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Annual surveillance mammography is recommended for breast cancer survivors on the basis of observational studies and meta-analyses showing reduced breast cancer mortality and improved quality of life. However, breast cancer survivors are at higher risk of subsequent breast cancer and have a fourfold increased risk of interval breast cancers compared with individuals without a personal history of breast cancer. Supplemental surveillance modalities offer increased cancer detection compared with mammography alone, but utilization is variable, and benefits must be balanced with possible harms of false-positive findings. In this review, we describe the current state of mammographic surveillance, summarize evidence for supplemental surveillance in breast cancer survivors, and explore a risk-based approach to selecting surveillance imaging strategies. Further research identifying predictors associated with increased risk of interval second breast cancers and development of validated risk prediction tools may help physicians and patients weigh the benefits and harms of surveillance breast imaging and decide on a personalized approach to surveillance for improved breast cancer outcomes.

PurposeThe aim of this study was to develop a prioritization strategy for scheduling immediate screening mammographic interpretation and possible diagnostic evaluation.MethodsA population-based cohort with screening mammograms performed from 2012 to 2020 at 126 radiology facilities from 7Breast Cancer Surveillance Consortium registries was identified. Classification trees identified combinations of clinical history (age, BI-RADS® density, time since prior mammogram, history of false-positive recall or biopsy result), screening modality (digital mammography, digital breast tomosynthesis), and facility characteristics (profit status, location, screening volume, practice type, academic affiliation) that grouped screening mammograms by recall rate, with ≥12/100 considered high and ≥16/100 very high. An efficiency ratio was estimated as the percentage of recalls divided by the percentage of mammograms.ResultsThe study cohort included 2,674,051 screening mammograms in 925,777 women, with 235,569 recalls. The most important predictor of recall was time since prior mammogram, followed by age, history of false-positive recall, breast density, history of benign biopsy, and screening modality. Recall rates were very high for baseline mammograms (21.3/100; 95% confidence interval, 19.7-23.0) and high for women with ≥5 years since prior mammogram (15.1/100; 95% confidence interval, 14.3-16.1). The 9.2% of mammograms in subgroups with very high and high recall rates accounted for 19.2% of recalls, an efficiency ratio of 2.1 compared with a random approach. Adding women

Background: Machine learning (ML) approaches facilitate risk prediction model development using high-dimensional predictors and higher-order interactions at the cost of model interpretability and transparency. We compared the relative predictive performance of statistical and ML models to guide modeling strategy selection for surveillance mammography outcomes in women with a personal history of breast cancer (PHBC). Methods: We cross-validated seven risk prediction models for two surveillance outcomes, failure (breast cancer within 12 months of a negative surveillance mammogram) and benefit (surveillance-detected breast cancer). We included 9,447 mammograms (495 failures, 1,414 benefits, and 7,538 nonevents) from years 1996 to 2017 using a 1:4 matched case–control samples of women with PHBC in the Breast Cancer Surveillance Consortium. We assessed model performance of conventional regression, regularized regressions (LASSO and elastic-net), and ML methods (random forests and gradient boosting machines) by evaluating their calibration and, among well-calibrated models, comparing the area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CI). Results: LASSO and elastic-net consistently provided well-calibrated predicted risks for surveillance failure and benefit. The AUCs of LASSO and elastic-net were both 0.63 (95% CI, 0.60–0.66) for surveillance failure and 0.66 (95% CI, 0.64–0.68) for surveillance benefit, the highest among well-calibrated models. Conclusions: For predicting breast cancer surveillance mammography outcomes, regularized regression outperformed other modeling approaches and balanced the trade-off between model flexibility and interpretability. Impact: Regularized regression may be preferred for developing risk prediction models in other contexts with rare outcomes, similar training sample sizes, and low-dimensional features.

