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Original Research: Genetic and Developmental Disorders |

Automated CT Scan Scores of Bronchiectasis and Air Trapping in Cystic FibrosisAutomated CT Scan Scores in Cystic Fibrosis

Emily M. DeBoer, MD; Waldemar Swiercz, PhD; Sonya L. Heltshe, PhD; Margaret M. Anthony; Paul Szefler, MD; Rebecca Klein, MD; John Strain, MD; Alan S. Brody, MD; Scott D. Sagel, MD, PhD
Author and Funding Information

From the Department of Pediatrics (Drs DeBoer, Szefler, Klein, and Sagel and Ms Anthony) and the Department of Radiology (Dr Strain), Children’s Hospital Colorado, University of Colorado School of Medicine, Aurora, CO; the Department of Neurology (Dr Swiercz), Massachusetts General Hospital and Harvard Medical School, Boston, MA; the Department of Pediatrics (Dr Heltshe), Seattle Children’s and University of Washington School of Medicine, Seattle, WA; and the Department of Radiology (Dr Brody), Cincinnati Children’s Hospital Medical Center, Cincinnati, OH.

Correspondence to: Emily DeBoer, MD, Children’s Hospital Colorado, 13123 E 16th Ave, B-395, Aurora, CO 80045; e-mail: emily.deboer@childrenscolorado.org


A prior abstract with preliminary data was presented at the 2006 North American Cystic Fibrosis Conference, November 2-5, 2006, Denver, CO, and part of this article has been presented in abstract form (Sagel SD, Swiercz W, Heltshe S, Kaess H, Anthony M. Pediatric Pulmonology 2006;41[S29]:387).

Funding/Support: This research was supported by the National Institutes of Health (NIH) [K23 RR018611] and by the NIH/National Center for Advancing Translational Sciences Colorado Clinical and Translational Science Institute [Grant UL1 TR000154].

Reproduction of this article is prohibited without written permission from the American College of Chest Physicians. See online for more details.


Chest. 2014;145(3):593-603. doi:10.1378/chest.13-0588
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Background:  Computer analysis of high-resolution CT (HRCT) scans may improve the assessment of structural lung injury in children with cystic fibrosis (CF). The goal of this cross-sectional pilot study was to validate automated, observer-independent image analysis software to establish objective, simple criteria for bronchiectasis and air trapping.

Methods:  HRCT scans of the chest were performed in 35 children with CF and compared with scans from 12 disease control subjects. Automated image analysis software was developed to count visible airways on inspiratory images and to measure a low attenuation density (LAD) index on expiratory images. Among the children with CF, relationships among automated measures, Brody HRCT scanning scores, lung function, and sputum markers of inflammation were assessed.

Results:  The number of total, central, and peripheral airways on inspiratory images and LAD (%) on expiratory images were significantly higher in children with CF compared with control subjects. Among subjects with CF, peripheral airway counts correlated strongly with Brody bronchiectasis scores by two raters (r = 0.86, P < .0001; r = 0.91, P < .0001), correlated negatively with lung function, and were positively associated with sputum free neutrophil elastase activity. LAD (%) correlated with Brody air trapping scores (r = 0.83, P < .0001; r = 0.69, P < .0001) but did not correlate with lung function or sputum inflammatory markers.

Conclusions:  Quantitative airway counts and LAD (%) on HRCT scans appear to be useful surrogates for bronchiectasis and air trapping in children with CF. Our automated methodology provides objective quantitative measures of bronchiectasis and air trapping that may serve as end points in CF clinical trials.

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