  Breast density is an important risk factor for breast cancer that also
affects the specificity and sensitivity of screening mammography. Current
federal legislation mandates reporting of breast density for all women
undergoing breast screening. Clinically, breast density is assessed visually
using the American College of Radiology Breast Imaging Reporting And Data
System (BI-RADS) scale. Here, we introduce an artificial intelligence (AI)
method to estimate breast percentage density (PD) from digital mammograms. Our
method leverages deep learning (DL) using two convolutional neural network
architectures to accurately segment the breast area. A machine-learning
algorithm combining superpixel generation, texture feature analysis, and
support vector machine is then applied to differentiate dense from non-dense
tissue regions, from which PD is estimated. Our method has been trained and
validated on a multi-ethnic, multi-institutional dataset of 15,661 images
(4,437 women), and then tested on an independent dataset of 6,368 digital
mammograms (1,702 women; cases=414) for both PD estimation and discrimination
of breast cancer. On the independent dataset, PD estimates from Deep-LIBRA and
an expert reader were strongly correlated (Spearman correlation coefficient =
0.90). Moreover, Deep-LIBRA yielded a higher breast cancer discrimination
performance (area under the ROC curve, AUC = 0.611 [95% confidence interval
(CI): 0.583, 0.639]) compared to four other widely-used research and commercial
PD assessment methods (AUCs = 0.528 to 0.588). Our results suggest a strong
agreement of PD estimates between Deep-LIBRA and gold-standard assessment by an
expert reader, as well as improved performance in breast cancer risk assessment
over state-of-the-art open-source and commercial methods.
