Longitudinal analysis of micro-calcifications features for breast cancer risk prediction with the Mirai model
Yao-Kuan Wang, Žan Klaneček, Tobias Wagner, Lesley Cockmartin, Nicholas Marshall, Andrej Studen, Robert Jeraj, Hilde Bosmans · 2024
Complementary relationship between computer-aided detection (CAD) and risk prediction has been identified. To understand the factors triggering either cancer detection or risk prediction, we previously studied the performance of the deep learning (DL)-based risk prediction model, Mirai, using a feature-centric explainable AI (XAI) approach. A total of 16 calcification features were identified from Mirai as major risk factor contributors. Several studies have revealed the existence of early detection signs on prior mammograms of screen-detected and interval cancers. Accordingly, the longitudinal behavior of calcifications may further improve the understanding of the causal relationship between Mirai calcification features and elevated risk. In this study, we hypothesize that the calcification features from Mirai have the ability to capture early suspicious signs, which may be important for the risk prediction of breast cancer development. Thus, we tracked the Mirai calcification features across two screening rounds using the breast polar coordinate system. Subsequently, we assessed the ability to predict the current Breast Imaging-Reporting and Data System (BI-RADS) assessment from prior mammograms. The results show that calcification features were able to capture early suspicious signs on prior mammograms at the same location with an average polar angle difference of 13 degrees compared to the current mammograms. In addition, the calcification features were able to classify the current BI-RADS assessment with an area under the receiver operating characteristic curve (AUC) of 0.74 using prior mammograms. In conclusion, the predictive power of calcification features in short-term risk prediction may arise from their ability to detect early suspicious signs.