Breast Cancer Risk Prediction with Density Independent Texture Features

K. Kallenberg, M. Nielsen, K. Holland, N. Karssemeijer and M. Lillholm

Annual Meeting of the Radiological Society of North America 2016.

PURPOSE In personalized breast cancer screening stratification is commonly based on breast density. It has been suggested though, that breast density is a too coarse descriptor for breast cancer risk. Several authors have developed texture features that are potentially more predictive of breast cancer. Yet, in several studies, strong correlation between both types of features is an issue. In this work we investigate a method to generate deep learning texture features that are independent of breast density.

METHOD AND MATERIALS From the Dutch breast cancer screening program we collected 394 cancers and 1182 age matched healthy controls. To obtain mammograms without signs of cancerous tissue, we took the contralateral mammograms. For each image breast density was computed using automated software. Texture features were automatically learned from the data by means of techniques that are commonly used in deep learning. In the initial matching, breast density was on average higher in the cases than in the controls, as breast density is associated with breast cancer risk. Texture features and scores learned on this set (Td) are determined to be correlated to density. In order to obtain density independent features and scores (Ti) we balanced breast density over the cases and the controls by performing a rematching based on breast density. Non-matching cases and controls were excluded during training; in the testing phase all images were scored. We trained and tested Td and Ti to separate between cancers and controls with 5-fold cross-validation. We compared the performance of Td and Ti in terms of predictive power.

RESULTS Spearman's rank correlation between density and Td was 0.81 (0.79-0.83). The density adjusted odds ratios for breast cancer were 1.15 (0.81-1.65), 1.40 (0.98-2.00), and 1.39 (0.92-2.09) for quartile 2-4 respectively, relative to quartile 1. For Ti the correlation with density was 0.00 (-0.06 - 0.05). The odds ratios were 1.15 (0.82-1.62), 1.33 (0.96-1.86), and 1.45 (1.05-2.01). The AUC for separating cancers from controls was 0.539 (0.506-0.572).

CONCLUSION We developed a method for generating density independent texture features and scores. The obtained texture scores were significantly associated with breast cancer risk.

CLINICAL RELEVANCE/APPLICATION The obtained density independent texture features may enhance breast cancer risk models beyond breast density, and as such offer opportunities to further optimize personalized breast cancer screening.