European Radiology

, Volume 17, Issue 10, pp 2598–2607 | Cite as

Effect of computer-aided detection as a second reader in multidetector-row CT colonography

  • Thomas Mang
  • Philipp Peloschek
  • Christina Plank
  • Andrea Maier
  • Anno Graser
  • Michael Weber
  • Christian Herold
  • Luca Bogoni
  • Wolfgang Schima


Our purpose was to assess the effect of computer-aided detection (CAD) on lesion detection as a second reader in computed tomographic colonography, and to compare the influence of CAD on the performance of readers with different levels of expertise. Fifty-two CT colonography patient data-sets (37 patients: 55 endoscopically confirmed polyps ≥0.5 cm, seven cancers; 15 patients: no abnormalities) were retrospectively reviewed by four radiologists (two expert, two nonexpert). After primary data evaluation, a second reading augmented with findings of CAD (polyp-enhanced view, Siemens) was performed. Sensitivities and reading time were calculated for each reader without CAD and supported by CAD findings. The sensitivity of expert readers was 91% each, and of nonexpert readers, 76% and 75%, respectively, for polyp detection. CAD increased the sensitivity of expert readers to 96% (P = 0.25) and 93% (P = 1), and that of nonexpert readers to 91% (P = 0.008) and 95% (P = 0.001), respectively. All four readers diagnosed 100% of cancers, but CAD alone only 43%. CAD increased reading time by 2.1 min (mean). CAD as a second reader significantly improves sensitivity for polyp detection in a high disease prevalence population for nonexpert readers. CAD causes a modest increase in reading time. CAD is of limited value in the detection of cancer.


CT colonography Computer-aided detection Virtual endoscopy Polyp Colon cancer 


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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Thomas Mang
    • 1
  • Philipp Peloschek
    • 1
  • Christina Plank
    • 1
  • Andrea Maier
    • 1
  • Anno Graser
    • 2
  • Michael Weber
    • 1
  • Christian Herold
    • 1
  • Luca Bogoni
    • 3
  • Wolfgang Schima
    • 1
  1. 1.Department of RadiologyMedical University of ViennaViennaAustria
  2. 2.Department of Clinical Radiology-Grosshadern CampusUniversity of MunichMunichGermany
  3. 3.Computer-Aided Diagnosis and TherapySiemens Medical SolutionsMalvernUSA

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