Data describing outcomes for patients with early stage lung cancer who undergo expectant management is lacking, despite evidence of a sub-population with indolent malignancies. We used the National Cancer Data Base (NCDB) to identify factors associated with active surveillance for early stage lung cancer. Additionally, we sought to describe outcomes of three different care plans: active surveillance, no treatment, and Stereotactic Body Radiation Therapy (SBRT).Patients diagnosed in 2010 to 2017 with early stage lung cancer who underwent active surveillance, no treatment, and SBRT were retrospectively identified in the NCDB. Multinomial logistic regression was used to assess care plan selection. Kaplan Meier analysis was used to assess overall survival (OS).We identified 30,107 patients that met our inclusion criteria: 838 (3%) underwent active surveillance, 6388 patients (21%) received no treatment, and 22,881 (76%) underwent SBRT. Black race (relative risk ratio (RRR): 1.66) and older age (RRR: 1.02) were significant positive predictors of active surveillance selection. Conversely, higher tumor stage (RRR: 0.26) and squamous cell carcinoma (RRR: 0.35) were significant negative predictors of active surveillance selection. Kaplan Meier analysis revealed a longer median OS associated with active surveillance compared to no treatment at 49.3 months versus 26.5 months, respectively. SBRT OS was 43.1 months.We identified a population of lung cancer patients who underwent expectant management with favorable outcomes. Additionally, we identified factors associated with active surveillance selection. The selection of active surveillance over no treatment was associated with significantly longer OS.

Supplementary Table S1 shows the calibration assessment in a sensitivity analysis on LASSO and Elastic-net by enforcing the adjustment of the matching factor.

Relationship of Terminal Duct Lobular Unit Involution of the Breast with Area and Volume Mammographic Densities

Supplementary Figure S1 shows frequency of predictor inclusion in regression-based models for surveillance failure and benefit across imputed datasets.

Supplementary Figure 1. Average Percent Dense Area by Age Supplementary Figure 2. Average Percent Dense Volume by Age.

Background:Machine learning (ML) approaches facilitate risk prediction model development using high-dimensional predictors and higher-order interactions at the cost of model interpretability and transparency. We compared the relative predictive performance of statistical and ML models to guide modeling strategy selection for surveillance mammography outcomes in women with a personal history of breast cancer (PHBC).Methods:We cross-validated seven risk prediction models for two surveillance outcomes, failure (breast cancer within 12 months of a negative surveillance mammogram) and benefit (surveillance-detected breast cancer). We included 9,447 mammograms (495 failures, 1,414 benefits, and 7,538 nonevents) from years 1996 to 2017 using a 1:4 matched case–control samples of women with PHBC in the Breast Cancer Surveillance Consortium. We assessed model performance of conventional regression, regularized regressions (LASSO and elastic-net), and ML methods (random forests and gradient boosting machines) by evaluating their calibration and, among well-calibrated models, comparing the area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CI).Results:LASSO and elastic-net consistently provided well-calibrated predicted risks for surveillance failure and benefit. The AUCs of LASSO and elastic-net were both 0.63 (95% CI, 0.60–0.66) for surveillance failure and 0.66 (95% CI, 0.64–0.68) for surveillance benefit, the highest among well-calibrated models.Conclusions:For predicting breast cancer surveillance mammography outcomes, regularized regression outperformed other modeling approaches and balanced the trade-off between model flexibility and interpretability.Impact:Regularized regression may be preferred for developing risk prediction models in other contexts with rare outcomes, similar training sample sizes, and low-dimensional features.

Supplementary Table 1. Spearman partial rank-order correlations for TDLU measures among pre- and postmenopausal women Supplementary Table 2. Spearman partial rank-order correlations between global and peri-lesional volumetric mammographic density measures among pre- and postmenopausal women with benign breast disease Supplementary Tables 3 to 5: Association between TDLU and mammographic density measures (percent, absolute and nondense) among postmenopausal women with benign breast disease

Supplementary Methods show the detailed information of model fitting using the machine learning approaches in this work.

Supplemental Tables S1-2. Supplemental Table S1. Case definitionsa for ductal carcinoma in situ (DCIS) and invasive breast cancer cases included in the age-period-cohort model, SEER Program and Connecticut Tumor Registry, 1935-2010. Supplemental Table S2. Rate ratios and 95% confidence intervals of breast cancer according to age, period and cohort.

