Abstract
Background
Black women have lower Breast Imaging Reporting and Data System (BI-RADS) breast density than White women, likely due to body mass index (BMI) differences. No studies have directly compared BMI, race, and volumetric breast density in relation to breast cancer risk. This study examines the associations between BI-RADS density and Volpara-derived volumetric breast density measures and breast cancer risk in non-Hispanic Black and White women, focusing on BMI’s influence.
Methods
A nested case-control study was conducted with 3699 women (526 Black, 3173 White) from Mayo Clinic and the University of Pennsylvania. Invasive breast cancer cases (n = 1013) were matched with controls (n = 2686). Breast density was assessed using BI-RADS density categories and continuous density measures from Volpara, including dense volume and volumetric percent density. Conditional logistic regression was used to evaluate associations between density measures and breast cancer risk, adjusting for BMI and age.
Results
Black women had higher dense volume but lower volumetric percent density and BI-RADS density compared with White women. All density measures were significantly associated with breast cancer risk in both groups, with stronger associations after BMI adjustment for BI-RADS density and volumetric percent density. BI-RADS density showed a stronger association in Black women (OR 2.06, 95% CI 1.45–2.91) than in White women (OR 1.55, 95% CI 1.38–1.74) when adjusted for BMI (p-interaction = 0.04). Dense volume showed similar predictive value for both groups, regardless of BMI adjustment.
Conclusion
Using BI-RADS density categories to assess breast cancer risk requires adjustment for BMI for equitable comparison of predictive values across race. Associations of dense volume are not altered when BMI is included as an adjustment factor.
Supplementary Information
The online version contains supplementary material available at 10.1186/s13058-025-02120-8.
Keywords: Quantitative breast density, Body mass index, Interaction, Breast cancer risk
Introduction
Breast density is a strong risk factor for invasive breast cancer risk, as women with dense (heterogeneously or extremely dense) breasts have up to a four to sixfold greater cancer risk compared with women with mostly fatty breast tissue [1–3]. In addition, having dense breasts can increase the rate of interval cancers missed by mammography, as dense tissue can mask tumors at screening [4, 5]. As such, mammography facilities in the U.S. are now required to inform women about their breast density under the Mammography Quality Standards Act (MQSA) [6].
In clinical practice, breast density is measured qualitatively by radiologists according to a visual assessment using the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) categories, ranging from category a: almost entirely fatty to category d: extremely dense breasts, but this measure has modest reproducibility [7, 8] Black women have a lower proportion of dense breasts than White women based on the subjective BI-RADS density grading [9]. These racial differences in the prevalence of dense breasts are partly attributed to differences in body mass index (BMI) [10–14]. BMI is inversely associated with BI-RADS density assessments, as women with higher BMI tend to have a higher volume of fatty breast tissue. Black women make up nearly 16% of the U.S. female population [15, 16] and have 40% higher breast cancer mortality than White women [17]. It is therefore important to better understand different breast density measures and their contribution to breast cancer risk in order to improve breast cancer outcomes.
Three-dimensional (volumetric) estimations of dense volume and volumetric percent density may more accurately quantify mammographic breast density than previous 2D area-based density measures, including qualitative BI-RADS density. Further, dense volume measures are not dependent on the non-dense tissue or BMI and may be more robust measures of density across racial groups. Several studies have examined the associations between BMI, race/ethnicity, and breast density, considering age and menopausal status [9–14]. There is conflicting evidence on whether Black women have greater odds of dense breasts—assessed by quantitative dense area, volumetric percent density, or BI-RADS density—compared with non-Hispanic White women, even after adjusting for age and BMI [9, 12–14].
Few studies have examined Breast Density Measures and Breast Cancer Risk for Black and White Women. The purpose of this study was to compare the associations of BI-RADS density with quantitative measurements of dense volume and volumetric percent density with breast cancer risk, examine in Black and White women, and how BMI may influence these associations.
Methods
We pooled data from two nested case-control studies from women ages 35 and older receiving full field digital mammography (FFDM) screening examinations at Mayo Clinic in Rochester, Minnesota (MN) (years 2008 to 2017) and the Hospital of the University of Pennsylvania in Philadelphia, Pennsylvania (PA, UPENN) (2011 to 2014) from Hologic-Selenia mammography units. Incident invasive breast cancers were identified from linkages to institutional and state cancer registries and included at least six months after screening mammography. Both screen-detected and symptomatically detected cancers were included. Three controls without a history of breast cancer were initially matched to cases based on facility, race, age, and year of screening mammogram; however, due to various reasons, some controls dropped out due to breast area exclusions and imaging artifacts, resulting in certain cases having only one matched control in the final analyses. For this analysis, only those with non-missing information on breast density measures and BMI, and who self-identified as non-Hispanic Black or non-Hispanic White, were included. The final study population included 3699 women, of whom 526 were Black and 3173 were White, with each patient contributing one screening mammogram to the analysis.
Self-reported demographic and reproductive breast cancer risk factors, such as family history of breast cancer, were available from mammography screening questionnaires administered as part of routine practice. BMI values were extracted from electronic medical records recorded on the screening date, if available, and if not, within 1 year before or after the screening mammogram. The study was HIPAA-compliant and approved by the Institutional Review Boards at both Mayo Clinic and University of Pennsylvania, and a waiver of informed consent was granted for this review of existing clinical data.
BI-RADS breast density was obtained from radiology reports, visually determined by one interpreting radiologist, as is standard practice in the U.S. The measures were derived according to the American College of Radiology BI-RADS Atlas 4th Edition [8] definitions and provide an assessment of a woman’s breast density in one of the four ordinal categories: almost entirely fatty (a), scattered fibroglandular density (b), heterogeneously dense (c), or extremely dense (d).
Quantitative measures of volumetric breast density were obtained using Volpara™, a fully automated software that computes volumetric breast density from FFDM images. Briefly, Volpara uses an area of entirely fatty tissue as a reference point to estimate the thickness of dense tissue at each pixel in the image [26]. Estimates of dense volume are obtained by summing the estimated dense tissue across all pixels in the breast image through multiplying the estimated breast area by the breast thickness. Volumetric percent density is obtained by dividing the estimated dense volume by the total breast volume. Breast density was measured on the cranio-caudal (CC) and medio-lateral oblique (MLO) views for both left and right breasts for both cases and controls. For each woman, the estimates from all 4 views were averaged to obtain the final density values. We focused on values of dense as opposed to non-dense tissues, as we are interested in translation to the clinical setting. Though Volpara software can estimate a BI-RADS-like categorical breast density measure, we did not include this in our analysis.
We used conditional logistic regression to compare matched cases and controls to assess the association between the density measures (BI-RADS density, dense volume DV, volumetric percent density VPD) and risk of invasive breast cancer. We estimated the association of density measures using odds ratios and discriminatory accuracy using the area under the receiver operator curve (AUC) and 95% confidence intervals (CI). The AUC was calculated within matched pairs. Odds ratios for volumetric percent density and dense volume are presented per 1 standard deviation in the log-transformed measure. BI-RADS density was modeled as an ordinal trend across categories. This was done given the small sample size of Black women in the extremely dense category. AUCs when BI-RADS density was modeled as a four-level categorical variable were very similar to models using ordinal trend (See Additional File 1). Analyses were adjusted for age and BMI as continuous variables, and comparisons were made between models adjusted for age alone and those adjusted for both age and BMI. We also tested models including family history as a covariate, as well as an interaction between menopausal status (age greater than or equal to 55 or age less than 55) [18] and BMI. Analyses were performed overall and stratified by race groups (Black vs. White). Differences in associations of density measures with overall invasive cancer by race groups (UPENN Black or White Mayo Clinic and UPENN) were tested with inclusion of interaction terms between race and density in the conditional logistic regression models.
For primary analyses, AUC was compared between race groups based on results from 1000 bootstrapped samples. In analyses of age and BMI subgroups, non-conditional logistic regression analysis was used, and matching factors were also included in the adjustment variables.
As a secondary analysis, we explored the association between each of the density measures and breast cancer risk stratified by age (as a proxy for menopausal status), obesity (BMI > 30 kg/m2), and time to breast cancer diagnosis. The time to breast cancer diagnosis was used to see if breast density was better at detecting breast cancers close to diagnosis compared with predicting breast cancers in the long term. Statistical analyses were carried out using SAS version 9.4. The type I error rate for CIs and statistical tests was set at 0.05, and two-sided tests were used.
