Quantitative CT Evidence of Airway Inflammation in WTC Workers and Volunteers with Low FVC Spirometric Pattern.

BACKGROUND: The most common abnormal spirometric pattern reported in WTC worker and volunteer cohorts has consistently been that of a nonobstructive reduced forced vital capacity (low FVC). Low FVC is associated with obesity, which is highly prevalent in these cohorts. We used quantitative CT (QCT) to investigate proximal and distal airway inflammation and emphysema in participants with stable low FVC pattern. METHODS: We selected study participants with at least two available longitudinal surveillance spirometries, and a chest CT with QCT measurements of proximal airway inflammation (wall area percent, WAP), end-expiratory air trapping, suggestive of distal airway obstruction (expiratory to inspiratory mean lung attenuation ratio, MLAEI), and emphysema (percentage of lung volume with attenuation below - 950 HU, LAV%). The comparison groups in multinomial logistic regression models were participants with consistently normal spirometries, and participants with stable fixed obstruction (COPD). RESULTS: Compared to normal spirometry participants, and after adjusting for age, sex, race/ethnicity, BMI, smoking, and early arrival at the WTC disaster site, low FVC participants had higher WAP (ORadj 1.24, 95% CI 1.06, 1.45, per 5% unit), suggestive of proximal airway inflammation, but did not differ in MLAEI, or LAV%. COPD participants did not differ in WAP with the low FVC ones and were more likely to have higher MLAEI or LAV% than the other two subgroups. DISCUSSION: WTC workers with spirometric low FVC have higher QCT-measured WAP compared to those with normal spirometries, but did not differ in distal airway and emphysema measurements, independently of obesity, smoking, and other covariates.

Association of Obesity with Quantitative Chest CT Measured Airway Wall Thickness in WTC Workers with Lower Airway Disease.

BACKGROUND: We previously reported that wall area percent (WAP), a quantitative CT (QCT) indicator of airway wall thickness and, presumably, inflammation, is associated with adverse longitudinal expiratory flow trajectories in WTC workers, but that obesity and weight gain also seemed to be independently predictive of the latter. Previous studies have reported no association between WAP and obesity, so we investigated that association in nonsmoking WTC-exposed individuals and healthy unexposed controls. METHODS: We assessed WAP using the Chest Imaging Platform QCT system in a segmental bronchus in 118 former WTC workers, and 89 COPDGene® WTC-unexposed and asymptomatic subjects. We used multiple regression to model WAP vs. body mass index (BMI) in the two groups, adjusting for important subject and CT image characteristics. RESULTS: Unadjusted analyses revealed significant differences between the two groups with regards to WAP, age, gender, scan pixel spacing and slice interval, but not BMI or total lung capacity. In adjusted analysis, there was a significant interaction between BMI and WTC exposure on WAP. BMI was significantly and positively associated with WAP in the WTC group, but not in the COPDGene® group, but stratified analyses revealed that the effect was significant in WTC subjects with clinical evidence of lower airway disease (LAD). DISCUSSION: Unlike non-diseased subjects, BMI was significantly associated with WAP in WTC workers and, in stratified analyses, the association was significant only among those with LAD. Our findings suggest that this adverse effect of obesity on airway structure and inflammation may be confined to already diseased individuals.

Increased Airway Wall Thickness is Associated with Adverse Longitudinal First-Second Forced Expiratory Volume Trajectories of Former World Trade Center workers.

