Family history of breast cancer associated with breast density in premenopausal women


Premenopausal women with a first-degree family history of breast cancer had greater mammographic breast density than those without a family history of breast cancer, according to a study in JAMA Network Open.

“Mammographic breast density and breast cancer share similar genetic pathways, hence, we hypothesize that [family history of breast cancer (FHBC)] will be associated with a higher risk of having dense breasts, especially in premenopausal women,” Yunan Han, MD, a postdoctoral research associate in the division of public health sciences, department of surgery at Washington University School of Medicine in St. Louis, and colleagues wrote. “However, only a few studies have investigated the associations between these two strong risk factors for breast cancer in premenopausal with conflicting results.”

Data derived from Han Y, et al. JAMA Netw Open. 2022;doi:10.1001/jamanetworkopen.2021.48983.
Data derived from Han Y, et al. JAMA Netw Open. 2022;doi:10.1001/jamanetworkopen.2021.48983.

Measuring breast density

Han and colleagues assessed two retrospective cohorts of premenopausal women who underwent mammograms at Siteman Cancer Center’s Joanne Knight Breast Health Center at Washington University School of Medicine in St. Louis.

In the discovery cohort, 375 women who underwent an annual mammography screening in 2016 reported demographics, reproductive and anthropometric measures and breast cancer-related risk factors through a questionnaire. The primary outcome in this cohort was volumetric percent density as measured by Volpara.

For the validation cohort, researchers included 14,040 women who underwent routine mammography from June 2010 to December 2015. They assessed breast density using four Breast Imaging Reporting and Data System (BI-RADS) categories, which ranged from 1 (“almost entirely fatty”) to 4 (“extremely dense”). Categories 1 and 2 were considered nondense and 3 and 4 were considered dense for analysis.

Women with and without FHBC in both cohorts were similar in age (mean age, 46.8-47.7 years) and race (non-Hispanic white, 55.8%-73.6%).

Discovery cohort findings

In the discovery cohort, women with FHBC were more likely to have higher volumetric percent density than those without FHBC (11.1% vs. 9%). Multivariable-adjusted modeling showed they also had a 25% higher volumetric percent density than those with no FHBC (OR = 1.25; 95% CI, 1.12-1.41).

For women whose mothers had breast cancer, volumetric percent density was 31% higher than that of women whose mothers did not have breast cancer (OR = 1.31; 95% CI, 1.15-1.48). However, having a sister with breast cancer was not associated with higher volumetric percent density, which Han and colleagues attributed to a small number of women who only had a sister with breast cancer.

Compared with women who had no relatives affected by FHBC, women who had one first-degree relative with breast cancer had a 24% higher (OR = 1.24; 95% CI, 1.1-1.4) volumetric percent density, but those with two or more affected relatives did not have a significantly higher density (OR = 1.4; 95% CI, 0.95-2.07).

Validation cohort findings

Women in the validation cohort who had FHBC were more likely to have dense breasts compared with women with no FHBC (BI-RADS 3: 41.1% vs. 38.8%; BI-RADS 4: 10.5% vs. 7.7%). In multivariable-adjusted modeling, the odds of having dense breasts were 30% higher (OR = 1.3; 95% CI, 1.17-1.45) for women with FHBC.

Dense breasts were 28% more likely (OR = 1.28; 95% CI, 1.12-1.46) in women whose mothers alone had breast cancer compared with women whose mothers did not. In women whose sisters alone had breast cancer, the odds of having dense breasts were 34% higher (OR = 1.34; 95% CI, 1.09-1.64) compared with women who did not have a sister with breast cancer.

Having one first-degree family member with breast cancer was associated with 29% greater odds of having dense breasts (OR = 1.29; 95% CI, 1.14-1.45). There was no significant association between BI-RADS breast density and having two or more family members with breast cancer (OR = 1.38; 95% CI, 0.85-2.23).

“This cohort study found that having an FHBC was positively associated with mammographic breast density in premenopausal women, and the association was consistent and robust irrespective of whether qualitative or quantitative measures of mammographic breast density were used,” Han and colleagues wrote. “Our findings indicate that women’s FHBC may play an important role in mammographic breast density and underscore the need to begin annual screening mammogram at an early age in premenopausal women with an FHBC.”

PERSPECTIVE

 Deborah Toppmeyer, MD)

Deborah Toppmeyer, MD

The recently published cohort study by Han et al. is an interesting study which may have screening implications given that mammographic density is a strong determinant of sporadic breast cancer risk. The authors show an “association” — though done in a very large validation set of 14,040 premenopausal women — of family history and breast density. This association is intriguing and suggests that breast density may reflect some heritable component of breast cancer risk, although there are many potential confounders. Furthermore, it is difficult to understand why having one affected relative would increase the likelihood of having dense breasts compared with having two or more affected relatives, where an association with increased breast density was not observed.

Interestingly, women with BRCA1/2 mutations in this study do not have higher breast density; but within carriers, breast density may be associated with increased risk. This finding is supported by several other studies suggesting breast density may be an independent risk factor from BRCA1/2 and may potentially be used to improve risk prediction in carriers as well.

In summary, these finding suggest that family history may be used to identify a population of premenopausal patients that may benefit from early mammographic screening. However, larger validation studies are required to better define this population.

A Quantitative Description of the Percentage of Breast Density Measurement Using Full-field Digital Mammography


Breast density is a significant breast cancer risk factor that is measured from mammograms. However, uncertainty remains in both understanding its underlying physical properties as it relates to the breast and determining the optimal method for its measurement. A quantitative description of the information captured by the standard operator-assisted percentage of breast density (PD) measure was developed using full-field digital mammography (FFDM) images that were calibrated to adjust for interimage acquisition technique differences.

Materials and Methods

The information captured by the standard PD measure was quantified by developing a similar measure of breast density (PDc) from calibrated mammograms automatically by applying a static threshold to each image. The specific threshold was estimated by first sampling the probability distributions for breast tissue in calibrated mammograms. A percent glandular (PG) measure of breast density was also derived from calibrated mammograms. The PD, PDc, and PG breast density measures were compared using both linear correlation (R) and quartile odds ratio measures derived from a matched case-control study.

Results

The standard PD measure is an estimate of the number of pixel values above a fixed idealized x-ray attenuation fraction. There was significant correlation (P < .0001) between the PDc-PD (r = 0.78), PDc-PG (r = 0.87), and PD-PG (r = 0.71) measures of breast density. Risk estimates associated with the lowest to highest quartiles for the PDc measure (odds ratio [OR]: 1.0 ref., 3.4, 3.6, and 5.6), and the standard PD measure (OR 1.0 ref., 2.9, 4.8, and 5.1) were similar and greater than that of the calibrated PG measure (OR 1.0 ref., 2.0, 2.4, and 2.4).

Conclusions

The information captured by the standard PD measure was quantified as it relates to calibrated mammograms and used to develop an automated method for measuring breast density. These findings represent an initial step for developing an automated measure built on an established calibration platform. A fully developed automated measure may be useful for both research- and clinical-based risk applications.

source: academic radiology