Quantification of Breast Desnity with Spectral Mamography

 

breast density 

 

Among American women breast cancer is the most common cancer and the 2nd leading cause of death from cancer. Breast density, which is defined as the ratio of fibroglandular tissue to the total fibroglandular and adipose tissue, is an important risk factor in the development of breast cancer. Previous reports have shown that women with high mammographic density have four to five-fold increased risk of developing breast cancer. Increased mammographic density has proven to be more prognostic of overall breast cancer risk than nearly all other risk factors. Furthermore, elevated breast density is fairly common. Previous reports indicate that approximately 25% of all women and 40% of women in their forties exhibit dense breasts. By contrast, 2%-10% of women exhibit the two breast cancer susceptibility genes, BRCA1 and BRCA2. Therefore, women with breast cancer attributable to increased breast density are likely to form a significant percentage of overall breast cancer cases. This indicates that developing new techniques to accurately quantify breast density is of particular importance in identifying women with high risk of developing breast cancer.

 

One of the important characteristics of breast density is the fact that it can be altered. This is very important since most of the known contributing factors to breast cancer, such as age and family history, cannot be changed. Due to the fact that breast density can be altered, there has been previous suggestion for its use as a surrogate marker, intermediate phenotype for breast cancer and therapeutic strategies. Hormones also play an important role in breast density change. Tamoxifen and other chemopreventive agents such as soy isoflavones have been shown to decrease breast density. Furthermore, identifying women with elevated breast cancer risk due in part to increased breast density can potentially better enable preventive measures such as more frequent screening, breast MRI or chemopreventive agents that have the potential to reduce breast cancer incidence. However, in order to assess changes in breast density, an accurate technique for quantification of breast density in conjunction with screening mammography is needed. One advantage of developing an accurate method to measure breast density was pointed out in a prior study which observed a 2% increase in the relative risk of breast cancer for every 1% increase in mammographic density.

 

 

 

The current standard of care for breast density evaluation involves visual assessment of mammograms using the four-category Breast Imaging Reporting and Data System (Bi-RADS). This subjective classification scheme is limited by its considerable intra- and inter-reader variability. Several groups have reported more quantitative approaches for measuring breast density. Area-based techniques have included qualitative and semi-quantitative classification schemes, quantitative estimations from manual or semi-manual segmentation of a digital image histogram. Although these quantitative measures provide a more quantitative measure of breast density, one of the limitations is the binary classification of a pixel into either 100% fibroglandular or 100% adipose tissue. Additionally, an important limitation is that an area measurement ignores the physical 3D character of a real breast. Breasts of different thicknesses can potentially all yield the same measurement of area breast density yet correspond to widely varying volumetric breast density values.

 

Breast cancer risk is most likely more strongly associated with the volume of dense tissue as opposed to the projected area. Volume-based techniques, which overcome some of the limitations of area-based techniques, have included attempts to standardize and calibrate mammographic image data. However, these techniques require assumptions to be made in order to measure breast density and thickness from a single image. Most of the current approaches use thickness models to estimate the total breast thickness. However, this technique depends critically on knowledge about the shape of the compressed breast, particularly in the periphery where the breast is not in contact with the compression plate. Unfortunately, such information is difficult to obtain in practice. Uncertainties in thickness estimation induced by the shape model and the mechanical precision of the compression paddle can lead to a two- to three- fold increase in measurement error for volumetric breast density. 

 

Our group at UC Irvine has been working to develop an accurate and quantitative measure of breast density with the most advanced technology. An ideal method for breast density quantification should be simple, accurate, reproducible, and most importantly, ready to be implemented with the current mammography screening technique so that minimum effort is required for its application in current clinical conditions. To this end, we recently investigated the feasibility of quantifying volumetric breast density with a spectral mammography system.   Spectral mammography allows the breast mammogram to be viewed at two different energies. A simple analogy is the viewing of a black and white television vs. that of a color television. The fundamental object being represented is the same but the color image has more information inside. Spectral imaging can exploit differences between the effective atomic numbers (Z) of different tissues to provide separate quantitative thickness measurements for each tissue. It does not require any assumption for breast density measurement since glandular and adipose thickness measurements are based on two physical measurements using low and high energy images.

 

Spectral mammography measures breast density based on the physics of the interaction between x-rays and different type of breast tissues. The process is fully automatic and does not depend on the experience of the radiologist. Therefore, it provides an accurate and reliable estimation of the breast density. Such information can be used in a quantitative statistical model for cancer risk analysis. Thus the link between breast density and cancer risk can be understood.     

 

Once clinically implemented, spectral mammography could become the standard of care for screening mammograms and help identify higher risk women, who would benefit from more sensitive screening modalities in future exams. Women with higher breast density, and thus higher cancer risk, might benefit from more frequent screening exams, or exams starting at a younger age. In other words, breast cancer could be caught earlier and lives could be saved. 

 

Another possible application of spectral mammography is to evaluate the effect of cancer therapy. Studies have suggested that breast density can be altered by hormonal therapies.  For example, tamoxifen, a hormonal cancer risk reducing therapy, may decrease the breast density by 4.3 to 5.3% annually. With the accurate quantification of breast density using spectral mammography, the individual response to such therapy can be monitored on a regular basis, which provides valuable information for optimized treatment planning.

 

With the plethora of pragmatic uses for spectral mammography, the researchers at UC Irvine are proud to be continuing research in this area and we are eager to see the results of more widespread implementation.