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.