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

TitleQuantifying the Extent of Emphysema:: Factors Associated with Radiologists’ Estimations and Quantitative Indices of Emphysema Severity Using the ECLIPSE Cohort
Publication TypeJournal Article
Year of Publication2011
AuthorsGietema, HA, Müller, NL, Fauerbach, PVNasute, Sharma, S, Edwards, LD, Camp, PG, Coxson, HO
JournalAcademic Radiology
Volume18
Issue6
Start Page661-671
Abstract

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.

DOI10.1016/j.acra.2011.01.011