP) technique for thoracic CT examinations with organ-based tube current modulation ( OTCM). Methods This study included 13 female anthropomorphic computational phantoms ( XCAT, age range: 27-65 y.o., weight range: 52-105.8 kg). Each phantom was modified to simulate three breast sizes in standard supine geometry. The modeled breasts were then morphed to emulate BP that constrained the majority of the breast tissue inside the 120° anterior tube current ( mA) reduction zone. The OTCM mA value was modeled using a ray-tracing program, which reduced the mA to 20% in the anterior region with a corresponding increase to the posterior region. The organ doses were estimated by a validated Monte Carlo program for a typical clinical CT system ( SOMATOM Definition Flash, Siemens Healthcare). The simulated organ doses and organ doses normalized by CTDIvol were used to compare three CT protocols: attenuation-based tube current modulation ( ATCM), OTCM, and OTCM with BP (OTCMBP). Results On average, compared to ATCM, OTCM reduced breast dose by 19.3 ± 4.5%, whereas OTCMBP reduced breast dose by 38.6 ± 8.1% (an additional 23.8 ± 9.4%). The dose saving of OTCMBP was more significant for larger breasts (on average 33, 38, and 44% reduction for 0.5, 1, and 2 kg breasts, respectively). Compared to ATCM, OTCMBP also reduced thymus and heart dose by 15.1 ± 7.4% and 15.9 ± 6.2% respectively. Conclusions In thoracic CT examinations, OTCM with a breast-positioning technique can markedly reduce unnecessary exposure to radiosensitive organs in anterior chest wall, specifically breast tissue. The breast dose reduction is more notable for women with larger breasts. [ABSTRACT FROM AUTHOR]

Background: Surgical lung biopsy (SLB) is invasive and not possible in all patients with undiagnosed interstitial lung disease (ILD). We hypothesized that transbronchial biopsy (TBB) findings combined with clinical and high-resolution CT (HRCT) data leads to a confident diagnosis congruent to SLB and therefore avoids the need for SLB in some patients.Methods: We evaluated 33 patients being investigated for suspected ILD who underwent HRCT, TBB, and SLB. First, clinicians, radiologists, and a pathologist reviewed the clinical information and HRCT and TBB findings. Clinicians were asked to provide a diagnosis and were also asked if SLB was needed for a more confident diagnosis. Subsequently, the clinical, HRCT, and SLB data were reviewed, and the same participants were asked to provide a final diagnosis. Clinician consensus and overall agreement between TBB- and SLB-based diagnoses were calculated.Results: Four patients had definite usual interstitial pneumonia (UIP) on HRCT and would not be considered for biopsy using current guidelines. Of the 29 patients without a definitive HRCT diagnosis, the clinicians felt confident of the diagnosis (ie, would not recommend SLB) in six cases. In these cases, there was 100% agreement between TBB and SLB diagnoses. UIP was the most common diagnosis (n = 3) and was associated with an HRCT diagnosis of possible UIP/nonspecific interstitial pneumonia-like. Agreement was poor (33%) between TBB and SLB diagnoses when confidence in the TBB diagnosis was low.Conclusions: Information from TBB, when combined with clinical and HRCT data, may provide enough information to make a confident and accurate diagnosis in approximately 20% to 30% of patients with ILD. [ABSTRACT FROM AUTHOR]

