title: Automated quantification of high-resolution CT scan findings in individuals at risk for pulmonary fibrosis.
abstract: 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.
authors.affiliation: Pulmonary-Critical Care Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD; Division of Pulmonary and Critical Care, Brigham and Women's Hospital, Boston, MA.
authors.forename: Ivan O
authors.initials: IO
authors.lastname: Rosas
authors.affiliation: Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD.
authors.forename: Jianhua
authors.initials: J
authors.lastname: Yao
authors.affiliation: Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD; Veterans Affairs Medical Center, Washington, DC.
authors.forename: Nilo A
authors.initials: NA
authors.lastname: Avila
authors.affiliation: Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD.
authors.forename: Catherine K
authors.initials: CK
authors.lastname: Chow
authors.affiliation: Medical Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD.
authors.forename: William A
authors.initials: WA
authors.lastname: Gahl
authors.affiliation: Medical Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD. Electronic address: gochuicb@mail.nih.gov.
authors.forename: Bernadette R
authors.initials: BR
authors.lastname: Gochuico
mesh_terms: D001172:Arthritis, Rheumatoid / Q000000981:diagnostic imaging* / Q000473:pathology; D016022:Case-Control Studies; D015331:Cohort Studies; D018450:Disease Progression; D005260:Female; D006801:Humans; D017563:Lung Diseases, Interstitial / Q000000981:diagnostic imaging* / Q000453:epidemiology / Q000473:pathology; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D011658:Pulmonary Fibrosis / Q000000981:diagnostic imaging* / Q000453:epidemiology / Q000473:pathology; D011856:Radiographic Image Enhancement; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012129:Respiratory Function Tests; D018570:Risk Assessment; D012720:Severity of Illness Index; D014057:Tomography, X-Ray Computed / Q000379:methods*