Dataset Information

Quantitative Imaging Markers of Lung Function in a Smoking Population Distinguish COPD Subgroups with Differential Lung Cancer Risk.

ABSTRACT: BACKGROUND:Chronic obstructive pulmonary disease (COPD) is a heterogeneous condition with respect to onset, progression, and response to therapy. Incorporating clinical- and imaging-based features to refine COPD phenotypes provides valuable information beyond that obtained from traditional clinical evaluations. We characterized the spectrum of COPD-related phenotypes in a sample of former and current smokers and evaluated how these subgroups differ with respect to sociodemographic characteristics, COPD-related comorbidities, and subsequent risk of lung cancer. METHODS:White (N = 659) and African American (N = 520) male and female participants without lung cancer (controls) in the INHALE study who completed a chest CT scan, interview, and spirometry test were used to define distinct COPD-related subgroups based on hierarchical clustering. Seven variables were used to define clusters: pack years, quit years, FEV1/FVC, % predicted FEV1, and from quantitative CT (qCT) imaging, % emphysema, % air trapping, and mean lung density ratio. Cluster definitions were then applied to INHALE lung cancer cases (N = 576) to evaluate lung cancer risk. RESULTS:Five clusters were identified that differed significantly with respect to sociodemographic (e.g., race, age) and clinical (e.g., BMI, limitations due to breathing difficulties) characteristics. Increased risk of lung cancer was associated with increasingly detrimental lung function clusters (when ordered from most detrimental to least detrimental). CONCLUSIONS:Measures of lung function vary considerably among smokers and are not fully explained by smoking intensity. IMPACT:Combining clinical (spirometry) and radiologic (qCT) measures of COPD defines a spectrum of lung disease that predicts lung cancer risk differentially among patient clusters.

SUBMITTER: Lusk CM

PROVIDER: S-EPMC6449213 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Quantitative Imaging Markers of Lung Function in a Smoking Population Distinguish COPD Subgroups with Differential Lung Cancer Risk.

Lusk Christine M CM Wenzlaff Angela S AS Watza Donovan D Sieren Jessica C JC Robinette Natasha N Walworth Garrett G Petrich Michael M Neslund-Dudas Christine C Flynn Michael J MJ Song Thomas T Spizarny David D Simoff Michael J MJ Soubani Ayman O AO Gadgeel Shirish S Schwartz Ann G AG

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20190114 4

<h4>Background</h4>Chronic obstructive pulmonary disease (COPD) is a heterogeneous condition with respect to onset, progression, and response to therapy. Incorporating clinical- and imaging-based features to refine COPD phenotypes provides valuable information beyond that obtained from traditional clinical evaluations. We characterized the spectrum of COPD-related phenotypes in a sample of former and current smokers and evaluated how these subgroups differ with respect to sociodemographic charac ...[more]

PMID: 30642838

Similar Datasets

Project description:Cigarette smoking is the main risk factor for COPD (Chronic Obstructive Pulmonary Disease), yet only a subset of smokers develops COPD. Family members of patients with severe early-onset COPD have an increased risk to develop COPD and are therefore defined as "susceptible individuals". Here we perform unbiased analyses of proteomic profiles to assess how "susceptible individuals" differ from age-matched "non-susceptible individuals" in response to cigarette smoking. Epithelial lining fluid (ELF) was collected at baseline and 24 hours after smoking 3 cigarettes in young individuals susceptible or non-susceptible to develop COPD and older subjects with established COPD. Controls at baseline were older healthy smoking and non-smoking individuals. Five samples per group were pooled and analysed by stable isotope labelling (iTRAQ) in duplicate. Six proteins were selected and validated by ELISA or immunohistochemistry. After smoking, 23 proteins increased or decreased in young susceptible individuals, 7 in young non-susceptible individuals, and 13 in COPD in the first experiment; 23 proteins increased or decreased in young susceptible individuals, 32 in young non-susceptible individuals, and 11 in COPD in the second experiment. SerpinB3 and Uteroglobin decreased after acute smoke exposure in young non-susceptible individuals exclusively, whereas Peroxiredoxin I, S100A9, S100A8, ALDH3A1 (Aldehyde dehydrogenase 3A1) decreased both in young susceptible and non-susceptible individuals, changes being significantly different between groups for Uteroglobin with iTRAQ and for Serpin B3 with iTRAQ and ELISA measures. Peroxiredoxin I, SerpinB3 and ALDH3A1 increased in COPD patients after smoking. We conclude that smoking induces a differential protein response in ELF of susceptible and non-susceptible young individuals, which differs from patients with established COPD. This is the first study applying unbiased proteomic profiling to unravel the underlying mechanisms that induce COPD. Our data suggest that SerpinB3 and Uteroglobin could be interesting proteins in understanding the processes leading to COPD.

