Breast Density Measurement Research Articles

AI-based selection of individuals for supplemental MRI in population-based breast cancer screening: the randomized ScreenTrustMRI trial

Screening mammography reduces breast cancer mortality, but studies analyzing interval cancers diagnosed after negative screens have shown that many cancers are missed. Supplemental screening using magnetic resonance imaging (MRI) can reduce the number of missed cancers. However, as qualified MRI staff are lacking, the equipment is expensive to purchase and cost-effectiveness for screening may not be convincing, the utilization of MRI is currently limited. An effective method for triaging individuals to supplemental MRI screening is therefore needed. We conducted a randomized clinical trial, ScreenTrustMRI, using a recently developed artificial intelligence (AI) tool to score each mammogram. We offered trial participation to individuals with a negative screening mammogram and a high AI score (top 6.9%). Upon agreeing to participate, individuals were assigned randomly to one of two groups: those receiving supplemental MRI and those not receiving MRI. The primary endpoint of ScreenTrustMRI is advanced breast cancer defined as either interval cancer, invasive component larger than 15 mm or lymph node positive cancer, based on a 27-month follow-up time from the initial screening. Secondary endpoints, prespecified in the study protocol to be reported before the primary outcome, include cancer detected by supplemental MRI, which is the focus of the current paper. Compared with traditional breast density measures used in a previous clinical trial, the current AI method was nearly four times more efficient in terms of cancers detected per 1,000 MRI examinations (64 versus 16.5). Most additional cancers detected were invasive and several were multifocal, suggesting that their detection was timely. Altogether, our results show that using an AI-based score to select a small proportion (6.9%) of individuals for supplemental MRI after negative mammography detects many missed cancers, making the cost per cancer detected comparable with screening mammography. ClinicalTrials.gov registration: NCT04832594.

The Prospective Association between Early Life Growth and Breast Density in Young Adult Women.

Breast density is a strong intermediate endpoint to investigate the association between early-life exposures and breast cancer risk. This study investigates the association between early-life growth and breast density in young adult women measured using Optical Breast Spectroscopy (OBS) and Dual X-ray Absorptiometry (DXA). OBS measurements were obtained for 536 female Raine Cohort Study participants at ages 27-28, with 268 completing DXA measurements. Participants with three or more height and weight measurements from ages 8 to 22 were used to generate linear growth curves for height, weight and body mass index (BMI) using SITAR modelling. Three growth parameters (size, velocity and timing) were examined for association with breast density measures, adjusting for potential confounders. Women who reached their peak height rapidly (velocity) and later in adolescence (timing) had lower OBS-breast density. Overall, women who were taller (size) had higher OBS-breast density. For weight, women who grew quickly (velocity) and later in adolescence (timing) had higher absolute DXA-breast density. Overall, weight (size) was also inversely associated with absolute DXA-breast density, as was BMI. These findings provide new evidence that adolescent growth is associated with breast density measures in young adult women, suggesting potential mediation pathways for breast cancer risk in later life.

Association of mammographic breast density measurements and hormone receptor status of breast cancer.

Breast cancer is the most frequent cancer in women, with significant mortality. Mammography is a routine investigation for breast disease. A known risk factor for breast cancer is increased breast density. Here, we tried to observe if mammographic density also affects the hormone receptor status of breast cancer, which will help in the understanding of the biological mechanisms of breast cancer development. Suspected breast cancer patients at Lok Nayak Hospital, Delhi, underwent mammography in the Department of Radiodiagnosis. The density of breast contralateral to the mass was assessed using Hologic Quantra software version 2.1.1 [Area Breast Density(ABD)]. The hormone receptor status of all the tumours was recorded on histopathology. Of these, 100 confirmed cases were included in the study. ER-positive, PR-positive, and HER2-positive tumours were seen in 41%, 33%, and 34% patients, respectively. Regarding ER receptor status, the mean ABD for positive and negative tumours was 27% and 23%, respectively, p-value = 0.01, showing significant relation between them. Mean ABD for HER2-positive and -negative tumours was 25% and 24%, respectively, p-value = 0.75. Mean ABD for PR-positive and PR-negative tumours was 23% and 25%, respectively, p-value = 0.42 (not significant). We found that ER-positive tumours were common in dense breasts, which was statistically significant. However, this was not true for PR and HER2 receptor status. Limited studies have been done to study MD using computerised software and its effect on hormone receptor status, with conflicting results. Further, large, multicentric studies can be useful in understanding the mechanism and providing better treatment for breast cancer patients.

