Breast density has been widely considered as an important risk factor for breast cancer.
The purpose of this study is to examine the association between mammogram density
results and background parenchymal enhancement (BPE) of breast MRI. A dataset involving
breast MR images was acquired from 65 high-risk women. Based on mammography density
(BIRADS) results, the dataset was divided into two groups of low and high breast density
cases. The Low-Density group has 15 cases with mammographic density (BIRADS 1 and
2), while the High-density group includes 50 cases, which were rated by radiologists
as mammographic density BIRADS 3 and 4. A computer aided detection (CAD) scheme was
applied to segment and register breast regions depicted on sequential images of breast
MRI scans. CAD scheme computed 20 global BPE features from the entire two breast regions,
separately from the left and right breast region, as well as from the bilateral difference
between left and right breast regions. An image feature selection method namely, CFS
method, was applied to remove the most redundant features and select optimal features
from the initial feature pool. Then, a logistic regression classifier was built using
the optimal features to predict the mammogram density from the BPE features. Using
a leave-one-case-out validation method, the classifier yields the accuracy of 82%
and area under ROC curve, AUC=0.81 +/- 0.09. Also, the box-plot based analysis shows
a negative association between mammogram density results and BPE features in the MRI
images. This study demonstrated a negative association between mammogram density and
BPE of breast MRI images.
