HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Figures Only Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Taylor, S. A. Articles by Dehmeshki, J. Search for Related Content PubMed PubMed Citation Articles by Taylor, S. A. Articles by Dehmeshki, J. Social Bookmarking                      What's this?

Computer-Assisted Reader Software Versus Expert Reviewers for Polyp Detection on CT Colonography

¹ Department of Intestinal Imaging, St. Mark's and Northwick Park Hospitals, Watford Rd., Harrow HA1 3UJ, United Kingdom.
² Present address: Department of Specialist X-ray, University College Hospital, 235 Euston Rd., London NW1 2BU, United Kingdom.
³ Department of Radiology, Charing Cross Hospital, London, United Kingdom.
⁴ Medicsight PLC, London, United Kingdom.

OBJECTIVE. The purpose of our study was to assess the sensitivity of computer-assisted reader (CAR) software for polyp detection compared with the performance of expert reviewers.

MATERIALS AND METHODS. A library of colonoscopically validated CT colonography cases were collated and separated into training and test sets according to the time of accrual. Training data sets were annotated in consensus by three expert radiologists who were aware of the colonoscopy report. A subset of 45 training cases containing 100 polyps underwent batch analysis using ColonCAR version 1.2 software to determine the optimum polyp enhancement filter settings for polyp detection. Twenty-five consecutive positive test data sets were subsequently interpreted individually by each expert, who was unaware of the endoscopy report, and before generation of the annotated reference via an unblinded consensus interpretation. ColonCAR version 1.2 software was applied to the test cases, at optimized polyp enhancement filter settings, to determine diagnostic performance. False-positive findings were classified according to importance.

RESULTS. The 25 test cases contained 32 nondiminutive polyps ranging from 6 to 35 mm in diameter. The ColonCAR version 1.2 software identified 26 (81%) of 32 polyps compared with an average sensitivity of 70% for the expert reviewers. Eleven (92%) of 12 polyps 10 mm were detected by ColonCAR version 1.2. All polyps missed by experts 1 (n = 4) and 2 (n = 3) and 12 (86%) of 14 polyps missed by expert 3 were detected by ColonCAR version 1.2. The median number of false-positive highlights per case was 13, of which 91% were easily dismissed.

CONCLUSION. ColonCAR version 1.2 is sensitive for polyp detection, with a clinically acceptable false-positive rate. ColonCAR version 1.2 has a synergistic effect to the reviewer alone, and its standalone performance may exceed even that of experts.

Keywords: colon • colonography • computer-assisted imaging • CT colonography • polyps • software

CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    What's this?

This article has been cited by other articles:

				R. M. Summers, L. R. Handwerker, P. J. Pickhardt, R. L. Van Uitert, K. K. Deshpande, S. Yeshwant, J. Yao, and M. Franaszek Performance of a Previously Validated CT Colonography Computer-Aided Detection System in a New Patient Population Am. J. Roentgenol., July 1, 2008; 191(1): 168 - 174. [Abstract] [Full Text] [PDF]

				S. A. Taylor, R. Greenhalgh, R. Ilangovan, E. Tam, V. A. Sahni, D. Burling, J. Zhang, P. Bassett, P. J. Pickhardt, and S. Halligan CT Colonography and Computer-aided Detection: Effect of False-Positive Results on Reader Specificity and Reading Efficiency in a Low-Prevalence Screening Population Radiology, April 1, 2008; 247(1): 133 - 140. [Abstract] [Full Text] [PDF]

				S A TAYLOR, D BURLING, M RODDIE, L HONEYFIELD, J MCQUILLAN, P BASSETT, and S HALLIGAN Computer-aided detection for CT colonography: incremental benefit of observer training Br. J. Radiol., March 1, 2008; 81(963): 180 - 186. [Abstract] [Full Text] [PDF]

				S. A. Taylor, S. C. Charman, P. Lefere, E. G. McFarland, E. K. Paulson, J. Yee, R. Aslam, J. M. Barlow, A. Gupta, D. H. Kim, et al. CT Colonography: Investigation of the Optimum Reader Paradigm by Using Computer-aided Detection Software Radiology, December 19, 2007; (2007) 2461070190. [Abstract] [Full Text]

				M. E. Baker, L. Bogoni, N. A. Obuchowski, C. Dass, R. M. Kendzierski, E. M. Remer, D. M. Einstein, P. Cathier, A. Jerebko, S. Lakare, et al. Computer-aided Detection of Colorectal Polyps: Can It Improve Sensitivity of Less-Experienced Readers? Preliminary Findings Radiology, October 1, 2007; 245(1): 140 - 149. [Abstract] [Full Text] [PDF]

				J. G. Fletcher, F. Booya, R. M. Summers, D. Roy, L. Guendel, B. Schmidt, C. H. McCollough, and J. L. Fidler Comparative Performance of Two Polyp Detection Systems on CT Colonography Am. J. Roentgenol., August 1, 2007; 189(2): 277 - 282. [Abstract] [Full Text] [PDF]

				T. Mang, A. Maier, C. Plank, C. Mueller-Mang, C. Herold, and W. Schima Pitfalls in Multi-Detector Row CT Colonography: A Systematic Approach RadioGraphics, March 1, 2007; 27(2): 431 - 454. [Abstract] [Full Text] [PDF]