Abstract PS10-06: Contribution of a breast cancer polygenic risk score to mammography artificial intelligence models and breast density for long term breast cancer risk prediction

Abstract Artificial intelligence (AI) models based on deep learning of mammogram images have been developed for lesion detection and diagnosis as well as breast cancer (BC) risk. We and others have shown that these AI models, coupled with breast density, predict long-term breast cancer risk. Common germline genetic variation, in particular a polygenic risk score (PRS) that captures the cumulative impact of hundreds of common risk variants, has been shown associated with 3-fold increased BC risk. The BC-PRS may complement imaging risk factors to improve identification of women at high BC risk and better target prevention strategies. We evaluated the contribution of a BC-PRS to two imaging-based AI models (Transpara AI cancer detection system and Mirai 5-year risk) and breast density measures to long-term BC risk. We hypothesized that the BC-PRS will improve long-term risk prediction above AI and density measures. We examined this hypothesis within a cohort of 10,271 women enrolled to the Mayo Clinic Biobank between 2009 and 2015. Eligible women had no prior cancer, were ages 35 to 85, had a full-field digital screening mammogram within two years of enrollment and genotyping by Regeneron. Incident invasive and DCIS BC was identified through the Mayo tumor registry and follow-up questionnaires, and person years were calculated from date of mammogram through date of BC, last mammogram at Mayo or last questionnaire. We estimated Transpara AI malignancy score (1-10), 5-year Mirai risk and Volpara volumetric density measures (volumetric percent density, dense volume) on screening mammograms at enrollment and obtained clinical BI-RADS density. We calculated the BC-PRS using 268 SNPs (of the 313-SNP score) and the weighted sum of the SNPs using odds ratios from the largest BC GWAS of European ancestry to date. We performed cox proportional hazards regression adjusting for age and BMI to estimate hazard ratios (HRs), 95% confidence intervals (CI), and C-statistics (AUC) to describe the contribution of BC-PRS (per standard deviation (SD)) to the association of AI scores and breast density with BC risk. Likelihood ratio tests (LRT) and bootstrapping were used to compare model performance with vs. without inclusion of BC-PRS, at 10-years follow-up. Comparisons to the Tyrer-Cuzick model are ongoing. Over a median of 9.1 years (Interquartile range, 6.8 to 10.6), 446 incident BC were identified of which 285 invasive and 103 DCIS were confirmed by pathology report (60 were self-reported); 399 (256 invasive) occurred within 10 years and were used in primary analyses. Transpara score [HR per 1 unit=1.18 (1.14, 1.23), AUC=0.655 (0.628, 0.682)] and Mirai risk [HR per SD log risk=1.54 (1.43, 1.65), AUC=0.681 (0.655, 0.706)] were both associated with BC. All three breast density measures were also significantly associated with BC, including in models with AI scores (Table). Addition of the BC-PRS to models with AI scores and breast density improved both model fit (All PLRT < 0.001; Table) and discrimination, with increases in AUC ranging from 0.033 to 0.034 for models with Transpara and density and 0.025 to 0.026 for models with Mirai and density (all P-values < 0.001; Table). Analyses restricted to invasive BC showed identical results. But, the BC-PRS contribution at 5-year follow-up was attenuated. In this cohort study, we found BC-PRS contributed to long-term risk prediction of BC above imaging AI models and breast density. Imaging and genetic risk factors are complementary and may outperform existing risk models and provide more accurate risk assessment to aid in precision prevention strategies. Citation Format: Celine Vachon, Christopher Scott, Imon Banerjee, Ramon Correa Moderno, Dan Hursh, Matthew Jensen, Yvonne Wang, Kathleen Brandt, Karla Kerlikowske, Sandhya Pruthi, Fergus Couch, Stacey Winham. Contribution of a breast cancer polygenic risk score to mammography artificial intelligence models and breast density for long term breast cancer risk prediction [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS10-06.

