Dataset Information

Texture-based Quantification of Centrilobular Emphysema and Centrilobular Nodularity in Longitudinal CT Scans of Current and Former Smokers.

ABSTRACT: The effect of smoking cessation on centrilobular emphysema (CLE) and centrilobular nodularity (CN), two manifestations of smoking-related lung injury on computed tomography (CT) images, has not been clarified. The objective of this study is to leverage texture analysis to investigate differences in extent of CLE and CN between current and former smokers.Chest CT scans from 350 current smokers, 401 former smokers, and 25 control subjects were obtained from the multicenter COPDGene Study, a Health Insurance Portability and Accountability Act-compliant study approved by the institutional review board of each participating clinical study center. Additionally, for 215 of these subjects, a follow-up CT scan was obtained approximately 5 years later. For each CT scan, 5000 circular regions of interest (ROIs) of 35-pixel diameter were randomly selected throughout the lungs. The patterns present in each ROI were summarized by 50 computer-extracted texture features. A logistic regression classifier was leveraged to classify each ROI as normal lung, CLE, or CN, and differences in the percentages of normal lung, CLE, and CN by study group were assessed.Former smokers had significantly more CLE (P?<0.01) but less CN (P?<0.001) than did current smokers, even after adjustment for important covariates such as patient age, GOLD stage, smoking history, forced expiratory volume in 1 second, gas trapping, and scanner model. Among patients with longitudinal CT scans, continued smoking led to a slight increase in CLE (P?=?0.13), whereas sustained abstinence from smoking led to further reduction in CN (P?<0.05).The proposed texture-based approach quantifies the extent of CN and CLE with high precision. Differences in smoking-related lung disease between longitudinal scans of current smokers and longitudinal scans of former smokers suggest that CN may be reversible on smoking cessation.

SUBMITTER: Ginsburg SB

PROVIDER: S-EPMC5096977 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Texture-based Quantification of Centrilobular Emphysema and Centrilobular Nodularity in Longitudinal CT Scans of Current and Former Smokers.

Ginsburg Shoshana B SB Zhao Jason J Humphries Stephen S Jou Sungshick S Yagihashi Kunihiro K Lynch David A DA Schroeder Joyce D JD

Academic radiology 20160827 11

<h4>Rationale and objectives</h4>The effect of smoking cessation on centrilobular emphysema (CLE) and centrilobular nodularity (CN), two manifestations of smoking-related lung injury on computed tomography (CT) images, has not been clarified. The objective of this study is to leverage texture analysis to investigate differences in extent of CLE and CN between current and former smokers.<h4>Materials and methods</h4>Chest CT scans from 350 current smokers, 401 former smokers, and 25 control subje ...[more]

PMID: 27575837

Similar Datasets

Project description:Background The correlation between visual emphysema patterns and subsequent progression of disease may provide a way to enrich a study population for treatment trials of emphysema. Purpose To evaluate the potential relationship between emphysema visual subtypes and progression of emphysema and gas trapping. Materials and Methods Current and former smokers with and without chronic obstructive pulmonary disease (COPD) enrolled in the prospective Genetic Epidemiology of COPD (COPDGene) study (ClinicalTrials.gov identifier: NCT02445183) between 2008 and 2011 had their Fleischner Society visual CT scores assessed at baseline, quantitative inspiratory, and expiratory CT and at 5 years. They also underwent pulmonary function testing at baseline CT and at 5 years. The dependent variables were inspiratory lung density at 15th percentile (adjusted for lung volume) as a measure of emphysema and percentage of lung volume with attenuation less than -856 HU at expiratory CT as a measure of air trapping. Statistical analysis used a linear mixed model, adjusted for age, height, sex, race, smoking status, and scanner make. Results A total of 4166 participants (mean age, 60 years ± 9 [standard deviation]; 2091 [50%] men) were evaluated. In participants with COPD (1655 participants, 40%), those with visual presence of mild, moderate, and confluent emphysema at baseline CT showed a mean decline in lung density of 4.6 g/L ± 1.1 (P < .001), 6.7 g/L ± 1.1 (P < .001), and 6.4 g/L ± 1.2 (P < .001), respectively, compared with 2.4 g/L ± 1.3 (P < .001) for those with trace emphysema. For participants without COPD, those with visual presence of mild and moderate emphysema at baseline CT showed a mean decline in lung density of 3.6 g/L ± 1.0 (P < .001) and 3.1 g/L ± 1.6 (P < .001), respectively, compared with 1.8 g/L ± 1.0 (P < .001) for those with trace emphysema. Conclusion The pattern of parenchymal emphysema at baseline CT was an independent predictor of subsequent progression of emphysema in participants who are current or former cigarette smokers with and without chronic obstructive pulmonary disease. © RSNA, 2020 Online supplemental material is available for this article.

