Literature DB >> 31903408

Is there a safety-net effect with computer-aided detection?

Ethan Du-Crow1, Susan M Astley1, Johan Hulleman2.   

Abstract

Computer-aided detection (CAD) systems are used to aid readers interpreting screening mammograms. An expert reader searches the image initially unaided and then once again with the aid of CAD, which prompts automatically detected suspicious regions. This could lead to a "safety-net" effect, where the initial unaided search of the image is adversely affected by the fact that it is preliminary to an additional search with CAD and may, therefore, be less thorough. To investigate the existence of such an effect, we created a visual search experiment for nonexpert observers mirroring breast screening with CAD. Each observer searched 100 images for microcalcification clusters within synthetic images in both prompted (CAD) and unprompted (no-CAD) conditions. Fifty-two participants were recruited for the study, 48 of whom had their eye movements tracked in real-time; the other 4 participants could not be accurately calibrated, so only behavioral data were collected. In the CAD condition, before prompts were displayed, image coverage was significantly lower than coverage in the no-CAD condition ( t 47 = 5.29 , p < 0.0001 ). Observer sensitivity was significantly greater for targets marked by CAD than the same targets in the no-CAD condition ( t 51 = 6.56 , p < 0.001 ). For targets not marked by CAD, there was no significant difference in observer sensitivity in the CAD condition compared with the same targets in the no-CAD condition ( t 51 = 0.54 , p = 0.59 ). These results suggest that the initial search may be influenced by the subsequent availability of CAD; if so, cross-sectional CAD efficacy studies should account for the effect when estimating benefit.
© The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Entities:  

Keywords:  breast cancer; computer-aided detection; eye-tracking; image perception; mammography; visual search

Year:  2019        PMID: 31903408      PMCID: PMC6931663          DOI: 10.1117/1.JMI.7.2.022405

Source DB:  PubMed          Journal:  J Med Imaging (Bellingham)        ISSN: 2329-4302


  16 in total

Review 1.  Computer-aided detection in mammography.

Authors:  S M Astley; F J Gilbert
Journal:  Clin Radiol       Date:  2004-05       Impact factor: 2.350

2.  Testing the effect of computer-assisted detection on interpretive performance in screening mammography.

Authors:  Stephen H Taplin; Carolyn M Rutter; Constance D Lehman
Journal:  AJR Am J Roentgenol       Date:  2006-12       Impact factor: 3.959

Review 3.  Improving the radiologist-CAD interaction: designing for appropriate trust.

Authors:  W Jorritsma; F Cnossen; P M A van Ooijen
Journal:  Clin Radiol       Date:  2014-10-30       Impact factor: 2.350

4.  Low prevalence search for cancers in mammograms: Evidence using laboratory experiments and computer aided detection.

Authors:  Melina A Kunar; Derrick G Watson; Sian Taylor-Phillips; Julia Wolska
Journal:  J Exp Psychol Appl       Date:  2017-05-25

5.  Colour and spatial cueing in low-prevalence visual search.

Authors:  Nicholas C C Russell; Melina A Kunar
Journal:  Q J Exp Psychol (Hove)       Date:  2012-04-12       Impact factor: 2.143

6.  Clinically missed cancer: how effectively can radiologists use computer-aided detection?

Authors:  Robert M Nishikawa; Robert A Schmidt; Michael N Linver; Alexandra V Edwards; John Papaioannou; Margaret A Stull
Journal:  AJR Am J Roentgenol       Date:  2012-03       Impact factor: 3.959

7.  Effects of incorrect computer-aided detection (CAD) output on human decision-making in mammography.

Authors:  Eugenio Alberdi; Andrey Povykalo; Lorenzo Strigini; Peter Ayton
Journal:  Acad Radiol       Date:  2004-08       Impact factor: 3.173

8.  Variability in interpretive performance at screening mammography and radiologists' characteristics associated with accuracy.

Authors:  Joann G Elmore; Sara L Jackson; Linn Abraham; Diana L Miglioretti; Patricia A Carney; Berta M Geller; Bonnie C Yankaskas; Karla Kerlikowske; Tracy Onega; Robert D Rosenberg; Edward A Sickles; Diana S M Buist
Journal:  Radiology       Date:  2009-10-28       Impact factor: 11.105

9.  Single reading with computer-aided detection for screening mammography.

Authors:  Fiona J Gilbert; Susan M Astley; Maureen G C Gillan; Olorunsola F Agbaje; Matthew G Wallis; Jonathan James; Caroline R M Boggis; Stephen W Duffy
Journal:  N Engl J Med       Date:  2008-10-01       Impact factor: 91.245

10.  If you don't find it often, you often don't find it: why some cancers are missed in breast cancer screening.

Authors:  Karla K Evans; Robyn L Birdwell; Jeremy M Wolfe
Journal:  PLoS One       Date:  2013-05-30       Impact factor: 3.240

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  2 in total

1.  Current Available Computer-Aided Detection Catches Cancer but Requires a Human Operator.

Authors:  Florentino Saenz Rios; Giri Movva; Hari Movva; Quan D Nguyen
Journal:  Cureus       Date:  2020-12-19

2.  The optimal use of computer aided detection to find low prevalence cancers.

Authors:  Melina A Kunar
Journal:  Cogn Res Princ Implic       Date:  2022-02-04
  2 in total

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