Purpose: The authors are developing an automated method to identify the best-quality coronary arterial segment from multiple-phase coronary CT angiography (cCTA) acquisitions, which may be used by either interpreting physicians or computer-aided detection systems to optimally and efficiently utilize the diagnostic information available in multiple-phase cCTA for the detection of coronary artery disease. Methods: After initialization with a manually identified seed point, each coronary artery tree is automatically extracted from multiple cCTA phases using our multiscale coronary artery response enhancement and 3D rolling balloon region growing vessel segmentation and tracking method. The coronary artery trees from multiple phases are then aligned by a global registration using an affine transformation with quadratic terms and nonlinear simplex optimization, followed by a local registration using a cubic B-spline method with fast localized optimization. The corresponding coronary arteries among the available phases are identified using a recursive coronary segment matching method. Each of the identified vessel segments is transformed by the curved planar reformation (CPR) method. Four features are extracted from each corresponding segment as quality indicators in the original computed tomography volume and the straightened CPR volume, and each quality indicator is used as a voting classifier for the arterial segment. A weighted voting ensemble (WVE) classifier is designed to combine the votes of the four voting classifiers for each corresponding segment. The segment with the highest WVE vote is then selected as the best-quality segment. In this study, the training and test sets consisted of 6 and 20 cCTA cases, respectively, each with 6 phases, containing a total of 156 cCTA volumes and 312 coronary artery trees. An observer preference study was also conducted with one expert cardiothoracic radiologist and four nonradiologist readers to visually rank vessel segment quality. The performance of our automated method was evaluated by comparing the automatically identified best-quality segments identified by the computer to those selected by the observers. Results: For the 20 test cases, 254 groups of corresponding vessel segments were identified after multiple phase registration and recursive matching. The AI-BQ segments agreed with the radiologist's top 2 ranked segments in 78.3% of the 254 groups (Cohen's kappa 0.60), and with the 4 nonradiologist observers in 76.8%, 84.3%, 83.9%, and 85.8% of the 254 groups. In addition, 89.4% of the AI-BQ segments agreed with at least two observers' top 2 rankings, and 96.5% agreed with at least one observer's top 2 rankings. In comparison, agreement between the four observers' top ranked segment and the radiologist's top 2 ranked segments were 79.9%, 80.7%, 82.3%, and 76.8%, respectively, with kappa values ranging from 0.56 to 0.68. Conclusions: The performance of our automated method for selecting the best-quality coronary segments from a multiple-phase cCTA acquisition was comparable to the selection made by human observers. This study demonstrates the potential usefulness of the automated method in clinical practice, enabling interpreting physicians to fully utilize the best available information in cCTA for diagnosis of coronary disease, without requiring manual search through the multiple phases and minimizing the variability in image phase selection for evaluation of coronary artery segments across the diversity of human readers with variations in expertise. C 2016 American Association of Physicists in Medicine. [ABSTRACT FROM AUTHOR]

The detection of stenotic plaques strongly depends on the quality of the coronary arterial tree imaged with coronary CT angiography (cCTA). However, it is time consuming for the radiologist to select the best-quality vessels from the multiple-phase cCTA for interpretation in clinical practice. We are developing an automated method for selection of the best-quality vessels from coronary arterial trees in multiple-phase cCTA to facilitate radiologist’s reading or computerized analysis. Our automated method consists of vessel segmentation, vessel registration, corresponding vessel branch matching, vessel quality measure (VQM) estimation, and automatic selection of best branches based on VQM. For every branch, the VQM was calculated as the average radial gradient. An observer preference study was conducted to visually compare the quality of the selected vessels. 167 corresponding branch pairs were evaluated by two radiologists. The agreement between the first radiologist and the automated selection was 76% with kappa of 0.49. The agreement between the second radiologist and the automated selection was also 76% with kappa of 0.45. The agreement between the two radiologists was 81% with kappa of 0.57. The observer preference study demonstrated the feasibility of the proposed automated method for the selection of the best-quality vessels from multiple cCTA phases. [ABSTRACT FROM AUTHOR]

