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.

Low-dose acetylsalicylic acid reduces local inflammation and tissue perfusion in dense breast tissue in postmenopausal women.

PURPOSE: One major risk factor for breast cancer is high mammographic density. It has been estimated that dense breast tissue contributes to ~ 30% of all breast cancer. Prevention targeting dense breast tissue has the potential to improve breast cancer mortality and morbidity. Anti-estrogens, which may be associated with severe side-effects, can be used for prevention of breast cancer in women with high risk of the disease per se. However, no preventive therapy targeting dense breasts is currently available. Inflammation is a hallmark of cancer. Although the biological mechanisms involved in the increased risk of cancer in dense breasts is not yet fully understood, high mammographic density has been associated with increased inflammation. We investigated whether low-dose acetylsalicylic acid (ASA) affects local breast tissue inflammation and/or structural and dynamic changes in dense breasts. METHODS: Postmenopausal women with mammographic dense breasts on their regular mammography screen were identified. A total of 53 women were randomized to receive ASA 160 mg/day or no treatment for 6 months. Magnetic resonance imaging (MRI) was performed before and after 6 months for a sophisticated and continuous measure breast density by calculating lean tissue fraction (LTF). Additionally, dynamic quantifications including tissue perfusion were performed. Microdialysis for sampling of proteins in vivo from breasts and abdominal subcutaneous fat, as a measure of systemic effects, before and after 6 months were performed. A panel of 92 inflammatory proteins were quantified in the microdialysates using proximity extension assay. RESULTS: After correction for false discovery rate, 20 of the 92 inflammatory proteins were significantly decreased in breast tissue after ASA treatment, whereas no systemic effects were detected. In the no-treatment group, protein levels were unaffected. Breast density, measured by LTF on MRI, were unaffected in both groups. ASA significantly decreased the perfusion rate. The perfusion rate correlated positively with local breast tissue concentration of VEGF. CONCLUSIONS: ASA may shape the local breast tissue microenvironment into an anti-tumorigenic state. Trials investigating the effects of low-dose ASA and risk of primary breast cancer among postmenopausal women with maintained high mammographic density are warranted. Trial registration EudraCT: 2017-000317-22.

Effect of testosterone therapy on breast tissue composition and mammographic breast density in trans masculine individuals.

BACKGROUND: The effect of gender-affirming testosterone therapy (TT) on breast cancer risk is unclear. This study investigated the association between TT and breast tissue composition and breast tissue density in trans masculine individuals (TMIs). METHODS: Of the 444 TMIs who underwent chest-contouring surgeries between 2013 and 2019, breast tissue composition was assessed in 425 TMIs by the pathologists (categories of lobular atrophy and stromal composition) and using our automated deep-learning algorithm (% epithelium, % fibrous stroma, and % fat). Forty-two out of 444 TMIs had mammography prior to surgery and their breast tissue density was read by a radiologist. Mammography digital files, available for 25/42 TMIs, were analyzed using the LIBRA software to obtain percent density, absolute dense area, and absolute non-dense area. Linear regression was used to describe the associations between duration of TT use and breast tissue composition or breast tissue density measures, while adjusting for potential confounders. Analyses stratified by body mass index were also conducted. RESULTS: Longer duration of TT use was associated with increasing degrees of lobular atrophy (p < 0.001) but not fibrous content (p = 0.82). Every 6 months of TT was associated with decreasing amounts of epithelium (exp(ß) = 0.97, 95% CI 0.95,0.98, adj p = 0.005) and fibrous stroma (exp(ß) = 0.99, 95% CI 0.98,1.00, adj p = 0.05), but not fat (exp(ß) = 1.01, 95%CI 0.98,1.05, adj p = 0.39). The effect of TT on breast epithelium was attenuated in overweight/obese TMIs (exp(ß) = 0.98, 95% CI 0.95,1.01, adj p = 0.14). When comparing TT users versus non-users, TT users had 28% less epithelium (exp(ß) = 0.72, 95% CI 0.58,0.90, adj p = 0.003). There was no association between TT and radiologist's breast density assessment (p = 0.58) or LIBRA measurements (p > 0.05). CONCLUSIONS: TT decreases breast epithelium, but this effect is attenuated in overweight/obese TMIs. TT has the potential to affect the breast cancer risk of TMIs. Further studies are warranted to elucidate the effect of TT on breast density and breast cancer risk.

