用AI量化乳腺MRI密度，发现与钼靶结果相关但有差异。
Breast density in MRI: an AI-based quantification and relationship to assessment in mammography
- 自研算法自动分析三组MRI数据中的乳腺密度
- 各数据集密度值稳定在0.104至0.114之间，随年龄下降
- MRI可捕捉钼靶未覆盖的密度成分，适合风险评估优化
钼靶乳腺密度是乳腺癌的重要风险因素。近年来，乳腺MRI因其三维、高定量特性成为钼靶的补充手段，但其复杂结构跨切片分割与聚合带来分析挑战。本文采用自研机器学习算法，在三组正常乳腺MRI数据中评估乳腺密度，结果显示不同数据集间密度值一致（0.104–0.114），且各年龄组呈现与已有研究一致的随年龄增长密度下降趋势。MRI乳腺密度与钼靶结果存在相关性，但部分差异表明某些密度成分仅在MRI中显现。未来工作将探索如何将MR乳腺密度整合至现有风险预测工具中，以提升预测能力。
原文摘要 · Abstract (English)
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.
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