Full Field Digital Mammography and Breast Density: Comparison of Calibrated and Noncalibrated Measurements

Abstract

Mammographic breast density is an important and widely accepted risk factor for breast cancer. A statement about breast density in the mammographic report is becoming a requirement in many States. However, there is significant inter-observer variation between radiologists in their interpretation of breast density. A properly designed automated system could provide benefits in maintaining consistency and reproducibility. We have developed a new automated and calibrated measure of breast density using full field digital mammography (FFDM). This new measure assesses spatial variation within a mammogram and produced significant associations with breast cancer in a small study. The costs of this automation are delays from advanced image and data analyses before the study can be processed. We evaluated this new calibrated variation measure using a larger dataset than previously. We also explored the possibility of developing an automated measure from unprocessed (raw data) mammograms as an approximation for this calibrated breast density measure. A case-control study comprised of 160 cases and 160 controls matched by age, screening history, and hormone replacement therapy was used to compare the calibrated variation measure of breast density with three variants of a noncalibrated measure of spatial variation. The operator-assisted percentage of breast density measure (PD) was used as a standard reference for comparison. Odds ratio (OR) quartile analysis was used to compare these measures. Linear regression analysis was applied to assess the calibration's impact on the raw pixel distribution. All breast density measures showed significant breast cancer associations. The calibrated spatial variation measure produced the strongest associations (OR: 1.0 [ref.], 4.6, 4.3, 7.4). The associations for PD were diminished in comparison (OR: 1.0 [ref.], 2.7, 2.9, 5.2). Two additional non-calibrated measures restricted in region size also showed significant associations (OR: 1.0 [ref.], 2.9, 4.4, 5.4), and (OR: 1.0 [ref.], 3.5, 3.1, 4.9). Regression analyses indicated the raw image mean is influenced by the calibration more so than its standard deviation. Breast density measures can be automated. The associated calibration produced risk information not retrievable from the raw data representation. Although the calibrated measure produced the stronger association, the non-calibrated measures may offer an alternative to PD and other operator based methods after further evaluation, because they can be implemented automatically with a simple processing algorithm.