Results section
The nested case-control study consisted of 1013 invasive breast cancer cases and 2686 matched controls, with 14% Black and 83% White women. White women contributed 875 cases, and 2298 controls, and Black women contributed 138 cases and 388 controls. There were differences in both BMI, family history, and breast density between Black and White women (Table 1). Among controls, Black women had a higher mean BMI than White women (30.5 ± 9.7 kg/m2 vs. 27.2 ± 8.2 kg/m2). White women who were controls had a higher percentage of first-degree relatives with a history of breast cancer (22.5%) compared with Black controls (17.5%). Using radiologist-reported clinical BI-RADS measures density, Black women were less likely to have heterogeneously dense (18.6% vs. 31.5% for) or extremely dense (0.3% vs. 4.8%) breasts than White women. Similar findings were seen for volumetric percent density, with lower mean volumetric percent density for Black (6.0% ± 4.2%) controls compared with White (8.0%±5.3) controls. However, mean dense volume was higher for Black (64.8 ± 30.5 cm [3]) controls vs. White controls (58.7 ± 29.2 cm [3]).
Table 1.
Demographic characteristics of the patient population by race and case-control status
| Black women | White women | |||||
|---|---|---|---|---|---|---|
| Control (N = 388) |
Case (N = 138) |
Total (N = 526) |
Control (N = 2298) |
Case (N = 875) |
Total (N = 3173) |
|
| Age at mammogram | 60.4 (11.38) | 60.6 (11.37) | 60.5 (11.37) | 60.5 (11.22) | 60.8 (11.55) | 60.6 (11.31) |
| BMI | 30.5 (9.67) | 30.2 (9.07) | 30.4 (9.51) | 27.2 (8.18) | 27.8 (7.73) | 27.4 (8.06) |
| Post menopausal, n (%) (n = 3611) | 305 (80) | 100 (73) | 405 (78) | 1711 (76) | 659 (78) | 2370 (77) |
| First degree relative B.C., n (%) (n = 3657) | 65 (17.5%) | 27 (20.1%) | 92 (18.2%) | 513 (22.5%) | 283 (32.6%) | 796 (25.3%) |
| Time in years from mammogram to breast cancer diagnosis, Median (IQR) | 3.0 (2.0, 4.4) | 3.4 (2.0, 4.8) | ||||
| ER+, n (%) (n = 1005) | 99 (72%) | 756 (87%) | ||||
| HER2+, n(%) (n = 977) | 18 (13%) | 87 (10%) | ||||
| BI-RADS density, n (%) (n = 3679) | ||||||
| a | 77 (19.8%) | 15 (10.9%) | 92 (17.5%) | 459 (20.0%) | 110 (12.6%) | 569 (17.9%) |
| b | 238 (61.3%) | 74 (54.0%) | 312 (59.4%) | 1003 (43.7%) | 364 (41.6%) | 1367 (43.1%) |
| c | 72 (18.6%) | 46 (33.6%) | 118 (22.5%) | 724 (31.5%) | 345 (39.4%) | 1069 (33.7%) |
| d | 1 (0.3%) | 2 (1.5%) | 3 (0.6%) | 111 (4.8%) | 56 (6.4%) | 167 (5.3%) |
| Volpara volumetric percent density, Median (IQR) | 4.6 (3.6, 6.9) | 5.9 (4.2, 8.8) | 4.8 (3.6, 7.2) | 5.9 (4.3, 9.8) | 6.4 (4.6, 11.5) | 6.1 (4.3, 10.3) |
| Volpara dense volume, Median (IQR) |
56.5 (44.4, 79.6) |
72.6 (50.4, 95.4) |
60.5 (45.4, 83.9) |
52.0 (38.9, 69.9) |
59.2 (45.2, 83.0) |
53.8 (40.5, 72.8) |
| Study, n (%) | ||||||
| Mayo | 0% | 0% | 0% | 1913 (83.2%) | 736 (84.1%) | 2649 (83.5%) |
| UPENN | 388 (100%) | 138 (100%) | 526 (100%) | 385 (16.8%) | 139 (15.9%) | 524 (16.5%) |
BI-RADS density, dense volume, and volumetric percent density were all significantly associated with breast cancer risk in both Black and White women, when adjusted for age alone, both age and BMI, and age, BMI, and menopausal status (Table 2). Adjustment for BMI in addition to age resulted in stronger associations for BI-RADS density and volumetric percent density, but not dense volume, whose association was unchanged after BMI adjustment. The OR for BI-RADS density increased from 1.44 (95% CI 1.30–1.59) to 1.58 (95% CI 1.42–1.77), and volumetric percent density increased from 1.25 (95% CI 1.15–1.36) to 1.40 (95% CI 1.28–1.54) after additionally adjusting for BMI, suggesting negative confounding of the association by BMI. Discriminatory accuracy also increased for all models after adjustment for BMI, except for dense volume. Similar findings with BMI adjustment were also observed in the race-stratified analyses (Table 2). We additionally adjusted for family history of breast cancer and included an interaction between menopausal status and BMI. Neither significantly changed our results and thus were not included in the final models.
