Mammographic density as a marker of susceptibility to breast cancer: a hypothesis.

We propose that radiological features of breast tissue provide an index of cumulative exposure to the current and past hormonal and reproductive events that influence breast cancer incidence. The changes in breast tissue that occur with ageing, and changes in the associated radiological features of the breast, are similar to the concept of "breast tissue ageing" proposed by Pike, and may explain features of the age-specific incidence of breast cancer, both within the population and between populations. These radiological features can be observed and measured, can be related directly to risk of breast cancer, and are likely to be of value in research into the etiology of breast cancer. Identification of the sources of variation in this radiological characteristic of the breast is likely to lead to a better understanding of the factors that cause breast cancer and to new approaches to prevention of the disease.

Mammographic densities and risk of breast cancer among subjects with a family history of this disease.

BACKGROUND: A family history of breast cancer is known to increase risk of the disease, but other genetic and environmental factors that modify this risk are likely to exist. One of these factors is mammographic density, and we have sought evidence that it is associated with increased risk of breast cancer among women with a family history of breast cancer. METHODS: We used data from a nested case-control study based on the Canadian National Breast Screening Study (NBSS). From 354 case patients with incident breast cancer detected at least 12 months after entry into the NBSS and 354 matched control subjects, we analyzed subjects who were identified as having a family history of breast cancer according to one of three, nonmutually exclusive, criteria. We compared the mammographic densities of case patients and control subjects by radiologic and computer-assisted methods of measurement. RESULTS: After adjustment for other risk factors for breast cancer, the relative risks (RRs) between the most and least extensive categories of breast density were as follows: For at least one first-degree relative with breast cancer, RR = 11.14 (95% confidence interval [CI] = 1.54-80.39); for at least two affected first- or second-degree relatives, RR = 2.57 (95% CI = 0.23-28.22); for at least one first- or second-degree relative with breast cancer, RR = 5.43 (95% CI = 1.85-15.88). CONCLUSIONS: These results suggest that mammographic density may be strongly associated with risk of breast cancer among women with a family history of the disease. Because mammographic densities can be modified by dietary and hormonal interventions, the results suggest potential approaches to the prevention of breast cancer in women with a family history of breast cancer.

Macronutrient intake and change in mammographic density at menopause: results from a randomized trial.

To examine the effects of dietary fat intake on breast cancer risk, we are conducting a randomized trial of dietary intervention in women with extensive areas of radiologically dense breast tissue on mammography, a risk factor for breast cancer. Early results show that after 2 years on a low-fat, high-carbohydrate diet there is a significant reduction in area of density, particularly in women going through menopause. In women who went through menopause during the 2-year follow-up, the mean decreases in area of density and percentage of density in the intervention group were 11.0 cm2 and 11.0%, respectively, whereas the control group decreased 4.5 cm2 and 5.2%. The purpose of this analysis was to determine whether changes in intake of specific macronutrients could account for the observed reduction in breast density in these women. Differences between 2-year and baseline values of macronutrients (averaged over 3 nonconsecutive days of food intake) were calculated. We examined the effect of dietary variables, adjusted for changes in total calorie intake and weight and for family history of breast cancer, on changes in area of density and percentage of density using linear regression. Reduction in total or saturated fat intake or cholesterol intake was significantly associated with decreased dense area (p < or = .004). The most significant dietary variable associated with reduction in percentage of density was reduction in dietary cholesterol intake (P = 0.001), although reducing saturated fat intake was of borderline significance (P = 0.05). The effect of the membership in the intervention and control groups on change in area of density or percentage of density was reduced by models that included changes in intake of any fat, or cholesterol, or carbohydrates. The observation of an effect of diet at menopause on breast density, a marker of increased risk of breast cancer, may be an indication that exposures at this time have an enhanced effect on subsequent risk.

Mammographic densities and breast cancer risk.