Associations between quantitative measures of mammographic density and terminal ductal lobular unit involution in Chinese breast cancer patients.

BACKGROUND: Higher mammographic density (MD), a radiological measure of the proportion of fibroglandular tissue in the breast, and lower terminal duct lobular unit (TDLU) involution, a histological measure of the amount of epithelial tissue in the breast, are independent breast cancer risk factors. Previous studies among predominantly white women have associated reduced TDLU involution with higher MD. METHODS: In this cohort of 611 invasive breast cancer patients (ages 23-91 years [58.4% ≥ 50 years]) from China, where breast cancer incidence rates are lower and the prevalence of dense breasts is higher compared with Western countries, we examined the associations between TDLU involution assessed in tumor-adjacent normal breast tissue and quantitative MD assessed in the contralateral breast obtained from the VolparaDensity software. Associations were estimated using generalized linear models with MD measures as the outcome variables (log-transformed), TDLU measures as explanatory variables (categorized into quartiles or tertiles), and adjusted for age, body mass index, parity, age at menarche and breast cancer subtype. RESULTS: We found that, among all women, percent dense volume (PDV) was positively associated with TDLU count (highest tertile vs. zero: Expbeta = 1.28, 95% confidence interval [CI] 1.08-1.51, ptrend = < .0001), TDLU span (highest vs. lowest tertile: Expbeta = 1.23, 95% CI 1.11-1.37, ptrend = < .0001) and acini count/TDLU (highest vs. lowest tertile: Expbeta = 1.22, 95% CI 1.09-1.37, ptrend = 0.0005), while non-dense volume (NDV) was inversely associated with these measures. Similar trend was observed for absolute dense volume (ADV) after the adjustment of total breast volume, although the associations for ADV were in general weaker than those for PDV. The MD-TDLU associations were generally more pronounced among breast cancer patients ≥ 50 years and those with luminal A tumors compared with patients < 50 years and with luminal B tumors. CONCLUSIONS: Our findings based on quantitative MD and TDLU involution measures among Chinese breast cancer patients are largely consistent with those reported in Western populations and may provide additional insights into the complexity of the relationship, which varies by age, and possibly breast cancer subtype.

Screening mammography performance according to breast density: a comparison between radiologists versus standalone intelligence detection.

BACKGROUND: Artificial intelligence (AI) algorithms for the independent assessment of screening mammograms have not been well established in a large screening cohort of Asian women. We compared the performance of screening digital mammography considering breast density, between radiologists and AI standalone detection among Korean women. METHODS: We retrospectively included 89,855 Korean women who underwent their initial screening digital mammography from 2009 to 2020. Breast cancer within 12 months of the screening mammography was the reference standard, according to the National Cancer Registry. Lunit software was used to determine the probability of malignancy scores, with a cutoff of 10% for breast cancer detection. The AI's performance was compared with that of the final Breast Imaging Reporting and Data System category, as recorded by breast radiologists. Breast density was classified into four categories (A-D) based on the radiologist and AI-based assessments. The performance metrics (cancer detection rate [CDR], sensitivity, specificity, positive predictive value [PPV], recall rate, and area under the receiver operating characteristic curve [AUC]) were compared across breast density categories. RESULTS: Mean participant age was 43.5 ± 8.7 years; 143 breast cancer cases were identified within 12 months. The CDRs (1.1/1000 examination) and sensitivity values showed no significant differences between radiologist and AI-based results (69.9% [95% confidence interval [CI], 61.7-77.3] vs. 67.1% [95% CI, 58.8-74.8]). However, the AI algorithm showed better specificity (93.0% [95% CI, 92.9-93.2] vs. 77.6% [95% CI, 61.7-77.9]), PPV (1.5% [95% CI, 1.2-1.9] vs. 0.5% [95% CI, 0.4-0.6]), recall rate (7.1% [95% CI, 6.9-7.2] vs. 22.5% [95% CI, 22.2-22.7]), and AUC values (0.8 [95% CI, 0.76-0.84] vs. 0.74 [95% CI, 0.7-0.78]) (all P < 0.05). Radiologist and AI-based results showed the best performance in the non-dense category; the CDR and sensitivity were higher for radiologists in the heterogeneously dense category (P = 0.059). However, the specificity, PPV, and recall rate consistently favored AI-based results across all categories, including the extremely dense category. CONCLUSIONS: AI-based software showed slightly lower sensitivity, although the difference was not statistically significant. However, it outperformed radiologists in recall rate, specificity, PPV, and AUC, with disparities most prominent in extremely dense breast tissue.

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.

Association of area- and volumetric-mammographic density and breast cancer risk in women of Asian descent: a case control study.

BACKGROUND: Mammographic density (MD) has been shown to be a strong and independent risk factor for breast cancer in women of European and Asian descent. However, the majority of Asian studies to date have used BI-RADS as the scoring method and none have evaluated area and volumetric densities in the same cohort of women. This study aims to compare the association of MD measured by two automated methods with the risk of breast cancer in Asian women, and to investigate if the association is different for premenopausal and postmenopausal women. METHODS: In this case-control study of 531 cases and 2297 controls, we evaluated the association of area-based MD measures and volumetric-based MD measures with breast cancer risk in Asian women using conditional logistic regression analysis, adjusting for relevant confounders. The corresponding association by menopausal status were assessed using unconditional logistic regression. RESULTS: We found that both area and volume-based MD measures were associated with breast cancer risk. Strongest associations were observed for percent densities (OR (95% CI) was 2.06 (1.42-2.99) for percent dense area and 2.21 (1.44-3.39) for percent dense volume, comparing women in highest density quartile with those in the lowest quartile). The corresponding associations were significant in postmenopausal but not premenopausal women (premenopausal versus postmenopausal were 1.59 (0.95-2.67) and 1.89 (1.22-2.96) for percent dense area and 1.24 (0.70-2.22) and 1.96 (1.19-3.27) for percent dense volume). However, the odds ratios were not statistically different by menopausal status [p difference = 0.782 for percent dense area and 0.486 for percent dense volume]. CONCLUSIONS: This study confirms the associations of mammographic density measured by both area and volumetric methods and breast cancer risk in Asian women. Stronger associations were observed for percent dense area and percent dense volume, and strongest effects were seen in postmenopausal individuals.

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.

Associations of stem cell markers CD44, CD24 and ALDH1A1 with mammographic breast density in women with benign breast biopsies.

BACKGROUND: We examined associations of CD44, CD24 and ALDH1A1 breast stem cell markers with mammographic breast density (MBD), a well-established breast cancer (BCa) risk factor. METHODS: We included 218 cancer-free women with biopsy-confirmed benign breast disease within the Nurses' Health Study (NHS) and NHSII. The data on BCa risk factors were obtained from biennial questionnaires. Immunohistochemistry (IHC) was done on tissue microarrays. For each core, the IHC expression was assessed using a semi-automated platform and expressed as percent of positively stained cells for each marker out of the total cell count. MBD was assessed with computer-assisted techniques. Generalised linear regression was used to examine the associations of each marker with square root-transformed percent density (PD), absolute dense and non-dense areas (NDA), adjusted for BCa risk factors. RESULTS: Stromal CD44 and ALDH1A1 expression was positively associated with PD (≥ 10% vs. <10% ß = 0.56, 95% confidence interval [CI] [0.06; 1.07] and ß = 0.81 [0.27; 1.34], respectively) and inversely associated with NDA (ß per 10% increase = -0.17 [-0.34; -0.01] and ß for ≥10% vs. <10% = -1.17 [-2.07; -0.28], respectively). Epithelial CD24 expression was inversely associated with PD (ß per 10% increase = -0.14 [-0.28; -0.01]. Stromal and epithelial CD24 expression was positively associated with NDA (ß per 10% increase = 0.35 [0.2 × 10-2; 0.70] and ß per 10% increase = 0.34 [0.11; 0.57], respectively). CONCLUSION: Expression of stem cell markers is associated with MBD.

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.

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.

False negative breast cancers on imaging and associated risk factors: a single institution six-year analysis.

PURPOSE: Mitigating false negative imaging studies remains an important issue given its association with worse morbidity and mortality in patients with breast cancer. We aimed to identify risk factors that predispose to false negative breast imaging exams. METHODS: In an IRB-approved, HIPAA compliant retrospective study, we identified all patients who were diagnosed with breast cancer within 365 days of a negative imaging study assessed as BI-RADS 1-3 between January 1, 2014 and January 31, 2020. A matched cohort based on mammographic breast density was created from randomly selected studies with BI-RADS 4-5 designation that yielded breast cancer at pathology within the same time frame. Patient and cancer characteristics, prior personal history of breast cancer and gene mutation status were collected from patient charts. Pearson chi-squared and Student's t-test on two independent groups with significance at < 0.05 was used for statistical analysis. RESULTS: We identified 155 false negative studies of 129 missed cancers and 128 breast density matched true positive cancers. False negative studies were screening mammograms in 57.42% (89/155), diagnostic mammograms in 29.68% (46/155), ultrasounds in 6.45% (10/155) and MRIs in 6.45% (10/155). Rates of personal (41.09% vs. 18.75%, p < 0.001) and family history of breast cancer (68.22% vs. 49.21%, p = 0.002) were higher in the false negative cohort and remained significant when asymptomatic MRI-detected cancers were removed. CONCLUSION: Our findings suggest that supplemental screening may be useful in breast cancer survivors.

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.

Impact of pre-operative MRI on surgical management of screening digital breast tomosynthesis-detected invasive lobular carcinoma.

PURPOSE: The purpose of this study is to determine the impact of pre-operative MRI on surgical management of screening digital breast tomosynthesis (DBT)-detected invasive lobular carcinoma (ILC). METHODS: A retrospective medical record analysis was conducted of women with screening DBT-detected ILC and subsequent surgery from 2017-2021. Clinical, imaging, and pathological features were compared between women who did and did not undergo MRI, and between women with and without additional disease detected on MRI, using the Pearson's chi-squared test and Wilcoxon signed-rank test. Concordance between imaging and surgical pathology sizes was also evaluated. RESULTS: Of 125 women (mean age 67 years, range 44-90) with screening-detected ILC, MRI was obtained in 62 women (49.6%) with a mean age of 63 years (range 45-80). Compared to women without MRI, women who had MRI examinations were younger, more likely to have dense breast tissue, and more likely to undergo mastectomy initially rather than lumpectomy (p < 0.001-0.01). Eighteen biopsies were performed based on MRI findings, of which 55.6% (10/18) were malignant. Conventional imaging more frequently underestimated ILC span at the biopsy site than MRI, using a 25% threshold difference (17.5% [7/40] versus 58.5% [24/41], p < 0.001). MRI detected more extensive disease at the biopsy site in six patients (9.7%, 6/62), additional ipsilateral disease in six patients (9.7%, 6/62), and contralateral disease in one patient (1.6%, 1/62). MRI therefore impacted surgical management in 21.0% (13/62) of patients. CONCLUSION: MRI led to the detection of additional disease, thus impacting surgical management, in one-fifth of patients with ILC.

Patient Characteristics Impact Performance of AI Algorithm in Interpreting Negative Screening Digital Breast Tomosynthesis Studies.

