Significance of Low-Attenuation Cluster Analysis on Quantitative CT in the Evaluation of Chronic Obstructive Pulmonary Disease.

Abstract

ObjectiveTo assess clinical feasibility of low-attenuation cluster analysis in evaluation of chronic obstructive pulmonary disease (COPD).Materials and MethodsSubjects were 199 current and former cigarette smokers that underwent CT for quantification of COPD and had physiological measurements. Quantitative CT (QCT) measurements included low-attenuation area percent (LAA%) (voxels ≤ −950 Hounsfield unit [HU]), and two-dimensional (2D) and three-dimensional D values of cluster analysis at three different thresholds of CT value (−856, −910, and −950 HU). Correlation coefficients between QCT measurements and physiological indices were calculated. Multivariable analyses for percentage of predicted forced expiratory volume at one second (%FEV1) was performed including sex, age, body mass index, LAA%, and D value had the highest correlation coefficient with %FEV1 as independent variables. These analyses were conducted in subjects including those with mild COPD (global initiative of chronic obstructive lung disease stage = 0–II).ResultsLAA% had a higher correlation coefficient (-0.549, p < 0.001) with %FEV1 than D values in subjects while 2D D−910HU (−0.350, p < 0.001) revealed slightly higher correlation coefficient than LAA% (−0.343, p < 0.001) in subjects with mild COPD. Multivariable analyses revealed that LAA% and 2D D value−910HU were significant independent predictors of %FEV1 in subjects and that only 2D D value−910HU revealed a marginal p value (0.05) among independent variables in subjects with mild COPD.ConclusionLow-attenuation cluster analysis provides incremental information regarding physiologic severity of COPD, independent of LAA%, especially with mild COPD.