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

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. 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. 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). 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. GOV : NCT02980848 registered December 2, 2016.

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

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. 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 709mL 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. Virtual phantom VBD measurements exhibited a strong correlation (Pearson's ) 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 =0.68). 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.

Evaluation of Automated Mammographic Density Classification in Tomosynthesis: Comparison with Radiologists

Objective: Breast cancer screening is a valuable field of health research conducted through mammography. However, mammography evaluation is the examination with the most frequent lack of to agrement among radiologists. In this study we aimed to show the compatibility of mammographic density classification with a new software, Bellus Breast Density Measurement Software (Option), with visual examination. Methods: The mammographic density classification of 500 patients was retrospectively determined by five radiologists with varying levels of experience, according to the 5th version of the breast imaging reporting and data system (BIRADS). The mean age of 500 women included in the study was calculated as 53.8±10.08. The obtained data were compared with the Bellus software mammographic density classification of the same patients. Then, the visual evaluation and the compatibility of the Bellus software and the readers were compared. Results: The agreement between the Bellus software and all five readers was poor (kappa value 0.07-0.12). The agreement of the readers with each other is moderate-good (kappa value 0.054-0.64). The Intraclass Correlation Coefficient (ICC) value for the five separate readers was calculated to be 0.80, indicating good compatibility, while the ICC value for the Bellus software with the five separate readers was calculated to be 0.74, indicating moderate compatibility. The Friedman test revealed that while the mammographic density classification of each reader remained consistent, the classification provided by the Bellus software differed. Conclusion: Bellus Breast Density Measurement Software (Option) diagnostic accuracy is lower than visual examination. We recommend that the manufacturer develop the software.

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

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. Breast density measures were estimated for 1,961 Australian women aged 18-97years 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. The mean age was 38years (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. 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.

Variability in Breast Density Estimation and Its Impact on Breast Cancer Risk Assessment.

Breast density is an independent risk factor for breast cancer, although variability exists in measurements. This study sought to evaluate the agreement between radiologists and automated breast density assessment software and assess the impact of breast density measures on breast cancer risk estimates using the Breast Cancer Surveillance Consortium (BCSC) model (v.2). A retrospective database search identified women who had undergone mammography between December 2021 and June 2022. The Breast Imaging Reporting and Data System (BI-RADS) breast composition index assigned by a radiologist (R) was recorded and analyzed using three commercially available software programs (S1, S2, and S3). The agreement rate and Cohen's kappa (κ) were used to evaluate inter-rater agreements concerning breast density measures. The 5-year risk of invasive breast cancer in women was calculated using the BCSC model (v.2) with breast density inputs from various density estimation methods. Absolute differences in risk between various density measurements were evaluated. Overall, 1,949 women (mean age, 53.2 years) were included. The inter-rater agreement between R, S1, and S2 was 75.0-75.6%, while that between S3 and the others was 60.2%-63.3%. Kappa was substantial between R, S1, and S2 (0.66-0.68), and moderate (0.49-0.50) between S3 and the others. S3 placed fewer women in mammographic density d (14.9%) than R, S1, and S2 (40.5%-44.0%). In BCSC risk assessment (v.2), S3 assessed fewer women with a high 5-year risk of invasive breast cancer than the other methods, resulting in an absolute difference of 0% between R, S1, and S2 in 75.0%-75.6% of cases, whereas the difference between S3 and the other methods occurs in 60.2%-63.3% of cases. Breast density assessment using various methods showed moderate-to-substantial agreement, potentially affecting risk assessments. Precise and consistent breast density measurements may lead to personalized and effective strategies for breast cancer prevention.