Background: Change in health behaviors can occur among women newly diagnosed with ductal carcinoma in situ (DCIS). We sought to understand whether partnership status and socioeconomic status (SES) affected behavioral changes in body weight, physical activity, alcohol consumption, and smoking.Methods: The Wisconsin In Situ Cohort (WISC) study comprises 1,382 women diagnosed with DCIS with information on demographics, SES factors, and pre- and post-DCIS diagnosis health-related behaviors. Logistic regression models were used to determine the association between partnership status, education, and income with change in behavior variables.Results: Higher educational attainment was associated with lower likelihood of stopping physical activity [OR, 0.45; 95% confidence interval (CI), 0.32–0.63; college vs. high school degree], or starting to drink alcohol (OR, 0.34; 95% CI, 0.15–0.80). Results suggested that higher family income was associated with lower likelihood of gaining >5% body mass index (P = 0.07) or stopping physical activity (P = 0.09). Living with a partner was not strongly associated with behavior changes.Conclusion: Higher educational attainment and higher income, but not living with a partner, were associated with positive health behaviors after a DCIS diagnosis.Impact: The associations between higher educational attainment and, to a lesser extent, higher income with positive health behaviors underscore the importance of considering SES when identifying those at risk for negative behavioral change after DCIS diagnosis. Cancer Epidemiol Biomarkers Prev; 25(1); 76–82. ©2015 AACR.

Background: One measure of Breast Imaging Reporting and Data System (BI-RADS) breast density improves 5-year breast cancer risk prediction, but the value of sequential measures is unknown. We determined whether two BI-RADS density measures improve the predictive accuracy of the Breast Cancer Surveillance Consortium 5-year risk model compared with one measure.Methods: We included 722,654 women of ages 35 to 74 years with two mammograms with BI-RADS density measures on average 1.8 years apart; 13,715 developed invasive breast cancer. We used Cox regression to estimate the relative hazards of breast cancer for age, race/ethnicity, family history of breast cancer, history of breast biopsy, and one or two density measures. We developed a risk prediction model by combining these estimates with 2000–2010 Surveillance, Epidemiology, and End Results incidence and 2010 vital statistics for competing risk of death.Results: The two-measure density model had marginally greater discriminatory accuracy than the one-measure model (AUC, 0.640 vs. 0.635). Of 18.6% of women (134,404 of 722,654) who decreased density categories, 15.4% (20,741 of 134,404) of women whose density decreased from heterogeneously or extremely dense to a lower density category with one other risk factor had a clinically meaningful increase in 5-year risk from Conclusion: The two-density model has similar overall discrimination to the one-density model for predicting 5-year breast cancer risk and improves risk classification for women with risk factors and a decrease in density.Impact: A two-density model should be considered for women whose density decreases when calculating breast cancer risk. Cancer Epidemiol Biomarkers Prev; 24(6); 889–97. ©2015 AACR.

Background: The impact of screening mammography on breast cancer incidence is difficult to disentangle from cohort- and age-related effects on incidence.Methods: We developed an age–period–cohort model of ductal carcinoma in situ (DCIS) and invasive breast cancer incidence in U.S. females using cancer registry data. Five functions were included in the model to estimate stage-specific effects for age, premenopausal birth cohorts, postmenopausal birth cohorts, period (for all years of diagnosis), and a mammography period effect limited to women ages ≥40 years after 1982. Incidence with and without the mammography period effect was calculated.Results: More recent birth cohorts have elevated underlying risk compared with earlier cohorts for both pre- and postmenopausal women. Comparing models with and without the mammography period effect showed that overall breast cancer incidence would have been 23.1% lower in the absence of mammography in 2010 (95% confidence intervals, 18.8–27.4), including 14.7% (9.5–19.3) lower for invasive breast cancer and 54.5% (47.4–59.6) lower for DCIS. Incidence of distant-staged breast cancer in 2010 would have been 29.0% (13.1–48.1) greater in the absence of mammography screening.Conclusions: Mammography contributes to markedly elevated rates of DCIS and early-stage invasive cancers, but also contributes to substantial reductions in the incidence of metastatic breast cancer.Impact: Mammography is an important tool for reducing the burden of breast cancer, but future work is needed to identify risk factors accounting for increasing underlying incidence and to distinguish between indolent and potentially lethal early-stage breast cancers that are detected via mammography. Cancer Epidemiol Biomarkers Prev; 24(6); 905–12. ©2015 AACR.