Background Artificial intelligence (AI) is increasingly used to manage radiologists' workloads. The impact of patient characteristics on AI performance has not been well studied. Purpose To understand the impact of patient characteristics (race and ethnicity, age, and breast density) on the performance of an AI algorithm interpreting negative screening digital breast tomosynthesis (DBT) examinations. Materials and Methods This retrospective cohort study identified negative screening DBT examinations from an academic institution from January 1, 2016, to December 31, 2019. All examinations had 2 years of follow-up without a diagnosis of atypia or breast malignancy and were therefore considered true negatives. A subset of unique patients was randomly selected to provide a broad distribution of race and ethnicity. DBT studies in this final cohort were interpreted by a U.S. Food and Drug Administration-approved AI algorithm, which generated case scores (malignancy certainty) and risk scores (1-year subsequent malignancy risk) for each mammogram. Positive examinations were classified based on vendor-provided thresholds for both scores. Multivariable logistic regression was used to understand relationships between the scores and patient characteristics. Results A total of 4855 patients (median age, 54 years [IQR, 46-63 years]) were included: 27% (1316 of 4855) White, 26% (1261 of 4855) Black, 28% (1351 of 4855) Asian, and 19% (927 of 4855) Hispanic patients. False-positive case scores were significantly more likely in Black patients (odds ratio [OR] = 1.5 [95% CI: 1.2, 1.8]) and less likely in Asian patients (OR = 0.7 [95% CI: 0.5, 0.9]) compared with White patients, and more likely in older patients (71-80 years; OR = 1.9 [95% CI: 1.5, 2.5]) and less likely in younger patients (41-50 years; OR = 0.6 [95% CI: 0.5, 0.7]) compared with patients aged 51-60 years. False-positive risk scores were more likely in Black patients (OR = 1.5 [95% CI: 1.0, 2.0]), patients aged 61-70 years (OR = 3.5 [95% CI: 2.4, 5.1]), and patients with extremely dense breasts (OR = 2.8 [95% CI: 1.3, 5.8]) compared with White patients, patients aged 51-60 years, and patients with fatty density breasts, respectively. Conclusion Patient characteristics influenced the case and risk scores of a Food and Drug Administration-approved AI algorithm analyzing negative screening DBT examinations. © RSNA, 2024.

Comparison of Mammography and Mammography with Supplemental Whole-Breast US Tomography for Cancer Detection in Patients with Dense Breasts.

BACKGROUND: Women with dense breasts benefit from supplemental cancer screening with US, but US has low specificity. PURPOSE: To evaluate the performance of breast US tomography (UST) combined with full-field digital mammography (FFDM) compared with FFDM alone for breast cancer screening in women with dense breasts. MATERIALS AND METHODS: This retrospective multireader multicase study included women with dense breasts who underwent FFDM and UST at 10 centers between August 2017 and October 2019 as part of a prospective case collection registry. All patients in the registry with cancer were included; patients with benign biopsy or negative follow-up imaging findings were randomly selected for inclusion. Thirty-two Mammography Quality Standards Act-qualified radiologists independently evaluated FFDM followed immediately by FFDM plus UST for suspicious findings and assigned a Breast Imaging Reporting and Data System (BI-RADS) category. The superiority of FFDM plus UST versus FFDM alone for cancer detection (assessed with area under the receiver operating characteristic curve [AUC]), BI-RADS 4 sensitivity, and BI-RADS 3 sensitivity and specificity were evaluated using the two-sided significance level of α = .05. Noninferiority of BI-RADS 4 specificity was evaluated at the one-sided significance level of α = .025 with a -10% margin. RESULTS: Among 140 women (mean age, 56 years ±10 [SD]; 36 with cancer, 104 without), FFDM plus UST achieved superior performance compared with FFDM alone (AUC, 0.60 [95% CI: 0.51, 0.69] vs 0.54 [95% CI: 0.45, 0.64]; P = .03). For FFDM plus UST versus FFDM alone, BI-RADS 4 mean sensitivity was superior (37% [428 of 1152] vs 30% [343 of 1152]; P = .03) and BI-RADS 4 mean specificity was noninferior (82% [2741 of 3328] vs 88% [2916 of 3328]; P = .004). For FFDM plus UST versus FFDM, no difference in BI-RADS 3 mean sensitivity was observed (40% [461 of 1152] vs 33% [385 of 1152]; P = .08), but BI-RADS 3 mean specificity was superior (75% [2491 of 3328] vs 69% [2299 of 3328]; P = .04). CONCLUSION: In women with dense breasts, FFDM plus UST improved cancer detection by radiologists versus FFDM alone. Clinical trial registration nos. NCT03257839 and NCT04260620 Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Mann in this issue.

Screening Outcomes of Mammography with AI in Dense Breasts: A Comparative Study with Supplemental Screening US.