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

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. 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. 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). Reproductive choices, particularly those related to childbearing and oral contraceptive use, may contribute to racial and ethnic variation in breast density.

Practical Challenges of DBT-Guided VABB: Harms and Benefits, from Literature to Clinical Experience.

Vacuum-assisted breast biopsy (VABB) guided by digital breast tomosynthesis (DBT) represents one of the best instruments to obtain a histological diagnosis of suspicious lesions with no ultrasound correlation or those which are visible only on DBT. After a review of the literature, we retrospectively analyzed the DBT-guided VABBs performed from 2019 to 2022 at our department. Descriptive statistics, Pearson's correlation and χ2 test were used to compare distributions of age, breast density (BD) and early performance measures including histopathology. We used kappa statistics to evaluate the agreement between histological assessment and diagnosis. Finally, we compared our experience to the literature to provide indications for clinical practice. We included 85 women aged 41-84 years old. We identified 37 breast cancers (BC), 26 stage 0 and 11 stage IA. 67.5% of BC was diagnosed in women with high BD. The agreement between VABB and surgery was 0.92 (k value, 95% CI: 0.76-1.08). We found a statistically significant inverse correlation between age and BD. The post-procedural clip was correctly positioned in 88.2%. The post-procedural hematoma rate was 14.1%. No infection or hemorrhage were recorded. When executed correctly, DBT-guided VABB represents a safe and minimally invasive technique with high histopathological concordance, for detecting nonpalpable lesions without ultrasound correlation.

Development and evaluation of a method to assess breast cancer risk using a longitudinal history of mammographic density: a cohort study

BackgroundWomen with dense breasts have an increased risk of breast cancer. However, breast density is measured with variability, which may reduce the reliability and accuracy of its association with breast cancer risk. This is particularly relevant when visually assessing breast density due to variation in inter- and intra-reader assessments. To address this issue, we developed a longitudinal breast density measure which uses an individual woman’s entire history of mammographic density, and we evaluated its association with breast cancer risk as well as its predictive ability.MethodsIn total, 132,439 women, aged 40–73 yr, who were enrolled in Kaiser Permanente Washington and had multiple screening mammograms taken between 1996 and 2013 were followed up for invasive breast cancer through 2014. Breast Imaging Reporting and Data System (BI-RADS) density was assessed at each screen. Continuous and derived categorical longitudinal density measures were developed using a linear mixed model that allowed for longitudinal density to be updated at each screen. Predictive ability was assessed using (1) age and body mass index-adjusted hazard ratios (HR) for breast density (time-varying covariate), (2) likelihood-ratio statistics (ΔLR-χ2) and (3) concordance indices.ResultsIn total, 2704 invasive breast cancers were diagnosed during follow-up (median = 5.2 yr; median mammograms per woman = 3). When compared with an age- and body mass index-only model, the gain in statistical information provided by the continuous longitudinal density measure was 23% greater than that provided by BI-RADS density (follow-up after baseline mammogram: ΔLR-χ2 = 379.6 (degrees of freedom (df) = 2) vs. 307.7 (df = 3)), which increased to 35% (ΔLR-χ2 = 251.2 vs. 186.7) for follow-up after three mammograms (n = 76,313, 2169 cancers). There was a sixfold difference in observed risk between densest and fattiest eight-category longitudinal density (HR = 6.3, 95% CI 4.7–8.7), versus a fourfold difference with BI-RADS density (HR = 4.3, 95% CI 3.4–5.5). Discriminatory accuracy was marginally greater for longitudinal versus BI-RADS density (c-index = 0.64 vs. 0.63, mean difference = 0.008, 95% CI 0.003–0.012).ConclusionsEstimating mammographic density using a woman’s history of breast density is likely to be more reliable than using the most recent observation only, which may lead to more reliable and accurate estimates of individual breast cancer risk. Longitudinal breast density has the potential to improve personal breast cancer risk estimation in women attending mammography screening.

