Longitudinal Interpretability of Deep-Learning based Breast Cancer Risk Prediction Model

When developing models intended for clinical applications, understanding which part of the input contributed the most to the final decision is crucial. Our study brings interpretability to a Breast Cancer Risk (BCR) prediction by exploring whether the Deep Learning (DL) model relies on the laterality of the breast, where cancer ultimately develops, and how this reliance evolves.

The Mirai DL model was employed for BCR predictions using a dataset containing 1210 mammography studies of patients who developed cancer within six years. Each study included four images (two views per breast). For each BCR prediction, gradients with respect to each pixel of the four input images were calculated. The attribution (importance to model prediction) across both views for each breast was summed. It was assumed that the cancer would form in the breast with higher relative attribution. ROC-AUC analysis was performed for six-time points (during cancer and each year before cancer from 2y-6y).

Results showed that the model heavily relies on the attribution from the breast where cancer is already detectable (AUC=0.92±0.02). For the studies where the cancer was not yet present, the high AUC for 2y (0.91±0.07) decreased monotonically to 0.51±0.15 for 6y before cancer.

This interpretability research suggests that the model's predictions for time points closer to cancer occurrence primarily rely on the signal derived from the breast where cancer will form. In contrast, the relative difference is random for more distant time points.