Breast density classification based on volumetric glandularity measured by spectral mammography

Abstract

Observations of significant variability in radiologists' classification of breast density signals the need for objective classification methods. In this study, we develop a model for a radiologist's BI-RADS classification based on the volumetric glandularity image measured by spectral mammography and a reader study where ten MQSA certified radiologists assigned BI-RADS scores to 300 screening cases. Several combinations of features such as area glandularity based on a certain volumetric glandularity threshold, breast thickness and the spread of glandular tissue were tested as linear classifier parameters. Logistic regression was used to optimize the parameters and cross-validation to assess the agreement with the radiologists' majority vote, regarded as truth. We show a clear indication that the automatic classification algorithm performs on par with or better than the average individual radiologist. © 2014 Springer International Publishing.