Literature DB >> 19268860

Subjective similarity of patterns of diffuse interstitial lung disease on thin-section CT: an observer performance study.

Feng Li1, Seiji Kumazawa, Junji Shiraishi, Qiang Li, Roger Engelmann, Philip Caligiuri, Heber MacMahon, Kunio Doi.   

Abstract

RATIONALE AND
OBJECTIVES: 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.
MATERIALS AND 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).
CONCLUSION: 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.

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Year:  2009        PMID: 19268860     DOI: 10.1016/j.acra.2008.10.016

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  2 in total

Review 1.  Potential clinical impact of advanced imaging and computer-aided diagnosis in chest radiology: importance of radiologist's role and successful observer study.

Authors:  Feng Li
Journal:  Radiol Phys Technol       Date:  2015-05-17

2.  Quantification of regional interstitial lung disease from CT-derived fractional tissue volume: a lung tissue research consortium study.

Authors:  Cuneyt Yilmaz; Snehal S Watharkar; Alberto Diaz de Leon; Christine K Garcia; Nova C Patel; Kirk G Jordan; Connie C W Hsia
Journal:  Acad Radiol       Date:  2011-05-18       Impact factor: 3.173

  2 in total

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