PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of annoncLink to Publisher's site
 
Ann Oncol. 2009 August; 20(8): 1447–1449.
PMCID: PMC2720819

Does breast density show difference in patients with estrogen receptor-positive and estrogen receptor-negative breast cancer measured on MRI?

Mammographic density is a known risk factor for breast cancer. Studies have shown that women with increased mammographic density have higher risk of developing breast cancer compared with women with lower mammographic density [1, 2]. Both endogenous and exogenous estrogen may influence mammographic density. Mammographic density decreases after menopause when ovarian function declines. Hormonal replacement therapy, with combination of estrogen and progesterone, increases mammographic density [2], while tamoxifen, which has antiestrogenic effect, decreases mammographic density [3]. Mammographic density therefore can be regarded as a marker of the effect of estrogen on the breast tissue. To what extent mammographic density is a predictor for both hormone receptor-positive and hormone receptor-negative tumors is still unclear. Postmenopausal hormonal replacement therapy is associated with an increased risk of developing an estrogen receptor (ER)-positive tumor [4]. In symptomatic breast cancers, a significant positive association was found between ER and progesterone receptor (PgR) status of the tumors and percent density [5]. If an increased risk of ER-positive breast cancer is associated with breast density, it may be possible to find a higher density in patients who develop ER-positive cancer than ER-negative cancer. In this study, we investigated the breast density using three-dimensional (3D) magnetic resonance imaging (MRI)-based method [6] in two groups of patients, with ER-positive and ER-negative cancer. The evaluation of breast density based on mammogram may not accurately analyze the density due to projection imaging with the tissue-overlapping problem. MRI, however, provides strong soft tissue contrast distinguishing between fibroglandular and fatty tissues and a 3D view of breast tissues without compression.

In a retrospective review of breast cancer patients who received breast MRI at our institution from 2004 to 2006, 80 pathologically proven patients with unilateral invasive ductal carcinoma and with complete information of ER and PgR status were included and studied (12 Asian women and 68 white women; 45 ER/PgR positive and 35 ER/PgR negative). These 80 subjects came from a sub-cohort of women in our another study focusing on the comparison of breast density between invasive ductal carcinoma and ductal carcinoma in situ. The breast density was analyzed from the contralateral normal breast of each subject, assuming symmetric bilateral breast density. It was noted that density measurements of the two breasts in women are highly correlated [7]. This study was approved by the institutional review board and was Health Insurance Portability and Accountability compliant. All patients gave written informed consent for receiving the MRI study.

The breast MRI was carried out in a 1.5-T magnetic resonance (MR) with a 4-channel phased-array bilateral breast coil (Philips Medical Systems, Cleveland, OH). The imaging protocol consisted of pre-contrast T1-weighted imaging and bilateral dynamic contrast-enhanced imaging using a 3D spoiled gradient recalled radiofrequency-spoiled (RF)-fast acquisition at steady rate (FAST) pulse sequence. Thirty-two axial images covering both breasts were acquired. Noncontrast 3D SPGR (RF-FAST) T1-weighted images without fat suppression (repetition time = 8.1 ms, echo time = 4.0 ms, flip angle = 20°, matrix size = 256 × 128, field of view = 38 cm, slice thickness = 3–4 mm) were used to calculate the breast density. In this study, since tumor volume will affect the measurement of the breast density, the contralateral breast not harboring tumor was selected for density measurement.

Breast density measurement was carried out based on our recently developed MR method [6]. With this method, the breast and the fibroglandular tissue were segmented using computer-assisted algorithms. After the breast was segmented out, the total breast volume was calculated. The adaptive Fuzzy C-Means was applied for segmentation of the fibroglandular tissue from the surrounding fatty tissue. After completing the segmentation from all 2D imaging slices, the volume of fibroglandular tissue was calculated, and the percent density was obtained by normalizing to the total breast volume.

The mean age was 54 years in the ER-positive group and 47 years in the ER-negative group (significantly younger, P < 0.005). In the ER-positive group, 20 patients were ≥55 years old and 25 were <55 years old. Overall, the measured breast density did not show significant difference between the ER-positive and ER-negative patient groups (9.9% ± 7.2% for the ER-positive group versus 12.6% ± 8.9% for the ER-negative group, P = 0.14). Figure 1 shows the scattered plot between the percent breast density and the age for the two groups of patients. It was noted that there were no obvious differences among patients with different cancer types. However, the age dependence was clearly noted, higher density with younger age. A logistic model was applied to analyze the difference in density, controlling for age, and the results show no significant difference between ER-positive and ER-negative cancers. Figure 2 shows two patients with ER-positive and ER-negative breast cancer, who had similar breast density in the contralateral normal breast.

