4.6 Article

Quantifying the Extent of Emphysema: Factors Associated with Radiologists' Estimations and Quantitative Indices of Emphysema Severity Using the ECLIPSE Cohort

期刊

ACADEMIC RADIOLOGY
卷 18, 期 6, 页码 661-671

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.acra.2011.01.011

关键词

Emphysema; chronic obstructive pulmonary disease; computed tomography; quantitative CT; small airways disease

资金

  1. GlaxoSmithKline
  2. American Thoracic Society
  3. GSK
  4. University of Pittsburgh COPD SCCOR [NIH 1P50 HL084948, R01 HL085096]
  5. National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD

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Rationale and Objectives: This study investigated what factors radiologists take into account when estimating emphysema severity and assessed quantitative computed tomography (CT) measurements of low attenuation areas. Materials and Methods: CT scans and spirometry were obtained on 1519 chronic obstructive pulmonary disease (COPD) subjects, 269 smoker controls, and 184 nonsmoker controls from the Evaluation of COPD Longitudinally to Indentify Surrogate Endpoints (ECLIPSE) study. CT scans were analyzed using the threshold technique (%<-950HU) and a low attenuation cluster analysis. Two radiologists scored emphysema severity (0 to 5 scale), described the predominant type and distribution of emphysema, and the presence of suspected small airways disease. Results: The percent low attenuation area (%LAA) and visual scores of emphysema severity correlated well (r=0.77, P<.001). %LAA, low attenuation cluster analysis, and absence of radiologist described gas trapping, distribution, and predominant type of emphysema were predictors of visual scores of emphysema severity (all P<.001). CT scans scored as showing regions of gas trapping had smaller lesions for a similar %LAA than those without (P<.001). Conclusions: Visual estimates of emphysema are not only determined by the extent of LAA, but also by lesion size, predominant type, and distribution of emphysema and presence/absence of areas of small airways disease. A computer analysis of low attenuation cluster size helps quantitative algorithms discriminate low attenuation areas from gas trapping, image noise, and emphysema.

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