Research Project
6174116
DESCRIPTION: (Adapted from the Investigator's Abstract) The long term goal of this proposal is to develop improved methods of measuring the breast tissue characteristics that we refer to as "mammographic densities" and have found to be strongly related to risk of breast cancer. These improved methods will be fully automated, will not require an observer, and will measure the volume, rather than the projected area, of the relevant tissues in the breast. Mammographically dense breast tissue in different individuals may have similar projected areas, but differ substantially in thickness and thus in quantity. Because we expect the quantity of the relevant tissue to be more directly related to risk of breast cancer than the projected area, we believe that measurement of volume will be a stronger risk factor for breast cancer than is the projected area. To assess the volume of radiologically dense breast tissue as a risk factor for breast cancer we must acquire images prospectively under controlled conditions, and we have developed a calibration device and a thickness gauge that will be incorporated into the field imaged during mammography to assess breast tissue composition and thickness. We will install the calibration device in selected mammography sites, and include it in the examination of all subjects having mammography. Cases will be subjects diagnosed with histologically verified invasive breast cancer. One control will be selected from the same mammography department, and age-matched to the case. A total of 800 case control pairs will be recruited. The hypotheses to be addressed in this project are: 1) Automated measurement of the volume of mammographically dense tissue in the breast will create larger gradients in risk than projected area. 2) The volume of mammographically dense breast tissue will be more strongly associated with other risk factors for breast cancer than the projected area. 3) Statistical models that combine volume, projected area, and other mammographic features will result in improved prediction of breast cancer risk compared to percent density alone.