Purpose: A test object (phantom) is an important tool to evaluate comparability and stability of CT scanners used in multicenter and longitudinal studies. However, there are many sources of error that can interfere with the test object-derived quantitative measurements. Here the authors investigated three major possible sources of operator error in the use of a test object employed to assess pulmonary density-related as well as airway-related metrics. Methods: Two kinds of experiments were carried out to assess measurement variability caused by imperfect scanning status. The first one consisted of three experiments. A COPDGene test object was scanned using a dual source multidetector computed tomographic scanner (Siemens Somatom Flash) with the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS) inspiration protocol (120 kV, 110 mAs, pitch = 1, slice thickness = 0.75 mm, slice spacing = 0.5 mm) to evaluate the effects of tilt angle, water bottle offset, and air bubble size. After analysis of these results, a guideline was reached in order to achieve more reliable results for this test object. Next the authors applied the above findings to 2272 test object scans collected over 4 years as part of the SPIROMICS study. The authors compared changes of the data consistency before and after excluding the scans that failed to pass the guideline. Results: This study established the following limits for the test object: tilt index ≤0.3, water bottle offset limits of [-6.6 mm, 7.4 mm], and no air bubble within the water bottle, where tilt index is a measure incorporating two tilt angles around x- and y-axis. With 95% confidence, the density measurement variation for all five interested materials in the test object (acrylic, water, lung, inside air, and outside air) resulting from all three error sources can be limited to ±0.9 HU (summed in quadrature), when all the requirements are satisfied. The authors applied these criteria to 2272 SPIROMICS scans and demonstrated a significant reduction in measurement variation associated with the test object. Conclusions: Three operator errors were identified which significantly affected the usability of the acquired scan images of the test object used for monitoring scanner stability in a multicenter study. The authors' results demonstrated that at the time of test object scan receipt at a radiology core laboratory, quality control procedures should include an assessment of tilt index, water bottle offset, and air bubble size within the water bottle. Application of this methodology to 2272 SPIROMICS scans indicated that their findings were not limited to the scanner make and model used for the initial test but was generalizable to both Siemens and GE scanners which comprise the scanner types used within the SPIROMICS study. [ABSTRACT FROM AUTHOR]

Purpose: To determine patient preferences for use of a chaperone during transvaginal sonography. Materials and methods: Informed consent was waived for this IRB-exempt, HIPAA-compliant, retrospective review of prospectively acquired quality improvement survey data. Willing unique consecutive adult patients (n = 155) scheduled to undergo a transvaginal pelvic ultrasound between March 1, 2015 and August 15, 2015 completed a survey before and after the ultrasound examination assessing their opinions regarding the need for a chaperone. Surveys were collected from three outpatient centers (n = 35/center) and an inpatient center (n = 50) in the same health system. Respondent opinions were compared with Chi-Square test or Fisher's Exact test. Results: Most respondents (78% [121/155]) had previously undergone a transvaginal pelvic ultrasound. Respondents were significantly more likely to prefer a chaperone if their sonographer was male than if their sonographer was female (pre-ultrasound: 46% [69/155] vs. 12% [19/155], p < 0.0001; post-ultrasound: 43% [66/155] vs. 6% [10/155], p < 0.0001). The fraction of respondents who said they would be somewhat or substantially reassured by a chaperone if their sonographer was female was similar to the fraction of respondents who said they would be somewhat or substantially embarrassed by presence of a female chaperone (pre-ultrasound: 12% [19/155] vs. 6% [9/155], p = 0.07; post-ultrasound: 6% [10/155] vs. 7% [11/155], p = 0.82; respectively). Conclusion: Approximately half of adult women scheduled to undergo transvaginal pelvic sonography prefer that a chaperone be present if their sonographer is male, but in general do not feel it is necessary if their sonographer is female. These opinions do not change after performance of a transvaginal pelvic ultrasound. [ABSTRACT FROM AUTHOR]

Purpose: The purpose was to compare first-pass and delayed-phase thoracic computed tomography (CT) venography for the evaluation of suspected central thoracic venous pathology. Material and methods: CT images and medical records of all patients who underwent thoracic CT venography over a 5-year period were retrospectively reviewed. Both first-pass (18 s) and delayed-phase (60 s) venous images were obtained in all patients. The images were reviewed in random order by three readers using a semiquantitative visual grading scheme for image quality, including artifacts and the uniformity of venous enhancement. In addition, enhancement was quantitatively evaluated. The presence and type of venous pathology, and overall diagnostic confidence were recorded and compared. Reference verification was performed when available. Results: Eighteen patients formed the study group, mean age 49.5 years and 28% male. Dual-arm injection was successful in 72% of exams. All readers reported more streak artifacts on first-pass imaging than delayed imaging (72%-94% vs. 27%-44%, respectively; Pb.05). First-pass imaging had significantly higher measured enhancement across all central venous segments than delayed imaging (mean HU range 212-906 HU vs. 173-414 HU; Pb.05) but also had significantly more heterogeneous enhancement (mean S.D. range 75-1058 HU vs. 67-378 HU; Pb.05). For overall diagnosis, reader agreement, accuracy, and confidence levels were higher for delayed-phase images (Pb.05). Conclusion: Indirect thoracic CT venography using delayed-phase imaging alone may be sufficient for evaluating clinically suspected central venous abnormality. [ABSTRACT FROM AUTHOR]