Background: The utility of incorporating detailed family history into breast cancer risk prediction hinges on its independent contribution to breast cancer risk. We evaluated associations between detailed family history and breast cancer risk while accounting for breast density.Methods: We followed 222,019 participants ages 35 to 74 in the Breast Cancer Surveillance Consortium, of whom 2,456 developed invasive breast cancer. We calculated standardized breast cancer risks within joint strata of breast density and simple (1st-degree female relative) or detailed (first-degree, second-degree, or first- and second-degree female relative) breast cancer family history. We fit log-binomial models to estimate age-specific breast cancer associations for simple and detailed family history, accounting for breast density.Results: Simple first-degree family history was associated with increased breast cancer risk compared with no first-degree history [Risk ratio (RR), 1.5; 95% confidence interval (CI), 1.0–2.1 at age 40; RR, 1.5; 95% CI, 1.3–1.7 at age 50; RR, 1.4; 95% CI, 1.2–1.6 at age 60; RR, 1.3; 95% CI, 1.1–1.5 at age 70). Breast cancer associations with detailed family history were strongest for women with first- and second-degree family history compared with no history (RR, 1.9; 95% CI, 1.1–3.2 at age 40); this association weakened in higher age groups (RR, 1.2; 95% CI, 0.88–1.5 at age 70). Associations did not change substantially when adjusted for breast density.Conclusions: Even with adjustment for breast density, a history of breast cancer in both first- and second-degree relatives is more strongly associated with breast cancer than simple first-degree family history.Impact: Future efforts to improve breast cancer risk prediction models should evaluate detailed family history as a risk factor. Cancer Epidemiol Biomarkers Prev; 26(6); 938–44. ©2017 AACR.

Supplementary Table S1 repeats the longitudinal association between annual increase in BMI and annual change in volumetric breast density provided in Table 4 of the manuscript with additional adjustment for change in total breast volume.

Background: Understanding how changes in body mass index (BMI) relate to changes in mammographic density is necessary to evaluate adjustment for BMI gain/loss in studies of change in density and breast cancer risk. Increase in BMI has been associated with a decrease in percent density, but the effect on change in absolute dense area or volume is unclear.Methods: We examined the association between change in BMI and change in volumetric breast density among 24,556 women in the San Francisco Mammography Registry from 2007 to 2013. Height and weight were self-reported at the time of mammography. Breast density was assessed using single x-ray absorptiometry measurements. Cross-sectional and longitudinal associations between BMI and dense volume (DV), non-dense volume (NDV), and percent dense volume (PDV) were assessed using multivariable linear regression models, adjusted for demographics, risk factors, and reproductive history.Results: In cross-sectional analysis, BMI was positively associated with DV [β, 2.95 cm3; 95% confidence interval (CI), 2.69–3.21] and inversely associated with PDV (β, −2.03%; 95% CI, −2.09, −1.98). In contrast, increasing BMI was longitudinally associated with a decrease in both DV (β, −1.01 cm3; 95% CI, −1.59, −0.42) and PDV (β, −1.17%; 95% CI, −1.31, −1.04). These findings were consistent for both pre- and postmenopausal women.Conclusion: Our findings support an inverse association between change in BMI and change in PDV. The association between increasing BMI and decreasing DV requires confirmation.Impact: Longitudinal studies of PDV and breast cancer risk, or those using PDV as an indicator of breast cancer risk, should evaluate adjustment for change in BMI. Cancer Epidemiol Biomarkers Prev; 24(11); 1724–30. ©2015 AACR.

Supplemental Figure. Diagram of origin of study population

Comparison of demographics, behavioral factors, and socioeconomic factors between DCIS cases that participated in the study only at baseline and DCIS cases that completed at least one follow-up questionnaire.

Supplemental Movie. Observed and modeled overall breast cancer incidence rates for each of the 9 Surveillance, Epidemiology and End Results (SEER) registries, 1935-2010 by age and period (year of diagnosis). Includes rates per 100,000 women for DCIS and invasive breast cancer combined. Observed crude rates shown with circles. Modeled rates shown with solid lines.

