Share Email Print

Proceedings Paper

A utility/cost analysis of breast cancer risk prediction algorithms
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Breast cancer risk prediction algorithms are used to identify subpopulations that are at increased risk for developing breast cancer. They can be based on many different sources of data such as demographics, relatives with cancer, gene expression, and various phenotypic features such as breast density. Women who are identified as high risk may undergo a more extensive (and expensive) screening process that includes MRI or ultrasound imaging in addition to the standard full-field digital mammography (FFDM) exam. Given that there are many ways that risk prediction may be accomplished, it is of interest to evaluate them in terms of expected cost, which includes the costs of diagnostic outcomes. In this work we perform an expected-cost analysis of risk prediction algorithms that is based on a published model that includes the costs associated with diagnostic outcomes (true-positive, false-positive, etc.). We assume the existence of a standard screening method and an enhanced screening method with higher scan cost, higher sensitivity, and lower specificity. We then assess expected cost of using a risk prediction algorithm to determine who gets the enhanced screening method under the strong assumption that risk and diagnostic performance are independent. We find that if risk prediction leads to a high enough positive predictive value, it will be cost-effective regardless of the size of the subpopulation. Furthermore, in terms of the hit-rate and false-alarm rate of the of the risk prediction algorithm, iso-cost contours are lines with slope determined by properties of the available diagnostic systems for screening.

Paper Details

Date Published: 24 March 2016
PDF: 7 pages
Proc. SPIE 9787, Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment, 97871J (24 March 2016); doi: 10.1117/12.2217850
Show Author Affiliations
Craig K. Abbey, Univ. of California, Santa Barbara (United States)
Yirong Wu, Univ. of Wisconsin-Madison (United States)
Elizabeth S. Burnside, Univ. of Wisconsin-Madison (United States)
Adam Wunderlich, U.S. Food and Drug Administration (United States)
Frank W. Samuelson, U.S. Food and Drug Administration (United States)
John M. Boone, UC Davis Medical 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)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?