scholarly journals Relationship of Serum Progesterone and Progesterone Metabolites with Mammographic Breast Density and Terminal Ductal Lobular Unit Involution among Women Undergoing Diagnostic Breast Biopsy

2020 ◽  
Vol 9 (1) ◽  
pp. 245
Author(s):  
Manila Hada ◽  
Hannah Oh ◽  
Shaoqi Fan ◽  
Roni T. Falk ◽  
Berta Geller ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Postmenopausal Women ◽  
Breast Density ◽  
Luteal Phase ◽  
Breast Biopsy ◽  
Mammographic Breast Density ◽  
Serum Progesterone ◽  
Progesterone Metabolites

The association of progesterone/progesterone metabolites with elevated mammographic breast density (MBD) and delayed age-related terminal duct lobular unit (TDLU) involution, strong breast cancer risk factors, has received limited attention. Using a reliable liquid chromatography-tandem mass-spectrometry assay, we quantified serum progesterone/progesterone metabolites and explored cross-sectional relationships with MBD and TDLU involution among women, ages 40–65, undergoing diagnostic breast biopsy. Quantitative MBD measures were estimated in pre-biopsy digital mammograms. TDLU involution was quantified in diagnostic biopsies. Adjusted partial correlations and trends across MBD/TDLU categories were calculated. Pregnenolone was positively associated with percent MBD-area (MBD-A, rho: 0.30; p-trend = 0.01) among premenopausal luteal phase women. Progesterone tended to be positively associated with percent MBD-A among luteal phase (rho: 0.26; p-trend = 0.07) and postmenopausal (rho: 0.17; p-trend = 0.04) women. Consistent with experimental data, implicating an elevated 5α-pregnanes/3α-dihydroprogesterone (5αP/3αHP) metabolite ratio in breast cancer, higher 5αP/3αHP was associated with elevated percent MBD-A among luteal phase (rho: 0.29; p-trend = 0.08), but not postmenopausal women. This exploratory analysis provided some evidence that endogenous progesterone and progesterone metabolites might be correlated with MBD, a strong breast cancer risk factor, in both pre- and postmenopausal women undergoing breast biopsy. Additional studies are needed to understand the role of progesterone/progesterone metabolites in breast tissue composition and breast cancer risk.

Mammographic breast density, its changes, and breast cancer risk in premenopausal and postmenopausal women

Cancer ◽  
2020 ◽  
Vol 126 (21) ◽  
pp. 4687-4696
Author(s):  
Eun Young Kim ◽  
Yoosoo Chang ◽  
Jiin Ahn ◽  
Ji‐Sup Yun ◽  
Yong Lai Park ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Postmenopausal Women ◽  
Breast Density ◽  
Mammographic Breast Density

scholarly journals Review of: Are breast density and bone mineral density independent risk factors for breast cancer?

2005 ◽  
Vol 8 (11) ◽  
Author(s):  
J. L. Hopper
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Bone Mineral Density ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Correlation Coefficient ◽  
Breast Density ◽  
Mammographic Breast Density ◽  
Mineral Density ◽  
Increased Risk

Citation of original article:K. Kerlikowske, J. Shepherd, J. Creasman, J. A. Tice, E. Ziv, S. R. Cummings. Are breast density and bone mineral density independent risk factors for breast cancer. Journal of the National Cancer Institute 2005; 97(7): 368–74.Abstract of the original articleBackground: Mammographic breast density and bone mineral density (BMD) are markers of cumulative exposure to estrogen. Previous studies have suggested that women with high mammographic breast density or high BMD are at increased risk of breast cancer. We determined whether mammographic breast density and BMD of the hip and spine are correlated and independently associated with breast cancer risk. Methods: We conducted a cross-sectional study (N = 15 254) and a nested case-control study (of 208 women with breast cancer and 436 control subjects) among women aged 28 years or older who had a screening mammography examination and hip BMD measurement within 2 years. Breast density for 3105 of the women was classified using the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) categories, and percentage mammographic breast density among the case patients and control subjects was quantified with a computer-based threshold method. Spearman rank partial correlation coefficient and Pearson's correlation coefficient were used to examine correlations between BI-RADS breast density and BMD and between percentage mammographic breast density and BMD, respectively, in women without breast cancer. Logistic regression was used to examine the association of breast cancer with percentage mammographic breast density and BMD. All statistical tests were two-sided. Results: Neither BI-RADS breast density nor percentage breast density was correlated with hip or spine BMD (correlation coefficient = −.02 and −.01 for BI-RADS, respectively, and −2.06 and .01 for percentage breast density, respectively). Neither hip BMD nor spine BMD had a statistically significant relationship with breast cancer risk. Women with breast density in the highest sextile had an approximately threefold increased risk of breast cancer compared with women in the lowest sextile (odds ratio: 2.7; 95% confidence interval: 1.4–5.4); adjusting for hip or spine BMD did not change the association between breast density and breast cancer risk. Conclusion: Breast density is strongly associated with increased risk of breast cancer, even after taking into account reproductive and hormonal risk factors, whereas BMD, although a possible marker of lifetime exposure to estrogen, is not. Thus, a component of breast density that is independent of estrogen-mediated effects may contribute to breast cancer risk.

Determination of breast density by bioimpedance.

