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Proceedings Paper

Assessment of two mammographic density related features in predicting near-term breast cancer risk
Author(s): Bin Zheng; Jules H. Sumkin; Margarita L. Zuley; Xingwei Wang; Amy H. Klym; David Gur
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Paper Abstract

In order to establish a personalized breast cancer screening program, it is important to develop risk models that have high discriminatory power in predicting the likelihood of a woman developing an imaging detectable breast cancer in near-term (e.g., <3 years after a negative examination in question). In epidemiology-based breast cancer risk models, mammographic density is considered the second highest breast cancer risk factor (second to woman's age). In this study we explored a new feature, namely bilateral mammographic density asymmetry, and investigated the feasibility of predicting near-term screening outcome. The database consisted of 343 negative examinations, of which 187 depicted cancers that were detected during the subsequent screening examination and 155 that remained negative. We computed the average pixel value of the segmented breast areas depicted on each cranio-caudal view of the initial negative examinations. We then computed the mean and difference mammographic density for paired bilateral images. Using woman's age, subjectively rated density (BIRADS), and computed mammographic density related features we compared classification performance in estimating the likelihood of detecting cancer during the subsequent examination using areas under the ROC curves (AUC). The AUCs were 0.63±0.03, 0.54±0.04, 0.57±0.03, 0.68±0.03 when using woman's age, BIRADS rating, computed mean density and difference in computed bilateral mammographic density, respectively. Performance increased to 0.62±0.03 and 0.72±0.03 when we fused mean and difference in density with woman's age. The results suggest that, in this study, bilateral mammographic tissue density is a significantly stronger (p<0.01) risk indicator than both woman's age and mean breast density.

Paper Details

Date Published: 22 February 2012
PDF: 6 pages
Proc. SPIE 8318, Medical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment, 83181E (22 February 2012); doi: 10.1117/12.910630
Show Author Affiliations
Bin Zheng, Univ. of Pittsburgh (United States)
Jules H. Sumkin, Univ. of Pittsburgh (United States)
Margarita L. Zuley, Univ. of Pittsburgh (United States)
Xingwei Wang, Univ. of Pittsburgh (United States)
Amy H. Klym, Univ. of Pittsburgh (United States)
David Gur, Univ. of Pittsburgh (United States)

Published in SPIE Proceedings Vol. 8318:
Medical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment
Craig K. Abbey; Claudia R. Mello-Thoms, Editor(s)

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