Time-specific impact of mono-benzyl phthalate (MBzP) and perfluorooctanoic acid (PFOA) on breast density of a Chilean adolescent Cohort

IntroductionHigh mammographic density is among the strongest and most established predictors for breast cancer risk. Puberty, the period during which breasts undergo exponential mammary growth, is considered one of the critical stages of breast development for environmental exposures. Benzylbutyl phthalate (BBP) and perfluorooctanoic acid (PFOA) are pervasive endocrine disrupting chemicals that may increase hormone-sensitive cancers. Evaluating the potential impact of BBP and PFOA exposure on pubertal breast density is important to our understanding of early-life environmental influences on breast cancer etiology. ObjectiveTo prospectively assess the effect of biomarker concentrations of monobenzyl phthalate (MBzP) and PFOA at specific pubertal window of susceptibility (WOS) on adolescent breast density. MethodThis study included 376 Chilean girls from the Growth and Obesity Cohort Study with data collection at four timepoints: Tanner breast stages 1 (B1) and 4 (B4), 1- year post- menarche (1YPM) and 2-years post-menarche (2YPM). Dual-energy X-ray absorptiometry was used to assess the absolute fibroglandular volume (FGV) and percent breast density (%FGV) at 2YPM. We used concentrations of PFOA in serum and MBzP in urine as an index of exposure to PFOA and BBP, respectively. Parametric G-formula was used to estimate the time-specific effects of MBzP and PFOA on breast density. The models included body fat percentage as a time-varying confounder and age, birthweight, age at menarche, and maternal education as fixed covariates. ResultsA doubling of serum PFOA concentration at B4 resulted in a non-significant increase in absolute FGV (β:11.25, 95% confidence interval (CI): −0.28, 23.49)), while a doubling of PFOA concentration at 1YPM resulted in a decrease in % FGV (β:-4.61, 95% CI: −7.45, −1.78). We observed no associations between urine MBzP and breast density measures. ConclusionIn this cohort of Latina girls, PFOA serum concentrations corresponded to a decrease in % FGV. No effect was observed between MBzP and breast density measures across pubertal WOS.

3 QUESTIONS ON . . . Using Artificial Intelligence in Breast Cancer Risk Prediction

Celine Vachon, PhD: Celine Vachon, PhDA rtificial intelligence (AI) technology is making headlines in many areas of research and sectors of business. Now, epidemiologists and others who study cancer screening methodology say they've found new evidence it could improve breast cancer detection efforts, too. In a study published in the Journal of Clinical Oncology (JCO), researchers found that using an AI algorithm designed to improve breast cancer detection on mammograms could improve breast cancer risk prediction, including interval and advanced cancers—particularly when combined with consideration of breast density (2023; doi: 10.1200/JCO.22.01153). “This adds further evidence to support computer-aided imaging assessments and will help motivate prospective studies to disentangle improved earlier diagnosis from an improved risk model for subsequent cancer,” Paul H. Frankel, PhD, JCO Associate Editor, wrote in a relevance statement published with the article. “Such prospective studies will also provide estimates that can more easily guide clinical practice.” In an interview with Oncology Times, the study's lead author, Celine Vachon, PhD, Professor and Consultant in the Division of Epidemiology within the Department of Quantitative Health Sciences at Mayo Clinic, shared more about the work. 1 What were the key findings from this research? “We evaluated whether the AI measure assessed on mammograms taken years prior to the cancer (2-5.5 years prior to the cancer diagnosis) contributed additional information to breast density for risk of breast cancer—in particular interval and advanced cancers. The AI algorithm was developed to be used on all women, not just those with dense breasts. It was run on the mammograms from women in our clinical practices who did and did not develop breast cancer. “AI score assessed on mammograms 2-5.5 years before cancer diagnosis was associated with invasive breast cancer, and similarly predictive of interval and advanced cancers in dense and non-dense breasts. These findings imply that the AI score is robust for long-term risk prediction by severity of cancer and independent of breast density for predicting cancer. “Furthermore, the AI score improved prediction of all cancer types in models with breast density measures. AI imaging algorithms coupled with breast density independently contribute to long-term risk prediction of invasive breast cancers, including advanced cancers.” 2 What are the implications of these findings and how might they improve real-world screening efforts? “Data from our study and others suggests that AI algorithms will help improve both breast cancer detection and breast cancer risk prediction, including interval cancers and advanced cancers. These will likely help better identify women for supplemental screening and, potentially, for identification of women who would benefit from early interventions. “The AI algorithm we used is commercially available. And there are several similar AI algorithms from multiple vendors available that likely would provide similar findings. However, it will be important to understand how these algorithms work among women from various races, BMI (those with obesity, for example), and how they contribute to factors already known for risk prediction (such as genetic factors). Also, these algorithms need to be able to identify prognostically poor cancers, not just DCIS or early cancers that may not progress. This research is underway by many groups to inform how best to use these algorithms in the clinical setting.” 3 What is the next step of this work? “We are pursuing additional research to understand the imaging factors that determine the high-risk score, including calcifications. We are also examining other AI models, in particular those designed for longer-term risk, and how the combination of these algorithms with genetic factors and breast density can better inform future risk. The bottom line that practicing oncologists and cancer care providers need to know now is that this research confirms the importance of both breast density and AI models to predict invasive breast cancer, including both interval and advanced cancers. Our data suggest that this AI algorithm is not only relevant at the time of cancer [for detection], but also 2-5.5 years prior to the breast cancer.”

