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Proceedings Paper

Applying a new mammographic imaging marker to predict breast cancer risk
Author(s): Faranak Aghaei; Gopichandh Danala; Alan B. Hollingsworth; Rebecca G. Stoug; Melanie Pearce; Hong Liu; Bin Zheng
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Paper Abstract

Identifying and developing new mammographic imaging markers to assist prediction of breast cancer risk has been attracting extensive research interest recently. Although mammographic density is considered an important breast cancer risk, its discriminatory power is lower for predicting short-term breast cancer risk, which is a prerequisite to establish a more effective personalized breast cancer screening paradigm. In this study, we presented a new interactive computer-aided detection (CAD) scheme to generate a new quantitative mammographic imaging marker based on the bilateral mammographic tissue density asymmetry to predict risk of cancer detection in the next subsequent mammography screening. An image database involving 1,397 women was retrospectively assembled and tested. Each woman had two digital mammography screenings namely, the “current” and “prior” screenings with a time interval from 365 to 600 days. All “prior” images were originally interpreted negative. In “current” screenings, these cases were divided into 3 groups, which include 402 positive, 643 negative, and 352 biopsy-proved benign cases, respectively. There is no significant difference of BIRADS based mammographic density ratings between 3 case groups (p < 0.6). When applying the CAD-generated imaging marker or risk model to classify between 402 positive and 643 negative cases using “prior” negative mammograms, the area under a ROC curve is 0.70±0.02 and the adjusted odds ratios show an increasing trend from 1.0 to 8.13 to predict the risk of cancer detection in the “current” screening. Study demonstrated that this new imaging marker had potential to yield significantly higher discriminatory power to predict short-term breast cancer risk.

Paper Details

Date Published: 27 February 2018
PDF: 7 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105753N (27 February 2018); doi: 10.1117/12.2287556
Show Author Affiliations
Faranak Aghaei, The Univ. of Oklahoma (United States)
Gopichandh Danala, The Univ. of Oklahoma (United States)
Alan B. Hollingsworth, Mercy Health Ctr. (United States)
Rebecca G. Stoug, Mercy Health Ctr. (United States)
Melanie Pearce, Mercy Health Ctr. (United States)
Hong Liu, The Univ. of Oklahoma (United States)
Bin Zheng, The Univ. of Oklahoma (United States)


Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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