• Samuel Y Ash, Rola Harmouche, James C Ross, Alejandro A Diaz, Gary M Hunninghake, Rachel K Putman, Jorge Onieva, Fernando J Martinez, Augustine M Choi, David A Lynch, Hiroto Hatabu, Ivan O Rosas, Estepar Raul San Jose RSJ Laboratory of Mathematics in Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts 02115., and George R Washko.
• 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.
• Acad Radiol. 2017 Aug 1; 24 (8): 941-946.

Rationale And ObjectivesPrevious 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 MethodsAn 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.ResultsThe 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.ConclusionsIn smokers, a fully automated image processing tool is able to identify those individuals who have interstitial lung abnormalities with moderate sensitivity and specificity.Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

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