Figure 1.
Scatter plot of percent breast density versus age in patients with ER-positive (ER+) and ER-negative (ER−) breast cancer. A clear age dependence is noted, but not between the two cancer types. ER, estrogen receptor.
Figure 2.
(A) A 41-year-old Hispanic woman with ER-positive cancer; (B) a 50-year-old Hispanic woman with ER-negative cancer. ER-positive cancer is a mass lesion, and ER-negative cancer is a nonmass lesion, indicated by arrows. The segmented fibroglandular density ...

Although mammographic density is a risk factor for breast cancer, it is not clear whether mammographic density can predict subtypes of breast cancer defined by expression of the their hormonal and human epidermal growth factor receptor 2 receptors. To clarify if increased mammographic density is related to ER status of breast cancer, studies from literature regarding if increased mammographic density is related to ER status of breast cancer showed conflicting results [4, 8, 9]. A study using Breast Imaging Reporting and Data System categories 1–4 for classifying mammographic density has found that increasing density was significantly correlated with negative ER status [8]; another study with same approach, however, showed no correlation of increased density with the ER status of the tumor [9]. Using quantitative mammographic percent density, increased mammographic density was associated with both ER-positive and ER-negative breast cancer, and both luminal type and triple-negative cancer, indicating breast density did not predict tumor type [4, 10].

This study was the first one to use 3D MR-based method for quantification of breast density between different cancer types. Our results did not show significant differences in breast density between ER-positive and ER-negative patients, indicating that the link between breast density and breast cancer may be due to factors other than or in addition to estrogen exposure [4].

funding

National Institutes of Health/National Cancer Institute (R01 CA90437, R21 CA121568, R03 CA136071); California Breast Cancer Research Program (9WB-0020, 14GB-0148); National Science Council (Taiwan) (97-2314-B-039-031).

Acknowledgments

This work was conducted at Tu and Yuen Center for Functional Onco-Imaging at University of California, Irvine.

References

1. Boyd NF, Guo H, Martin LJ, et al. Mammographic density and the risk and detection of breast cancer. N Engl J Med. 2007;356(3):227–236. [PubMed]
2. Vachon CM, Brandt KR, Ghosh K, et al. Mammographic breast density as a general marker of breast cancer risk. Cancer Epidemiol Biomarkers Prev. 2007;16(1):43–49. [PubMed]
3. Boyd NF, Lockwood GA, Martin LJ, et al. Mammographic densities and breast cancer risk. Breast Dis. 1998;10:113–126. [PubMed]
4. Ziv E, Tice J, Smith-Bindman R, et al. Mammographic density and estrogen receptor status of breast cancer. Cancer Epidemiol Biomarkers Prev. 2004;13:2090–2095. [PubMed]
5. Ghosh K, Brandt KR, Sellers TA, et al. Association of mammographic density with the pathology of subsequent breast cancer among postmenopausal women. Cancer Epidemiol Biomarkers Prev. 2008;17(4):872–879. [PMC free article] [PubMed]
6. Nie K, Chen JH, Chan S, et al. Development of a quantitative method for analysis of breast density based on three-dimensional breast MRI. Med Phys. 2008;35:5253–5262. [PubMed]
7. Byng JW, Boyd NF, Little L, et al. Symmetry of projection in the quantitative analysis of mammographic images. Eur J Cancer Prev. 1996;5:319–327. [PubMed]
8. Roubidoux MA, Bailey JE, Wray LA, Helvie MA. Invasive cancers detected after breast cancer screening yielded a negative result: relationship of mammographic density to tumor prognostic factors. Radiology. 2004;230(1):42–48. [PubMed]
9. Aiello EJ, Buist DS, White E, Porter PL. Association between mammographic breast density and breast cancer tumor characteristics. Cancer Epidemiol Biomarkers Prev. 2005;14(3):662–668. [PubMed]
10. Ma H, Luo J, Press MF, et al. Is there a difference in the association between percent mammographic density and subtypes of breast cancer? Luminal A and triple-negative breast cancer. Cancer Epidemiol Biomarkers Prev. 2009;18(2):479–485. [PubMed]

Articles from Annals of Oncology are provided here courtesy of Oxford University Press