CT colonography with computer-aided detection: automated recognition of ileocecal valve to reduce number of false-positive detections

Radiology. 2004 Oct;233(1):266-72. doi: 10.1148/radiol.2331031326.

Abstract

The ileocecal valve (ICV) is a common cause of false-positive detections of polyps at computed tomographic (CT) colonography with computer-aided detection (CAD). The authors developed a CAD algorithm for differentiating the ICV from a true polyp and evaluated this algorithm by using two colonoscopy-confirmed CT colonography data sets. Data sets 1 and 2 consisted of the data obtained at CT colonographic examinations performed in 20 and 40 patients, respectively. Forty of these patients had at least one polyp 1 cm or larger. For data set 1, the proposed ICV recognition algorithm eliminated three of nine (33%; 95% confidence interval [CI]: 8%, 70%) false-positive CAD detections that were attributable to the ICV and none of the true-positive polyp detections. For data set 2, with use of identical parameters, the algorithm eliminated 11 of 18 (61%; 95% CI: 36%, 83%) false-positive detections that were attributable to the ICV and none of the true-positive detections. The thresholds used to recognize the ICV were a mean internal CT attenuation of less than -124 HU and a volume of greater than 1.5 cm(3). The proposed algorithm successfully recognized the ICV and eliminated it in some cases. This result is clinically important because, by reducing the frequency of a common cause of false-positive detections, this algorithm may improve the efficiency of physicians who use CAD.

Publication types

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

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Algorithms
  • Cohort Studies
  • Colonic Neoplasms / diagnostic imaging
  • Colonic Polyps / diagnostic imaging
  • Colonography, Computed Tomographic*
  • Colonoscopy
  • False Positive Reactions
  • Female
  • Humans
  • Ileocecal Valve / diagnostic imaging*
  • Image Processing, Computer-Assisted
  • Male
  • Middle Aged
  • Pattern Recognition, Automated*
  • Radiographic Image Interpretation, Computer-Assisted*
  • Retrospective Studies
  • Sensitivity and Specificity
  • Software