Title
Applying a new mammographic imaging marker to predict breast cancer risk.
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-teen 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.
Year
DOI
Venue
2018
10.1117/12.2287556
Proceedings of SPIE
Keywords
DocType
Volume
Breast cancer screening,Short-term breast cancer risk prediction,Quantitative mammographic imaging marker,Computer-aided detection
Conference
10575
ISSN
Citations 
PageRank 
0277-786X
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Faranak Aghaei103.72
Gopichandh Danala2174.88
Alan B. Hollingsworth301.01
Rebecca G. Stough400.68
Melanie Pearce500.34
Hong Liu65617.01
Bin Zheng713528.83