RATIONALE: Occupational exposures at the WTC site after September 11, 2001 have been associated with several presumably inflammatory lower airway diseases. In this study, we describe the trajectories of expiratory air flow decline, identify subgroups with adverse progression, and investigate the association of a quantitative computed tomography (QCT) imaging measurement of airway wall thickness, and other risk factors for adverse progression. METHODS: We examined the trajectories of expiratory air flow decline in a group of 799 former WTC workers and volunteers with QCT-measured (with two independent systems) wall area percent (WAP) and at least 3 periodic spirometries. We calculated individual regression lines for first-second forced expiratory volume (FEV1), identified subjects with rapidly declining and increasing ("gainers"), and compared them to subjects with normal and "stable" FEV1 decline. We used multivariate logistic regression to model decliner vs. stable trajectories. RESULTS: The mean longitudinal FEV1slopes for the entire study population, and its stable, decliner, and gainer subgroups were, respectively, - 35.8, - 8, - 157.6, and + 173.62 ml/year. WAP was associated with "decliner" status (ORadj 1.08, 95% CI 1.02, 1.14, per 5% increment) compared to stable. Age, weight gain, baseline FEV1 percent predicted, bronchodilator response, and pre-WTC occupational exposures were also significantly associated with accelerated FEV1 decline. Analyses of gainers vs. stable subgroup showed WAP as a significant predictor in unadjusted but not consistently in adjusted analyses. CONCLUSIONS: The apparent normal age-related rate of FEV1 decline results from averaging widely divergent trajectories. WAP is significantly associated with accelerated air flow decline in WTC workers.

Low-Dose CT Screening for Lung Cancer: Computer-aided Detection of Missed Lung Cancers.

Purpose To update information regarding the usefulness of computer-aided detection (CAD) systems with a focus on the most critical category, that of missed cancers at earlier imaging, for cancers that manifest as a solid nodule. Materials and Methods By using a HIPAA-compliant institutional review board-approved protocol where informed consent was obtained, 50 lung cancers that manifested as a solid nodule on computed tomographic (CT) scans in annual rounds of screening (time 1) were retrospectively identified that could, in retrospect, be identified on the previous CT scans (time 0). Four CAD systems were compared, which were referred to as CAD 1, CAD 2, CAD 3, and CAD 4. The total number of accepted CAD-system-detected nodules at time 0 was determined by consensus of two radiologists and the number of CAD-system-detected nodules that were rejected by the radiologists was also documented. Results At time 0 when all the cancers had been missed, CAD system detection rates for the cancers were 56%, 70%, 68%, and 60% (κ = 0.45) for CAD systems 1, 2, 3, and 4, respectively. At time 1, the rates were 74%, 82%, 82%, and 78% (κ = 0.32), respectively. The average diameter of the 50 cancers at time 0 and time 1 was 4.8 mm and 11.4 mm, respectively. The number of CAD-system-detected nodules that were rejected per CT scan for CAD systems 1-4 at time 0 was 7.4, 1.7, 0.6, and 4.5 respectively. Conclusion CAD systems detected up to 70% of lung cancers that were not detected by the radiologist but failed to detect about 20% of the lung cancers when they were identified by the radiologist, which suggests that CAD may be useful in the role of second reader. (©) RSNA, 2016.

Metrology Standards for Quantitative Imaging Biomarkers.

Although investigators in the imaging community have been active in developing and evaluating quantitative imaging biomarkers (QIBs), the development and implementation of QIBs have been hampered by the inconsistent or incorrect use of terminology or methods for technical performance and statistical concepts. Technical performance is an assessment of how a test performs in reference objects or subjects under controlled conditions. In this article, some of the relevant statistical concepts are reviewed, methods that can be used for evaluating and comparing QIBs are described, and some of the technical performance issues related to imaging biomarkers are discussed. More consistent and correct use of terminology and study design principles will improve clinical research, advance regulatory science, and foster better care for patients who undergo imaging studies.

Disease severity in a mouse model of ataxia telangiectasia is modulated by the DNA damage checkpoint gene Hus1.

The human genomic instability syndrome ataxia telangiectasia (A-T), caused by mutations in the gene encoding the DNA damage checkpoint kinase ATM, is characterized by multisystem defects including neurodegeneration, immunodeficiency and increased cancer predisposition. ATM is central to a pathway that responds to double-strand DNA breaks, whereas the related kinase ATR leads a parallel signaling cascade that is activated by replication stress. To dissect the physiological relationship between the ATM and ATR pathways, we generated mice defective for both. Because complete ATR pathway inactivation causes embryonic lethality, we weakened the ATR mechanism to different degrees by impairing HUS1, a member of the 911 complex that is required for efficient ATR signaling. Notably, simultaneous ATM and HUS1 defects caused synthetic lethality. Atm/Hus1 double-mutant embryos showed widespread apoptosis and died mid-gestationally. Despite the underlying DNA damage checkpoint defects, increased DNA damage signaling was observed, as evidenced by H2AX phosphorylation and p53 accumulation. A less severe Hus1 defect together with Atm loss resulted in partial embryonic lethality, with the surviving double-mutant mice showing synergistic increases in genomic instability and specific developmental defects, including dwarfism, craniofacial abnormalities and brachymesophalangy, phenotypes that are observed in several human genomic instability disorders. In addition to identifying tissue-specific consequences of checkpoint dysfunction, these data highlight a robust, cooperative configuration for the mammalian DNA damage response network and further suggest HUS1 and related genes in the ATR pathway as candidate modifiers of disease severity in A-T patients.

