Breast Cancer Risks in Early Detection and Prevention

–Ongoing Projects–

Breast Density Identification Tool

Breast cancer is the most common cancer in Canadian women and early detection dramatically increases a woman’s chance of survival. Until recently, women’s ages were considered the single most influential risk factor for developing breast cancer. Today, the density of fibroglandular tissue within the breast is considered just as important a risk factor as age. Because of this, accuracy and consistency while estimating tissue density is paramount. Currently, radiologists use the BI-RADS classification system to place mammographic images into one of four different categories. However, inter-observer variance has been shown to be as high as 30% and the methodology can be highly subjective. Many computer vision algorithms have been developed to automatically quantify breast density but only a few of these algorithms take advantage of the latest digital mammographic imaging technology. One algorithm, specifically designed to use digital mammography images, is explored in detail. Its ability to quantify and classify fibroglandular breast tissue is demonstrated and its accuracy is shown to be consistent with experienced radiologists.

Two types of images are produced from digital mammography machines: the raw image, acquired from the imaging sensor, and the processed image which contains propriety techniques for visual enhancement. Currently, automated breast density algorithms focus on utilizing the raw image while radiologists use the processed image for visual inspection. The processed image is then stored within a patient’s medical file. Discovering a means to detect breast density from processed images would allow radiologists to assess retroactively the breast cancer risk of any patient who has previously received a digital mammogram using minimal financial and human resources.

Development of a Natural History Model for Breast Cancer using CADe/CADx

Mammography is a valuable tool for reducing breast cancer mortality, however the frequency with which women should receive mammography is still controversial, and guidelines for screening mammography vary across different jurisdictions. Micro-simulation modeling has the potential to aid health policy researchers in evaluating the impact of a policy change (and other changes) at both the individual level and the population level. Micro-simulation modeling has two components: a natural history model and an intervention model.

To create a micro-simulation model, the natural history of the disease must first be modelled. Several previous studies have created mathematical models of breast cancer growth; however, these models required assumptions be made about certain aspects of cancer development, e.g. the distribution of tumour growth rates2-5 . In order to minimize the need for assumptions, this project will use a technology that has yet to be utilized in breast cancer modelling: computer-aided detection (CAD) software. CAD is a system that aids radiologic interpretation of mammograms by highlighting areas on breast images that display suspicious lesions, and assigning a level-of-certainty score to each of those regions.

We propose that by comparing abnormal CAD scores (taken from the abnormal mammogram that led to breast cancer detection) with normal CAD scores from previous normal mammograms of women who have developed breast cancer, we expect to be able to create a preliminary natural history model that is a more realistic representation of the biological development of screen-detected breast cancer than existing models. This natural history model would inform the microsimulation model currently under development at the BC Cancer Agency, which will ultimately be used to evaluate preferable screening frequencies for women in British Columbia and elsewhere.

Impact of Coronary Artery Calcium on Radiation Induced Cardiac Toxicity in Breast Cancer Survivors

A number of studies have demonstrated a link between radiotherapy of the chest area and subsequent cardiovascular disease, a relationship seen in breast cancer survivors specifically. The risk has been shown to persist, and even increase, long after the completion of radiation treatment. This pilot study will seek to determine whether there are a sufficient number of breast cancer patients with Coronary Artery Calcium who underwent breast radiation therapy in the BC Cancer Agency, in order to design a formal study to evaluate the impact of coronary calcification on radiation induced cardiac toxicity. These patients will then be categorized based on the extent of coronary calcification to determine if there is a correlation between these groups and radiation induced cardiac toxicity.

Validation of Volumetric Breast Density as a Risk Factor for Breast Cancer Screening

Personalized mammography screening has emerged as the way of the future for improving breast cancer screening effectiveness. In order for individualized screening to be applicable in a clinical setting, one must first improve methods of evaluating individual risk by validating risk factors. Breast density, a measure of the dense tissue in the breast, has the potential to be used as a strong predictor for breast cancer risk. Strong evidence relating breast density and cancer risk has been established for 2-dimensional methods of estimating breast density, even with the subjective nature of that process. Considering that the breast is a 3-dimensional structure, efforts were made to develop methods to assess volumetric breast density (VBD). With the implementation of digital mammography in B.C., the quantitative analysis of mammograms to estimate VBD is now available. However, validation data to support the direct correlation of VBD to breast cancer risk for an ethnically diverse population like British Columbia is not available. 

This study aims to validate three-dimensional measures of breast density as a risk factor for breast cancer. Furthermore, the study will examine if and how VBD is associated with tumour characteristics, breast cancer subtypes and survival outcomes following treatment to better understand the biological mechanisms through which breast density influences breast tumour prognosis and to potentially provide a biomarker for risk of breast cancer recurrence. Screening mammograms and risk assessment questionnaires will be collected from 190,000 women in B.C. over two years. Women who are asymptomatic and aged 40 and older will be eligible to participate in the study and will be invited at their routine bilateral, two-view, screening mammogram.

Screening mammograms and questionnaires will be collected from twenty-two digital screening sites. Women in the study will be followed-up for 10 years. The breast cancer outcomes for the study participants will be extracted from the Screening Mammography Program of British Columbia (SMPBC) database. VBD measurements will be determined for all mammograms collected using Quantra and Volpara. The study will then establish the relative breast cancer risk attributable to mammographic density. The study will then examine whether there is a relationship between breast density and tumour characteristics, breast cancer subtypes and survival outcomes following treatment using data from the BC Cancer Registry. How well volumetric breast density represents the risk of developing breast cancer and predicts response to therapy and breast cancer recurrence, are critical pieces of information needed for a screening program to develop an effective personalized strategy to detect and treat breast cancer.

This study proposes to establish the direct link between VBD and breast cancer risk for women in British Columbia. Once substantiated, VBD, together with other risk factor information, will be necessary for the screening mammography program to develop effective personalized breast screening strategies such as ultrasound screening for women with dense breasts. Therefore the results of this study may have the potential to change the paradigm of early breast cancer detection from a uniform-screening method toward an individualized, risk/benefit based personal approach.