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定量肺气肿程度:利用 ECLIPSE 队列评估放射科医生的估计值和肺气肿严重程度的定量指标的相关因素。

Quantifying the extent of emphysema: factors associated with radiologists' estimations and quantitative indices of emphysema severity using the ECLIPSE cohort.

机构信息

Department of Radiology, University of British Columbia, Vancouver General Hospital, Canada.

出版信息

Acad Radiol. 2011 Jun;18(6):661-71. doi: 10.1016/j.acra.2011.01.011. Epub 2011 Mar 9.

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.

摘要

背景与目的

本研究旨在探讨放射科医生在评估肺气肿严重程度时考虑的因素,并评估低衰减区的定量计算机断层扫描(CT)测量。

材料与方法

来自 COPD 纵向评估以确定替代终点(ECLIPSE)研究的 1519 例慢性阻塞性肺疾病(COPD)患者、269 例吸烟者对照和 184 例非吸烟者对照接受了 CT 扫描和肺量测定。使用阈值技术(%<-950HU)和低衰减聚类分析对 CT 扫描进行分析。两位放射科医生对肺气肿严重程度进行评分(0 至 5 分),描述肺气肿的主要类型和分布以及疑似小气道疾病的存在。

结果

低衰减区百分比(%LAA)和肺气肿严重程度的视觉评分相关性良好(r = 0.77,P <.001)。%LAA、低衰减聚类分析以及放射科医生未描述的气体陷闭、肺气肿的分布和主要类型均为肺气肿严重程度视觉评分的预测因素(均 P <.001)。CT 扫描评分显示存在气体陷闭的区域,其具有相似的%LAA 的病变较小(P <.001)。

结论

肺气肿的视觉评估不仅取决于 LAA 的程度,还取决于病变大小、肺气肿的主要类型和分布以及小气道疾病的存在/缺失。低衰减聚类大小的计算机分析有助于定量算法将低衰减区与气体陷闭、图像噪声和肺气肿区分开来。

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