Impact of Artificial Intelligence System and Volumetric Density on Risk Prediction of Interval, Screen-Detected, and Advanced Breast Cancer.

Artificial intelligence (AI) algorithms improve breast cancer detection on mammography, but their contribution to long-term risk prediction for advanced and interval cancers is unknown. We identified 2,412 women with invasive breast cancer and 4,995 controls matched on age, race, and date of mammogram, from two US mammography cohorts, who had two-dimensional full-field digital mammograms performed 2-5.5 years before cancer diagnosis. We assessed Breast Imaging Reporting and Data System density, an AI malignancy score (1-10), and volumetric density measures. We used conditional logistic regression to estimate odds ratios (ORs), 95% CIs, adjusted for age and BMI, and C-statistics (AUC) to describe the association of AI score with invasive cancer and its contribution to models with breast density measures. Likelihood ratio tests (LRTs) and bootstrapping methods were used to compare model performance. On mammograms between 2-5.5 years prior to cancer, a one unit increase in AI score was associated with 20% greater odds of invasive breast cancer (OR, 1.20; 95% CI, 1.17 to 1.22; AUC, 0.63; 95% CI, 0.62 to 0.64) and was similarly predictive of interval (OR, 1.20; 95% CI, 1.13 to 1.27; AUC, 0.63) and advanced cancers (OR, 1.23; 95% CI, 1.16 to 1.31; AUC, 0.64) and in dense (OR, 1.18; 95% CI, 1.15 to 1.22; AUC, 0.66) breasts. AI score improved prediction of all cancer types in models with density measures (PLRT values < .001); discrimination improved for advanced cancer (ie, AUC for dense volume increased from 0.624 to 0.679, Δ AUC 0.065, P = .01) but did not reach statistical significance for interval cancer. AI imaging algorithms coupled with breast density independently contribute to long-term risk prediction of invasive breast cancers, in particular, advanced cancer.

Longitudinal Analysis of Change in Mammographic Density in Each Breast and Its Association With Breast Cancer Risk