The increased detection and treatment of early stage breast cancer as well as ductal carcinoma in situ (DCIS) has not led to significant survival benefits. Therefore, the current standard treatment of DCIS is questionable. An informed evidence-based treatment strategy, and likely de-escalation from the current standards requires new prognostic models built from more comprehensive characterization with objective criteria. Parallel profiling of the molecular landscape and micro-environment in pure DCIS remains challenging due to histological heterogeneity and the inevitable reliance on small archived specimens. Leveraging recent methodological advances, we characterized the mutational, transcriptional, histological and microenvironmental landscape across multiple micro-dissected regions from 39 cases to generate a multi-modal breast precancer atlas. The histological architecture was associated with grade, adiposity, and intrinsic expression subtypes. Similar to previous findings, high-grade lesions had higher mutational burden, including TP53 mutations, while low-grade lesions had more frequent 16q losses and GATA3 mutations. Multi-region analysis revealed most somatic alterations, including whole genome duplication events, were clonal, but genetic divergence increased with distance between regions. In 7/12 evaluable cases, somatic mutations in putative driver genes affected a subset of regions only. This genetic heterogeneity often accompanied phenotypic heterogeneity and regions with low risk features (Normal-like, Luminal A) occurred earlier than those with high-risk features (Her2-like, Basal or necrosis) according to the phylogenetic analysis. The immune-environment was evaluated using multiplex immuno-histochemistry to measure relative stromal and epithelial densities of B lymphocyte (B-cell), T lymphocyte (T-cell) and regulatory T cells (T-reg) and identify 3 immune-states: Active, Suppressed and Excluded (lower epithelial density). All states included both DCIS and adjacent benign regions, and none associated with intrinsic subtypes. The Excluded state was enriched in high-grade DCIS and, compared to benign areas, more likely acquired in DCIS, showing transcriptional evidence of stronger immune-suppression and possible evasion. The breast pre-cancer atlas therefore reveals correlated levels of phenotypic and genotypic heterogeneity, including at sub-histological resolution. These uniquely integrated observations will help scope future studies, prioritize candidate markers for progression risk modelling and identify functional similarities in precursor lesions from other types of adenocarcinomas.

Background Minimally invasive breast biopsy (MIBB) is the standard of care for the diagnosis of breast cancer, with consensus guidelines suggesting MIBB goals of 90% of total biopsies. In a previous study of patients in the rural state of Vermont, USA (population size of 640,000), rural breast cancer patients had open biopsies 42% of the time compared to 29% of urban breast cancer patients. The aim of this study was to assess overall population‐based biopsy trends in Vermont. Methods The Vermont Breast Cancer Surveillance System (VBCSS) was used to identify women receiving MIBB and excisional breast biopsies in Vermont. Patient zip code at the time of initial biopsy was used to determine the patient residence rurality by rural–urban commuting area codes (RUCA 2.0™). Results There were 9122 diagnostic episodes from 1999 to 2018. MIBB was the initial biopsy method in 7524 (82.5%) cases, while surgical excision was the initial biopsy method in 1598 (17.5%) cases. A linear trend fit estimated an increase of 1.3% per year (p, Minimally invasive needle biopsy has been the standard of care for breast cancer diagnosis since the early 2000’s. However, a number of studies through 2010, including our prior report from Vermont, showed persistently high rates of open surgical biopsies in rural populations. In this manuscript, we report updated results from a state‐wide retrospective study of breast biopsies, stratified by rurality of patient residence; we evaluated the trend of minimally invasive breast biopsies from 1999 to 2018 and show for the first time that the state as a whole has reached the standard of care of over 90% breast biopsies performed as minimally invasive.

