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Published online before print March 16, 2006, 10.1148/radiol.2392050483
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(Radiology 2006;239:759-767.)
© RSNA, 2006


Gastrointestinal Imaging

Polyp Detection with CT Colonography: Primary 3D Endoluminal Analysis versus Primary 2D Transverse Analysis with Computer-assisted Reader Software1

Stuart A. Taylor, BSc, MD, MRCP, FRCR, Steve Halligan, MD, FRCP, FRCR2, Andrew Slater, MRCP, FRCR, Vicky Goh, MRCP, FRCR3, David N. Burling, MRCP, FRCR, Mary E. Roddie, MRCP, FRCR, Leslie Honeyfield, DCR(R), Justine McQuillan, DCR(R), Hamdan Amin, PhD and Jamshid Dehmeshki, PhD

1 From the Department of Intestinal Imaging, St Mark's Hospital, Northwick Park, Harrow, United Kingdom (S.A.T., S.H., A.S., V.G., D.N.B.); Charing Cross Hospital, London, United Kingdom (M.E.R.); and Medicsight, London, United Kingdom (M.E.R., L.H., J.M., H.A., J.D.). Received March 22, 2005; revision requested May 17; revision received May 24; accepted June 21; final version accepted July 18. Supported by Medicsight, London, United Kingdom. Address correspondence to S.A.T., Department of Specialist X-Ray, Level 2 Podium, University College Hospital, 235 Euston Rd, London NW1 2BU, UK (e-mail: csytaylor{at}yahoo.co.uk).

Purpose: To retrospectively compare primary three-dimensional (3D) endoluminal analysis with primary two-dimensional (2D) transverse analysis supplemented by computer-assisted reader (CAR) software for computed tomographic (CT) polyp detection and reader reporting times.

Materials and Methods: Ethical permission and patient consent were obtained from all donor institutions for use of CT colonography data sets. Twenty CT colonography data sets from 14 men (median age, 61 years; age range, 52–78 years) with 48 endoscopically proved polyps were selected. Polyp coordinates were documented in consensus by three unblinded radiologists to create a reference standard. Two radiologists read the data sets, which were randomized between primary 3D endoluminal views with 2D problem solving and 2D views supplemented by CAR software. Reading times and diagnostic confidence were documented. The CAR software highlighted possible polyps by superimposing circles on the 2D transverse images. Data sets were reread after 1 month by using the opposing analysis method. Detection rates were compared by using the McNemar test. Reporting times and diagnostic confidence were compared by using the paired t test and Mann-Whitney U test, respectively.

Results: Mean sensitivity values for polyps measuring 1–5, 6–9, and 10 mm or larger were 14%, 53%, and 83%, respectively, for 2D CAR analysis and 16%, 53%, and 67%, respectively, for primary 3D analysis. Overall sensitivity values were 41% for 2D CAR analysis and 39% for primary 3D analysis (P = .77). Reader 1 detected more polyps than reader 2, particularly when using the 3D fly-through method (P = .002). Mean reading times were significantly longer with the 3D method (P = .001). Mean false-positive findings were 1.5 for 2D analysis and 5.5 for 3D analysis. Reader confidence was not significantly different between analysis methods (P = .42).

Conclusion: Two-dimensional CAR analysis is quicker and at least matches the sensitivity of primary 3D endoluminal analysis, with fewer false-positive findings.

© RSNA, 2006




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