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Proceedings Paper

Exploring a new bilateral focal density asymmetry based image marker to predict breast cancer risk
Author(s): Faranak Aghaei; Seyedehnafiseh Mirniaharikandehei; Alan B. Hollingsworth; Yunzhi Wang; Yuchen Qiu; Hong Liu; Bin Zheng
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Paper Abstract

Although breast density has been widely considered an important breast cancer risk factor, it is not very effective to predict risk of developing breast cancer in a short-term or harboring cancer in mammograms. Based on our recent studies to build short-term breast cancer risk stratification models based on bilateral mammographic density asymmetry, we in this study explored a new quantitative image marker based on bilateral focal density asymmetry to predict the risk of harboring cancers in mammograms. For this purpose, we assembled a testing dataset involving 100 positive and 100 negative cases. In each of positive case, no any solid masses are visible on mammograms. We developed a computer-aided detection (CAD) scheme to automatically detect focal dense regions depicting on two bilateral mammograms of left and right breasts. CAD selects one focal dense region with the maximum size on each image and computes its asymmetrical ratio. We used this focal density asymmetry as a new imaging marker to divide testing cases into two groups of higher and lower focal density asymmetry. The first group included 70 cases in which 62.9% are positive, while the second group included 130 cases in which 43.1% are positive. The odds ratio is 2.24. As a result, this preliminary study supported the feasibility of applying a new focal density asymmetry based imaging marker to predict the risk of having mammography-occult cancers. The goal is to assist radiologists more effectively and accurately detect early subtle cancers using mammography and/or other adjunctive imaging modalities in the future.

Paper Details

Date Published: 10 March 2017
PDF: 7 pages
Proc. SPIE 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment, 101361P (10 March 2017); doi: 10.1117/12.2254073
Show Author Affiliations
Faranak Aghaei, The Univ. of Oklahoma (United States)
Seyedehnafiseh Mirniaharikandehei, The Univ. of Oklahoma (United States)
Alan B. Hollingsworth, Mercy Health Ctr. (United States)
Yunzhi Wang, The Univ. of Oklahoma (United States)
Yuchen Qiu, The Univ. of Oklahoma (United States)
Hong Liu, The Univ. of Oklahoma (United States)
Bin Zheng, The Univ. of Oklahoma (United States)

Published in SPIE Proceedings Vol. 10136:
Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment
Matthew A. Kupinski; Robert M. Nishikawa, Editor(s)

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