Learning density independent texture features

Abstract

Breast cancer risk assessment is becoming increasingly important in clinical practice. It has been suggested that features that characterize mammographic texture are more predictive for breast cancer than breast density. Yet, strong correlation between both types of features is an issue in many studies. In this work we investigate a method to generate texture features and/or scores that are independent of breast density. The method is especially useful in settings where features are learned from the data itself. We evaluate our method on a case control set comprising 394 cancers, and 1182 healthy controls. We show that the learned density independent texture features are significantly associated with breast cancer risk. As such it may aid in exploring breast characteristics that are predictive of breast cancer irrespective of breast density. Furthermore it offers opportunities to enhance personalized breast cancer screening beyond breast density.