Observer variation in computed tomography of pleural lesions in subjects exposed to indoor asbestos

Eur Respir J. 2001 May;17(5):916-21. doi: 10.1183/09031936.01.17509160.

Abstract

To assess the reliability of computed tomography (CT) in detecting discrete pleural lesions, the interobserver and intra-observer variability in reading the conventional and high-resolution CT (HRCT) scans of 100 volunteers, who had worked for > or = 10 yrs in a building with known asbestos contamination, was evaluated. In the first session, pleural abnormalities were detected by a single radiologist (A1) in 13 subjects. In the second session, the scans were read again independently by the same radiologist (A2) and two other experienced radiologists (B, C). The final decision for the presence of pleural lesions was made in a final consensus reading. This gave a diagnosis of pleural abnormalities in 18 subjects, of whom eight (44%) had been detected by all three readers, five (28%) by two readers and four (22%) by only one reader; one scan, rated normal by all readers during the second session, was reconsidered because pleural abnormalities had been noted at the first reading (A1). The intra-observer agreement for reader A was good (kappa (kappa) 0.68) but the interobserver agreement between the readers was only fair to moderate (weighted kappa: A2-B=0.43, A2-C = 0.45, B-C = 0.26) in the second reading session. In conclusion, when looking for the prevalence of pleural lesions in indoor asbestos exposed subjects, the potential lack of consistency in reporting the presence of small pleural abnormalities must be borne in mind and strict precautions must be taken.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Air Pollution, Indoor / adverse effects*
  • Asbestos / adverse effects*
  • Asbestosis / diagnostic imaging*
  • Belgium
  • Female
  • Humans
  • Male
  • Middle Aged
  • Observer Variation
  • Pleura / diagnostic imaging*
  • Reproducibility of Results
  • Tomography, X-Ray Computed / statistics & numerical data*

Substances

  • Asbestos