Visual scoring of osteoporosis on low-dose CT in lung cancer screening population.

OBJECTIVES: The risk factors for lung cancer screening eligibility, age as well as smoking history, are also present for osteoporosis. This study aims to develop a visual scoring system to identify osteoporosis that can be applied to low-dose CT scans obtained for lung cancer screening. MATERIALS AND METHODS: We retrospectively reviewed 1000 prospectively enrolled participants in the lung cancer screening program at the Mount Sinai Hospital. Optimal window width and level settings for the visual assessment were chosen based on a previously described approach. Visual scoring of osteoporosis and automated measurement using dedicated software were compared. Inter-reader agreement was conducted using six readers with different levels of experience who independently visually assessed 30 CT scans. RESULTS: Based on previously validated formulas for choosing window and level settings, we chose osteoporosis settings of Width = 230 and Level = 80. Of the 1000 participants, automated measurement was successfully performed on 774 (77.4 %). Among these, 138 (17.8 %) had osteoporosis. There was a significant correlation between the automated measurement and the visual score categories for osteoporosis (Kendall's Tau = -0.64, p < 0.0001; Spearman's rho = -0.77, p < 0.0001). We also found substantial to excellent inter-reader agreement on the osteoporosis classification among the 6 radiologists (Fleiss κ = 0.91). CONCLUSIONS: Our study shows that a simple approach of applying specific window width and level settings to already reconstructed sagittal images obtained in the context of low-dose CT screening for lung cancer is highly feasible and useful in identifying osteoporosis.

Cluster Analysis of World Trade Center Related Lower Airway Diseases.

ABSTRACT: Introduction: Cluster analysis can classify without a priori assumptions the heterogeneous chronic lower airway diseases found in former workers at the World Trade Center (WTC) disaster site. Methods: We selected the first available chest computed tomography scan with quantitative computed tomography measurements on 311 former WTC workers with complete clinical, and spirometric data from their closest surveillance visit. We performed a nonhierarchical iterative algorithm K-prototype cluster analysis, using gap measure. Results: A five-cluster solution was most satisfactory. Cluster 5 had the healthiest individuals. In cluster 4, smoking was most prevalent and intense but there was scant evidence of respiratory disease. Cluster 3 had symptomatic subjects with reduced forced vital capacity impairment (low FVC). Clusters 1 and 2 had less dyspneic subjects, but more functional and quantitative computed tomography evidence of chronic obstructive pulmonary disease (COPD) in cluster 1, or low FVC in cluster 2. Clusters 1 and 4 had the highest proportion of rapid first-second forced expiratory volume decliners. Conclusions: Cluster analysis confirms low FVC and COPD/pre-COPD as distinctive chronic lower airway disease phenotypes on long-term surveillance of the WTC workers.

AI-enabled left atrial volumetry in coronary artery calcium scans (AI-CACTM) predicts atrial fibrillation as early as one year, improves CHARGE-AF, and outperforms NT-proBNP: The multi-ethnic study of atherosclerosis.