Project description:BackgroundReduced ventilation in lung regions affected by chronic obstructive pulmonary disease (COPD), reflected as inhomogeneities in the single-photon emission computed tomography (SPECT) lung image, is correlated to disease advancement. An analysis method for measuring these inhomogeneities is proposed in this work. The first aim was to develop a quantitative analysis method that could discriminate between Monte Carlo simulated normal and COPD lung SPECT images. A second aim was to evaluate the ability of the present method to discriminate between human subjects with advanced COPD and healthy volunteers.MethodsIn the simulated COPD study, different activity distributions in the lungs were created to mimic the healthy lung (normal) and different levels of COPD. Gamma camera projections were Monte Carlo simulated, representing clinically acquired projections of a patient who had inhaled 125 MBq 99mTc-Technegas followed by a 10-min SPECT examination. Reconstructions were made with iterative ordered subset expectation maximisation. The coefficient of variance (CV) was calculated for small overlapping volumes covering the 3D reconstructed activity distribution. A CV threshold value (CVT) was calculated as the modal value of the CV distribution of the simulated normal. The area under the distribution curve (AUC), for CV values greater than CVT, AUC(CVT), was then calculated. Moreover, five patients with advanced emphysema and five healthy volunteers inhaled approximately 75 MBq 99mTc-Technegas immediately before the 20-min SPECT acquisition. In the human study, CVT was based on the mean CV distribution of the five healthy volunteers.ResultsA significant difference (p < 0.001) was found between the Monte-Carlo simulated normal and COPD lung SPECT examinations. The present method identified a total reduction of ventilation of approximately 5%, not visible to the human eye in the reconstructed image. In humans the same method clearly discriminated between the five healthy volunteers and five patients with advanced COPD (p < 0.05).ConclusionsWhile our results are promising, the potential of the AUC(CVT) method to detect less advanced COPD in patients needs further clinical studies.

Project description:BackgroundMultiple studies have identified COPD subtypes by using visual or quantitative evaluation of CT images. However, there has been no systematic assessment of a combined visual and quantitative CT imaging classification. We integrated visually defined patterns of emphysema with quantitative imaging features and spirometry data to produce a set of 10 nonoverlapping CT imaging subtypes, and we assessed differences between subtypes in demographic features, physiological characteristics, longitudinal disease progression, and mortality.MethodsWe evaluated 9,080 current and former smokers in the COPDGene study who had available volumetric inspiratory and expiratory CT images obtained using a standardized imaging protocol. We defined 10 discrete, nonoverlapping CT imaging subtypes: no CT imaging abnormality, paraseptal emphysema (PSE), bronchial disease, small airway disease, mild emphysema, upper lobe predominant centrilobular emphysema (CLE), lower lobe predominant CLE, diffuse CLE, visual without quantitative emphysema, and quantitative without visual emphysema. Baseline and 5-year longitudinal characteristics and mortality were compared across these CT imaging subtypes.ResultsThe overall mortality differed significantly between groups (P < .01) and was highest in the 3 moderate to severe CLE groups. Subjects having quantitative but not visual emphysema and subjects with visual but not quantitative emphysema were unique groups with mild COPD, at risk for progression, and with likely different underlying mechanisms. Subjects with PSE and/or moderate to severe CLE had substantial progression of emphysema over 5 years compared with findings in subjects with no CT imaging abnormality (P < .01).ConclusionsThe combination of visual and quantitative CT imaging features reflects different underlying pathological processes in the heterogeneous COPD syndrome and provides a useful approach to reclassify types of COPD.Trial registryClinicalTrials.gov; No.: NCT00608764; URL: www.clinicaltrials.gov.

Project description:BACKGROUND:Imaging variables, including airway diameter, wall thickness, and air trapping, have been found to be important metrics when differentiating patients with severe asthma from those with nonsevere asthma and healthy subjects. OBJECTIVE:The objective of this study was to identify imaging-based clusters and to explore the association of the clusters with existing clinical metrics. METHODS:We performed an imaging-based cluster analysis using quantitative computed tomography-based structural and functional variables extracted from the respective inspiration and expiration scans of 248 asthmatic patients. The imaging-based metrics included a broader set of multiscale variables, such as inspiratory airway dimension, expiratory air trapping, and registration-based lung deformation (inspiration vs expiration). Asthma subgroups derived from a clustering method were associated with subject demographics, questionnaire results, medication history, and biomarker variables. RESULTS:Cluster 1 was composed of younger patients with early-onset nonsevere asthma and reversible airflow obstruction and normal airway structure. Cluster 2 was composed of patients with a mix of patients with nonsevere and severe asthma with marginal inflammation who exhibited airway luminal narrowing without wall thickening. Clusters 3 and 4 were dominated by patients with severe asthma. Cluster 3 patients were obese female patients with reversible airflow obstruction who exhibited airway wall thickening without airway narrowing. Cluster 4 patients were late-onset older male subjects with persistent airflow obstruction who exhibited significant air trapping and reduced regional deformation. Cluster 3 and 4 patients also showed decreased lymphocyte and increased neutrophil counts, respectively. CONCLUSIONS:Four image-based clusters were identified and shown to be correlated with clinical characteristics. Such clustering serves to differentiate asthma subgroups that can be used as a basis for the development of new therapies.

Dataset Information

Quantitative Imaging Markers of Lung Function in a Smoking Population Distinguish COPD Subgroups with Differential Lung Cancer Risk.

Publications

Quantitative Imaging Markers of Lung Function in a Smoking Population Distinguish COPD Subgroups with Differential Lung Cancer Risk.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure