Breast density in MRI: an AI-based quantification and relationship to assessment in mammography
This work addresses breast cancer risk prediction by providing an AI-based method for MRI density assessment, but it is incremental as it builds on existing knowledge and tools.
The study tackled the challenge of quantifying breast density in MRI, a 3D modality with analytic difficulties, by applying a machine-learning algorithm to three datasets, finding consistent density values (0.104-0.114) and correlation with mammographic density, though with notable differences suggesting MRI captures unique components.
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 integrate MR breast density with current tools to improve future breast cancer risk prediction.