Variables affecting pulmonary nodule detection with computed tomography: Evaluation with three-dimensional computer simulation

David P. Naidich, Henry Rusinek, Georgeann McGuinness, Barry Leitman, Dorothy I. McCauley, Claudia I. Henschke

Research output: Contribution to journalArticlepeer-review

73 Scopus citations

Abstract

To meaningfully evaluate factors determining the overall accuracy of computed tomography (CT) for identifying pulmonary nodules, computer-generated nodules were superimposed on normal CT scans and interpreted independently by three experienced chest radiologists. Variables evaluated included nodule size, shape, number, density, location, edge characteristics, and relationship to adjacent vessels, as well as technical factors, including slice thickness and electronic windowing. The overall sensitivity in identifying nodules was 62% and the specificity was 80%. On average, the observers identified 56, 67, and 63% of nodules on 1.5-, 5-, and 10-mm-thick sections, respectively (p = 0.037). Nodules were more difficult to identify on 1.5-mm-thick sections. On average, observers identified 1, 48, 82, and 91% of nodules <1.5, <3, <4.5, and <7 mm in diameter, respectively (p < 0.001). Other factors that made a significant contribution (p < 0.01) in identifying nodules, as determined by linear discriminant function analysis, included nodule location, angiocentricity, and density. We concluded that computer-generated nodules can be used to assess a large number of imaging variables. We anticipate that this approach will be of considerable utility in assessing the accuracy of interpretation of a wide range of pathologic entities as well as in optimizing three-dimensional scan protocols within the thorax.

Original languageEnglish
Pages (from-to)291-299
Number of pages9
JournalJournal of Thoracic Imaging
Volume8
Issue number4
DOIs
StatePublished - 1993
Externally publishedYes

Keywords

  • Pulmonary nodules
  • Three-dimensional computer simulation

Fingerprint

Dive into the research topics of 'Variables affecting pulmonary nodule detection with computed tomography: Evaluation with three-dimensional computer simulation'. Together they form a unique fingerprint.

Cite this