Breast cancer is a significant public health concern and early detection is critical for triaging high risk patients. Sequential screening mammograms can provide important spatiotemporal information about changes in breast tissue over time. In this study, we propose a deep learning architecture called RADIFUSION that utilizes sequential mammograms and incorporates a linear image attention mechanism, radiomic features, a new gating mechanism to combine different mammographic views, and bilateral asymmetry-based finetuning for breast cancer risk assessment. We evaluate our model on a screening dataset called Cohort of Screen-Aged Women (CSAW) dataset. Based on results obtained on the independent testing set consisting of 1,749 women, our approach achieved superior performance compared to other state-of-the-art models with area under the receiver operating characteristic curves (AUCs) of 0.905, 0.872 and 0.866 in the three respective metrics of 1-year AUC, 2-year AUC and > 2-year AUC. Our study highlights the importance of incorporating various deep learning mechanisms, such as image attention, radiomic features, gating mechanism, and bilateral asymmetry-based fine-tuning, to improve the accuracy of breast cancer risk assessment. We also demonstrate that our model's performance was enhanced by leveraging spatiotemporal information from sequential mammograms. Our findings suggest that RADIFUSION can provide clinicians with a powerful tool for breast cancer risk assessment.

翻译：乳腺癌是一项重大公共卫生问题，早期检测对于高危患者的分诊至关重要。序贯筛查乳腺X光图像能提供关于乳腺组织随时间变化的重要时空信息。本研究提出一种名为RADIFUSION的深度学习架构，该架构利用序贯乳腺X光图像，并融合了线性图像注意力机制、放射组学特征、一种用于整合不同乳腺X光视图的新型门控机制，以及基于双侧不对称性的微调方法，以实现乳腺癌风险评估。我们在名为Cohort of Screen-Aged Women（CSAW）的筛查数据集上评估了该模型。基于包含1,749名女性的独立测试集结果，我们的方法在三个评估指标——1年AUC、2年AUC和大于2年AUC——上分别取得了0.905、0.872和0.866的受试者工作特征曲线下面积（AUC），性能优于其他最先进模型。本研究强调了整合多种深度学习机制（如图像注意力、放射组学特征、门控机制和基于双侧不对称性的微调）对于提升乳腺癌风险评估准确性的重要性。我们还证明了模型通过利用序贯乳腺X光图像的时空信息而增强了性能。研究结果表明，RADIFUSION可为临床医生提供一种强大的乳腺癌风险评估工具。