BackgroundBeyond lung cancer, screening CT contains additional information on other smoking related diseases (e.g. chronic obstructive pulmonary disease, COPD). Since pulmonary function testing is not regularly incorporated in lung cancer screening, imaging biomarkers for COPD are likely to provide important surrogate measures for disease evaluation. Therefore, this study aims to determine the independent diagnostic value of CT emphysema, CT air trapping and CT bronchial wall thickness for COPD in low-dose screening CT scans.MethodsPrebronchodilator spirometry and volumetric inspiratory and expiratory chest CT were obtained on the same day in 1140 male lung cancer screening participants. Emphysema, air trapping and bronchial wall thickness were automatically quantified in the CT scans. Logistic regression analysis was performed to derivate a model to diagnose COPD. The model was internally validated using bootstrapping techniques.ResultsEach of the three CT biomarkers independently contributed diagnostic value for COPD, additional to age, body mass index, smoking history and smoking status. The diagnostic model that included all three CT biomarkers had a sensitivity and specificity of 73.2% and 88.%, respectively. The positive and negative predictive value were 80.2% and 84.2%, respectively. Of all participants, 82.8% was assigned the correct status. The C-statistic was 0.87, and the Net Reclassification Index compared to a model without any CT biomarkers was 44.4%. However, the added value of the expiratory CT data was limited, with an increase in Net Reclassification Index of 4.5% compared to a model with only inspiratory CT data.ConclusionQuantitatively assessed CT emphysema, air trapping and bronchial wall thickness each contain independent diagnostic information for COPD, and these imaging biomarkers might prove useful in the absence of lung function testing and may influence lung cancer screening strategy. Inspiratory CT biomarkers alone may be sufficient to identify patients with COPD in lung cancer screening setting.
Background In the search for specific phenotypes of chronic obstructive pulmonary disease (COPD) computed tomography (CT) derived Parametric Response Mapping (PRM) has been introduced. This study evaluates the association between PRM and currently available biomarkers of disease severity in COPD. Methods Smokers with and without COPD were characterized based on questionnaires, pulmonary function tests, body plethysmography, and low-dose chest CT scanning. PRM was used to calculate the amount of emphysema (PRMEmph) and non-emphysematous air trapping (i.e. functional small airway disease, PRMfSAD). PRM was first compared with other biomarkers for emphysema (Perc15) and air trapping (E/I-ratioMLD). Consequently, linear regression models were utilized to study associations of PRM measurements with clinical parameters. Results 166 participants were included with a mean ± SD age of 50.5 ± 17.7 years. Both PRMEmph and PRMfSAD were more strongly correlated with lung function parameters as compared to Perc15 and E/I-ratioMLD. PRMEmph and PRMfSAD were higher in COPD participants than non-COPD participants (14.0% vs. 1.1%, and 31.6% vs. 8.2%, respectively, both p < 0.001) and increased with increasing GOLD stage (all p < 0.001). Multivariate analysis showed that PRMfSAD was mainly associated with total lung capacity (TLC) (β = −7.90, p < 0.001), alveolar volume (VA) (β = 7.79, p < 0.001), and residual volume (β = 6.78, p < 0.001), whilst PRMEmph was primarily associated with Kco (β = 8.95, p < 0.001), VA (β = −6.21, p < 0.001), and TLC (β = 6.20, p < 0.001). Conclusions PRM strongly associates with the presence and severity of COPD. PRM therefore appears to be a valuable tool in differentiating COPD phenotypes.
