Halligan, Steve, Altman, Douglas G., Mallett, Susan, Taylor, Stuart A., Burling, David, Roddie, MaryE., Honeyfield, Lesley, McQuillan, Justine, Amin, Hamdan and Dehmeshki, Jamshid (2006) Computed tomographic colonography: assessment of radiologist performance with and without computer-aided detection. Gastroenterology, 131(6), pp. 1690-1699. ISSN (print) 0016-5085Full text not available from this archive.
BACKGROUND & AIMS: In isolation, computer-aided detection (CAD) for computed tomographic (CT) colonography is as effective as optical colonoscopy for detection of significant adenomas. However, the unavoidable interaction between CAD and the reader has not been addressed. METHODS: Ten readers trained in CT but without special expertise in colonography interpreted CT colonography images of 107 patients (60 with 142 polyps), first without CAD and then with CAD after temporal separation of 2 months. Per-patient and per-polyp detection were determined by comparing responses with known patient status. RESULTS: With CAD, 41 (68%; 95% confidence interval [CI], 55%-80%) of the 60 patients with polyps were identified more frequently by readers. Per-patient sensitivity increased significantly in 70% of readers, while specificity dropped significantly in only one. Polyp detection increased significantly with CAD; on average, 12 more polyps were detected by each reader (9.1%, 95% CI, 5.2%-12.8%). Small- (< or =5 mm) and medium-sized (6-9 mm) polyps were significantly more likely to be detected when prompted correctly by CAD. However, overall performance was relatively poor; even with CAD, on average readers detected only 10 polyps (51.0%) > or =10 mm and 24 (38.2%) > or =6 mm. Interpretation time was shortened significantly with CAD: by 1.9 minutes (95% CI, 1.4-2.4 minutes) for patients with polyps and by 2.9 minutes (95% CI, 2.5-3.3 minutes) for patients without. Overall, 9 readers (90%) benefited significantly from CAD, either by increased sensitivity and/or by reduced interpretation time. CONCLUSIONS: CAD for CT colonography significantly increases per-patient and per-polyp detection and significantly reduces interpretation times but cannot substitute for adequate training.
|Faculty, School or Research Centre:||Faculty of Computing, Information Systems and Mathematics (until 2011) > Digital Imaging Research Centre (DIRC)|
|Depositing User:||Automatic Import Agent|
|Date Deposited:||06 Jul 2010 10:53|
|Last Modified:||26 Sep 2011 13:16|
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