BACKGROUND: Coronary artery calcium (CAC) scans contain actionable information beyond CAC scores that is not currently reported. METHODS: We have applied artificial intelligence-enabled automated cardiac chambers volumetry to CAC scans (AI-CACTM) to 5535 asymptomatic individuals (52.2% women, ages 45-84) that were previously obtained for CAC scoring in the baseline examination (2000-2002) of the Multi-Ethnic Study of Atherosclerosis (MESA). AI-CAC took on average 21 â€‹s per CAC scan. We used the 5-year outcomes data for incident atrial fibrillation (AF) and assessed discrimination using the time-dependent area under the curve (AUC) of AI-CAC LA volume with known predictors of AF, the CHARGE-AF Risk Score and NT-proBNP. The mean follow-up time to an AF event was 2.9 â€‹± â€‹1.4 years. RESULTS: At 1,2,3,4, and 5 years follow-up 36, 77, 123, 182, and 236 cases of AF were identified, respectively. The AUC for AI-CAC LA volume was significantly higher than CHARGE-AF for Years 1, 2, and 3 (0.83 vs. 0.74, 0.84 vs. 0.80, and 0.81 vs. 0.78, respectively, all p â€‹< â€‹0.05), but similar for Years 4 and 5, and significantly higher than NT-proBNP at Years 1-5 (all p â€‹< â€‹0.01), but not for combined CHARGE-AF and NT-proBNP at any year. AI-CAC LA significantly improved the continuous Net Reclassification Index for prediction of AF over years 1-5 when added to CHARGE-AF Risk Score (0.60, 0.28, 0.32, 0.19, 0.24), and NT-proBNP (0.68, 0.44, 0.42, 0.30, 0.37) (all p â€‹< â€‹0.01). CONCLUSION: AI-CAC LA volume enabled prediction of AF as early as one year and significantly improved on risk classification of CHARGE-AF Risk Score and NT-proBNP.

AI-enabled Left Atrial Volumetry in Cardiac CT Scans Improves CHARGE-AF and Outperforms NT-ProBNP for Prediction of Atrial Fibrillation in Asymptomatic Individuals: Multi-Ethnic Study of Atherosclerosis.

Background: Coronary artery calcium (CAC) scans contain actionable information beyond CAC scores that is not currently reported. Methods: We have applied artificial intelligence-enabled automated cardiac chambers volumetry to CAC scans (AI-CAC), taking on average 21 seconds per CAC scan, to 5535 asymptomatic individuals (52.2% women, ages 45-84) that were previously obtained for CAC scoring in the baseline examination (2000-2002) of the Multi-Ethnic Study of Atherosclerosis (MESA). We used the 5-year outcomes data for incident atrial fibrillation (AF) and compared the time-dependent AUC of AI-CAC LA volume with known predictors of AF, the CHARGE-AF Risk Score and NT-proBNP (BNP). The mean follow-up time to an AF event was 2.9±1.4 years. Results: At 1,2,3,4, and 5 years follow-up 36, 77, 123, 182, and 236 cases of AF were identified, respectively. The AUC for AI-CAC LA volume was significantly higher than CHARGE-AF or BNP at year 1 (0.836, 0.742, 0.742), year 2 (0.842, 0.807,0.772), and year 3 (0.811, 0.785, 0.745) (p<0.02), but similar for year 4 (0.785, 0.769, 0.725) and year 5 (0.781, 0.767, 0.734) respectively (p>0.05). AI-CAC LA volume significantly improved the continuous Net Reclassification Index for prediction of AF over years 1-5 when added to CAC score (0.74, 0.49, 0.53, 0.39, 0.44), CHARGE-AF Risk Score (0.60, 0.28, 0.32, 0.19, 0.24), and BNP (0.68, 0.44, 0.42, 0.30, 0.37) respectively (p<0.01). Conclusion: AI-CAC LA volume enabled prediction of AF as early as one year and significantly improved on risk classification of CHARGE-AF Risk Score and BNP.

Dynamic Differential Image Circle Diameter Measurement Precision Assessment: Application to Burning Droplets.

Dynamic measurement precision assessment has been achieved for a differential circle measurement application. Differential circle diameter measurement, in image analysis, typically requires fitting a circle model that optimizes for image distortions, defects or occlusions. The differential task occurs when precise measurements of diameter change are required given object size variation with time. An automated system was designed to provide diameter measurements and associated measurement precision of images of a fuel droplet undergoing combustion in zero gravity for the FLEX-2 dataset. An image gradient-based, least-squares boundary point fitting method to a circle or ellipse model is used for diameter measurement. The presence of soot aggregates poses significant challenges for diameter measurements when it occludes part of the droplet boundary. The precision of the diameter measurements depends upon the image quality. Using synthetic image simulations that model the soot behavior, we developed a model based on image quality measures that assesses the measurement precision for each individual diameter measurement. Thus, diameter measurements with precision assessments were made available for follow-up scientific analysis. The algorithm's success rate for measurable runs was 98%. In cases of limited occlusion, a measurement precision of ±0.2 pixels for the FLEX-2 dataset was achieved.

Internal growth of nonsolid lung nodules: radiologic-pathologic correlation.

PURPOSE: To determine whether computed tomographic (CT) attenuation values correlate with the histologic measurements of a lung cancer manifesting as a nonsolid nodule and to quantify the extent to which the tumor replaces the airspace within the nodule. MATERIALS AND METHODS: Informed consent was obtained to analyze images from CT and pathologic examination under an institutional review board-approved protocol. Fifteen patients who had undergone resection of nonsolid lung cancer were evaluated. On the basis of the CT attenuation values of nonsolid nodules, nonneoplastic lung, soft tissue, and air, the overall proportion of soft tissue in the nodule and nonneoplastic lung and the difference between these two measures were calculated. The analogous measures were obtained from a representative digitized histologic slide. The area of each nodule and the proportion of air within it were measured, and the proportion of soft tissue in the nodule and nonneoplastic lung and the difference between the two were calculated. The difference between the two proportions at CT and histologic examination are the proportions attributable to the cancer on the basis of CT and histologic examinations, respectively. Linear regression was performed to assess the relationship between these measures. RESULTS: The average proportions of soft tissue in the nodule at CT and histologic examination were 48% and 69%, respectively, and they showed significant correlation with each other (P = .02); in addition, each showed significant correlation with the attenuation of the nodule (P < .0001 and P = .02, respectively). The difference between the proportions of soft tissue in nodule and nonneoplastic lung at CT and histologic examination were 37% and 30%, respectively, and both were independent of the tumor diameter (P = .26 and P = .41). CONCLUSION: The proportion of soft tissue within a nonsolid nodule is correlated with attenuation at CT. This allows for measurement of change within the nodule. An increase of 100 HU in nodule attenuation represents an approximately 10% increase in tumor volume.

Lung cancers diagnosed at annual CT screening: volume doubling times.

PURPOSE: To empirically address the distribution of the volume doubling time (VDT) of lung cancers diagnosed in repeat annual rounds of computed tomographic (CT) screening in the International Early Lung Cancer Action Program (I-ELCAP), first and foremost with respect to rates of tumor growth but also in terms of cell types. MATERIALS AND METHODS: All CT screenings in I-ELCAP from 1993 to 2009 were performed according to HIPAA-compliant protocols approved by the institutional review boards of the collaborating institutions. All instances of first diagnosis of primary lung cancer after a negative screening result 7-18 months earlier were identified, with symptom-prompted diagnoses included. Lesion diameter was calculated by using the measured length and width of each cancer at the time when the nodule was first identified for further work-up and at the time of the most recent prior screening, 7-18 months earlier. The length and width were measured a second time for each cancer, and the geometric mean of the two calculated diameters was used to calculate the VDT. The χ(2) statistic was used to compare the VDT distributions. RESULTS: The median VDT for 111 cancers was 98 days (interquartile range, 108). For 56 (50%) cancers it was less than 100 days, and for three (3%) cancers it was more than 400 days. Adenocarcinoma was the most frequent cell type (50%), followed by squamous cell carcinoma (19%), small cell carcinoma (19%), and others (12%). Lung cancers manifesting as subsolid nodules had significantly longer VDTs than those manifesting as solid nodules (P < .0001). CONCLUSION: Lung cancers diagnosed in annual repeat rounds of CT screening, as manifest by the VDT and cell-type distributions, are similar to those diagnosed in the absence of screening.

The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans.

PURPOSE: The development of computer-aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography (CT) scans. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process. METHODS: Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories ("nodule > or =3 mm," "nodule <3 mm," and "non-nodule > or =3 mm"). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus. RESULTS: The Database contains 7371 lesions marked "nodule" by at least one radiologist. 2669 of these lesions were marked "nodule > or =3 mm" by at least one radiologist, of which 928 (34.7%) received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings. CONCLUSIONS: The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.

