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1.
Clin Imaging ; 53: 195-199, 2019.
Article in English | MEDLINE | ID: mdl-30419414

ABSTRACT

PURPOSE: To evaluate whether visual CT findings could account for the effect of current smoking. METHODS: 500 CT scans were visually evaluated within each lobe. A multivariate model for emphysema index was constructed containing previously described confounders in addition to the visual components associated with smoking status. RESULTS: Current smokers displayed 23% less visual emphysema, 19% more airway wall thickening, and 188% more centrilogular nodule than former smokers (all p < 0.001). The effect of current smoking on the emphysema index decreased after adjustment with confounders and visual parameters. CONCLUSIONS: Visual CT findings could partially account for the effect of current smoking.


Subject(s)
Lung/diagnostic imaging , Pulmonary Emphysema/diagnosis , Smokers , Smoking/adverse effects , Tomography, X-Ray Computed/methods , Aged , Female , Humans , Male , Middle Aged , Pulmonary Emphysema/etiology
2.
Invest Radiol ; 47(10): 596-602, 2012 Oct.
Article in English | MEDLINE | ID: mdl-22836310

ABSTRACT

OBJECTIVES: The purposes of this study were to evaluate the reference range of quantitative computed tomography (QCT) measures of lung attenuation and airway parameter measurements in healthy nonsmoking adults and to identify sources of variation in those measures and possible means to adjust for them. MATERIALS AND METHODS: Within the COPDGene study, 92 healthy non-Hispanic white nonsmokers (29 men, 63 women; mean [SD] age, 62.7 [9.0] years; mean [SD] body mass index [BMI], 28.1 [5.1] kg/m(2)) underwent volumetric computed tomography (CT) at full inspiration and at the end of a normal expiration. On QCT analysis (Pulmonary Workstation 2, VIDA Diagnostics), inspiratory low-attenuation areas were defined as lung tissue with attenuation values -950 Hounsfield units or less on inspiratory CT (LAA(I-950)). Expiratory low-attenuation areas were defined as lung tissue -856 Hounsfield units or less on expiratory CT (LAA(E-856)). We used simple linear regression to determine the impact of age and sex on QCT parameters and multiple regression to assess the additional impact of total lung capacity and functional residual capacity measured by CT (TLC(CT) and FRC(CT)), scanner type, and mean tracheal air attenuation. Airways were evaluated using measures of airway wall thickness, inner luminal area, wall area percentage (WA%), and standardized thickness of an airway with inner perimeter of 10 mm (Pi10). RESULTS: Mean (SD) %LAA(I-950) was 2.0% (2.7%), and mean (SD) %LAA(E-856) was 9.2% (6.8%). Mean (SD) %LAA(I-950) was 3.6% (3.2%) in men, compared with 1.3% (2.0%) in women (P < 0.001). The %LAA(I-950) did not change significantly with age (P = 0.08) or BMI (P = 0.52). %LAA(E-856) did not show any independent relationship with age (P = 0.33), sex (P = 0.70), or BMI (P = 0.32). On multivariate analysis, %LAA(I-950) showed a direct relationship to TLC(CT) (P = 0.002) and an inverse relationship to mean tracheal air attenuation (P = 0.003), and %LAA(E-856) was related to age (P = 0.001), FRC(CT) (P = 0.007), and scanner type (P < 0.001). Multivariate analysis of segmental airways showed that inner luminal area and WA% were significantly related to TLC(CT) (P < 0.001) and age (0.006). Moreover, WA% was associated with sex (P = 0.05), axial pixel size (P = 0.03), and slice interval (P = 0.04). Lastly, airway wall thickness was strongly influenced by axial pixel size (P < 0.001). CONCLUSIONS: Although the attenuation characteristics of normal lung differ by age and sex, these differences do not persist on multivariate analysis. Potential sources of variation in measurement of attenuation-based QCT parameters include depth of inspiration/expiration and scanner type. Tracheal air attenuation may partially correct variation because of scanner type. Sources of variation in QCT airway measurements may include age, sex, BMI, depth of inspiration, and spatial resolution.


Subject(s)
Lung/radiation effects , Tomography, X-Ray Computed , Age Factors , Aged , Aged, 80 and over , Female , Health Status , Humans , Linear Models , Male , Middle Aged , Respiratory Function Tests , Respiratory System/radiation effects , Statistics as Topic
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