The radiological appearance of the female breast varies among individuals because of differences in the relative amounts and X-ray attenuation characteristics of fat and epithelial and stromal tissues. Fat is radiolucent and appears dark on a mammogram, and epithelium and stroma are radiodense and appear light. We review here the evidence that these variations, known as mammographic parenchymal patterns, are related to risk of breast cancer. Studies that used quantitative measurement to classify mammographic patterns have consistently found that women with dense tissue in more than 60-75% of the breast are at four to six times greater risk of breast cancer than those with no densities. These risk estimates are independent of the effects of other risk factors and have been shown to persist over at least 10 years of follow up. Estimates of attributable risk suggest that this risk factor may account for as many as 30% of breast cancer cases. Mammographically dense breast tissue is associated both with epithelial proliferation and with stromal fibrosis. The relationship between these histological features and risk of breast cancer may by explained by the known actions of growth factors that are thought to play important roles in breast development and carcinogenesis. Mammographically dense tissue differs from most other breast cancer risk factors in the strength of the associated relative and attributable risks for breast cancer, and because it can be changed by hormonal and dietary interventions. This risk factor may be most useful as a means of investigating the etiology of breast cancer and of testing hypotheses about potential preventive strategies.

The relationship of anthropometric measures to radiological features of the breast in premenopausal women.

We studied 273 premenopausal women recruited from mammography units who had different degrees of density of the breast parenchyma on mammography, in whom we measured height, weight and skinfold thicknesses. Mammograms were digitized to high spatial resolution by a scanning densitometer and images analysed to measure the area of dense tissue and the total area of the breast. Per cent density and the area of non-dense tissue were calculated from these measurements. We found that the mammographic measures had different associations with body size. Weight and the Quetelet index of obesity were strongly and positively associated with the area of non-dense tissue and with the total area of the breast, but less strongly and negatively correlated with the area of dense tissue. We also found a strong inverse relationship between the areas of radiologically dense and non-dense breast tissue. Statistical models containing anthropometric variables explained up to 8% of the variance in dense area, but explained up to 49% of the variance in non-dense area and 43% of variance in total area. These results suggest that aetiological studies in breast cancer that use mammographic density should consider dense and non-dense tissues separately. In addition to per cent density, methods should be examined that combine information from these two tissues.

Analysis of mammographic density and breast cancer risk from digitized mammograms.

To evaluate the association between mammographic density and breast cancer risk, a simple, observer-assisted technique called interactive thresholding was developed that allows reliable quantitative assessment of mammographic density with use of a computer workstation. Use of this technique helps confirm that mammographic density is one of the strongest risk factors for breast cancer and is present in a large proportion of breast cancer cases. The strong relationship between mammographic density and breast cancer risk suggests that the causes of breast cancer may be better understood by identifying the factors associated with mammographically dense tissue and determining how such tissue changes as these factors vary. Furthermore, because it can be modified, mammographic density may also be a good vehicle for the development and monitoring of potential preventive strategies. Areas of ongoing investigation include evaluating a potential genetic component of mammographic density by comparing density measurements in twins and understanding changes in density relative to age, menopausal status, exogenous hormone use, and exposure to environmental carcinogens. In addition, work is ongoing to establish measurements from imaging modalities other than mammography and to relate these measurements directly to breast cancer risk.

X-ray characterization of breast phantom materials.

A pulse-height spectroscopic technique is used to measure the linear attenuation coefficients of commercially available composite phantom materials designed to simulate the attenuation characteristics of breast fat and breast glandular tissue. The manufacturers have specified the composition of these materials with the goal of matching the linear attenuation coefficients of breast tissues, calculated using the mixture rule. Over the energy range 18 to 100 keV, measurements from these materials are in close agreement with manufacturers' predictions and with previously measured linear attenuation coefficients of breast tissue samples.

Mammographic densities and breast cancer risk.

