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Proceedings Paper

The interplay of attention economics and computer-aided detection marks in screening mammography
Author(s): Tayler M. Schwartz; Radhika Sridharan; Wei Wei; Olga Lukyanchenko; William Geiser; Gary J. Whitman; Tamara Miner Haygood
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Paper Abstract

Introduction: According to attention economists, overabundant information leads to decreased attention for individual pieces of information. Computer-aided detection (CAD) alerts radiologists to findings potentially associated with breast cancer but is notorious for creating an abundance of false-positive marks. We suspected that increased CAD marks do not lengthen mammogram interpretation time, as radiologists will selectively disregard these marks when present in larger numbers. We explore the relevance of attention economics in mammography by examining how the number of CAD marks affects interpretation time. Methods: We performed a retrospective review of bilateral digital screening mammograms obtained between January 1, 2011 and February 28, 2014, using only weekend interpretations to decrease distractions and the likelihood of trainee participation. We stratified data according to reader and used ANOVA to assess the relationship between number of CAD marks and interpretation time. Results: Ten radiologists, with median experience after residency of 12.5 years (range 6 to 24,) interpreted 1849 mammograms. When accounting for number of images, Breast Imaging Reporting and Data System category, and breast density, increasing numbers of CAD marks was correlated with longer interpretation time only for the three radiologists with the fewest years of experience (median 7 years.) Conclusion: For the 7 most experienced readers, increasing CAD marks did not lengthen interpretation time. We surmise that as CAD marks increase, the attention given to individual marks decreases. Experienced radiologists may rapidly dismiss larger numbers of CAD marks as false-positive, having learned that devoting extra attention to such marks does not improve clinical detection.

Paper Details

Date Published: 24 March 2016
PDF: 9 pages
Proc. SPIE 9787, Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment, 978711 (24 March 2016); doi: 10.1117/12.2208253
Show Author Affiliations
Tayler M. Schwartz, The Warren Alpert Medical School of Brown Univ. (United States)
Radhika Sridharan, UKM Medical Ctr. (Malaysia)
Wei Wei, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
Olga Lukyanchenko, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
William Geiser, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
Gary J. Whitman, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
Tamara Miner Haygood, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)


Published in SPIE Proceedings Vol. 9787:
Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment
Craig K. Abbey; Matthew A. Kupinski, Editor(s)

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