Dataset Information

Prediction of the rate of decline in FEV(1) in smokers using quantitative Computed Tomography.

ABSTRACT: A study was undertaken to determine if quantitative CT estimates of lung parenchymal overinflation and airway dimensions in smokers with a normal forced expiratory volume in 1 s (FEV(1)) can predict the rapid decline in FEV(1) that leads to chronic obstructive pulmonary disease (COPD).Study participants (n = 143; age 45-72 years; 54% male) were part of a lung cancer screening trial, had a smoking history of >30 pack years and a normal FEV(1) and FEV(1)/forced vital capacity (FVC) at baseline (mean (SD) FEV(1) 99.4 (12.8)%, range 80.2-140.7%; mean (SD) FEV(1)/FVC 77.9 (4.4), range 70.0-88.0%). An inspiratory multislice CT scan was acquired for each subject at baseline. Custom software was used to measure airway lumen and wall dimensions; the percentage of the lung inflated beyond a predicted maximal lung inflation, the low attenuation lung area with an x ray attenuation lower than -950 HU and the size distribution of the overinflated lung areas and the low attenuation area were described using a cluster analysis. Multiple regression analysis was used to test the hypothesis that these CT measurements combined with other baseline characteristics might identify those who would develop an excessive annual decline in FEV(1).The mean (SD) annual change in FEV(1) was -2.3 (4.7)% predicted (range -23.0% to +8.3%). Multiple regression analysis revealed that the annual change in FEV(1)%predicted was significantly associated with baseline percentage overinflated lung area measured on quantitative CT, FEV(1)% predicted, FEV(1)/FVC and gender.Quantitative CT scan evidence of overinflation of the lung predicts a rapid annual decline in FEV(1) in smokers with normal FEV(1).

SUBMITTER: Yuan R

PROVIDER: S-EPMC3035577 | biostudies-literature | 2009 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prediction of the rate of decline in FEV(1) in smokers using quantitative Computed Tomography.

Yuan R R Hogg J C JC Paré P D PD Sin D D DD Wong J C JC Nakano Y Y McWilliams A M AM Lam S S Coxson H O HO

Thorax 20090903 11

<h4>Background</h4>A study was undertaken to determine if quantitative CT estimates of lung parenchymal overinflation and airway dimensions in smokers with a normal forced expiratory volume in 1 s (FEV(1)) can predict the rapid decline in FEV(1) that leads to chronic obstructive pulmonary disease (COPD).<h4>Methods</h4>Study participants (n = 143; age 45-72 years; 54% male) were part of a lung cancer screening trial, had a smoking history of >30 pack years and a normal FEV(1) and FEV(1)/forced v ...[more]

PMID: 19734130

Similar Datasets

Project description:Background:Lung cancer screening guidelines recommend using individualized risk models to refer ever-smokers for screening. However, different models select different screening populations. The performance of each model in selecting ever-smokers for screening is unknown. Objective:To compare the U.S. screening populations selected by 9 lung cancer risk models (the Bach model; the Spitz model; the Liverpool Lung Project [LLP] model; the LLP Incidence Risk Model [LLPi]; the Hoggart model; the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Model 2012 [PLCOM2012]; the Pittsburgh Predictor; the Lung Cancer Risk Assessment Tool [LCRAT]; and the Lung Cancer Death Risk Assessment Tool [LCDRAT]) and to examine their predictive performance in 2 cohorts. Design:Population-based prospective studies. Setting:United States. Participants:Models selected U.S. screening populations by using data from the National Health Interview Survey from 2010 to 2012. Model performance was evaluated using data from 337 388 ever-smokers in the National Institutes of Health-AARP Diet and Health Study and 72 338 ever-smokers in the CPS-II (Cancer Prevention Study II) Nutrition Survey cohort. Measurements:Model calibration (ratio of model-predicted to observed cases [expected-observed ratio]) and discrimination (area under the curve [AUC]). Results:At a 5-year risk threshold of 2.0%, the models chose U.S. screening populations ranging from 7.6 million to 26 million ever-smokers. These disagreements occurred because, in both validation cohorts, 4 models (the Bach model, PLCOM2012, LCRAT, and LCDRAT) were well-calibrated (expected-observed ratio range, 0.92 to 1.12) and had higher AUCs (range, 0.75 to 0.79) than 5 models that generally overestimated risk (expected-observed ratio range, 0.83 to 3.69) and had lower AUCs (range, 0.62 to 0.75). The 4 best-performing models also had the highest sensitivity at a fixed specificity (and vice versa) and similar discrimination at a fixed risk threshold. These models showed better agreement on size of the screening population (7.6 million to 10.9 million) and achieved consensus on 73% of persons chosen. Limitation:No consensus on risk thresholds for screening. Conclusion:The 9 lung cancer risk models chose widely differing U.S. screening populations. However, 4 models (the Bach model, PLCOM2012, LCRAT, and LCDRAT) most accurately predicted risk and performed best in selecting ever-smokers for screening. Primary Funding Source:Intramural Research Program of the National Institutes of Health/National Cancer Institute.

Project description:Extra-thoracic tumors send out pilot cells that attach to the pulmonary endothelium. We hypothesized that this could alter regional lung mechanics (tissue stiffening or accumulation of fluid and inflammatory cells) through interactions with host cells. We explored this with serial inspiratory computed tomography (CT) and image matching to assess regional changes in lung expansion.We retrospectively assessed 44 pairs of two serial CT scans on 21 sarcoma patients: 12 without lung metastases and 9 with lung metastases. For each subject, two or more serial inspiratory clinically-derived CT scans were retrospectively collected. Two research-derived control groups were included: 7 normal nonsmokers and 12 asymptomatic smokers with two inspiratory scans taken the same day or one year apart respectively. We performed image registration for local-to-local matching scans to baseline, and derived local expansion and density changes at an acinar scale. Welch two sample t test was used for comparison between groups. Statistical significance was determined with a p value < 0.05.Lung regions of metastatic sarcoma patients (but not the normal control group) demonstrated an increased proportion of normalized lung expansion between the first and second CT. These hyper-expanded regions were associated with, but not limited to, visible metastatic lung lesions. Compared with the normal control group, the percent of increased normalized hyper-expanded lung in sarcoma subjects was significantly increased (p < 0.05). There was also evidence of increased lung "tissue" volume (non-air components) in the hyper-expanded regions of the cancer subjects relative to non-hyper-expanded regions. "Tissue" volume increase was present in the hyper-expanded regions of metastatic and non-metastatic sarcoma subjects. This putatively could represent regional inflammation related to the presence of tumor pilot cell-host related interactions.This new quantitative CT (QCT) method for linking serial acquired inspiratory CT images may provide a diagnostic and prognostic means to objectively characterize regional responses in the lung following oncological treatment and monitoring for lung metastases.

Dataset Information

Prediction of the rate of decline in FEV(1) in smokers using quantitative Computed Tomography.

Publications

Prediction of the rate of decline in FEV(1) in smokers using quantitative Computed Tomography.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets