Temporal changes in mammographic breast density and breast cancer risk among women with benign breast disease.

INTRODUCTION: Benign breast disease (BBD) and high mammographic breast density (MBD) are prevalent and independent risk factors for invasive breast cancer. It has been suggested that temporal changes in MBD may impact future invasive breast cancer risk, but this has not been studied among women with BBD. METHODS: We undertook a nested case-control study within a cohort of 15,395 women with BBD in Kaiser Permanente Northwest (KPNW; 1970-2012, followed through mid-2015). Cases (n = 261) developed invasive breast cancer > 1 year after BBD diagnosis, whereas controls (n = 249) did not have breast cancer by the case diagnosis date. Cases and controls were individually matched on BBD diagnosis age and plan membership duration. Standardized %MBD change (per 2 years), categorized as stable/any increase (≥ 0%), minimal decrease of less than 5% or a decrease greater than or equal to 5%, was determined from baseline and follow-up mammograms. Associations between MBD change and breast cancer risk were examined using adjusted unconditional logistic regression. RESULTS: Overall, 64.5% (n = 329) of BBD patients had non-proliferative and 35.5% (n = 181) had proliferative disease with/without atypia. Women with an MBD decrease (≤ - 5%) were less likely to develop breast cancer (Odds Ratio (OR) 0.64; 95% Confidence Interval (CI) 0.38, 1.07) compared with women with minimal decreases. Associations were stronger among women ≥ 50 years at BBD diagnosis (OR 0.48; 95% CI 0.25, 0.92) and with proliferative BBD (OR 0.32; 95% CI 0.11, 0.99). DISCUSSION: Assessment of temporal MBD changes may inform risk monitoring among women with BBD, and strategies to actively reduce MBD may help decrease future breast cancer risk.

Breast density knowledge and willingness to delay treatment for pre-operative breast cancer imaging among women with a personal history of breast cancer.

BACKGROUND: Following a breast cancer diagnosis, it is uncertain whether women's breast density knowledge influences their willingness to undergo pre-operative imaging to detect additional cancer in their breasts. We evaluated women's breast density knowledge and their willingness to delay treatment for pre-operative testing. METHODS: We surveyed women identified in the Breast Cancer Surveillance Consortium aged ≥ 18 years, with first breast cancer diagnosed within the prior 6-18 months, who had at least one breast density measurement within the 5 years prior to their diagnosis. We assessed women's breast density knowledge and correlates of willingness to delay treatment for 6 or more weeks for pre-operative imaging via logistic regression. RESULTS: Survey participation was 28.3% (969/3,430). Seventy-two percent (469/647) of women with dense and 11% (34/322) with non-dense breasts correctly knew their density (p < 0.001); 69% (665/969) of all women knew dense breasts make it harder to detect cancers on a mammogram; and 29% (285/969) were willing to delay treatment ≥ 6 weeks to undergo pre-operative imaging. Willingness to delay treatment did not differ by self-reported density (OR:0.99 for non-dense vs. dense; 95%CI: 0.50-1.96). Treatment with chemotherapy was associated with less willingness to delay treatment (OR:0.67; 95%CI: 0.46-0.96). Having previously delayed breast cancer treatment more than 3 months was associated with an increased willingness to delay treatment for pre-operative imaging (OR:2.18; 95%CI: 1.26-3.77). CONCLUSIONS: Understanding of personal breast density was not associated with willingness to delay treatment 6 or more weeks for pre-operative imaging, but aspects of a woman's treatment experience were. CLINICALTRIALS: GOV : NCT02980848 registered December 2, 2016.

Time-specific impact of trace metals on breast density of adolescent girls in Santiago, Chile.

Whether trace metals modify breast density, the strongest predictor for breast cancer, during critical developmental stages such as puberty remains understudied. Our study prospectively evaluated the association between trace metals at Tanner breast stage B1 (n = 291) and at stages both B1 and B4 (n = 253) and breast density at 2 years post-menarche among Chilean girls from the Growth and Obesity Cohort Study. Dual-energy x-ray absorptiometry assessed the volume of dense breast tissue (absolute fibroglandular volume [FGV]) and percent breast density (%FGV). Urine trace metals included arsenic, barium, cadmium, cobalt, cesium, copper, magnesium, manganese, molybdenum, nickel, lead, antimony, selenium, tin, thallium, vanadium, and zinc. At B1, a doubling of thallium concentration resulted in 13.69 cm3 increase in absolute FGV (ß: 13.69, 95% confidence interval [CI]: 2.81, 24.52), while a doubling of lead concentration resulted in a 7.76 cm3 decrease in absolute FGV (ß: -7.76, 95%CI: -14.71, -0.73). At B4, a doubling of barium concentration was associated with a 10.06 cm3 increase (ß: 10.06, 95% CI: 1.44, 18.60), copper concentration with a 12.29 cm3 increase (ß: 12.29, 95% CI: 2.78, 21.56), lead concentration with a 9.86 cm3 increase (ß: 9.86, 95% CI: 0.73, 18.98), antimony concentration with a 12.97 cm3 increase (ß: 12.97, 95% CI: 1.98, 23.79) and vanadium concentration with a 13.14 cm3 increase in absolute FGV (ß: 13.14, 95% CI: 2.73, 23.58). Trace metals may affect pubertal breast density at varying developmental stages with implications for increased susceptibility for breast cancer.

Baseline breast tissue characteristics determine the effect of tamoxifen on mammographic density change.

Tamoxifen prevents recurrence of breast cancer and is also approved for preventive, risk-reducing, therapy. Tamoxifen alters the breast tissue composition and decreases the mammographic density. We aimed to test if baseline breast tissue composition influences tamoxifen-associated density change. This biopsy-based study included 83 participants randomised to 6 months daily intake of placebo, 20, 10, 5, 2.5, or 1 mg tamoxifen. The study is nested within the double-blinded tamoxifen dose-determination trial Karolinska Mammography Project for Risk Prediction of Breast Cancer Intervention (KARISMA) Study. Ultrasound-guided core-needle breast biopsies were collected at baseline before starting treatment. Biopsies were quantified for epithelial, stromal, and adipose distributions, and epithelial and stromal expression of proliferation marker Ki67, oestrogen receptor (ER) and progesterone receptor (PR). Mammographic density was measured using STRATUS. We found that greater mammographic density at baseline was positively associated with stromal area and inversely associated with adipose area and stromal expression of ER. Premenopausal women had greater mammographic density and epithelial tissue, and expressed more epithelial Ki67, PR, and stromal PR, compared to postmenopausal women. In women treated with tamoxifen (1-20 mg), greater density decrease was associated with higher baseline density, epithelial Ki67, and stromal PR. Women who responded to tamoxifen with a density decrease had on average 17% higher baseline density and a 2.2-fold higher PR expression compared to non-responders. Our results indicate that features in the normal breast tissue before tamoxifen exposure influences the tamoxifen-associated density decrease, and that the age-associated difference in density change may be related to age-dependant differences in expression of Ki67 and PR.

Breast density and risk of breast cancer: masking and detection bias.

Breast density is associated with risk of breast cancer (BC) diagnosis, impacting risk prediction tools and patient notification policies. Density affects mammography sensitivity and may influence screening intensity. Therefore, the observed association between density and BC diagnosis may not reflect the relationship between density and disease risk. We investigate the association between breast density and BC risk using data sourced from 33,542 women in the Breast Cancer Surveillance Consortium, 2000-2018. We estimated mammogram sensitivity and rates of screening mammography among dense (BI-RADS c, d) and non-dense (BI-RADS a, b) breasts. We used Kaplan-Meier estimates to summarize the relative risks of BC diagnosis (RRdx) by density and fit a natural history model to estimate the relative risks of BC onset (RRonset) given density-specific sensitivities. RRdx for dense versus non-dense breasts was 1.80 (95% CI 1.46 to 2.57). Based on estimated screening sensitivities of 0.88 and .78 for non-dense and dense breasts, respectively, RRonset was 1.73 (95% CI 1.43 to 2.25). Sensitivity analyses suggested higher breast density is robustly associated with increased risk of BC onset, similar in magnitude to the increased risk of BC diagnosis. These finding support laws requiring notifications to women with dense breasts of their increased BC risk.

Reproductive risk factors for breast cancer and association with novel breast density measurements among Hispanic, Black, and White women.