Background Elevated mammographic breast density is a strong breast cancer risk factor with poorly understood etiology. Increased deposition of collagen, one of the main fibrous proteins present in breast stroma, has been associated with increased mammographic density. Collagen fiber architecture has been linked to poor outcomes in breast cancer. However, relationships of quantitative collagen fiber features assessed in diagnostic biopsies with mammographic density and lesion severity are not well-established. Methods Clinically indicated breast biopsies from 65 in situ or invasive breast cancer cases and 73 frequency matched-controls with a benign biopsy result were used to measure collagen fiber features (length, straightness, width, alignment, orientation and density (fibers/µm2)) using second harmonic generation microscopy in up to three regions of interest (ROIs) per biopsy: normal, benign breast disease, and cancer. Local and global mammographic density volumes were quantified in the ipsilateral breast in pre-biopsy full-field digital mammograms. Associations of fibrillar collagen features with mammographic density and severity of biopsy diagnosis were evaluated using generalized estimating equation models with an independent correlation structure to account for multiple ROIs within each biopsy section. Results Collagen fiber density was positively associated with the proportion of stroma on the biopsy slide (p p = 0.035) and within a 2 mm perilesional ring (p = 0.02), but not with global mammographic density measures. As severity of the breast biopsy diagnosis increased at the ROI level, collagen fibers tended to be less dense, shorter, straighter, thinner, and more aligned with one another (p Conclusions Collagen fiber density was positively associated with local, but not global, mammographic density, suggesting that collagen microarchitecture may not translate into macroscopic mammographic features. However, collagen fiber features may be markers of cancer risk and/or progression among women referred for biopsy based on abnormal breast imaging.

Background A paucity of research addresses breast cancer screening strategies for women at lower-than-average breast cancer risk. The aim of this study was to examine screening harms and benefits among women aged 50-74 years at lower-than-average breast cancer risk by breast density. Methods Three well-established, validated Cancer Intervention and Surveillance Network models were used to estimate the lifetime benefits and harms of different screening scenarios, varying by screening interval (biennial, triennial). Breast cancer deaths averted, life-years and quality-adjusted life-years gained, false-positives, benign biopsies, and overdiagnosis were assessed by relative risk (RR) level (0.6, 0.7, 0.85, 1 [average risk]) and breast density category, for US women born in 1970. Results Screening benefits decreased proportionally with decreasing risk and with lower breast density. False-positives, unnecessary biopsies, and the percentage overdiagnosis also varied substantially by breast density category; false-positives and unnecessary biopsies were highest in the heterogeneously dense category. For women with fatty or scattered fibroglandular breast density and a relative risk of no more than 0.85, the additional deaths averted and life-years gained were small with biennial vs triennial screening. For these groups, undergoing 4 additional screens (screening biennially [13 screens] vs triennially [9 screens]) averted no more than 1 additional breast cancer death and gained no more than 16 life-years and no more than 10 quality-adjusted life-years per 1000 women but resulted in up to 232 more false-positives per 1000 women. Conclusion Triennial screening from age 50 to 74 years may be a reasonable screening strategy for women with lower-than-average breast cancer risk and fatty or scattered fibroglandular breast density.

Background: Women at high risk for breast cancer due to genetics or risk factor profiles are counseled to adopt lifestyle, behavioral, and dietary changes to help reduce their risk. These recommendations are based on studies of women at average risk, so their effectiveness in high-risk women is unclear. Methods: We evaluated the impact of physical activity, smoking, alcohol consumption, and intake of folate and carotenoids on mammographic breast density—a proxy for breast cancer risk—among 387 high-risk women. Exposures were self-reported on questionnaires. Breast dense area, nondense area, and percent dense area were measured from screening mammograms with Library for Breast Radiodensity Assessment software. Cross-sectional associations were estimated with multivariable quantile regression models. Results: After adjusting for age, adiposity, reproductive history, and use of postmenopausal hormones, no breast density measure was associated with physical activity level, smoking status, alcohol consumption, or estimated intake of folate, alpha-carotene, beta-carotene, lutein/zeaxanthin, and beta-cryptoxanthin. Lycopene intake was associated with lower dense area when comparing the highest and lowest intake categories (adjusted difference in median = −14 cm2, 95% confidence interval: −29 to 1.3 cm2). This association may be explained by incomplete adjustment for adiposity. Conclusions: Recommended lifestyle, behavioral, and dietary changes to mitigate personal risk of breast cancer do not substantially impact mammographic breast density measures. Impact: Alternative strategies, such as increased uptake of chemoprevention, may better serve risk reduction efforts in women at high risk for breast cancer.

