End-to-End Mammographic Breast Density Quantification with Deep Learning: Preliminary Study on Simulated Mammograms

High breast density (BD) is recognized as an independent risk factor for breast cancer development, in addition to negatively impacting the sensitivity of mammography. Although BD is normally assessed with the BI-RADS reporting system, this evaluation is qualitative and has been shown to vary considerably across readers. In this pilot study, we present a deep learning (DL) method to quantify BD from a standard two-view (cranio-caudal, and medio-lateral-oblique) mammography exam. With the aim of developing a method based on an objective ground truth, the DL model was trained and validated using 88 simulated mammograms from an equal number of distinct 3D digital breast phantoms for which BD is known. The phantoms had been previously generated through segmentation and simulated mechanical compression of patient dedicated breast CT images, allowing for the exact calculation of BD in each case. Different data augmentations were applied prior to simulation, to increase the dataset size, yielding a total of 528 cases. These were divided, randomly and on a patient level, into training (N=360), validation (N=60), and test sets (N=108). The DL model performance was tested by stratifying the breasts into four different density ranges: 1-15%, 15-25%, 25-60%, and >60%. The median absolute errors and interquartile ranges (IQR), in percentage points, were 3.3 (IQR: 3.5), 3.4 (IQR: 2.5), 3.5 (IQR: 3.9), and 14.8 (IQR: 8.4), respectively. Although preliminary, these results show the potential of the proposed approach for accurate BD quantification, which is based, as opposed to most previously proposed approaches, on an objective ground truth.