Table 2.
Conditional logistic regression models for breast cancer outcome adjusted for age only and age and BMI, with the respective volumetric density exposures
| Variables/ adjustment |
White/black combined (1013 cases/2686 controls) |
Black women (138 cases/388 controls) |
White women (875 cases/2298 controls) |
||||
|---|---|---|---|---|---|---|---|
| OR (95% CI) |
AUC (95% CI) |
Race/ Density interaction p value |
OR (95% CI) |
AUC (95% CI) |
OR (95% CI) |
AUC (95% CI) |
|
| BI-RADS* Density per 1 category | |||||||
| Age adjusted |
1.44 (1.30, 1.59) |
0.575 (0.556, 0.593) |
0.07 |
1.92 (1.39, 2.66) |
0.605 (0.557, 0.654) |
1.39 (1.25, 1.55) |
0.568 (0.548, 0.588) |
| Age + BMI adjusted |
1.58 (1.42, 1.77) |
0.590 (0.572, 0.609) |
0.04 |
2.06 (1.45, 2.91) |
0.634 (0.586, 0.682) |
1.55 (1.38, 1.74) |
0.593 (0.573, 0.613) |
| Volumetric percent density (log) per 1 SD | |||||||
| Age adjusted |
1.25 (1.15, 1.36) |
0.561 (0.543, 0.580) |
0.15 |
1.45 (1.17, 1.79) |
0.582 (0.533, 0.632) |
1.22 (1.12, 1.33) |
0.565 (0.545, 0.585) |
| Age + BMI adjusted |
1.40 (1.28, 1.54) |
0.591 (0.572, 0.610) |
0.16 |
1.53 (1.21, 1.93) |
0.611 (0.562, 0.659) |
1.39 (1.25, 1.54) |
0.591 (0.571, 0.611) |
| Dense volume (log) per 1 SD | |||||||
| Age adjusted |
1.46 (1.35, 1.59) |
0.600 (0.582, 0.619) |
0.38 |
1.61 (1.29, 2.00) |
0.603 (0.554, 0.652) |
1.44 (1.33, 1.57) |
0.596 (0.576, 0.616) |
| Age + BMI adjusted |
1.46 (1.35, 1.58) |
0.601 (0.583, 0.620) |
0.37 |
1.61 (1.29, 2.00) |
0.603 (0.554, 0.652) |
1.44 (1.32, 1.57) |
0.603 (0.583, 0.623) |
* Radiologist reported BI-RADS Density
*One black case and 1 white control were dropped in the analysis of BI-RADS density due to missing data
When comparing differences in the breast density associations by race, BI-RADS density was more strongly associated with breast cancer risk for Black women (OR 2.06, 95% (1.45, 2.91) compared with White women (OR 1.55, 95% CI 1.38, 1.74) (p-interaction = 0.04, adjusted for BMI and age). For volumetric percent density and dense volume, ORs were larger for Black women than White women, but not statistically different. AUCs were generally higher for Black compared with White women, with the exception of dense volume, which had the same AUC for Black and White women. For Black women, BI-RADS density (adjusted for age and BMI) had the highest discriminatory accuracy (AUC = 0.634) of all models, and for White women, dense volume (adjusted for age and BMI had the highest AUC (AUC = 0.603).