Variations between individuals in the radiographic appearance, or mammographic pattern, of the female breast arise because of differences in the relative amounts and X-ray attenuation characteristics of fat and connective and epithelial tissue. Studies using quantitative methods of assessment have consistently shown these variations to be strongly related to risk of breast cancer. Individuals with extensive areas of radiologically dense breast tissue on the mammogram have been found to have a risk of breast cancer that is four to six times higher than women with little or no density. In this paper, we propose a model for the relationship of mammographic densities to risk of breast cancer. We propose that the risk of breast cancer associated with mammographically dense breast tissue is due to the combined effects of two processes: cell proliferation (mitogenesis), induced by growth factors and sex hormones and influenced by reproductive risk factors for breast cancer; and damage to the DNA of dividing cells (mutagenesis) by mutagens generated by lipid peroxidation. We review the evidence that each of these processes is associated with mammographic densities and propose further work that we believe should be done to clarify these relationships.

Breast cancer risk and measured mammographic density.

It has been well established that there is a positive correlation between the dense appearance of breast stroma and parenchyma on a mammogram and the risk of breast cancer. Subjective assessment by radiologists indicated relative risks on the order of 4 to 6 for the group of women whose mammograms showed a density of over 75% or more of the projected area compared to those with an absence of density. In order to obtain a more quantitative, continuous and reproducible means of estimating breast density, which is sensitive to small changes, we have developed quantitative methods for the analysis of mammographic density, which can be applied to digitized mammograms. These techniques have been validated in a nested case-control study on 708 women aged 40-59 years (on entry) who participated in a national mammographic screening study. An interactive image segmentation method and two completely automated techniques based on image texture and grey scale histogram measures have been developed and evaluated. While our methods all show statistically significant risk factors for dense breasts, the interactive method currently provides the highest risk values (relative risk 4.0, 95% confidence interval (CI) = 2.12-7.56) compared to a measure based on the shape of the image histogram (relative risk 3.35, 95% CI = 1.57-7.12) or the fractal dimension of the mammogram (relative risk 2.54, 95% CI = 1.14-5.68). All methods were highly consistent between images of the left and right breast and between the two standard views (cranio-caudal and medio-lateral oblique) of each breast, so that studies can be done by sampling only one of the four views per examination. There is a large number of factors in addition to breast density which affect the appearance of the mammogram. In particular, the assessment of density is made difficult where the breast is not uniformly compressed, e.g. at the periphery. We have designed and are currently evaluating an image processing algorithm that effectively corrects for this problem and have considered methods for controlling some of the variables of image acquisition in prospective studies. Measurements of breast density may be helpful in assigning risk groups to women. Such measurements might guide the frequency of mammographic screening, aid the study of breast cancer aetiology, and be useful in monitoring possible risk-modifying interventions. Using our techniques, we have been able to show that reduction of the proportion of fat in the diet can result in reductions of breast density, although the direct connection to risk has not yet been made. The relationship between breast density and hormone-related and genetic factors is also of great interest. It is often not possible or ethical to obtain mammograms on some groups of women for whom information on density would be very useful. This includes younger women as well as groups in which it would be desirable to obtain such information at frequent intervals. For this reason, we are exploring the use of imaging approaches such as ultrasound and magnetic resonance imaging, which do not require ionizing radiation, to make measurements analogous to those now being performed by using X-ray mammograms.

Automated analysis of mammographic densities and breast carcinoma risk.

BACKGROUND: There is considerable evidence that one of the strongest risk factors for breast carcinoma can be assessed from the mammographic appearance of the breast. However, the magnitude of the risk factor and the reliability of the prediction depend on the method of classification. Subjective classification requires specialized observer training and suffers from inter- and intraobserver variability. Furthermore, the categoric scales make it difficult to distinguish small differences in mammographic appearance. To address these limitations, automated analysis techniques that characterize mammographic density on a continuous scale have been considered, but as yet, these have been evaluated only for their ability to reproduce subjective classifications of mammographic parenchyma. METHODS: In this study, using a nested case-control design, the authors evaluated the direct association between breast carcinoma risk and quantitative image features derived from automated analysis of digitized film mammograms. Two parameters, one describing the distribution of breast tissue density as reflected by brightness of the mammogram (regional skewness) and the other characterizing texture (fractal dimension), were calculated for images from 708 subjects identified from the Canadian National Breast Screening Study. RESULTS: These parameters were evaluated for their ability to distinguish cases (those women who developed breast carcinoma) from controls. It was found that both the skewness and fractal parameters were significantly related to risk of developing breast carcinoma. CONCLUSIONS: Although the relative risk estimates were moderate (typically > 2.0) and less than those from subjective classification or for an interactive computer method the authors have previously described, they are comparable to other risk factors for the disease. The observer independence and reproducibility of the automated methods may facilitate their more widespread use.

