Breast cancer risk prediction using background parenchymal enhancement, radiomics, and symmetry features on MRI

Breast cancer is the world's most prevalent cancer type. Risk models predicting the chance of near future cancer development can help to increase the efficiency of screening programs by targeting high risk patients specifically. In this study we develop machine learning models for predicting the 2 year risk for breast cancer and current breast cancer detection. Therefore, we leverage feature sets based on background parenchymal enhancement (BPE), radiomics and breast symmetry. We train and evaluate our models on longitudinal MRI data from a German high risk screening program using random forests and 5-fold cross validation. The models, which are developed similar to prior work for breast cancer risk prediction, have low predictive power on our dataset. The best performing model is based on BPE features and achieves an AUC of 0.57 for 2 year breast cancer risk prediction.