As a secondary analysis, we explored the association between each of the density measures and breast cancer risk stratified by age (as a proxy for menopausal status), obesity (BMI > 30 kg/m2), and time to breast cancer diagnosis (less than or greater than three years, Table 3). In the age-stratified models, ORs for BI-RADS density, volumetric percent density, and dense volume were higher for Black women < 55 years, but only statistically significantly higher for volumetric percent density (p-interaction = 0.01). There were no significant differences in ORs by age for White women. In the BMI stratified models, all density measures had stronger associations with breast cancer risk for obese women, however, the difference was only statistically significant for volumetric percent density for obese Black women (BMI ≥ 30 kg/m2 OR = 2.55 vs. BMI < 30 kg/m2 OR = 1.31, p-interaction = 0.01). In the models stratified by time to breast cancer diagnosis, there were no significant differences in breast density associations for Black or White women.
Table 3.
Logistic regression models for breast cancer outcome stratified by obesity, age, and time until breast cancer diagnosis for the respective volumetric density exposures
| Age groups | BMI groups | Time to breast cancer diagnosis | ||||
|---|---|---|---|---|---|---|
| Age < 55 | Age ≥ 55 | BMI < 30 | BMI ≥ 30 | Time < 3 years | Time ≥ 3 years | |
| Black women |
45 cases 127 controls |
93 cases 261 controls |
59 cases 185 controls |
79 cases 203 controls |
68 cases 193 controls |
70 cases 195 controls |
| BI-RADS density* per 1 category# |
4.05 (2.01, 8.15) |
1.61 (1.07, 2.45) |
1.81 (1.07, 3.06) |
2.58 (1.58, 4.21) |
2.23 (1.34, 3.69) |
1.98 (1.20, 3.25) |
| p-interaction | p-value = 0.11 | p-value = 0.35 | p-value = 0.32 | |||
| Volumetric percent density (log) per 1 SD |
2.69 (1.70, 4.25) |
1.20 (0.90, 1.59) |
1.31 (0.96, 1.77) |
2.55 (1.66, 3.91) |
1.59 (1.14, 2.21) |
1.53 (1.09, 2.14) |
| p-interaction | p-value = 0.01 | p-value = 0.01 | p-value = 0.30 | |||
| Dense volume (log) per 1 SD |
2.12 (1.41, 3.17) |
1.50 (1.14, 1.97) |
1.57 (1.16, 2.14) |
1.85 (1.31, 2.60) |
1.78 (1.29, 2.46) |
1.46 (1.08, 1.98) |
| p-interaction | p-value = 0.25 | p-value = 0.90 | p-value = 0.42 | |||
| White women |
292 cases 790 controls |
583 cases 1508 controls |
565 cases 1595 controls |
310 cases 703 controls |
372 cases 958 controls |
503 cases 1340 controls |
| BI-RADS density per 1 category |
1.39 (1.15, 1.67) |
1.60 (1.39, 1.83) |
1.50 (1.31, 1.72) |
1.72 (1.40, 2.12) |
1.45 (1.21, 1.73) |
1.63 (1.40, 1.90) |
| p-interaction | p-value = 0.40 | p-value = 0.20 | p-value = 0.20 | |||
| Volumetric percent density (log) per 1 SD |
1.38 (1.19, 1.61) |
1.35 (1.19, 1.54) |
1.38 (1.23, 1.54) |
1.63 (1.29, 2.06) |
1.27 (1.09, 1.49) |
1.48 (1.29, 1.70) |
| p-interaction | p-value = 0.46 | p-value = 0.08 | p-value = 0.07 | |||
| Dense volume log per 1 SD |
1.53 (1.33, 1.77) |
1.39 (1.25, 1.55) |
1.41 (1.28, 1.56) |
1.58 (1.32, 1.90) |
1.34 (1.17, 1.52) |
1.52 (1.35, 1.70) |
| p-interaction | p-value = 0.33 | p-value = 0.58 | p-value = 0.23 | |||
Results presented as odds ratios (95% CI) adjusted for age and BMI
*Radiologist reported BI-RADS density
# One black case and 1 white control dropped in the analysis of B-IRADS density due to missing data
Discussion section
Our findings highlight the importance of race and BMI to both density measures as well as their association with breast cancer. First, we found that Black women had higher levels of dense volume, but lower BI-RADS density and percent volumetric density compared with White women. Second, although all measures of breast density were strongly associated with invasive breast cancer risk for both Black and White women, adjustment for BMI strengthened associations of BI-RADS density and volumetric percent density measures with breast cancer, while dense volume had a stable association with breast cancer risk regardless of additional adjustment for BMI. Taken together, our study suggests that using BI-RADS density or volumetric percent density unadjusted for BMI will identify fewer Black women as high risk, but using BMI-adjusted BI-RADS density or volumetric percent density measures or dense volume combined with BMI may better identify Black women at elevated breast cancer risk.