Thickness-equalization processing for mammographic images.

A digital postprocessing technique was used to compensate for the limitations of laser film or cathode-ray-tube devices used to display digital mammograms. An algorithm identified and equalized for the large change in digital signal caused by the reduction in thickness at the margin of the compressed breast. The resulting images reflected only breast composition, and so the number of gray levels needed to display the processed image was greatly reduced, which facilitated presentation and analysis.

Symmetry of projection in the quantitative analysis of mammographic images.

Mammographic parenchymal patterns are among the strongest indicators of the risk of developing breast cancer. Risk evaluation through breast patterns may have an important role in studies of the aetiology of breast cancer and for monitoring changes in the breast in evaluating potential risk-modifying interventions. Typically, patterns are assessed by an experienced radiologist according to Wolfe grade, or on a coarse quantitative scale according to percent density. Parenchymal characterization methods, to overcome variability of classification by human observer, are under investigation. These include image segmentation using semi-automatic thresholding and automatic classification through textural and density measures. An important practical question relates to the extent to which information about mammographic pattern is carried by any one of the four views obtained in a typical examination. Specifically, variations of right-left breast symmetry and variations between the two standard views of each breast were tested. The mammograms of 30 premenopausal women, comprising 90 images [30 each of the right cranial-caudal (RCC), left cranial-caudal (LCC) and right medial-lateral oblique (RMLO)] were evaluated. Parameters included both subjective (radiologist classification and interactive image thresholding) and objective (fractal and skewness indices) quantitative measurements of parenchymal pattern. For the parameters tested, a high degree of correlations was observed for measurements on the RCC, LCC and RMLO views. Pearson correlation coefficients between 0.86-0.96 were found for the comparisons of quantitative parameters. The strong correlations suggest that, in the study and application of mammographic density classification, representative information is provided in a single view.

Automated analysis of mammographic densities.

Information derived from mammographic parenchymal patterns provides one of the strongest indicators of the risk of developing breast cancer. To address several limitations of subjective classification of mammographic parenchyma into coarse density categories, we have been investigating more quantitative, objective methods of analysing the film-screen mammogram. These include measures of the skewness of the image brightness histogram, and of image texture characterized by the fractal dimension. Both measures were found to be strongly correlated with radiologists' subjective classifications of mammographic parenchyma (Spearman correlation coefficients, Rs = -0.88 and -0.76 for skewness and fractal dimension measurements, respectively). Further, neither measure was strongly dependent on simulated changes in mammographic technique. Correlation with subjective classification of mammographic density was better when both the skewness and fractal measures were used in combination than when either was used alone. This suggests that each feature provides some independent information.

Quantitative correlation of breast tissue parameters using magnetic resonance and X-ray mammography.

Previous investigators have shown that there is a strong association between the fraction of fibroglandular tissue within the breast as determined by X-ray mammography (per cent density) and breast cancer risk. In this study, the quantitative correlation between per cent density and two objective magnetic resonance (MR) parameters of breast tissue, relative water content and mean T2 relaxation time, as investigated for 42 asymptomatic subjects. Using newly developed, rapid techniques MR measurements were performed on a volume-of-interest incorporating equal, representative portions of both breasts. X-ray mammograms of each subject were digitised and analysed semiautomatically to determine per cent density. Relative water content showed a strong positive correlation with per cent density (Pearson correlation coefficient rp = 0.79, P < 0.0001) and mean T2 value showed a strong negative correlation with per cent density (rp = -0.61, P < 0.0001). The MR and X-ray parameters were also associated with sociodemographic and anthropometric risk factors for breast cancer (P < 0.05). The potential use of MR parameters to assess risk of breast cancer and to provide a frequent, non-hazardous monitor of breast parenchyma is discussed.

