Breast density in MRI: an AI-based quantification and relationship to assessment in mammography.
Authors
Affiliations (6)
Affiliations (6)
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA.
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA.
- Department of Surgery, Duke University School of Medicine, Durham, NC, USA.
- Department of Biostatistics & Bioinformatics, Duke University, Durham, NC, USA.
- Department of Computer Science, Duke University, Durham, NC, USA.
- Department of Surgery, Duke University School of Medicine, Durham, NC, USA. [email protected].
Abstract
Mammographic breast density is a well-established risk factor for breast cancer. Recently, there has been interest in breast MRI as an adjunct to mammography, as this modality provides an orthogonal and highly quantitative assessment of breast tissue. However, its 3D nature poses analytic challenges related to delineating and aggregating complex structures across slices. Here, we applied an in-house machine-learning algorithm to assess breast density on normal breasts in three MRI datasets. Breast density was consistent across different datasets (0.104-0.114). Analysis across different age groups also demonstrated strong consistency across datasets and confirmed a trend of decreasing density with age as reported in previous studies. MR breast density was correlated with mammographic breast density, although some notable differences suggest that certain breast density components are captured only on MRI. Future work will determine how to best integrate MR breast density with current tools to improve future breast cancer risk prediction.