Multivariate compensation of quantitative pulmonary emphysema metric variation from low-dose, whole-lung CT scans.

OBJECTIVE: Emphysema is a disease of the lung characterized by the destruction of the alveolar sac walls. Several quantitative densitometric measures of emphysema from wholelung CT have been proposed for evaluating disease severity and progression. However, a concern with these quantitative measures has been the large interscan variability observed during longitudinal studies of emphysema. To account for and reduce inherent measure variability, this work implements and evaluates the use of a multivariate random-effects model for correcting longitudinal variation in densitometric scores of emphysema due to inspiration. MATERIALS AND METHODS: The method of multivariate compensation was validated on three of the most commonly reported densitometric measures of emphysema: the emphysema index, histogram percentile, and fractal dimension. Two short-interval, zero-change datasets, one for model development (n = 105) and one for validation (n = 106), were retrospectively identified and used to ensure that all variation was caused by inherent measure variability. RESULTS: A statistically significant (F test, p < 0.001) reduction of 42.40% in measurement limits of agreement could be obtained after model application, with compensated emphysema metric differences showing 31-33% of the variance compared with uncompensated metric variance. CONCLUSION: Compensation was still effective when the trained model was applied to the second validation dataset. Multivariate compensation was found to be useful in reducing interscan measurement variability and should be applied to future longitudinal studies of emphysema.

The Regimen of Computed Tomography Screening for Lung Cancer: Lessons Learned Over 25 Years From the International Early Lung Cancer Action Program.

We learned many unanticipated and valuable lessons since we started planning our study of low-dose computed tomography (CT) screening for lung cancer in 1991. The publication of the baseline results of the Early Lung Cancer Action Project (ELCAP) in Lancet 1999 showed that CT screening could identify a high proportion of early, curable lung cancers. This stimulated large national screening studies to be quickly started. The ELCAP design, which provided evidence about screening in the context of a clinical program, was able to rapidly expand to a 12-institution study in New York State (NY-ELCAP) and to many international institutions (International-ELCAP), ultimately working with 82 institutions, all using the common I-ELCAP protocol. This expansion was possible because the investigators had developed the ELCAP Management System for screening, capturing data and CT images, and providing for quality assurance. This advanced registry and its rapid accumulation of data and images allowed continual assessment and updating of the regimen of screening as advances in knowledge and new technology emerged. For example, in the initial ELCAP study, introduction of helical CT scanners had allowed imaging of the entire lungs in a single breath, but the images were obtained in 10 mm increments resulting in about 30 images per person. Today, images are obtained in submillimeter slice thickness, resulting in around 700 images per person, which are viewed on high-resolution monitors. The regimen provides the imaging acquisition parameters, imaging interpretation, definition of positive result, and the recommendations for further workup, which now include identification of emphysema and coronary artery calcifications. Continual updating is critical to maximize the benefit of screening and to minimize potential harms. Insights were gained about the natural history of lung cancers, identification and management of nodule subtypes, increased understanding of nodule imaging and pathologic features, and measurement variability inherent in CT scanners. The registry also provides the foundation for assessment of new statistical techniques, including artificial intelligence, and integration of effective genomic and blood-based biomarkers, as they are developed.

Association of quantitative CT lung density measurements and lung function decline in World Trade Center workers.

