Investigating early low and no dose predictors of breast density, for the purpose of breast cancer detection
Summary
Breast density is the key predictor of breast cancer, we seek to measure breast density at an early stage to help with early identification of patients who are susceptible to breast cancer.
Supervisor(s)
Dr Mark McEntee, Professor Patrick Brennan
Research Location
Medical Imaging and Radiation Sciences Research Group
Program Type
Masters/PHD
Synopsis
The evidence is now clear that women with higher breast density are at higher risk of breast cancer. Their higher risk continues across the entire life span. Women who are in the higher spectrum of dense breasts in youth will continue to be in the higher spectrum of dense breasts as they grow older.
Increasingly breast density is seen as a prognostic indicator of breast cancer development in later years. It is also a complicating factor in the diagnosis of breast cancer as higher density breasts obscure visualisation of nodules within the breast.
Breast tissue consists mainly of two types of tissue: fatty and fibroglandular tissue. The fibroglandular tissue is denser, and is often similar in density to breast cancer. This can lead to fibroglandular tissue obscuring breast cancers, leading to a later diagnosis. Mammography is still the primary way in which we diagnose breast cancer. In mammography there are difficulties in differentiating fibroglandular tissue from breast cancer tissue. It is the fibroglandular tissue is often referred to as the breast density.
Digital systems have been replacing film systems over the past 20 years, and in mammography Digital systems have replaced them. With new digital systems being introduced here comes the potential to use new digital image processing methodologies. One of these allows us to quantify breast density. The quantification can be used as a prognostic indicator for breast cancer. However this does not come without it’s risks. Mammography uses ionising radiation, which can be harmful to biological tissue and can in some cases induce breast cancer. Therefore, using radiation as a predictor of breast density could initiate cancer. Our research seeks to identify low dose or no dose methods of imaging and measuring breast density.
Students involved in this research will be working with a team of researchers including professors, senior lecturers, postdoctoral students and Ph.D. students. The research team currently exceeds 20 members all of whom have an interest in diagnosis of breast cancer.
Additional Information
Students interested in this area of research should read some of the important research papers published on the issue below and should put together a 500 word research proposal indicating their interest in the topic and identifying some for the reading that they have undertaken.
Carney, P. A., Miglioretti, D. L., Yankaskas, B. C., Kerlikowske, K., Rosenberg, R., Rutter, C. M., Ballard-Barbash, R. (2003). Individual and Combined Effects of Age, Breast Density, and Hormone Replacement Therapy Use on the Accuracy of Screening Mammography. Annals of Internal Medicine, 138(3), 168-175.
Kolb, T. M., Lichy, J., & Newhouse, J. H. (2002). Comparison of the Performance of Screening Mammography, Physical Examination, and Breast US and Evaluation of Factors that Influence Them: An Analysis of 27,825 Patient Evaluations1. Radiology, 225(1), 165-175.
Mandelson, M. T., Oestreicher, N., Porter, P. L., White, D., Finder, C. A., Taplin, S. H., & White, E. (2000). Breast Density as a Predictor of Mammographic Detection: Comparison of Interval- and Screen-Detected Cancers. Journal of the National Cancer Institute, 92(13), 1081-1087.
Pisano, E. D., Hendrick, R. E., Yaffe, M. J., Baum, J. K., Acharyya, S., Cormack, J. B., Gatsonis, C. A. (2008). Diagnostic Accuracy of Digital versus Film Mammography: Exploratory Analysis of Selected Population Subgroups in DMIST1. Radiology, 246(2), 376-383.
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Keywords
Breast Cancer, Early Detection, Mammography, diagnostic accuracy, Radiography, Radiology, Cancer, radiation dose, Ultrasound, x-ray
Opportunity ID
The opportunity ID for this research opportunity is: 1614
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