Time-to-event learning paradigm as a generalized approach to estimate risk of breast cancer using image-based deep learning models
Thomas A. Louis, Serena Pacilè, Pierre Fillard · 2024
Evaluation of an AI-based model to estimate cancer risk that aims at improving early detection of cancer by leveraging information untapped by detection models. We converted a breast cancer detection model into a risk model with light architectural changes and by using the survival analysis / time to event paradigm within the machine learning framework. The new model is able to predict cumulative risk function of a breast/patient from mammogram images. A longitudinal dataset of 2,460 positive patients and 5,466 negative patients over average timespan avg 4.6 years and q75 = 5.5 years, q90 = 7.1 years), independent from our training set, is used to evaluate the performance of our approach. We compare our methods against the open source baseline MIRAI, considered as the state of the art. To do so we used both concordance index aka. C-index and dynamic AUC restricted to the 5 year range that MIRAI model allows. We obtain a concordance index of 0.758 (ci=(0.752, 0.763)). While the baseline reaches a concordance index of 0.736 (ci=(0.730, 0.743)). Regarding cumulative dynamic AUC, our AI model reach 0.796 (ci=(0.791, 805)) remaining close to MIRAI, which is at 0.801 (ci=(0.794, 0.810)). Our model demonstrates performance similar to the state of the art with few modifications.