Rola Harmouche1, Samuel Y Ash2, Rachel K Putman3, Gary M Hunninghake3, Ruben San Jose Estepar1, Fernando J Martinez4, Augustine M Choi4, David A Lynch5, Hiroto Hatabu6, MeiLan K Han7, Russell P Bowler8, Ravi Kalhan9, Ivan O Rosas1, George R Washko10, Raul San Jose Estepar1. 1. Applied Chest Imaging Laboratory, Brigham and Women's Hospital; Boston, MA. 2. Applied Chest Imaging Laboratory, Brigham and Women's Hospital; Boston, MA; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA. Electronic address: syash@bwh.harvard.edu. 3. Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA. 4. Department of Medicine, Weil Cornell Medical College, New York, NY. 5. Department of Radiology, National Jewish Health, Denver, CO. 6. Department of Radiology, Brigham and Women's Hospital, Boston, MA. 7. Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI. 8. Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, National Jewish Health, Denver, CO. 9. Division of Pulmonary and Critical Care Medicine, Departments of Medicine and Preventative Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL. 10. Applied Chest Imaging Laboratory, Brigham and Women's Hospital; Boston, MA; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA.
Abstract
BACKGROUND: Tobacco smoke exposure is associated with emphysema and pulmonary fibrosis, both of which are irreversible. We have developed a new objective CT analysis tool that combines densitometry with machine learning to detect high attenuation changes in visually normal appearing lung (NormHA) that may precede these diseases. METHODS: We trained the classification tool by placing 34,528 training points in chest CT scans from 297 COPDGene participants. The tool was then used to classify lung tissue in 9,038 participants as normal, emphysema, fibrotic/interstitial, or NormHA. Associations between the quartile of NormHA and plasma-based biomarkers, clinical severity, and mortality were evaluated using Jonckheere-Terpstra, pairwise Wilcoxon rank-sum tests, and multivariable linear and Cox regression. RESULTS: A higher percentage of lung occupied by NormHA was associated with higher C-reactive protein and intercellular adhesion molecule 1 (P for trend for both < .001). In analyses adjusted for multiple covariates, including high and low attenuation area, compared with those in the lowest quartile of NormHA, those in the highest quartile had a 6.50 absolute percent lower percent predicted lower FEV1 (P < .001), an 8.48 absolute percent lower percent predicted forced expiratory volume, a 10.78-meter shorter 6-min walk distance (P = .011), and a 56% higher risk of death (P = .003). These findings were present even in those individuals without visually defined interstitial lung abnormalities. CONCLUSIONS: A new class of NormHA on CT may represent a unique tissue class associated with adverse outcomes, independent of emphysema and fibrosis.
BACKGROUND:Tobacco smoke exposure is associated with emphysema and pulmonary fibrosis, both of which are irreversible. We have developed a new objective CT analysis tool that combines densitometry with machine learning to detect high attenuation changes in visually normal appearing lung (NormHA) that may precede these diseases. METHODS: We trained the classification tool by placing 34,528 training points in chest CT scans from 297 COPDGeneparticipants. The tool was then used to classify lung tissue in 9,038 participants as normal, emphysema, fibrotic/interstitial, or NormHA. Associations between the quartile of NormHA and plasma-based biomarkers, clinical severity, and mortality were evaluated using Jonckheere-Terpstra, pairwise Wilcoxon rank-sum tests, and multivariable linear and Cox regression. RESULTS: A higher percentage of lung occupied by NormHA was associated with higher C-reactive protein and intercellular adhesion molecule 1 (P for trend for both < .001). In analyses adjusted for multiple covariates, including high and low attenuation area, compared with those in the lowest quartile of NormHA, those in the highest quartile had a 6.50 absolute percent lower percent predicted lower FEV1 (P < .001), an 8.48 absolute percent lower percent predicted forced expiratory volume, a 10.78-meter shorter 6-min walk distance (P = .011), and a 56% higher risk of death (P = .003). These findings were present even in those individuals without visually defined interstitial lung abnormalities. CONCLUSIONS: A new class of NormHA on CT may represent a unique tissue class associated with adverse outcomes, independent of emphysema and fibrosis.
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Authors: Samuel Y Ash; Rola Harmouche; Diego Lassala Lopez Vallejo; Julian A Villalba; Kris Ostridge; River Gunville; Carolyn E Come; Jorge Onieva Onieva; James C Ross; Gary M Hunninghake; Souheil Y El-Chemaly; Tracy J Doyle; Pietro Nardelli; Gonzalo V Sanchez-Ferrero; Hilary J Goldberg; Ivan O Rosas; Raul San Jose Estepar; George R Washko Journal: Respir Res Date: 2017-03-07