Project Summary The current breast cancer screening recommendations are essentially a one-size fits all approach and, therefore, not optimal in terms of effectiveness and resource utilization. This is because the typical approach focuses on finding subgroups of women who are at “higher than average risk” for developing breast cancer and aggressively promoting additional imaging techniques. However, most women (approximately 70%) who get breast cancer do not have any known risk factors. In addition, the majority of women (approximately 88%) never get breast cancer and these women benefit the least from breast cancer screening. To maximize the benefit to all women and minimize possible harms, investigators have advocated personalized screening using a woman's individual breast cancer risk. To do so, it is essential to have a marker that can provide an accurate near term mammography-detectable breast cancer (mBCa) risk to identify women with very high or very low near term mBCa risk. The goal of this application is to provide person-centered markers of mBCa risk, thus, offering a personalized screening strategy. We hypothesize that we can use temporal changes and lateral differences in images extracted by a novel imaging transformation from sequential mammograms to develop image-based risk markers that can provide women with an accurate near-term mBCa risk from their last negative mammography exam. We will build a database (N= 1,200, 400 cases and 800 controls) of sequential (≥ 5 years) full field digital mammograms collected from the medical records of women over 40 years of age for development and additional independent validation dataset (N = 600, 200 cases, 400 controls) for validation. We will develop year-specific risk markers using a novel Radon Cumulative Distribution Transform (RCDT), convolutional neural network (CNN), and traditional non-imaging markers (such as age). RCDT effectively compares any two lateral and temporal mammograms and highlights differences between the two without having to explicitly align the two images. We will use CNN as a robust imaging marker to analyze the resulting RCDT images from mammograms. Using a statistical approach for handling longitudinal data based on risk sets, we will combine imaging-based risk markers and conventional non-imaging risk factors to develop two near-term risk markers, one for accurately predicting very high risk of having mBCa within a few years and another for predicting very low risk of having mBCa within a few years. High-risk and low-risk markers will be optimized separately to maximize the sizes of accurately predicted high and low risk groups.