To the best of our knowledge, this is the largest study to explore differences in the association between volumetric density measures and breast cancer risk between Black and White women. Our results confirm previous literature, which states that Black women have less dense breasts compared with White women using BI-RADS density or volumetric percent density [9–14]. McCarthy et al. (2016) also found that Black women had higher average dense volume but lower percentages of women in the BI-RADS c and d categories compared with White women [10]. However, Oppong et al. (2018) and Moore et al. (2020) found that Black women had higher BI-RADS density compared with White women [11, 12] A more recent paper led by Kerlikowske and colleagues found that Non-Hispanic White women had a higher prevalence ratio of dense breasts compared with non-Hispanic Black women, assessed as BI-RADS density, after adjusting for BMI, age and menopausal status [9]. We found that only the BI-RADS density distribution and breast cancer association was statistically significantly different by race. Also, although the associations with cancer risk for volumetric percent density and BI-RADS density were stronger for obese vs. non-obese women, only the volumetric percent density association was statistically significant for Black, obese women compared with Black, non-obese women. This finding is consistent with those of Engmann et al. 2019 that found stronger associations of volumetric percent density with breast cancer risk for overweight and obese women [19]. Furthermore, we found a stronger association of volumetric percent density with breast cancer risk among Black women younger than 55 compared with older women. This is in contrast with recent findings from the Black Women’s Health Study that found the association of percent mammographic density measured by Cumulus to be stronger among women older than 55 [20].
We confirm studies that Black women have a lower percentage of breast density or BI-RADS density and higher absolute breast density compared with White women, prior to adjustment for BMI. BI-RADS density is currently relied upon to determine which women are eligible for supplemental screening. Based on our findings, using BI-RADS density measure or relative measures such as volumetric percent density, which reflect BMI instead of absolute measures of glandular tissue such as dense volume, may lead to inequitable screening practices between Black and White women [9, 21]. Therefore, if BI-RADS density alone is used to determine eligibility for supplemental screening, Black women are less likely to qualify, despite their greater risk of breast cancer death [22, 23] Additionally, our results highlight the importance of controlling for both BMI and age when considering differences in the association between relative breast density measures and breast cancer risk between Black and White women, as the discriminatory accuracy increased in the volumetric percent density and BI-RADS density measures for all models after adjustment for BMI.
We found that dense volume is a strong predictor of invasive breast cancer risk for both Black and White women, with similar discriminatory accuracy regardless of BMI considerations. Given that dense volume has a strong association with breast cancer risk and similar discriminatory accuracy for both Black and White women, this may be a more robust measure of risk to use in diverse populations [21]. If dense volume is used for density notification, it could potentially be a more equitable density measure to consider in future policy decisions. A previous study found that adding quantitative percent measures of breast density didn’t improve predictive accuracy of the Gail Model for Black or White women [24]. However, a study by our co-authors found that adding quantitative dense volume to the Breast Cancer Surveillance Consortium model (with BI-RADS breast density and BMI) increased discriminatory accuracy compared with a model with only BCSC risk model factors, but without specific focus on racial differences in model performance [21]. Thus, dense volume may be a promising marker for risk prediction. Recently the Breast Cancer Surveillance Consortium (BCSC) added BMI to its risk models as a way to better inform risk based screening and improve the model [25]. Inclusion of BMI has shown the largest improvement in estimated risk for individual women, particularly Black women who saw an improvement in the true positive rate for women estimated to be at high breast cancer risk (> 3% 5-year risk).
