Mammographic density is a dynamic risk factor for breast cancer and affects the sensitivity of mammography-based screening. While automated machine and deep learning-based methods provide more consistent and precise measurements compared to subjective BI-RADS assessments, they often fail to account for the longitudinal evolution of density. Many of these methods assess mammographic density in a cross-sectional manner, overlooking correlations in repeated measures, irregular visit intervals, missing data, and informative dropouts. Joint models, however, are well-suited for capturing the longitudinal relationship between biomarkers and survival outcomes. We present the DeepJoint algorithm, an open-source solution that integrates deep learning for quantitative mammographic density estimation with joint modeling to assess the longitudinal relationship between mammographic density and breast cancer risk. Our method efficiently analyzes processed mammograms from various manufacturers, estimating both dense area and percent density--established risk factors for breast cancer. We utilize a joint model to explore their association with breast cancer risk and provide individualized risk predictions. Bayesian inference and the Monte Carlo consensus algorithm make the approach reliable for large screening datasets. Our method allows for accurate analysis of processed mammograms from multiple manufacturers, offering a comprehensive view of breast cancer risk based on individual longitudinal density profiles. The complete pipeline is publicly available, promoting broader application and comparison with other methods.

翻译：乳腺密度是乳腺癌的动态风险因素，并影响基于乳腺X线摄影筛查的敏感性。虽然自动化的机器学习和深度学习方法相较于主观的BI-RADS评估能提供更一致和精确的测量，但它们往往未能考虑密度的纵向演变。许多此类方法以横断面方式评估乳腺密度，忽略了重复测量中的相关性、不规则访视间隔、数据缺失以及信息性脱落。然而，联合模型非常适合捕捉生物标志物与生存结果之间的纵向关系。我们提出了深度联合算法，这是一个开源解决方案，它将用于定量乳腺密度估计的深度学习与联合建模相结合，以评估乳腺密度与乳腺癌风险之间的纵向关系。我们的方法能高效分析来自不同制造商的已处理乳腺X光片，估计致密面积和百分比密度——这两个均是已确立的乳腺癌风险因素。我们利用联合模型来探索它们与乳腺癌风险的关联，并提供个体化的风险预测。贝叶斯推断和蒙特卡洛共识算法使该方法适用于大型筛查数据集。我们的方法能够准确分析来自多个制造商的已处理乳腺X光片，基于个体纵向密度谱提供乳腺癌风险的全面视图。完整的流程已公开可用，以促进更广泛的应用及与其他方法的比较。