BackgroundRegional right ventricular (RV) dysfunction is the hallmark of Arrhythmogenic Right Ventricular Dysplasia/Cardiomyopathy (ARVD/C), but is currently only qualitatively evaluated in the clinical setting. Feature Tracking Cardiovascular Magnetic Resonance (FT-CMR) is a novel quantitative method that uses cine CMR to calculate strain values. However, most prior FT-CMR studies in ARVD/C have focused on global RV strain using different software methods, complicating implementation of FT-CMR in clinical practice. We aimed to assess the clinical value of global and regional strain using FT-CMR in ARVD/C and to determine differences between commercially available FT-CMR software packages.MethodsWe analyzed cine CMR images of 110 subjects (39 overt ARVD/C [mutation+/phenotype+], 40 preclinical ARVD/C [mutation+/phenotype-] and 31 control) for global and regional (subtricuspid, anterior, apical) RV strain in the horizontal longitudinal axis using four FT-CMR software methods (Multimodality Tissue Tracking, TomTec, Medis and Circle Cardiovascular Imaging). Intersoftware agreement was assessed using Bland Altman plots.ResultsFor global strain, all methods showed reduced strain in overt ARVD/C patients compared to control subjects (p < 0.041), whereas none distinguished preclinical from control subjects (p > 0.275).For regional strain, overt ARVD/C patients showed reduced strain compared to control subjects in all segments which reached statistical significance in the subtricuspid region for all software methods (p < 0.037), in the anterior wall for two methods (p < 0.005) and in the apex for one method (p = 0.012). Preclinical subjects showed abnormal subtricuspid strain compared to control subjects using one of the software methods (p = 0.009). Agreement between software methods for absolute strain values was low (Intraclass Correlation Coefficient = 0.373).ConclusionsDespite large intersoftware variability of FT-CMR derived strain values, all four software methods distinguished overt ARVD/C patients from control subjects by both global and subtricuspid strain values. In the subtricuspid region, one software package distinguished preclinical from control subjects, suggesting the potential to identify early ARVD/C prior to overt disease expression.Electronic supplementary materialThe online version of this article (10.1186/s12968-017-0380-4) contains supplementary material, which is available to authorized users.
Rationale Accelerated lung function decline is a key COPD phenotype; however its genetic control remains largely unknown. Methods We performed a genome-wide association study using the Illumina Human660W-Quad v.1_A BeadChip. Generalized estimation equations were used to assess genetic contributions to lung function decline over a 5-year period in 4,048 European-American Lung Health Study participants with largely mild COPD. Genotype imputation was performed using reference HapMap II data. To validate regions meeting genome-wide significance, replication of top SNPs was attempted in independent cohorts. Three genes (TMEM26, ANK3 and FOXA1) within the regions of interest were selected for tissue expression studies using immunohistochemistry. Measurements and Main Results Two intergenic SNPs (rs10761570, rs7911302) on chromosome 10 and one SNP on chromosome 14 (rs177852) met genome-wide significance after Bonferroni. Further support for the chromosome 10 region was obtained by imputation, the most significantly associated imputed SNPs (rs10761571, rs7896712) being flanked by observed markers rs10761570 and rs7911302. Results were not replicated in four general population cohorts or a smaller cohort of subjects with moderate to severe COPD; however, we show novel expression of genes near regions of significantly associated SNPS, including TMEM26 and FOXA1 in airway epithelium and lung parenchyma, and ANK3 in alveolar macrophages. Levels of expression were associated with lung function and COPD status. Conclusions We identified two novel regions associated with lung function decline in mild COPD. Genes within these regions were expressed in relevant lung cells and their expression related to airflow limitation suggesting they may represent novel candidate genes for COPD susceptibility.
We studied the vertebral fracture prevalence on low-dose chest computed tomography (CT) in male lung cancer screening participants and the association of fractures and bone density with chronic obstructive pulmonary disease (COPD) and smoking. 1140 male current and former smokers with !16.5 packyears from the NELSON lung cancer screening trial were included. Age, body mass index, and smoking status were registered. CT scans and pulmonary function tests were obtained on the same day. On CT, vertebral fractures and bone density were measured. The cohort had a mean age of 62.5 years (standard deviation 5.2) old; 531 (46.6%) had quit smoking; and 437 (38.3%) had COPD. Of the group, 100 (8.8%) participants had a vertebral fracture. Fracture prevalence was higher in current compared to former smokers (11.3% versus 5.8%, p ¼ 0.001), but similar in participants with COPD compared to those without (9.6% versus 8.3%, p ¼ 0.430). The multivariable adjusted odds ratio for fracture presence was 1.79 (95% CI: 1.13-2.84) in current smokers and 1.08 (95% CI: 0.69-1.67) in COPD participants. Bone density was lower in current compared to former smokers (103.2HU versus 108.7HU, p ¼ 0.006) and in participants with COPD compared to those without [100.7 Hounsfield Units (HU) versus 108.9HU, p < 0.001]. In multivariate analysis, smoking status and COPD status were independently associated with bone density, corrected for age and body mass index. In conclusion, our study shows that lung cancer screening participants have a substantial vertebral fracture burden. Fractures are more common in current smokers, who also have lower bone density. We could not confirm that COPD is independently associated with vertebral fractures.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.