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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.15192 |
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| _version_ | 1866914163784155136 |
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| author | Chen, Yaqian Li, Lin Gu, Hanxue Dong, Haoyu Nguyen, Derek L. Kirk, Allan D. Mazurowski, Maciej A. Hwang, E. Shelley |
| author_facet | Chen, Yaqian Li, Lin Gu, Hanxue Dong, Haoyu Nguyen, Derek L. Kirk, Allan D. Mazurowski, Maciej A. Hwang, E. Shelley |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15192 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Breast density in MRI: an AI-based quantification and relationship to assessment in mammography Chen, Yaqian Li, Lin Gu, Hanxue Dong, Haoyu Nguyen, Derek L. Kirk, Allan D. Mazurowski, Maciej A. Hwang, E. Shelley Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Breast density in MRI: an AI-based quantification and relationship to assessment in mammography |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2504.15192 |