Although breast density is an established risk factor for breast cancer, longitudinal changes in breast density have not been extensively studied to determine whether this factor is associated with breast cancer risk. To prospectively evaluate the association between change in mammographic density in each breast over time and risk of subsequent breast cancer. This nested case-control cohort study was sampled from the Joanne Knight Breast Health Cohort of 10 481 women free from cancer at entry and observed from November 3, 2008, to October 31, 2020, with routine screening mammograms every 1 to 2 years, providing a measure of breast density. Breast cancer screening was provided for a diverse population of women in the St Louis region. A total of 289 case patients with pathology-confirmed breast cancer were identified, and approximately 2 control participants were sampled for each case according to age at entry and year of enrollment, yielding 658 controls with a total number of 8710 craniocaudal-view mammograms for analysis. Exposures included screening mammograms with volumetric percentage of density, change in volumetric breast density over time, and breast biopsy pathology-confirmed cancer. Breast cancer risk factors were collected via questionnaire at enrollment. Longitudinal changes over time in each woman's volumetric breast density by case and control status. The mean (SD) age of the 947 participants was 56.67 (8.71) years at entry; 141 were Black (14.9%), 763 were White (80.6%), 20 were of other race or ethnicity (2.1%), and 23 did not report this information (2.4%). The mean (SD) interval was 2.0 (1.5) years from last mammogram to date of subsequent breast cancer diagnosis (10th percentile, 1.0 year; 90th percentile, 3.9 years). Breast density decreased over time in both cases and controls. However, there was a significantly slower decrease in rate of decline in density in the breast that developed breast cancer compared with the decline in controls (estimate = 0.027; 95% CI, 0.001-0.053; P = .04). This study found that the rate of change in breast density was associated with the risk of subsequent breast cancer. Incorporation of longitudinal changes into existing models could optimize risk stratification and guide more personalized risk management.

Quantitative background parenchymal enhancement and fibro-glandular density at breast MRI: Association with BRCA status.

To investigate whether MRI-based measurements of fibro-glandular tissue volume, breast density (MRBD), and background parenchymal enhancement (BPE) could be used to stratify two cohorts of healthy women: BRCA carriers and women at population risk of breast cancer. Pre-menopausal women aged 40-50years old were scanned at 3T, employing a standard breast protocol including a DCE-MRI (35 and 30 participants in high- and low-risk groups, respectively). The dynamic range of the DCE protocol was characterised and both breasts were masked and segmented with minimal user input to produce measurements of fibro-glandular tissue volume, MRBD, and voxelwise BPE. Statistical tests were performed to determine inter- and intra-user repeatability, evaluate the symmetry between metrics derived from left and right breasts, and investigate MRBD and BPE differences between the high- and low-risk cohorts. Intra- and inter-user reproducibility in estimates of fibro-glandular tissue volume, MRBD, and median BPE estimations were good, with coefficients of variation < 15%. Coefficients of variation between left and right breasts were also low (< 25%). There were no significant correlations between fibro-glandular tissue volume, MRBD, and BPE for either risk group. However, the high-risk group had higher BPE kurtosis, although linear regression analysis did not reveal significant associations between BPE kurtosis and breast cancer risk. This study found no significant differences or correlations in fibro-glandular tissue volume, MRBD, or BPE metrics between the two groups of women with different levels of breast cancer risk. However, the results support further investigation into the heterogeneity of parenchymal enhancement. • A semi-automated method enabled quantitative measurements of fibro-glandular tissue volume, breast density, and background parenchymal enhancement with minimal user intervention. • Background parenchymal enhancement was quantified over the entire parenchyma, segmented in pre-contrast images, thus avoiding region selection. • No significant differences and correlations in fibro-glandular tissue volume, breast density, and breast background parenchymal enhancement were found between two cohorts of women at high and low levels of breast cancer risk.

Associations of age, body mass index, and breast size with mammographic breast density in Korean women