Quantitative classification of mammographic densities and breast cancer risk: results from the Canadian National Breast Screening Study.

BACKGROUND: The radiographic appearance of the female breast varies from woman to woman depending on the relative amounts of fat and connective and epithelial tissues present. Variations in the mammographic density of breast tissue are referred to as the parenchymal pattern of the breast. Fat is radiologically translucent or clear (darker appearance), and both connective and epithelial tissues are radiologically dense (lighter appearance). Previous studies have generally supported an association between parenchymal patterns and breast cancer risk (greater risk with increasing densities), but there has been considerable heterogeneity in risk estimates reported. PURPOSE: Our objective was to determine the level of breast cancer risk associated with varying mammographic densities by quantitatively classifying breast density with conventional radiological methods and novel computer-assisted methods. METHODS: From the medical records of a cohort of 45,000 women assigned to mammography in the Canadian National Breast Cancer Screening Study (NBSS), a multicenter, randomized trial, mammograms from 354 case subjects and 354 control subjects were identified. Case subjects were selected from those women in whom histologically verified invasive breast cancer had developed 12 months or more after entering the trial. Control subjects were selected from those of similar age who, after a similar period of observation, had not developed breast cancer. The mammogram taken at the beginning of the NBSS was the image used for measurements. Mammograms were classified into six categories of density, either by radiologists or by computer-assisted measurements. All radiological classification and computer-assisted measurements were made using one craniocaudal view from the breast contralateral to the cancer site in case subjects and the corresponding breast of control subjects. All P values represent two-sided tests of statistical significance. RESULTS: For all subjects, there was a 43% increase in the relative risk (RR) between the lower and the next higher category of density, as determined by radiologists, and there was a 32% increase as determined by the computer-assisted method. For all subjects, the RR in the most extensive category relative to the least was 6.05 (95% confidence interval [CI] = 2.82-12.97) for radiologists and 4.04 (95% CI = 2.12-7.69) for computer-assisted methods. Statistically significant increases in breast cancer risk associated with increasing mammographic density were found by both radiologists and computer-assisted methods for women in the age category 40-49 years (P = .005 for radiologists and P = .003 for computer-assisted measurements) and the age category 50-59 years (P = .002 for radiologists and P = .001 for computer-assisted measurements). CONCLUSION: These results show that increases in the level of breast tissue density as assessed by mammography are associated with increases in risk for breast cancer.

The quantitative analysis of mammographic densities.

Quantitative classification of mammographic parenchyma based on radiological assessment has been shown to provide one of the strongest estimates of the risk of developing breast cancer. Existing classification schemes, however, are limited by coarse category scales. In addition, subjectivity can lead to sizeable interobserver and intraobserver variations. Here, we propose an interactive thresholding technique applied to digitized film-screen mammograms, which assesses the proportion of the mammographic image representing radiographically dense tissue. Observers viewed images on a CRT display and selected grey-level thresholds from which the breast and regions of dense tissue in the breast were identified. The proportion of radiographic density was then calculated from the image histogram. The technique was evaluated for the mammograms of 30 women and is well correlated (R > 0.91, Spearman coefficient) with a six-category subjective classification of radiographic density by radiologists. The technique was found to be very reliable with an intraclass correlation coefficient between observers typically R > 0.9. This technique may have a role in routine mammographic analysis for the purpose of assessing risk categories and as a tool in studies of the etiology of breast cancer, in particular for monitoring changes in breast parenchyma during potential preventive interventions.

Anthropomorphic radiological phantoms for mammography.

Anthropomorphic radiological phantoms are useful in evaluating image quality in mammography by providing realistic detection tasks to the observer. Methods for creating such phantoms, based on original patient mammograms, are described. Photochemical enhancement techniques and application of fractal interpolation methods for improving the fine detail information contained in such phantoms are discussed. Approaches to incorporating additional calibrated test targets within the phantom are also described.