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Original Research |

Automated Quantification of High-Resolution CT Scan Findings in Individuals at Risk for Pulmonary FibrosisAutomated High-Resolution CT Scan Quantification

Ivan O. Rosas, MD; Jianhua Yao, PhD; Nilo A. Avila, MD; Catherine K. Chow, MD; William A. Gahl, MD, PhD; Bernadette R. Gochuico, MD
Author and Funding Information

From the Pulmonary-Critical Care Medicine Branch, National Heart, Lung, and Blood Institute (Dr Rosas); Radiology and Imaging Sciences, Clinical Center (Drs Yao, Avila, and Chow); Medical Genetics Branch, National Human Genome Research Institute (Drs Gahl and Gochuico), National Institutes of Health, Bethesda, MD; the Division of Pulmonary and Critical Care (Dr Rosas), Brigham and Women’s Hospital, Boston, MA; and the Veterans Affairs Medical Center (Dr Avila), Washington, DC.

Correspondence to: Bernadette R. Gochuico, MD, 10 Center Dr, MSC 1851, Bethesda, MD 20892-1851; e-mail: gochuicb@mail.nih.gov

Data are presented as No. unless otherwise noted. F = female; HRCT = high-resolution CT; ILD = interstitial lung disease; M = male.

See Table 1 legend for expansion of abbreviations.

Drs Rosas and Yao contributed equally to this article.

Funding/Support: This study was sponsored by the National Heart, Lung, and Blood Institute; the Clinical Center; and the National Human Genome Research Institute, Intramural Research Program, National Institutes of Health. This work was also supported by the National Institutes of Health [Grant HL087030-02 (I. O. R.)].

Reproduction of this article is prohibited without written permission from the American College of Chest Physicians (http://www.chestpubs.org/site/misc/reprints.xhtml).


Drs Rosas and Yao contributed equally to this article.

Drs Rosas and Yao contributed equally to this article.

Funding/Support: This study was sponsored by the National Heart, Lung, and Blood Institute; the Clinical Center; and the National Human Genome Research Institute, Intramural Research Program, National Institutes of Health. This work was also supported by the National Institutes of Health [Grant HL087030-02 (I. O. R.)].

Funding/Support: This study was sponsored by the National Heart, Lung, and Blood Institute; the Clinical Center; and the National Human Genome Research Institute, Intramural Research Program, National Institutes of Health. This work was also supported by the National Institutes of Health [Grant HL087030-02 (I. O. R.)].

Reproduction of this article is prohibited without written permission from the American College of Chest Physicians (http://www.chestpubs.org/site/misc/reprints.xhtml).

Reproduction of this article is prohibited without written permission from the American College of Chest Physicians (http://www.chestpubs.org/site/misc/reprints.xhtml).


Chest. 2011;140(6):1590-1597. doi:10.1378/chest.10-2545
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Background:  Automated methods to quantify interstitial lung disease (ILD) on high-resolution CT (HRCT) scans in people at risk for pulmonary fibrosis have not been developed and validated.

Methods:  Cohorts with familial pulmonary fibrosis (n = 126) or rheumatoid arthritis with and without ILD (n = 86) were used to develop and validate a computer program capable of quantifying ILD on HRCT scans, which imaged the lungs semicontinuously from the apices to the lung bases during end-inspiration in the prone position. This method uses segmentation, texture analysis, training, classification, and grading to score ILD.

Results:  Quantification of HRCT scan findings of ILD using an automated computer program correlated with radiologist readings and detected disease of varying severity in a derivation cohort with familial pulmonary fibrosis or their first-degree relatives. This algorithm was validated in an independent cohort of subjects with rheumatoid arthritis with and without ILD. Automated classification of HRCT scans as normal or ILD was significant in the derivation and validation cohorts (P < .001 and P < .001, respectively). Areas under receiver operating characteristic curves performed independently for each group were 0.888 for the derivation cohort and 0.885 for the validation cohort. Pulmonary function test results, including FVC and diffusion capacity, correlated with computer-generated HRCT scan scores for ILD (r = −0.483 and r = −0.532, respectively).

Conclusions:  Automated computer scoring of HRCT scans can objectively identify ILD and potentially quantify radiographic severity of lung disease in populations at risk for pulmonary fibrosis.

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