The Objective Identification and Quantifiicaton of Intertitial Lung Abnormalities in Smokers
The Objective Identification and Quantifiicaton of Intertitial Lung Abnormalities in Smokers
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DOI：
10.1016/j.acra.2016.08.023
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发表时间：
2017-08-01
影响因子：
4.8
通讯作者：
Washko, George R.
中科院分区：
文献类型：
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作者：
Ash, Samuel Y.;Harmouche, Rola;Washko, George R.
基本原理和目标：以前的研究表明，视觉检测吸烟者肺实质的间质变化具有高度临床相关性，并可预测包括死亡在内的结局。检测这些变化的视觉主观分析是耗时的，对细微变化不敏感，并且需要训练以提高再现性。对这些变化的客观检测可以提供一种没有这些限制的疾病鉴定方法。本研究的目的是开发和测试一种全自动的图像处理工具，以客观地识别与大型吸烟者队列的计算机断层扫描中的间质异常相关的放射学特征。材料和方法：使用局部直方图分析结合与胸膜表面距离的自动化工具检测与计算机断层扫描中的间质性肺异常一致的放射学特征来自COPD遗传流行病学研究的2257名个体的扫描，这是一项针对吸烟者的纵向观察研究。该工具的灵敏度和特异性是基于其检测这些异常的视觉识别存在的能力来确定的。该工具检测间质性肺异常的灵敏度为87.8%，特异性为57.5%，c统计量为0.82，对于检测间质异常（称为纤维化实质异常）的视觉亚型，敏感性为100%，特异性为56.7%，c统计量为0.89。结论：在吸烟者中，全自动图像处理工具能够以中等灵敏度和特异性识别具有间质性肺异常的个体。
Rationale and Objectives: Previous investigation suggests that visually detected interstitial changes in the lung parenchyma of smokers are highly clinically relevant and predict outcomes, including death. Visual subjective analysis to detect these changes is time-consuming, insensitive to subtle changes, and requires training to enhance reproducibility. Objective detection of such changes could provide a method of disease identification without these limitations. The goal of this study was to develop and test a fully automated image processing tool to objectively identify radiographic features associated with interstitial abnormalities in the computed tomography scans of a large cohort of smokers.Materials and Methods: An automated tool that uses local histogram analysis combined with distance from the pleural surface was used to detect radiographic features consistent with interstitial lung abnormalities in computed tomography scans from 2257 individuals from the Genetic Epidemiology of COPD study, a longitudinal observational study of smokers. The sensitivity and specificity of this tool was determined based on its ability to detect the visually identified presence of these abnormalities.Results: The tool had a sensitivity of 87.8% and a specificity of 57.5% for the detection of interstitial lung abnormalities, with a c-statistic of 0.82, and was 100% sensitive and 56.7% specific for the detection of the visual subtype of interstitial abnormalities called fibrotic parenchymal abnormalities, with a c-statistic of 0.89.Conclusions: In smokers, a fully automated image processing tool is able to identify those individuals who have interstitial lung abnormalities with moderate sensitivity and specificity.