Predicting up to 10 year breast cancer risk using longitudinal mammographic screening history

Risk assessment of breast cancer (BC) seeks to enhance individualized screening and prevention strategies. BC risk informs healthy individuals of the short- and long-term likelihood of cancer development, also enabling detection of existing BC. Recent mammographic-based deep learning (DL) risk models outperform traditional risk factor-based models and achieve state-of-the-art (SOTA) at short-term risk prediction, but mainly use single-time exams, which seem to rely more on detecting existing lesions. We present a novel temporospatial and explainable deep learning risk model, the Multi-Time Point Breast Cancer Risk Model (MTP-BCR), which learns from longitudinal mammography data to identify subtle changes in breast tissue that may signal future malignancy. Utilizing a large in-house dataset of 171,168 screening mammograms from 42,792 consecutive exams involving 9,133 women, our model demonstrates a significant improvement in long-term (10-year) risk prediction with an area under the receiver operating characteristics (AUC) of 0.80, outperforming the traditional BCSC 10-year risk model and other SOTA methods at 5-year AUC in various screening cohorts. Furthermore, MTP-BCR provides unilateral breast-level predictions, achieving AUCs up to 0.81 and 0.77 for 5-year risk and 10-year risk assessments, respectively. The heatmaps derived from our model may help clinicians better understand the progression from normal tissue to cancerous growth, enhancing interpretability in breast cancer risk assessment. Teaser MTP-BCR model uses multi-time points mammograms and rich risk factors to predict 10-year breast cancer risk more accurately.