BACKGROUND: Occupational exposures at the WTC site after 11 September 2001 have been associated with presumably inflammatory chronic lower airway diseases. AIMS: In this study, we describe the trajectories of expiratory air flow decline, identify subgroups with adverse progression, and investigate the association of those trajectories with quantitative computed tomography (QCT) imaging measurement of increased and decreased lung density. METHODS: We examined the trajectories of expiratory air flow decline in a group of 1,321 former WTC workers and volunteers with at least three periodic spirometries, and using QCT-measured low (LAV%, -950 HU) and high (HAV%, from -600 to -250 HU) attenuation volume percent. We calculated the individual regression line slopes for first-second forced expiratory volume (FEV1 slope), identified subjects with rapidly declining ("accelerated decliners") and increasing ("improved"), and compared them to subjects with "intermediate" (0 to -66.5 mL/year) FEV1 slope. We then used multinomial logistic regression to model those three trajectories, and the two lung attenuation metrics. RESULTS: The mean longitudinal FEV1 slopes for the entire study population, and its intermediate, decliner, and improved subgroups were, respectively, -40.4, -34.3, -106.5, and 37.6 mL/year. In unadjusted and adjusted analyses, LAV% and HAV% were both associated with "accelerated decliner" status (ORadj , 95% CI 2.37, 1.41-3.97, and 1.77, 1.08-2.89, respectively), compared to the intermediate decline. CONCLUSIONS: Longitudinal FEV1 decline in this cohort, known to be associated with QCT proximal airway inflammation metric, is also associated with QCT indicators of increased and decreased lung density. The improved FEV1 trajectory did not seem to be associated with lung density metrics.

Zone of transition: a potential source of error in tumor volume estimation.

PURPOSE: To measure the width of the zone of transition (ZOT) between nonaerated solid tumor and surrounding nonneoplastic lung parenchyma and determine the extent to which ZOT influences computer-derived estimates of tumor volume based on computed tomographic (CT) images. MATERIALS AND METHODS: This HIPAA-compliant study was approved by the institutional research board. The histologic slide containing the maximum tumor area was digitized for 20 consecutive patients with solid adenocarcinoma. The outer border of the tumor (A2) was marked; it included all lung parenchyma having any tumor cells. The inner border of the tumor (A1) was marked; it included only solid tumor where lung parenchyma was no longer preserved. Assuming two circles with areas of A2 and A1, the corresponding two radii, R2 and R1, were calculated. The average width of the ZOT was defined as R2 minus R1. The relationship between ZOT and tumor diameter on the CT images prior to surgery was assessed by using regression analysis. The relationship between ZOT and tumor volume was assessed by using a theoretical model of idealized spheres with varying diameters. RESULTS: The mean width of the ZOT was 0.78 mm (median, 0.48 mm). The proportional effect of ZOT on tumor volume estimates decreased with increasing tumor diameter and increased with increasing width of ZOT. Correlation between ZOT and tumor diameter was not significant (P = .87). CONCLUSION: The average width of ZOT is about a single pixel width on a full field of view CT scan; thus, the ZOT can have a large effect on volume estimates, particularly for small tumors.

Automated measurement of liver attenuation to identify moderate-to-severe hepatic steatosis from chest CT scans.

PURPOSE: Develop and validate an automated method for measuring liver attenuation in non-contrast low-dose chest CT (LDCT) scans and compare it to the standard manual method for identifying moderate-to-severe hepatic steatosis (HS). METHOD: The automated method identifies a region below the right lung within the liver and uses statistical sampling techniques to exclude non-liver parenchyma. The method was used to assess moderate-to-severe HS on two IRB-approved cohorts: 1) 24 patients with liver disease examined between 1/2013-1/2017 with non-contrast chest CT and abdominal MRI scans obtained within three months of liver biopsy, and 2) 319 lung screening participants with baseline LDCT performed between 8/2011-1/2017. Agreement between the manual and automated CT methods, the manual MRI method, and pathology for determining moderate-to-severe HS was assessed using Cohen's Kappa by applying a 40 HU threshold to the CT method and 17.4% fat fraction to MRI. Agreement between the manual and automated CT methods was assessed using the intraclass correlation coefficient (ICC). Variability was assessed using Bland-Altman limits of agreement (LoA). RESULTS: In the first cohort, the manual and automated CT methods had almost perfect agreement (ICC = 0.97, κ = 1.00) with LoA of -7.6 to 4.7 HU. Both manual and automated CT methods had almost perfect agreement with MRI (κ = 0.90) and substantial agreement with pathology (κ = 0.77). In the second cohort, the manual and automated CT methods had almost perfect agreement (ICC = 0.94, κ = 0.87). LoA were -10.6 to 5.2 HU. CONCLUSION: Automated measurements of liver attenuation from LDCT scans can be used to identify moderate-to-severe HS on LDCT.