This application is responsive to PA-10-026, "Development, Application, and Evaluation of Prediction Models for Cancer Risk and Prognosis (R21)". This research is disease and organ-specific and is aimed at developing and validating an accurate method for determining breast density, a known risk factor for breast cancer. Developing an accurate method for estimating breast density is essential to ensure the accuracy of prediction models for cancer risk and prognosis. Several studies have shown the association between breast density and breast cancer risk. A meta-analysis observed that the relative risk was ~4.7 for women with greater than 75% fibroglandular content compared to those with less than 5% fibroglandular content. Breast density is now considered the third highest risk factor in terms of relative risk after age and BRCA mutation. Recognizing the significance of breast density as a risk factor and the reduced sensitivity of mammography for women with dense breasts, several states now regulate that a woman undergoing screening mammography is informed of her breast density. In the United States, breast density is reported as per the American College of Radiology, Breast Imaging Reporting and Data System by the interpreting Radiologist that uses four categories. It is has been shown that agreement between Radiologists for such categorical assignment is only moderate. Hence, there is a need to develop accurate quantitative techniques for estimating breast density. While some studies use quantitative estimates of breast density from the projected area of the breast, often referred to as percent mammographic density, recent research has shown that volumetric estimates of breast density are more accurate predictors of breast cancer risk. In this research, we propose to develop and evaluate a quantitative algorithm that serves as a tool for the estimation of volumetric breast density and associated measures based on parenchymal texture analysis using 3-D images provided by digital breast tomosynthesis (DBT). At least one DBT system manufacturer has obtained FDA-approval for routine clinical use and several manufacturers are working towards FDA approval. Hence, the developed quantitative tool will be designed to be compatible with DBT systems from multiple vendors, facilitating its widespread use for clinical studies. This is particularly important for studies where image data are pooled from multiple sites that may use DBT systems from different vendors.
The specific aims of this research include developing the quantitative tool, evaluating its quantitative accuracy, and conducting a feasibility study aimed a demonstrating its accuracy in a clinical population with breast MRI serving as the truth. The validated 3-D imaging based quantitative tool can be used in future studies to accurately determine the cancer risk associated with breast density, to assess the effect of pharmacologic intervention on breast density and cancer risk, and to develop personalized breast cancer screening regimens.
Breast density is now considered a major risk factor for breast cancer. Recent research has shown that volumetric measures of breast density can predict breast cancer risk more accurately. In addition, more cancers are missed for women with dense breasts compared to women with fatty breasts with mammography. Digital breast tomosynthesis (DBT) is a new technique that is FDA-approved for clinical use, which provides 3-D images that can be used for volumetric estimates of breast density and can reduce the possibility of missing a cancer due to overlapping tissue. In this research, we propose to develop and evaluate an accurate quantitative tool for determining breast density to obtain personalized breast cancer risk assessment that can be used for breast cancer screening tailored to the individual, and for assessment of the effect of pharmacologic intervention on breast density and cancer risk.
|Vedantham, Srinivasan; Shi, Linxi; Michaelsen, Kelly E et al. (2015) Digital Breast Tomosynthesis guided Near Infrared Spectroscopy: Volumetric estimates of fibroglandular fraction and breast density from tomosynthesis reconstructions. Biomed Phys Eng Express 1:|
|Vedantham, Srinivasan; Karellas, Andrew; Vijayaraghavan, Gopal R et al. (2015) Digital Breast Tomosynthesis: State of the Art. Radiology 277:663-84|
|Vedantham, Srinivasan; Shi, Linxi; Karellas, Andrew (2014) Large-angle x-ray scatter in Talbot-Lau interferometry for breast imaging. Phys Med Biol 59:6387-400|
|O'Connell, Avice M; Karellas, Andrew; Vedantham, Srinivasan (2014) The potential role of dedicated 3D breast CT as a diagnostic tool: review and early clinical examples. Breast J 20:592-605|