Background Comparative performance between artificial intelligence (AI) and breast US for women with dense breasts undergoing screening mammography remains unclear. Purpose To compare the performance of mammography alone, mammography with AI, and mammography plus supplemental US for screening women with dense breasts, and to investigate the characteristics of the detected cancers. Materials and Methods A retrospective database search identified consecutive asymptomatic women (≥40 years of age) with dense breasts who underwent mammography plus supplemental whole-breast handheld US from January 2017 to December 2018 at a primary health care center. Sequential reading for mammography alone and mammography with the aid of an AI system was conducted by five breast radiologists, and their recall decisions were recorded. Results of the combined mammography and US examinations were collected from the database. A dedicated breast radiologist reviewed marks for mammography alone or with AI to confirm lesion identification. The reference standard was histologic examination and 1-year follow-up data. The cancer detection rate (CDR) per 1000 screening examinations, sensitivity, specificity, and abnormal interpretation rate (AIR) of mammography alone, mammography with AI, and mammography plus US were compared. Results Among 5707 asymptomatic women (mean age, 52.4 years ± 7.9 [SD]), 33 (0.6%) had cancer (median lesion size, 0.7 cm). Mammography with AI had a higher specificity (95.3% [95% CI: 94.7, 95.8], P = .003) and lower AIR (5.0% [95% CI: 4.5, 5.6], P = .004) than mammography alone (94.3% [95% CI: 93.6, 94.8] and 6.0% [95% CI: 5.4, 6.7], respectively). Mammography plus US had a higher CDR (5.6 vs 3.5 per 1000 examinations, P = .002) and sensitivity (97.0% vs 60.6%, P = .002) but lower specificity (77.6% vs 95.3%, P < .001) and higher AIR (22.9% vs 5.0%, P < .001) than mammography with AI. Supplemental US alone helped detect 12 cancers, mostly stage 0 and I (92%, 11 of 12). Conclusion Although AI improved the specificity of mammography interpretation, mammography plus supplemental US helped detect more node-negative early breast cancers that were undetected using mammography with AI. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Whitman and Destounis in this issue.

Performance of Supplemental US Screening in Women with Dense Breasts and Varying Breast Cancer Risk: Results from the Breast Cancer Surveillance Consortium.

Background It is unclear whether breast US screening outcomes for women with dense breasts vary with levels of breast cancer risk. Purpose To evaluate US screening outcomes for female patients with dense breasts and different estimated breast cancer risk levels. Materials and Methods This retrospective observational study used data from US screening examinations in female patients with heterogeneously or extremely dense breasts conducted from January 2014 to October 2020 at 24 radiology facilities within three Breast Cancer Surveillance Consortium (BCSC) registries. The primary outcomes were the cancer detection rate, false-positive biopsy recommendation rate, and positive predictive value of biopsies performed (PPV3). Risk classification of participants was performed using established BCSC risk prediction models of estimated 6-year advanced breast cancer risk and 5-year invasive breast cancer risk. Differences in high- versus low- or average-risk categories were assessed using a generalized linear model. Results In total, 34 791 US screening examinations from 26 489 female patients (mean age at screening, 53.9 years ± 9.0 [SD]) were included. The overall cancer detection rate per 1000 examinations was 2.0 (95% CI: 1.6, 2.4) and was higher in patients with high versus low or average risk of 6-year advanced breast cancer (5.5 [95% CI: 3.5, 8.6] vs 1.3 [95% CI: 1.0, 1.8], respectively; P = .003). The overall false-positive biopsy recommendation rate per 1000 examinations was 29.6 (95% CI: 22.6, 38.6) and was higher in patients with high versus low or average 6-year advanced breast cancer risk (37.0 [95% CI: 28.2, 48.4] vs 28.1 [95% CI: 20.9, 37.8], respectively; P = .04). The overall PPV3 was 6.9% (67 of 975; 95% CI: 5.3, 8.9) and was higher in patients with high versus low or average 6-year advanced cancer risk (15.0% [15 of 100; 95% CI: 9.9, 22.2] vs 4.9% [30 of 615; 95% CI: 3.3, 7.2]; P = .01). Similar patterns in outcomes were observed by 5-year invasive breast cancer risk. Conclusion The cancer detection rate and PPV3 of supplemental US screening increased with the estimated risk of advanced and invasive breast cancer. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Helbich and Kapetas in this issue.

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.