Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program

Radiology. 2002 Dec;225(3):685-92. doi: 10.1148/radiol.2253011376.


Purpose: To evaluate the performance of a fully automated computerized method for the detection of lung nodules in computed tomographic (CT) scans in the identification of lung cancers that may be missed during visual interpretation.

Materials and methods: A database of 38 low-dose CT scans with 50 lung nodules was obtained from a lung cancer screening program. Thirty-eight of the nodules represented biopsy-confirmed lung cancers that had not been reported during initial clinical interpretation. A computer detection method that involved the use of gray-level thresholding techniques to identify three-dimensionally contiguous structures within the lungs was applied to the CT data. Computer-extracted volume was used to determine whether a structure became a nodule candidate. A rule-based scheme and a cascaded automated classifier were applied to the set of nodule candidates to distinguish actual nodules from areas of normal anatomy. Overall performance of the computer detection method was evaluated with free-response receiver operating characteristic (FROC) analysis.

Results: At a specific operating point on the FROC curve, the method achieved a sensitivity of 80% (40 of 50 nodules), with an average of 1.0 false-positive detection per section. Missed cancers were detected by the computerized method with a sensitivity of 84% (32 of 38 nodules) and a false-positive rate of 1.0 per section.

Conclusion: With an automated lung nodule detection method, a large fraction (84%, 32 of 38) of missed cancers in a database of low-dose CT scans were detected correctly.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Aged
  • Databases, Factual
  • Diagnosis, Computer-Assisted*
  • Diagnostic Errors
  • Female
  • Humans
  • Lung Neoplasms / diagnostic imaging
  • Male
  • Mass Screening
  • ROC Curve
  • Sensitivity and Specificity
  • Solitary Pulmonary Nodule / diagnostic imaging*
  • Tomography, X-Ray Computed*