PURPOSE: There are differences in the distributions of breast cancer incidence and risk factors by race and ethnicity. Given the strong association between breast density and breast cancer, it is of interest describe racial and ethnic variation in the determinants of breast density. METHODS: We characterized racial and ethnic variation in reproductive history and several measures of breast density for Hispanic (n = 286), non-Hispanic Black (n = 255), and non-Hispanic White (n = 1694) women imaged at a single hospital. We quantified associations between reproductive factors and percent volumetric density (PVD), dense volume (DV), non-dense volume (NDV), and a novel measure of pixel intensity variation (V) using multivariable-adjusted linear regression, and tested for statistical heterogeneity by race and ethnicity. RESULTS: Reproductive factors most strongly associated with breast density were age at menarche, parity, and oral contraceptive use. Variation by race and ethnicity was most evident for the associations between reproductive factors and NDV (minimum p-heterogeneity:0.008) and V (minimum p-heterogeneity:0.004) and least evident for PVD (minimum p-heterogeneity:0.042) and DV (minimum p-heterogeneity:0.041). CONCLUSION: Reproductive choices, particularly those related to childbearing and oral contraceptive use, may contribute to racial and ethnic variation in breast density.

The distribution of breast density in women aged 18 years and older.

PURPOSE: Age and body mass index (BMI) are critical considerations when assessing individual breast cancer risk, particularly for women with dense breasts. However, age- and BMI-standardized estimates of breast density are not available for screen-aged women, and little is known about the distribution of breast density in women aged < 40. This cross-sectional study uses three different modalities: optical breast spectroscopy (OBS), dual-energy X-ray absorptiometry (DXA), and mammography, to describe the distributions of breast density across categories of age and BMI. METHODS: Breast density measures were estimated for 1,961 Australian women aged 18-97 years using OBS (%water and %water + %collagen). Of these, 935 women had DXA measures (percent and absolute fibroglandular dense volume, %FGV and FGV, respectively) and 354 had conventional mammographic measures (percent and absolute dense area). The distributions for each breast density measure were described across categories of age and BMI. RESULTS: The mean age was 38 years (standard deviation = 15). Median breast density measures decreased with age and BMI for all three modalities, except for DXA-FGV, which increased with BMI and decreased after age 30. The variation in breast density measures was largest for younger women and decreased with increasing age and BMI. CONCLUSION: This unique study describes the distribution of breast density measures for women aged 18-97 using alternative and conventional modalities of measurement. While this study is the largest of its kind, larger sample sizes are needed to provide clinically useful age-standardized measures to identify women with high breast density for their age or BMI.

Disentangling the relationships of body mass index and circulating sex hormone concentrations in mammographic density using Mendelian randomization.

PURPOSE: Mammographic density phenotypes, adjusted for age and body mass index (BMI), are strong predictors of breast cancer risk. BMI is associated with mammographic density measures, but the role of circulating sex hormone concentrations is less clear. We investigated the relationship between BMI, circulating sex hormone concentrations, and mammographic density phenotypes using Mendelian randomization (MR). METHODS: We applied two-sample MR approaches to assess the association between genetically predicted circulating concentrations of sex hormones [estradiol, testosterone, sex hormone-binding globulin (SHBG)], BMI, and mammographic density phenotypes (dense and non-dense area). We created instrumental variables from large European ancestry-based genome-wide association studies and applied estimates to mammographic density phenotypes in up to 14,000 women of European ancestry. We performed analyses overall and by menopausal status. RESULTS: Genetically predicted BMI was positively associated with non-dense area (IVW: ß = 1.79; 95% CI = 1.58, 2.00; p = 9.57 × 10-63) and inversely associated with dense area (IVW: ß = - 0.37; 95% CI = - 0.51,- 0.23; p = 4.7 × 10-7). We observed weak evidence for an association of circulating sex hormone concentrations with mammographic density phenotypes, specifically inverse associations between genetically predicted testosterone concentration and dense area (ß = - 0.22; 95% CI = - 0.38, - 0.053; p = 0.009) and between genetically predicted estradiol concentration and non-dense area (ß = - 3.32; 95% CI = - 5.83, - 0.82; p = 0.009), although results were not consistent across a range of MR approaches. CONCLUSION: Our findings support a positive causal association between BMI and mammographic non-dense area and an inverse association between BMI and dense area. Evidence was weaker and inconsistent for a causal effect of circulating sex hormone concentrations on mammographic density phenotypes. Based on our findings, associations between circulating sex hormone concentrations and mammographic density phenotypes are weak at best.

Screening mammography frequency following dense breast notification among a predominantly Hispanic/Latina screening cohort.

PURPOSE: Nationally legislated dense breast notification (DBN) informs women of their breast density (BD) and the impact of BD on breast cancer risk and detection, but consequences for screening participation are unclear. We evaluated the association of DBN in New York State (NYS) with subsequent screening mammography in a largely Hispanic/Latina cohort. METHODS: Women aged 40-60 were surveyed in their preferred language (33% English, 67% Spanish) during screening mammography from 2016 to 2018. We used clinical BD classification from mammography records from 2013 (NYS DBN enactment) through enrollment (baseline) to create a 6-category variable capturing prior and new DBN receipt (sent only after clinically dense mammograms). We used this variable to compare the number of subsequent mammograms (0, 1, ≥ 2) from 10 to 30 months after baseline using ordinal logistic regression. RESULTS: In a sample of 728 women (78% foreign-born, 72% Hispanic, 46% high school education or less), first-time screeners and women who received DBN for the first time after prior non-dense mammograms had significantly fewer screening mammograms within 30 months of baseline (Odds Ratios range: 0.33 (95% Confidence Interval (CI) 0.12-0.85) to 0.38 (95% CI 0.17-0.82)) compared to women with prior mammography but no DBN. There were no differences in subsequent mammogram frequency between women with multiple DBN and those who never received DBN. Findings were consistent across age, language, health literacy, and education groups. CONCLUSION: Women receiving their first DBN after previous non-dense mammograms have lower mammography participation within 2.5 years. DBN has limited influence on screening participation of first-time screeners and those with persistent dense mammograms.

Untargeted serum metabolomic profiles and breast density in young women.

PURPOSE OF THE STUDY: Breast density is an established risk factor for breast cancer. However, little is known about metabolic influences on breast density phenotypes. We conducted untargeted serum metabolomics analyses to identify metabolic signatures associated with breast density phenotypes among young women. METHODS: In a cross-sectional study of 173 young women aged 25-29 who participated in the Dietary Intervention Study in Children 2006 Follow-up Study, 449 metabolites were measured in fasting serum samples using ultra-high-performance liquid chromatography-tandem mass spectrometry. Multivariable-adjusted mixed-effects linear regression identified metabolites associated with magnetic resonance imaging measured breast density phenotypes: percent dense breast volume (%DBV), absolute dense breast volume (ADBV), and absolute non-dense breast volume (ANDBV). Metabolite results were corrected for multiple comparisons using a false discovery rate adjusted p-value (q). RESULTS: The amino acids valine and leucine were significantly inversely associated with %DBV. For each 1 SD increase in valine and leucine, %DBV decreased by 20.9% (q = 0.02) and 18.4% (q = 0.04), respectively. ANDBV was significantly positively associated with 16 lipid and one amino acid metabolites, whereas no metabolites were associated with ADBV. Metabolite set enrichment analysis also revealed associations of distinct metabolic signatures with %DBV, ADBV, and ANDBV; branched chain amino acids had the strongest inverse association with %DBV (p = 0.002); whereas, diacylglycerols and phospholipids were positively associated with ANDBV (p ≤ 0.002), no significant associations were observed for ADBV. CONCLUSION: Our results suggest an inverse association of branched chain amino acids with %DBV. Larger studies in diverse populations are needed.

Quantitative measurement of mammographic density in breast-tissue explants using portable NMR: Precision and accuracy.

PURPOSE: Single-sided portable NMR (pNMR) has previously been demonstrated to be suitable for quantification of mammographic density (MD) in excised breast tissue samples. Here we investigate the precision and accuracy of pNMR measurements of MD ex vivo as compared with the gold standards. METHODS: Forty-five breast-tissue explants from 9 prophylactic mastectomy patients were measured. The relative tissue water content was taken as the MD-equivalent quantity. In each sample, the water content was measured using some combination of three pNMR techniques (apparent T2, diffusion, and T1 measurements) and two gold-standard techniques (computed microtomography [µCT] and hematoxylin and eosin [H&E] histology). Pairwise correlation plots and Bland-Altman analysis were used to quantify the degree of agreement between pNMR techniques and the gold standards. RESULTS: Relative water content measured from both apparent T2 relaxation spectra, and diffusion decays exhibited strong correlation with the H&E and µCT results. Bland-Altman analysis yielded average bias values of -0.4, -2.6, 2.6, and 2.8 water percentage points (pp) and 95% confidence intervals of 13.1, 7.5, 11.2, and 11.8 pp for the H&E - T2, µCT - T2, H&E - diffusion, and µCT - diffusion comparison pairs, respectively. T1-based measurements were found to be less reliable, with the Bland-Altman confidence intervals of 27.7 and 33.0 pp when compared with H&E and µCT, respectively. CONCLUSION: Apparent T2-based and diffusion-based pNMR measurements enable quantification of MD in breast-tissue explants with the precision of approximately 10 pp and accuracy of approximately 3 pp or better, making pNMR a promising measurement modality for radiation-free quantification of MD.

Associations of Vitamins and Related Cofactor Metabolites with Mammographic Breast Density in Premenopausal Women.

BACKGROUND: Identifying biological drivers of mammographic breast density (MBD), a strong risk factor for breast cancer, could provide insight into breast cancer etiology and prevention. Studies on dietary factors and MBD have yielded conflicting results. There are, however, very limited data on the associations of dietary biomarkers and MBD. OBJECTIVE: We aimed to investigate the associations of vitamins and related cofactor metabolites with MBD in premenopausal women. METHODS: We measured 37 vitamins and related cofactor metabolites in fasting plasma samples of 705 premenopausal women recruited during their annual screening mammogram at the Washington University School of Medicine, St. Louis, MO. Volpara was used to assess volumetric percent density (VPD), dense volume (DV), and nondense volume (NDV). We estimated the least square means of VPD, DV, and NDV across quartiles of each metabolite, as well as the regression coefficient of a metabolite in continuous scale from multiple covariate-adjusted linear regression. We corrected for multiple testing using the Benjamini-Hochberg procedure to control the false discover rate (FDR) at a 5% level. RESULTS: Participants' mean VPD was 10.5%. Two vitamin A metabolites (ß-cryptoxanthin and carotene diol 2) were positively associated, and one vitamin E metabolite (γ-tocopherol) was inversely associated with VPD. The mean VPD increased across quartiles of ß-cryptoxanthin (Q1 = 7.2%, Q2 = 7.7%, Q3 = 8.4%%, Q4 = 9.2%; P-trend = 1.77E-05, FDR P value = 1.18E-03). There was a decrease in the mean VPD across quartiles of γ-tocopherol (Q1 = 9.4%, Q2 = 8.1%, Q3 = 8.0%, Q4 = 7.8%; P -trend = 4.01E-03, FDR P value = 0.04). Seven metabolites were associated with NDV: 3 vitamin E (γ-CEHC glucuronide, δ-CEHC, and γ-tocopherol) and 1 vitamin C (gulonate) were positively associated, whereas 2 vitamin A (carotene diol 2 and ß-cryptoxanthin) and 1 vitamin C (threonate) were inversely associated with NDV. No metabolite was significantly associated with DV. CONCLUSION: We report novel associations of vitamins and related cofactor metabolites with MBD in premenopausal women.

Mammographic features differ with body composition in women with breast cancer.

OBJECTIVES: There are several breast cancer (BC) risk factors-many related to body composition, hormonal status, and fertility patterns. However, it is not known if risk factors in healthy women are associated with specific mammographic features at the time of BC diagnosis. Our aim was to assess the potential association between pre-diagnostic body composition and mammographic features in the diagnostic BC image. MATERIALS AND METHODS: The prospective Malmö Diet and Cancer Study includes women with invasive BC from 1991 to 2014 (n = 1116). BC risk factors at baseline were registered (anthropometric measures, menopausal status, and parity) along with mammography data from BC diagnosis (breast density, mammographic tumor appearance, and mode of detection). We investigated associations between anthropometric measures and mammographic features via logistic regression analyses, yielding odds ratios (OR) with 95% confidence intervals (CI). RESULTS: There was an association between high body mass index (BMI) (≥ 30) at baseline and spiculated tumor appearance (OR 1.370 (95% CI: 0.941-2.010)), primarily in women with clinically detected cancers (OR 2.240 (95% CI: 1.280-3.940)), and in postmenopausal women (OR 1.580 (95% CI: 1.030-2.440)). Furthermore, an inverse association between high BMI (≥ 30) and high breast density (OR 0.270 (95% CI: 0.166-0.438)) was found. CONCLUSION: This study demonstrated an association between obesity and a spiculated mass on mammography-especially in women with clinically detected cancers and in postmenopausal women. These findings offer insights on the relationship between risk factors in healthy women and related mammographic features in subsequent BC. CLINICAL RELEVANCE STATEMENT: With increasing numbers of both BC incidence and women with obesity, it is important to highlight mammographic findings in women with an unhealthy weight. KEY POINTS: Women with obesity and BC may present with certain mammographic features. Spiculated masses were more common in women with obesity, especially postmenopausal women, and those with clinically detected BCs. Insights on the relationship between obesity and related mammographic features will aid mammographic interpretation.

Breast density effect on the sensitivity of digital screening mammography in a UK cohort.

OBJECTIVES: To assess the performance of breast cancer screening by category of breast density and age in a UK screening cohort. METHODS: Raw full-field digital mammography data from a single site in the UK, forming a consecutive 3-year cohort of women aged 50 to 70 years from 2016 to 2018, were obtained retrospectively. Breast density was assessed using Volpara software. Examinations were grouped by density category and age group (50-60 and 61-70 years) to analyse screening performance. Statistical analysis was performed to determine the association between density categories and age groups. Volumetric breast density was assessed as a binary classifier of interval cancers (ICs) to find an optimal density threshold. RESULTS: Forty-nine thousand nine-hundred forty-eight screening examinations (409 screen-detected cancers (SDCs) and 205 ICs) were included in the analysis. Mammographic sensitivity, SDC/(SDC + IC), decreased with increasing breast density from 75.0% for density a (p = 0.839, comparisons made to category b), to 73.5%, 59.8% (p = 0.001), and 51.3% (p < 0.001) in categories b, c, and d, respectively. IC rates were highest in the densest categories with rates of 1.8 (p = 0.039), 3.2, 5.7 (p < 0.001), and 7.9 (p < 0.001) per thousand for categories a, b, c, and d, respectively. The recall rate increased with breast density, leading to more false positive recalls, especially in the younger age group. There was no significant difference between the optimal density threshold found, 6.85, and that Volpara defined as the b/c boundary, 7.5. CONCLUSIONS: The performance of screening is significantly reduced with increasing density with IC rates in the densest category four times higher than in women with fatty breasts. False positives are a particular issue for the younger subgroup without prior examinations. CLINICAL RELEVANCE STATEMENT: In women attending screening there is significant underdiagnosis of breast cancer in those with dense breasts, most marked in the highest density category but still three times higher than in women with fatty breasts in the second highest category. KEY POINTS: Breast density can mask cancers leading to underdiagnosis on mammography. Interval cancer rate increased with breast density categories 'a' to 'd'; 1.8 to 7.9 per thousand. Recall rates increased with increasing breast density, leading to more false positive recalls.

AI performance by mammographic density in a retrospective cohort study of 99,489 participants in BreastScreen Norway.

OBJECTIVE: To explore the ability of artificial intelligence (AI) to classify breast cancer by mammographic density in an organized screening program. MATERIALS AND METHOD: We included information about 99,489 examinations from 74,941 women who participated in BreastScreen Norway, 2013-2019. All examinations were analyzed with an AI system that assigned a malignancy risk score (AI score) from 1 (lowest) to 10 (highest) for each examination. Mammographic density was classified into Volpara density grade (VDG), VDG1-4; VDG1 indicated fatty and VDG4 extremely dense breasts. Screen-detected and interval cancers with an AI score of 1-10 were stratified by VDG. RESULTS: We found 10,406 (10.5% of the total) examinations to have an AI risk score of 10, of which 6.7% (704/10,406) was breast cancer. The cancers represented 89.7% (617/688) of the screen-detected and 44.6% (87/195) of the interval cancers. 20.3% (20,178/99,489) of the examinations were classified as VDG1 and 6.1% (6047/99,489) as VDG4. For screen-detected cancers, 84.0% (68/81, 95% CI, 74.1-91.2) had an AI score of 10 for VDG1, 88.9% (328/369, 95% CI, 85.2-91.9) for VDG2, 92.5% (185/200, 95% CI, 87.9-95.7) for VDG3, and 94.7% (36/38, 95% CI, 82.3-99.4) for VDG4. For interval cancers, the percentages with an AI score of 10 were 33.3% (3/9, 95% CI, 7.5-70.1) for VDG1 and 48.0% (12/25, 95% CI, 27.8-68.7) for VDG4. CONCLUSION: The tested AI system performed well according to cancer detection across all density categories, especially for extremely dense breasts. The highest proportion of screen-detected cancers with an AI score of 10 was observed for women classified as VDG4. CLINICAL RELEVANCE STATEMENT: Our study demonstrates that AI can correctly classify the majority of screen-detected and about half of the interval breast cancers, regardless of breast density. KEY POINTS: • Mammographic density is important to consider in the evaluation of artificial intelligence in mammographic screening. • Given a threshold representing about 10% of those with the highest malignancy risk score by an AI system, we found an increasing percentage of cancers with increasing mammographic density. • Artificial intelligence risk score and mammographic density combined may help triage examinations to reduce workload for radiologists.

Radiation exposure and screening yield by digital breast tomosynthesis compared to mammography: results of the TOSYMA Trial breast density related.

OBJECTIVES: The randomized TOmosynthesis plus SYnthesized MAmmography (TOSYMA) screening trial has shown that digital breast tomosynthesis plus synthesized mammography (DBT + SM) is superior to digital mammography (DM) in invasive breast cancer detection varying with breast density. On the other hand, the overall average glandular dose (AGD) of DBT is higher than that of DM. Comparing the DBT + SM and DM trial arm, we analyzed here the mean AGD and their determinants per breast density category and related them to the respective invasive cancer detection rates (iCDR). METHODS: TOSYMA screened 99,689 women aged 50 to 69 years. Compression force, resulting breast thickness, the calculated AGD obtained from each mammography device, and previously published iCDR were used for comparisons across breast density categories in the two trial arms. RESULTS: There were 196,622 exposures of 49,227 women (DBT + SM) and 197,037 exposures of 49,132 women (DM) available for analyses. Mean breast thicknesses declined from breast density category A (fatty) to D (extremely dense) in both trial arms. However, while the mean AGD in the DBT + SM arm declined concomitantly from category A (2.41 mGy) to D (1.89 mGy), it remained almost unchanged in the DM arm (1.46 and 1.51 mGy, respectively). In relative terms, the AGD elevation in the DBT + SM arm (64.4% (A), by 44.5% (B), 27.8% (C), and 26.0% (D)) was lowest in dense breasts where, however, the highest iCDR were observed. CONCLUSION: Women with dense breasts may specifically benefit from DBT + SM screening as high cancer detection is achieved with only moderate AGD elevations. CLINICAL RELEVANCE STATEMENT: TOSYMA suggests a favorable constellation for screening with digital breast tomosynthesis plus synthesized mammography (DBT + SM) in dense breasts when weighing average glandular dose elevation against raised invasive breast cancer detection rates. There is potential for density-, i.e., risk-adapted population-wide breast cancer screening with DBT + SM. KEY POINTS: Breast thickness declines with visually increasing density in digital mammography (DM) and digital breast tomosynthesis (DBT). Average glandular doses of DBT decrease with increasing density; digital mammography shows lower and more constant values. With the smallest average glandular dose difference in dense breasts, DBT plus SM had the highest difference in invasive breast cancer detection rates.

Re-attendance in supplemental breast MRI screening rounds of the DENSE trial for women with extremely dense breasts.

OBJECTIVES: Supplemental MRI screening improves early breast cancer detection and reduces interval cancers in women with extremely dense breasts in a cost-effective way. Recently, the European Society of Breast Imaging recommended offering MRI screening to women with extremely dense breasts, but the debate on whether to implement it in breast cancer screening programs is ongoing. Insight into the participant experience and willingness to re-attend is important for this discussion. METHODS: We calculated the re-attendance rates of the second and third MRI screening rounds of the DENSE trial. Moreover, we calculated age-adjusted odds ratios (ORs) to study the association between characteristics and re-attendance. Women who discontinued MRI screening were asked to provide one or more reasons for this. RESULTS: The re-attendance rates were 81.3% (3458/4252) and 85.2% (2693/3160) in the second and third MRI screening round, respectively. A high age (> 65 years), a very low BMI, lower education, not being employed, smoking, and no alcohol consumption were correlated with lower re-attendance rates. Moderate or high levels of pain, discomfort, or anxiety experienced during the previous MRI screening round were correlated with lower re-attendance rates. Finally, a plurality of women mentioned an examination-related inconvenience as a reason to discontinue screening (39.1% and 34.8% in the second and third screening round, respectively). CONCLUSIONS: The willingness of women with dense breasts to re-attend an ongoing MRI screening study is high. However, emphasis should be placed on improving the MRI experience to increase the re-attendance rate if widespread supplemental MRI screening is implemented. CLINICAL RELEVANCE STATEMENT: For many women, MRI is an acceptable screening method, as re-attendance rates were high - even for screening in a clinical trial setting. To further enhance the (re-)attendance rate, one possible approach could be improving the overall MRI experience. KEY POINTS: • The willingness to re-attend in an ongoing MRI screening study is high. • Pain, discomfort, and anxiety in the previous MRI screening round were related to lower re-attendance rates. • Emphasis should be placed on improving MRI experience to increase the re-attendance rate in supplemental MRI screening.

Imaging the dense breast.

The sensitivity of mammography reduces as breast density increases, which impacts breast screening and locoregional staging in breast cancer. Supplementary imaging with other modalities can offer improved cancer detection, but this often comes at the cost of more false positives. Magnetic resonance imaging and contrast-enhanced mammography, which assess tumour enhancement following contrast administration, are more sensitive than digital breast tomosynthesis and ultrasound, which predominantly rely on the assessment of tumour morphology.

Mammographic density assessment: comparison of radiologists, automated volumetric measurement, and artificial intelligence-based computer-assisted diagnosis.

BACKGROUND: Artificial intelligence-based computer-assisted diagnosis (AI-CAD) is increasingly used for mammographic exams, and its role in mammographic density assessment should be evaluated. PURPOSE: To assess the inter-modality agreement between radiologists, automated volumetric density measurement program (Volpara), and AI-CAD system in breast density categorization using the Breast Imaging-Reporting and Data System (BI-RADS) density categories. MATERIAL AND METHODS: A retrospective review was conducted on 1015 screening digital mammograms that were performed in Asian female patients (mean age = 56 years ± 10 years) in our health examination center between December 2022 and January 2023. Four radiologists with two different levels of experience (expert and general radiologists) performed density assessments. Agreement between the radiologists, Volpara, and AI-CAD (Lunit INSIGHT MMG) was evaluated using weighted kappa statistics and matched rates. RESULTS: Inter-reader agreement between expert and general radiologists was substantial (k = 0.65) with a matched rate of 72.8%. The agreement was substantial between expert or general radiologists and Volpara (k = 0.64-0.67) with a matched rate of 72.0% but moderate between expert or general radiologists and AI-CAD (k = 0.45-0.58) with matched rates of 56.7%-67.0%. The agreement between Volpara and AI-CAD was moderate (k = 0.53) with a matched rate of 60.8%. CONCLUSION: The agreement in breast density categorization between radiologists and automated volumetric density measurement program (Volpara) was higher than the agreement between radiologists and AI-CAD (Lunit INSIGHT MMG).

Breast density quantification in dual-energy mammography using virtual anthropomorphic phantoms.

PURPOSE: Breast density is a significant risk factor for breast cancer and can impact the sensitivity of screening mammography. Area-based breast density measurements may not provide an accurate representation of the tissue distribution, therefore volumetric breast density (VBD) measurements are preferred. Dual-energy mammography enables volumetric measurements without additional assumptions about breast shape. In this work we evaluated the performance of a dual-energy decomposition technique for determining VBD by applying it to virtual anthropomorphic phantoms. METHODS: The dual-energy decomposition formalism was used to quantify VBD on simulated dual-energy images of anthropomorphic virtual phantoms with known tissue distributions. We simulated 150 phantoms with volumes ranging from 50 to 709 mL and VBD ranging from 15% to 60%. Using these results, we validated a correction for the presence of skin and assessed the method's intrinsic bias and variability. As a proof of concept, the method was applied to 14 sets of clinical dual-energy images, and the resulting breast densities were compared to magnetic resonance imaging (MRI) measurements. RESULTS: Virtual phantom VBD measurements exhibited a strong correlation (Pearson's r > 0.95 $r > 0.95$ ) with nominal values. The proposed skin correction eliminated the variability due to breast size and reduced the bias in VBD to a constant value of -2%. Disagreement between clinical VBD measurements using MRI and dual-energy mammography was under 10%, and the difference in the distributions was statistically non-significant. VBD measurements in both modalities had a moderate correlation (Spearman's ρ $\rho \ $ = 0.68). CONCLUSIONS: Our results in virtual phantoms indicate that the material decomposition method can produce accurate VBD measurements if the presence of a third material (skin) is considered. The results from our proof of concept showed agreement between MRI and dual-energy mammography VBD. Assessment of VBD using dual-energy images could provide complementary information in dual-energy mammography and tomosynthesis examinations.