High-throughput mammographic density measurement: a tool for risk prediction of breast cancer

Mammographic density (MD) is a strong, independent risk factor of breast cancer, but measuring MD is time-consuming and reader-dependent. Objective MD measurement in a high-throughput fashion would enable its wider use as a biomarker for breast cancer. We use a public domain image processing software for the fully automated analysis of MD and penalised regression to construct a measure which mimics a well-established semi-automated measure (Cumulus). We also describe measures which incorporate additional features of mammographic images for improving the risk associations of MD and breast cancer risk.

We randomly partitioned our dataset into a training set for model building (733 cases, 748 controls) and a test set for model assessment (765 cases, 747 controls). Pearson’s product moment correlation coefficient (r) was used to compare the MD measurements by Cumulus and our automated measure which mimics Cumulus. The likelihood ratio test was used to validate the performance of logistic regression models for breast cancer risk, which included our measure capturing additional information in mammographic images.

We observed a high correlation between the Cumulus measure and our measure mimicking Cumulus (r = 0.884, 95% CI: 0.872 to 0.894) in an external test set. Adding a variable, which includes extra information to percent density, significantly improved the fit of the logistic regression model of breast cancer risk (P=0.0002).

Our results demonstrate the potential to facilitate the integration of mammographic density measurements into large-scale research studies and subsequently into clinical practice.

Li J, Szekely L, Eriksson L, Heddson B, Sundbom A, Czene K, Hall P, Humphreys K.
Breast Cancer Res. 2012 Jul 30;14(4):R114.