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COMPARATIVE STUDY
JOURNAL ARTICLE
RESEARCH SUPPORT, NON-U.S. GOV'T
3D lung motion assessments on inspiratory/expiratory thin-section CT: Capability for pulmonary functional loss of smoking-related COPD in comparison with lung destruction and air trapping.
European Journal of Radiology 2016 Februrary
PURPOSE: To evaluate the utility of three-dimensional (3D) lung motion on inspiratory and expiratory CT for pulmonary functional loss in smoking-related COPD in comparison with lung destruction and air trapping assessments.
METHOD AND MATERIALS: Forty-four consecutive smokers and COPD patients prospectively underwent inspiratory and expiratory CT. A 3D motion vector map was generated from these CTs, and regional motion magnitudes were measured at the horizontal axis (X-axis), the ventrodorsal axis (Y-axis), and the craniocaudal axis (Z-axis). All mean magnitudes within the entire lung (MMLX, MMLY, and MMLZ) were normalized by expiratory CT lung volume. Moreover, CT-based functional lung volume (FLV) on inspiratory CT and air trapping lung volume (ATLV) on expiratory CT were assessed quantitatively. To evaluate the capability for pulmonary function loss assessment, all MMLs were correlated with pulmonary function tests. Then, discrimination analysis was performed to determine the concordance capability for clinical stage, and correct classification capabilities were compared by means of McNemar's test.
RESULTS: Multiple regression analysis showed MMLY (β=0.657, p<0.001) and FLV (β=0.375, p=0.019) were correlated with percentage of predicted forced expiratory volume in 1 second. Correct classification capabilities using patient characteristics and MMLs (68.2 (30/44)%) were significantly higher than those obtained by patient characteristics, FLV, and ATLV (54.5 (24/44)%), p=0.031).
CONCLUSION: 3D lung motion parameter assessment is useful for smoking-related COPD assessment as well as lung parenchymal destruction and/or air trapping evaluations.
METHOD AND MATERIALS: Forty-four consecutive smokers and COPD patients prospectively underwent inspiratory and expiratory CT. A 3D motion vector map was generated from these CTs, and regional motion magnitudes were measured at the horizontal axis (X-axis), the ventrodorsal axis (Y-axis), and the craniocaudal axis (Z-axis). All mean magnitudes within the entire lung (MMLX, MMLY, and MMLZ) were normalized by expiratory CT lung volume. Moreover, CT-based functional lung volume (FLV) on inspiratory CT and air trapping lung volume (ATLV) on expiratory CT were assessed quantitatively. To evaluate the capability for pulmonary function loss assessment, all MMLs were correlated with pulmonary function tests. Then, discrimination analysis was performed to determine the concordance capability for clinical stage, and correct classification capabilities were compared by means of McNemar's test.
RESULTS: Multiple regression analysis showed MMLY (β=0.657, p<0.001) and FLV (β=0.375, p=0.019) were correlated with percentage of predicted forced expiratory volume in 1 second. Correct classification capabilities using patient characteristics and MMLs (68.2 (30/44)%) were significantly higher than those obtained by patient characteristics, FLV, and ATLV (54.5 (24/44)%), p=0.031).
CONCLUSION: 3D lung motion parameter assessment is useful for smoking-related COPD assessment as well as lung parenchymal destruction and/or air trapping evaluations.
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