Project description:Background DNA methylation (DNAm) performs excellently in the discrimination of current and former smokers from never smokers, where AUCs > 0.9 are regularly reported using a single CpG site (cg05575921; AHRR). However, there is a paucity of DNAm models which attempt to distinguish current, former and never smokers as individual classes. Derivation of a robust DNAm model that accurately distinguishes between current, former and never smokers would be particularly valuable to epidemiological research (as a more accurate smoking definition vs. self-report) and could potentially translate to clinical settings. Therefore, we appraise 4 DNAm models of ternary smoking status (that is, current, former and never smokers): methylation at cg05575921 (AHRR model), weighted scores from 13 CpGs created by Maas et al. (Maas model), weighted scores from a LASSO model of candidate smoking CpGs from the literature (candidate CpG LASSO model), and weighted scores from a LASSO model supplied with genome-wide 450K data (agnostic LASSO model). Discrimination is assessed by AUC, whilst classification accuracy is assessed by accuracy and kappa, derived from confusion matrices. Results We find that DNAm can classify ternary smoking status with reasonable accuracy, including when applied to external data. Ternary classification using only DNAm far exceeds the classification accuracy of simply assigning all classes as the most prevalent class (63.7% vs. 36.4%). Further, we develop a DNAm classifier which performs well in discriminating current from former smokers (agnostic LASSO model AUC in external validation data: 0.744). Finally, across our DNAm models, we show evidence of enrichment for biological pathways and human phenotype ontologies relevant to smoking, such as haemostasis, molybdenum cofactor synthesis, body fatness and social behaviours, providing evidence of the generalisability of our classifiers. Conclusions Our findings suggest that DNAm can classify ternary smoking status with close to 65% accuracy. Both the ternary smoking status classifiers and current versus former smoking status classifiers address the present lack of former smoker classification in epigenetic literature; essential if DNAm classifiers are to adequately relate to real-world populations. To improve performance further, additional focus on improving discrimination of current from former smokers is necessary. Supplementary Information The online version contains supplementary material available at 10.1186/s13148-021-01191-6.

Project description:The human microbiome has a role in the development of multiple diseases. Individual microbiome profiles are highly personalized, though many species are shared. Understanding the relationship between the human microbiome and disease may inform future individualized treatments. We hypothesize the blood microbiome signature may be a surrogate for some lung microbial characteristics. We sought associations between the blood microbiome signature and lung-relevant host factors. Based on reads not mapped to the human genome, we detected microbial nucleic acids through secondary use of peripheral blood RNA-sequencing from 2,590 current and former smokers with and without chronic obstructive pulmonary disease (COPD) from the COPDGene study. We used the Genome Analysis Toolkit (GATK) microbial pipeline PathSeq to infer microbial profiles. We tested associations between the inferred profiles and lung disease relevant phenotypes and examined links to host gene expression pathways. We replicated our analyses using a second independent set of blood RNA-seq data from 1,065 COPDGene study subjects and performed a meta-analysis across the two studies. The four phyla with highest abundance across all subjects were Proteobacteria, Actinobacteria, Firmicutes and Bacteroidetes. In our meta-analysis, we observed associations (q-value < 0.05) between Acinetobacter, Serratia, Streptococcus and Bacillus inferred abundances and Modified Medical Research Council (mMRC) dyspnea score. Current smoking status was associated (q < 0.05) with Acinetobacter, Serratia and Cutibacterium abundance. All 12 taxa investigated were associated with at least one white blood cell distribution variable. Abundance for nine of the 12 taxa was associated with sex, and seven of the 12 taxa were associated with race. Host-microbiome interaction analysis revealed clustering of genera associated with mMRC dyspnea score and smoking status, through shared links to several host pathways. This study is the first to identify a bacterial microbiome signature in the peripheral blood of current and former smokers. Understanding the relationships between systemic microbial signatures and lung-related phenotypes may inform novel interventions and aid understanding of the systemic effects of smoking.

Dataset Information

Texture-based Quantification of Centrilobular Emphysema and Centrilobular Nodularity in Longitudinal CT Scans of Current and Former Smokers.

Publications

Texture-based Quantification of Centrilobular Emphysema and Centrilobular Nodularity in Longitudinal CT Scans of Current and Former Smokers.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets