Mammographic percent density (MD) is recognized as one of the strongest risk factors associated with breast cancer. This matched case-control study investigated whether MD represents an independent risk factor. Mammograms were obtained from 1025 breast cancer patients and from 520 healthy controls. MD was measured using a quantitative computer-based threshold method (0-100%). Breast cancer patients had a higher MD than healthy controls (38 vs. 32%, P<0.01). MD was significantly higher in association with factors such as age over 60 years, body mass index (BMI) of 25-30 kg/m², nulliparity or low parity (one to two births). Average MD was inversely associated with age, BMI, parity and positively associated with age at first full-term pregnancy. MD was higher in women with at least one first-degree relative affected, but only among patients and not in the group of healthy controls (P<0.01/P=0.61). In women with an MD of 25% or more, the risk of breast cancer was doubled compared with women with an MD of less than 10% (odds ratio: 2.1; 95% confidence interval: 1.3-3.4; P<0.01); in the postmenopausal subgroup, the risk was nearly tripled (odds ratio: 2.7; 95% confidence interval: 1.6-4.7; P<0.001). This study provides further evidence that MD is an important risk factor for breast cancer. These results indicate strong associations between MD and the risk of breast cancer in a matched case-control study in Germany.

For many breast cancer (BC) risk factors, there is growing evidence concerning molecular subtypes for which the risk factor is specific. With regard to mammographic density (MD), there are inconsistent data concerning its association with estrogen receptor (ER) and progesterone receptor (PR) expression. The aim of our study was to analyze the association between ER and PR expression and MD. In our case-only study, data on BC risk factors, hormone receptor expression and MD were available for 2,410 patients with incident BC. MD was assessed as percent MD (PMD) using a semiautomated method by two readers for every patient. The association of ER/PR and PMD was studied with multifactorial analyses of covariance with PMD as the target variable and including well-known factors that are also associated with MD, such as age, parity, use of hormone replacement therapy, and body mass index (BMI). In addition to the commonly known associations between PMD and age, parity, BMI and hormone replacement therapy, a significant inverse association was found between PMD and ER expression levels. Patients with ER-negative tumors had an average PMD of 38%, whereas patients with high ER expression had a PMD of 35%. A statistical trend toward a positive association between PMD and PR expression was also seen. PMD appears to be inversely associated with ER expression and may correlate positively with PR expression. These effects were independent of other risk factors such as age, BMI, parity, and hormone replacement therapy, possibly suggesting other pathways that mediate this effect.

There is growing evidence that certain breast cancer (BC) risk factors specifically increase the risk for specific molecular tumor subtypes. Different molecular subtypes of BC can partly be described by analyzing proliferation in tumors. Very few data are available regarding the association of mammographic density (MD), as a BC risk factor, with proliferation. The aim of this study was to analyze the association between Ki-67 expression in BCs and MD. In this case-only study, data on BC risk factors, hormone receptor expression, and MD were available for 1,975 patients with incident BC. MD was assessed as percentage mammographic density (PMD) using a semiautomated method by two readers for every patient. The association of the Ki-67 proliferation index and PMD was studied using multifactorial analyses of covariance (ANCOVA), with PMD as the target variable and including well-known factors that are also associated with MD such as age, parity, use of hormone replacement therapy (HRT), and body mass index (BMI). There were no significant differences in PMD between women with BC who had low and high Ki-67 values (P = 0.31). However, there were relevant differences in women with low BMI (P = 0.07), and in women using postmenopausal HRT (P = 0.06) as well as in women with low PR values (P = 0.07). In these subgroups, the Ki-67 expression index increased with decreasing PMD. Likewise PMD is correlated with BMI, parity status, and menopausal status stronger in patients with low proliferating tumors, and with progesterone receptor expression in patients with high proliferating tumors. MD correlates inversely with Ki-67 proliferation in BC tumors only in some subgroups of BC patients, defined by commonly known BC risk factors that are usually associated with MD as well.

The aim of our study involved the assessment of B-mode imaging and elastography with regard to their ability to predict mammographic density (MD) without X-rays. Women, who underwent routine mammography, were prospectively examined with additional B-mode ultrasound and elastography. MD was assessed quantitatively with a computer-assisted method (Madena). The B-mode and elastography images were assessed by histograms with equally sized gray-level intervals. Regression models were built and cross validated to examine the ability to predict MD. The results of this study showed that B-mode imaging and elastography were able to predict MD. B-mode seemed to give a more accurate prediction. R for B-mode image and elastography were 0.67 and 0.44, respectively. Areas in the B-mode images that correlated with mammographic dense areas were either dark gray or of intermediate gray levels. Concerning elastography only the gray levels that represent extremely stiff tissue correlated positively with MD. In conclusion, ultrasound seems to be able to predict MD. Easy and cheap utilization of regular breast ultrasound machines encourages the use of ultrasound in larger case-control studies to validate this method as a breast cancer risk predictor. Furthermore, the application of ultrasound for breast tissue characterization could enable comprehensive research concerning breast cancer risk and breast density in young and pregnant women.

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