title: Subjective similarity of patterns of diffuse interstitial lung disease on thin-section CT: an observer performance study.
abstract: OBJECTIVE
  The aim of this study was to investigate the subjective similarity for pairs of images with various abnormal patterns of diffuse interstitial lung disease on thin-section computed tomography by experienced radiologists to explore a basis for selecting similar images to assist radiologists' interpretation.
  
  
  METHODS
  Four major patterns (ground-glass opacity, nodular opacity, reticular opacity, and honeycombing) on thin-section computed tomographic images were identified by at least two of three radiologists. One radiologist manually selected 104 image pairs, in which the images in each pair had the same pattern and were similar in appearance. An additional 208 image pairs were randomly selected and evenly divided among the four patterns. These pairs were then rated for subjective similarity (on a continuous scale ranging from 0 = not similar at all to 1.0 = almost identical) by 12 radiologists.
  
  
  RESULTS
  For radiologist-selected pairs, the mean similarity rated by the 12 radiologists was 0.72. For randomly selected pairs, the mean similarity was higher for the same pattern (0.47) than for the varying patterns (0.27) (P < .001), and among the same pattern, the mean similarity was 0.63 for ground-glass opacity, 0.58 for honeycombing, 0.45 for nodular opacity, and 0.32 for reticular opacity. The mean standard deviation for similarity ratings on all pairs given by the 12 radiologists was 0.05 (rang, 0.01-0.09).
  
  
  CONCLUSIONS
  Subjective similarity ratings for pairs of abnormal images can be measured reliably and reproducibly by radiologists and will provide a basis for the selection of similar images to assist radiologists' interpretation.
authors.affiliation: Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, MC-2026, The University of Chicago, 5841 S Maryland Avenue, Chicago, IL 60637, USA. feng@uchicago.edu
authors.forename: Feng
authors.initials: F
authors.lastname: Li
authors.forename: Seiji
authors.initials: S
authors.lastname: Kumazawa
authors.forename: Junji
authors.initials: J
authors.lastname: Shiraishi
authors.forename: Qiang
authors.initials: Q
authors.lastname: Li
authors.forename: Roger
authors.initials: R
authors.lastname: Engelmann
authors.forename: Philip
authors.initials: P
authors.lastname: Caligiuri
authors.forename: Heber
authors.initials: H
authors.lastname: MacMahon
authors.forename: Kunio
authors.initials: K
authors.lastname: Doi
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D017563:Lung Diseases, Interstitial / Q000000981:diagnostic imaging*; D008875:Middle Aged; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*