Learning knowledge from longitudinal data of mammograms to improving breast cancer risk prediction

Abstract

Breast cancer risk prediction is becoming increasingly important especially after recent advances in deep learning models. In breast cancer screening, it is common that patients have multiple longitudinal mammogram examinations, where the longitudinal imaging data may provide additional information to boost the learning of a risk prediction model. In this study, we aim to leverage quantitative imaging features extracted from prior mammograms to augment the training of a risk prediction model, through two technical approaches: 1) prior data-enabled transfer learning, and 2) multi-task learning. We evaluated the two approaches on a study cohort of 306 patients in a case-control setting, where each patient has 3 longitudinal screening mammogram examinations. Our results show that both two approaches improved the 1-, 2-, and 3-year risk prediction, indicating that additional knowledge can be learned by our approaches from longitudinal imaging data to improve near-term risk prediction.