2012 ◽  
Vol 30 (27_suppl) ◽  
pp. 56-56
Author(s):  
Karina Bukhanov ◽  
Joel S. Ironstone ◽  
Cindy Basso ◽  
Tina Bilodeau
Keyword(s):  
Breast Cancer ◽  
Primary Care ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Correlation Coefficient ◽  
Breast Density ◽  
Pearson Correlation ◽  
Mammographic Breast Density ◽  
Pearson Correlation Coefficient ◽  
Dense Breasts

56 Background: Mammographic breast density is a significant risk factor for breast cancer. Women with extremely dense breasts are at 4-to-6 times the risk of developing breast cancer than women with primarily fatty breast tissue. Electrical Breast Densitometry (EBD) is a new technique that assesses breast density. EBD is non-ionizing, fast, has low cost per test ($20-$30) and may help in breast cancer risk assessment in the primary care setting. Methods: This study evaluated the feasibility of the EBD in an IRB-approved pilot study of 20 patients. The study used a custom-made self-adhesive electrode (SenoSENSE Medical Systems, Toronto, Canada) interfaced to an off-the-shelf impedance meter (Bodystat 1500, Bodystat, Isle of Man, UK) with a customized cable. On the same day as the subject’s scheduled mammogram, impedance measurements were acquired for each breast. Mammogram densities were scored by a trained radiologist using standard BiRADS breast density categories 1 to 4. Results: A high correlation coefficient was observed (Pearson correlation coefficient >0.80) between breast density determined by the EBD and the BiRADS breast density score. In addition a statistically significant difference was observed between dense categories (BiRADS 3,4) and fatty categories (BiRADS 1,2) (p<0.01), as well as between extremely dense breasts (BiRADS 4) and all other categories (p<0.01). Very high correlation (Pearson correlation coefficient >0.95) was observed between EBD measurements on the left and right breasts. Previous studies have reported a left/right correlation of 0.89 for blinded mammography readers. Conclusions: These results suggests that the EBD measure may be less variable than mammographic estimates of density. The results of the study suggest that Electrical Breast Densitometry is a promising technique for the assessment of breast density and the ability to aid in evaluation of breast cancer risk. It can be reasonably deployed at primary care facilities.

scholarly journals Postmenopausal mammographic breast density and subsequent breast cancer risk according to selected tissue markers

2015 ◽  
Vol 113 (7) ◽  
pp. 1104-1113 ◽  
Author(s):  
Lusine Yaghjyan ◽  
Andreas Pettersson ◽  
Graham A Colditz ◽  
Laura C Collins ◽  
Stuart J Schnitt ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Breast Density ◽  
Mammographic Breast Density

Predicting risk of estrogen receptor positive breast cancers in postmenopausal women

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 1507-1507
Author(s):  
R. T. Chlebowski ◽  
G. L. Anderson ◽  
D. S. Lane ◽  
A. Aragaki ◽  
T. Rohan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Estrogen Receptor ◽  
Family History ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Postmenopausal Women ◽  
Breast Biopsy ◽  
Breast Cancers ◽  
Estrogen Receptor Positive

1507 Background: Chemoprevention strategies for estrogen receptor positive (ER+) breast cancers are emerging, especially for postmenopausal women, but require methods of targeting appropriate populations. Our objective was to improve the Breast Cancer Risk Assessment Tool [Gail Model (GM)] for estimating ER+ breast cancer risk. Methods: A prospective cohort involving 161,809 postmenopausal women aged 50–79 years, (93,676 in the observational study (OS) and 68,132 in clinical trials (CT)) at Women’s Health Initiative (WHI) Clinical Centers had comprehensive assessment of lifestyle, medication use and breast cancer risk factors. Breast cancer risk from the GM and other models incorporating additional or fewer risk factors and five year incidence of ER + and ER negative (ER-) invasive breast cancers were determined. Main outcome measures were concordance statistics for models predicting breast cancer risk. Results: Of 148,266 women meeting eligibility criteria, (no prior breast cancer and/or mastectomy), 3,236 developed breast cancer. Chronological age and age at menopause, both GM components, were significantly associated with only ER+ but not ER- breast cancer risk (p<0.05 for heterogeneity test). The GM predicted population-based ER+ cancer risk with reasonable accuracy (concordance statistic 0.60, 95% confidence interval (CI) 0.58 to 0.62) but for ER- cancers, the results were equivalent to chance allocation (concordance statistic 0.49, 95% CI 0.45 to 0.54). For ER+ cancers, no additional risk factors improved the GM prediction. However, a simpler model, developed in the OS and tested in the CT population, including only age, family history, and benign breast biopsy was comparable to GM in ER+ breast cancer prediction (concordance statistics 0.58, 95% CI 0.56 to 0.60). Using this model, all women ≥ 55 years old (or ≥ 60 year old if African American) with either a prior breast biopsy or first degree breast cancer family history had five year breast cancer risk of ≥ 1.8%. Conclusions: In postmenopausal women with comprehensive mammography use, the GM identifies populations at increased risk for ER+ breast cancer but not for ER- cancer. A model with fewer variables provides a simpler alternative for identifying populations appropriate for breast cancer chemoprevention interventions. No significant financial relationships to disclose.

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