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Acad Radiol. 2017 Aug;24(8):941-946. doi: 10.1016/j.acra.2016.08.023. Epub 2016 Dec 15.

The Objective Identification and Quantification of Interstitial Lung Abnormalities in Smokers.

Author information

1
Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, 75 Francis St., PBB, CA-3, Boston, MA 02115. Electronic address: syash@partners.org.
2
Laboratory of Mathematics in Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts 02115.
3
Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, 75 Francis St., PBB, CA-3, Boston, MA 02115.
4
Department of Medicine, Weil Cornell Medical College, New York, New York 10065.
5
Department of Radiology, National Jewish Health, Denver, Colorado 80206.
6
Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts 02115.

Abstract

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.

KEYWORDS:

CT scanner; Pulmonary fibrosis; cigarettes; diffuse parenchymal lung diseases; idiopathic; interstitial; pneumonia; x-ray

PMID:
27989445
PMCID:
PMC5472510
DOI:
10.1016/j.acra.2016.08.023
[Indexed for MEDLINE]
Free PMC Article

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