  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.