Strengths of this study include the multi-institutional collaboration and investigation of volumetric breast density measures and breast cancer risk for both Black and White women. Several limitations should be considered. Though this was the largest study on the topic to date, our study included only 138 breast cancer cases for Black women. Larger studies should confirm our findings. We utilized only one software program to quantitatively estimate breast density, Volpara. Though this measure was developed in a European population, it has been widely validated in populations from Europe, the U.S., and Asia and shown to be correlated with other quantitative density measurement software [26]. We did not consider menopausal status in our analysis but used an age cutoff of 55 in our stratified analysis as a surrogate [18]. BMI from UPENN was obtained from medical records at the time of mammography screening or shortly before, and may have been a combination of both self-report and measured weight and height. This may lead to potential measurement error with BMI. Also, almost all Black women were from UPENN, introducing possible confounding by site. Finally, we were not able to analyze other measures of adiposity, such as central adiposity or waist-to-hip ratios, which may provide a more accurate assessment of the risk of adiposity (over the currently relied upon BMI) on breast cancer.
In summary, all the qualitative and percent density measures were strongly associated with breast cancer risk after adjustment for both age and BMI. Only a statistically significant race and density interaction existed between BI-RADS density and race, with Black women having a stronger breast cancer risk association compared with White women. The association of dense volume and breast cancer risk was similar after adjustment for BMI or stratification by BMI, age, and time until breast cancer diagnosis. Future studies should consider investigating dense volume as a more equitable breast density measure, both for clinical use and for inclusion in risk prediction models when considering supplemental screening.
Supplementary Information
Below is the link to the electronic supplementary material.
Abbreviations
- AUC
Area under the receiver operator curve
- BI-RADS
Breast imaging reporting and data system
- BMI
Body mass index
- CI
Confidence interval
- DV
Dense volume
- FFDM
Full field digital mammography
- HIPAA
Health Insurance Portability and Accountability Act
- MN
Minnesota
- MQSA
Mammography Quality Standards Act
- OR
Odds ratio
- PA
Pennsylvania
- SAS
Statistical analysis system
- UPENN
University of Pennsylvania
- VPD
Volumetric percent density
Author contributions
MAM (Investigation; Writing—original draft; Writing—review & editing)SW (Conceptualization; Methodology; Formal analysis; Investigation; Writing—review & editing)CS (Formal analysis; Methodology; Data curation; Visualization; Writing-original draft; Writing—review & editing)SE (Writing—review & editing)AN (Project administration; Resources; Writing—review & editing)MJ (Formal analysis; Data Curation;, Writing—review & editing)EFC (Conceptualization; Resources; Writing—review & editing)KK (Conceptualization; Methodology; Funding acquisition; Investigation; Supervision; Resources; Project administration; Writing—original draft; Writing—review & editing)DK (Conceptualization; Methodology; Funding acquisition; Investigation; Supervision; Project administration; Resources; Writing—review & editing)CMV (Conceptualization; Methodology; Funding acquisition; Investigation; Supervision; Project administration; Writing-original draft; Resources; Writing—review & editing)AMM (Conceptualization; Methodology; Investigation; Supervision; Resources; Writing-original draft; Writing—review & editing).
Funding
Evaluation of novel tomosynthesis density measures in breast cancer risk prediction. Grant Number: NIH CA275074, PIs CV, KK, DK. Radiomic phenotypes of breast parenchyma and their association with breast cancer risk and detection. Grant Number: NIH CA207084, PIs CV, KK, DK. Background Parenchymal Enhancement and Breast Cancer Risk in Black Women: Susan G. Komen ASP241260597, PI AMM. Combining volumetric breast density and polygenic risk scores to improve breast cancer risk assessment for black and white women: American Cancer Society RSG-23-1038098-01-HOPS, PI AMM. Genetic and radiomic markers to guide supplemental screening for breast cancer: American Cancer Society PASD-22-1003156-01-PASD, PI AMM.
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
This study was approved by the Institutional Review Boards at both Mayo Clinic and University of Pennsylvania, and a waiver of informed consent was granted for this review of existing clinical data.
Consent for publication
Not applicable.
Competing interests
EFC reports research support outside of current work from OM1/Hologic and iCAD, Inc. There are no other conflicts of interest to report at this time.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Celine M. Vachon and Anne Marie McCarthy: Joint senior authors.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.