Gastroenterology

Gastroenterology

Volume 129, Issue 6, December 2005, Pages 1832-1844
Gastroenterology

Clinical–alimentary tract
Computed Tomographic Virtual Colonoscopy Computer-Aided Polyp Detection in a Screening Population

https://doi.org/10.1053/j.gastro.2005.08.054Get rights and content

Background & Aims: The sensitivity of computed tomographic (CT) virtual colonoscopy (CT colonography) for detecting polyps varies widely in recently reported large clinical trials. Our objective was to determine whether a computer program is as sensitive as optical colonoscopy for the detection of adenomatous colonic polyps on CT virtual colonoscopy. Methods: The data set was a cohort of 1186 screening patients at 3 medical centers. All patients underwent same-day virtual and optical colonoscopy. Our enhanced gold standard combined segmental unblinded optical colonoscopy and retrospective identification of precise polyp locations. The data were randomized into separate training (n = 394) and test (n = 792) sets for analysis by a computer-aided polyp detection (CAD) program. Results: For the test set, per-polyp and per-patient sensitivities for CAD were both 89.3% (25/28; 95% confidence interval, 71.8%–97.7%) for detecting retrospectively identifiable adenomatous polyps at least 1 cm in size. The false-positive rate was 2.1 (95% confidence interval, 2.0–2.2) false polyps per patient. Both carcinomas were detected by CAD at a false-positive rate of 0.7 per patient; only 1 of 2 was detected by optical colonoscopy before segmental unblinding. At both 8-mm and 10-mm adenoma size thresholds, the per-patient sensitivities of CAD were not significantly different from those of optical colonoscopy before segmental unblinding. Conclusions: The per-patient sensitivity of CT virtual colonoscopy CAD in an asymptomatic screening population is comparable to that of optical colonoscopy for adenomas ≥8 mm and is generalizable to new CT virtual colonoscopy data.

Section snippets

Patient Population

The patient population consisted of 1253 asymptomatic adults between 40 and 79 years of age at 3 medical centers (institutions 1–3), of whom 1233 underwent complete same-day virtual and optical colonoscopy.4 Twenty of the 1253 patients were excluded because of incomplete optical colonoscopy, inadequate preparation, or failure of the CT colonographic system. The study was approved by the institutional review boards at all 3 centers. Written informed consent was obtained from all patients. This

Results

The patients were distributed into the training and test sets as shown in Table 1, with similar age and sex distributions, accounting for the 2:1 split. The polyp distributions are shown in Table 2.

The FROC curves are shown in Figure 1 for the 3 different classifiers trained to detect adenomatous polyps ≥10, ≥8, and ≥6 mm. These curves indicate that at a constant false-positive rate, sensitivity was higher for larger polyps. Sensitivity was also higher on the training set compared with the test

Discussion

CT virtual colonoscopy has progressed rapidly since its inception in 1994.29 Several large clinical trials have been reported.4, 6, 8, 30 Some of these trials have reported excellent sensitivity, but others have shown relatively poor sensitivity. The causes of poor sensitivities have been variously attributed to out-of-date CT scanner technology, absence of bowel opacification, inadequate interpretation software, improper interpretation approach (2-dimensional rather than 3-dimensional), or

References (56)

  • T.M. Gluecker et al.

    Colorectal cancer screening with CT colonography, colonoscopy, and double-contrast barium enema examinationprospective assessment of patient perceptions and preferences

    Radiology

    (2003)
  • R.E. van Gelder et al.

    CT colonography and colonoscopyassessment of patient preference in a 5-week follow-up study

    Radiology

    (2004)
  • P.J. Pickhardt et al.

    Computed tomographic virtual colonoscopy to screen for colorectal neoplasia in asymptomatic adults

    N Engl J Med

    (2003)
  • P.B. Cotton et al.

    Computed tomographic colonography (virtual colonoscopy)a multicenter comparison with standard colonoscopy for detection of colorectal neoplasia

    JAMA

    (2004)
  • R.M. Summers et al.

    Future directionscomputer-aided diagnosis

  • H. Yoshida et al.

    Computer-aided diagnosis scheme for detection of polyps at CT colonography

    Radiographics

    (2002)
  • R.M. Summers et al.

    Automated polyp detection at CT colonographyfeasibility assessment in a human population

    Radiology

    (2001)
  • G. Kiss et al.

    Computer-aided diagnosis in virtual colonography via combination of surface normal and sphere fitting methods

    Eur Radiol

    (2002)
  • D.S. Paik et al.

    Surface normal overlapa computer-aided detection algorithm, with application to colonic polyps and lung nodules in helical CT

    IEEE Trans Med Imaging

    (2004)
  • R.M. Summers et al.

    Colonic polypscomplementary role of computer-aided detection in CT colonography

    Radiology

    (2002)
  • P.J. Pickhardt et al.

    Electronic cleansing and stool tagging in CT colonographyadvantages and pitfalls with primary three-dimensional evaluation

    AJR Am J Roentgenol

    (2003)
  • P.J. Pickhardt et al.

    Location of adenomas missed by optical colonoscopy

    Ann Intern Med

    (2004)
  • R.M. Summers et al.

    Computer-aided detection of polyps on oral contrast-enhanced CT colonography

    Am J Roentgenol

    (2005)
  • J. Yao et al.
  • J.D. Malley et al.
  • J.H. Yao et al.
  • I. Bitter et al.
  • D.P. Chakraborty

    The FROC, AFROC and DROC variants of the ROC analysis. Chapter 16

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    P.J.P.’s current affiliation is: Department of Radiology, University of Wisconsin Medical School, Madison, Wisconsin.

    This research was supported by the Intramural Research Program of the National Institutes of Health, Warren G. Magnuson Clinical Center. Viatronix supplied the V3D Colon software free of charge. This study used the high-performance computational capabilities of the Biowulf Linux cluster at the National Institutes of Health in Bethesda, Maryland (http://biowulf.nih.gov).

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