We aimed (a) to investigate the associations between age, body mass index (BMI), and breast size with mammographic density based on the breast imaging reporting and data system (BI-RADS) and volumetric breast density measurement (VBDM) with Volpara, (b) to evaluate the associations of age, BMI, and breast size with fibroglandular tissue volume (FGV), and (c) to demonstrate the association of mammographic density grade with FGV. From April 2012 to May 2012, 1,203 women consecutively underwent mammography, and their breast density was calculated using the density grade and volume determined by Volpara. In total, 427 women were included in this study. The BMI and breast size of the 427 women were determined. The associations between mammographic density and age, BMI, and bra cup size were assessed. In addition, the associations between FGV and age, BMI, bra cup size, and mammographic density were assessed. The mean age of the women was 51 years (range, 27−83). Age was associated with mammographic density based on BI-RADS (&lt;i&gt;P&lt;/i&gt;&lt;0.0001), and both age and BMI were associated with mammographic density based on Volpara (&lt;i&gt;P&lt;/i&gt;&lt;0.0001). The mean FGV significantly decreased as age increased (&lt;i&gt;P&lt;/i&gt;&lt;0.0001) and increased as BMI and bra cup size increased (&lt;i&gt;P&lt;/i&gt;&lt;0.0001 and &lt;i&gt;P&lt;/i&gt;=0.0007, respectively). Age was associated with mammographic density, according to both the BI-RADS and VBDM; however, BMI was only associated with mammographic density based on the VBDM. Larger FGV was associated with younger age, higher BMI, larger bra cup size, and higher mammographic density

Short-term changes in ultrasound tomography measures of breast density and treatment-associated endocrine symptoms after tamoxifen therapy

Although breast density decline with tamoxifen therapy is associated with greater therapeutic benefit, limited data suggest that endocrine symptoms may also be associated with improved breast cancer outcomes. However, it is unknown whether endocrine symptoms are associated with reductions in breast density after tamoxifen initiation. We evaluated treatment-associated endocrine symptoms and breast density change among 74 women prescribed tamoxifen in a 12-month longitudinal study. Treatment-associated endocrine symptoms and sound speed measures of breast density, assessed via novel whole breast ultrasound tomography (m/s), were ascertained before tamoxifen (T0) and at 1–3 (T1), 4–6 (T2), and 12 months (T3) after initiation. CYP2D6 status was genotyped, and tamoxifen metabolites were measured at T3. Using multivariable linear regression, we estimated mean change in breast density by treatment-associated endocrine symptoms adjusting for age, race, menopausal status, body mass index, and baseline density. Significant breast density declines were observed in women with treatment-associated endocrine symptoms (mean change (95% confidence interval) at T1:−0.26 m/s (−2.17,1.65); T2:−2.12 m/s (−4.02,−0.22); T3:−3.73 m/s (−5.82,−1.63); p-trend = 0.004), but not among women without symptoms (p-trend = 0.18) (p-interaction = 0.02). Similar declines were observed with increasing symptom frequency (p-trends for no symptoms = 0.91; low/moderate symptoms = 0.03; high symptoms = 0.004). Density declines remained among women with detectable tamoxifen metabolites or intermediate/efficient CYP2D6 metabolizer status. Emergent/worsening endocrine symptoms are associated with significant, early declines in breast density after tamoxifen initiation. Further studies are needed to assess whether these observations predict clinical outcomes. If confirmed, endocrine symptoms may be a proxy for tamoxifen response and useful for patients and providers to encourage adherence.

Alternative methods to measure breast density in younger women

BackgroundBreast density is a strong and potentially modifiable breast cancer risk factor. Almost everything we know about breast density has been derived from mammography, and therefore, very little is known about breast density in younger women aged <40. This study examines the acceptability and performance of two alternative breast density measures, Optical Breast Spectroscopy (OBS) and Dual X-ray Absorptiometry (DXA), in women aged 18–40.MethodsBreast tissue composition (percent water, collagen, and lipid content) was measured in 539 women aged 18–40 using OBS. For a subset of 169 women, breast density was also measured via DXA (percent fibroglandular dense volume (%FGV), absolute dense volume (FGV), and non-dense volume (NFGV)). Acceptability of the measurement procedures was assessed using an adapted validated questionnaire. Performance was assessed by examining the correlation and agreement between the measures and their associations with known determinants of mammographic breast density.ResultsOver 93% of participants deemed OBS and DXA to be acceptable. The correlation between OBS-%water + collagen and %FGV was 0.48. Age and BMI were inversely associated with OBS-%water + collagen and %FGV and positively associated with OBS-%lipid and NFGV.ConclusionsOBS and DXA provide acceptable and viable alternative methods to measure breast density in younger women aged 18–40 years.