Background: Lung cancer screening with low-dose computed tomography (LDCT) has been recommended, based primarily on the results of the NLST (National Lung Screening Trial). The American College of Radiology recently released Lung-RADS, a classification system for LDCT lung cancer screening. Objective: To retrospectively apply the Lung-RADS criteria to the NLST. Design: Secondary analysis of a group from a randomized trial. Setting: 33 U.S. screening centers. Patients: Participants were randomly assigned to the LDCT group of the NLST, were aged 55 to 74 years, had at least a 30-pack-year history of smoking, and were current smokers or had quit within the past 15 years. Intervention: 3 annual LDCT lung cancer screenings. Measurements: Lung-RADS classifications for LDCT screenings. Lung-RADS categories 1 to 2 constitute negative screening results, and categories 3 to 4 constitute positive results. Results: Of 26 722 LDCT group participants, 26 455 received a baseline screening; 48 671 screenings were done after baseline. At baseline, the false-positive result rate (1 minus the specificity rate) for Lung-RADS was 12.8% (95% CI, 12.4% to 13.2%) versus 26.6% (CI, 26.1% to 27.1%) for the NLST; after baseline, the false-positive result rate was 5.3% (CI, 5.1% to 5.5%) for Lung-RADS versus 21.8% (CI, 21.4% to 22.2%) for the NLST. Baseline sensitivity was 84.9% (CI, 80.8% to 89.0%) for Lung-RADS versus 93.5% (CI, 90.7% to 96.3%) for the NLST, and sensitivity after baseline was 78.6% (CI, 74.6% to 82.6%) for Lung-RADS versus 93.8% (CI, 91.4% to 96.1%) for the NLST. Limitation: Lung-RADS criteria were applied retrospectively. Conclusion: Lung-RADS may substantially reduce the false-positive result rate; however, sensitivity is also decreased. The effect of using Lung-RADS criteria in clinical practice must be carefully studied. [ABSTRACT FROM AUTHOR]

Introduction Left ventricular ( LV) and right ventricular ( RV) volumetric and functional parameters are important biomarkers for morbidity and mortality in patients with heart failure. Purpose To retrospectively determine reference mean values of LV and RV volume, function and mass normalised by age, gender and body surface area ( BSA) from retrospectively electrocardiographically gated 64-slice cardiac computed tomography ( CCT) by using automated analysis software in healthy adults. Materials and Methods The study was approved by the institutional review board with a waiver of informed consent. Seventy-four healthy subjects (49% female, mean age 49.6 ± 11) free of hypertension and hypercholesterolaemia with a normal CCT formed the study population. Analyses of LV and RV volume (end-diastolic, end-systolic and stroke volumes), function (ejection fraction), LV mass and inter-rater reproducibility were performed with commercially available analysis software capable of automated contour detection. General linear model analysis was performed to assess statistical significance by age group after adjustment for gender and BSA. Bland-Altman analysis assessed the inter-rater agreement. Results The reference range for LV and RV volume, function, and LV mass was normalised to age, gender and BSA. Statistically significant differences were noted between genders in both LV mass and RV volume ( P-value < 0.0001). Age, in concert with gender, was associated with significant differences in RV end-diastolic volume and LV ejection fraction ( P-values 0.027 and 0.03). Bland-Altman analysis showed acceptable limits of agreement (±1.5% for ejection fraction) without systematic error. Conclusion LV and RV volume, function and mass normalised to age, gender and BSA can be reported from CCT datasets, providing additional information important for patient management. [ABSTRACT FROM AUTHOR]