Core facilities have a ubiquitous and increasingly valuable presence at research institutions. Although many shared cores were originally created to provide routine services and access to complex and expensive instrumentation for the research community, they are frequently called upon by investigators to design protocols and procedures to help answer complex research questions. For instance, shared microscopy resources are evolving from providing access to and training on complex imaging instruments to developing detailed innovative protocols and experimental strategies, including sample preparation techniques, staining, complex imaging parameters, and high-level image analyses. These approaches require close intellectual collaboration between core staff and research investigators to formulate and coordinate plans for protocol development suited to the research question. Herein, we provide an example of such coordinated collaboration between a shared microscopy facility and a team of scientists and clinician–investigators to approach a complex multiprobe immunostaining, imaging, and image analysis project investigating the tumor microenvironment from human breast cancer samples. Our hope is that this example may be used to convey to institute administrators the critical importance of the intellectual contributions of the scientific staff in core facilities to research endeavors.

Background Estimating advanced breast cancer risk in women undergoing annual or biennial mammography could identify women who may benefit from less or more intensive screening. We developed an actionable model to predict cumulative 6-year advanced cancer (prognostic pathologic stage II or higher) risk according to screening interval. Methods We included 931 186 women aged 40-74 years in the Breast Cancer Surveillance Consortium undergoing 2 542 382 annual (prior mammogram within 11-18 months) or 752 049 biennial (prior within 19-30 months) screening mammograms. The prediction model includes age, race and ethnicity, body mass index, breast density, family history of breast cancer, and prior breast biopsy subdivided by menopausal status and screening interval. We used fivefold cross-validation to internally validate model performance. We defined higher than 95th percentile as high risk (>0.658%), higher than 75th percentile to 95th or less percentile as intermediate risk (0.380%-0.658%), and 75th or less percentile as low to average risk ( Results Obesity, high breast density, and proliferative disease with atypia were strongly associated with advanced cancer. The model is well calibrated and has an area under the receiver operating characteristics curve of 0.682 (95% confidence interval = 0.670 to 0.694). Based on women’s predicted advanced cancer risk under annual and biennial screening, 69.1% had low or average risk regardless of screening interval, 12.4% intermediate risk with biennial screening and average risk with annual screening, and 17.4% intermediate or high risk regardless of screening interval. Conclusion Most women have low or average advanced cancer risk and can undergo biennial screening. Intermediate-risk women may consider annual screening, and high-risk women may consider supplemental imaging in addition to annual screening.

Background: There is widespread interest in discriminating indolent from aggressive ductal carcinoma in situ (DCIS). We sought to evaluate collagen organization in the DCIS tumor microenvironment in relation to pathologic characteristics and patient outcomes. Methods: We retrieved fixed tissue specimens for 90 DCIS cases within the population-based Vermont DCIS Cohort. We imaged collagen fibers within 75 μm of the tumor/stromal boundary on hematoxylin and eosin–stained slides using multiphoton microscopy with second-harmonic generation. Automated software quantified collagen fiber length, width, straightness, density, alignment, and angle to the tumor/stroma boundary. Factor analysis identified linear combinations of collagen fiber features representing composite attributes of collagen organization. Results: Multiple collagen features were associated with DCIS grade, necrosis pattern, or periductal fibrosis (P < 0.05). After adjusting for treatments and nuclear grade, risk of recurrence (defined as any second breast cancer diagnosis) was lower among cases with greater collagen fiber width [hazard ratio (HR), 0.57 per one standard deviation increase; 95% confidence interval (CI), 0.39–0.84] and fiber density (HR, 0.60; 95% CI, 0.42–0.85), whereas risk was elevated among DCIS cases with higher fiber straightness (HR, 1.47; 95% CI, 1.05–2.06) and distance to the nearest two fibers (HR, 1.47; 95% CI, 1.06–2.02). Fiber length, alignment, and fiber angle were not associated with recurrence (P > 0.05). Five composite factors were identified, accounting for 72.4% of the total variability among fibers; three were inversely associated with recurrence (HRs ranging from 0.60 to 0.67; P ≤ 0.01). Conclusions: Multiple aspects of collagen organization around DCIS lesions are associated with recurrence risk. Impact: Collagen organization should be considered in the development of prognostic DCIS biomarker signatures.

Background: Social determinants of health (SDOH) contribute to health care disparities, with social and economic barriers often leading to difficulties in obtaining necessary care. We evaluated barriers to receiving health care, focusing on caretaker responsibilities, health insurance and cost, and transportation. Materials and Methods: We included women ages ≥40 years receiving screening mammography across three Breast Cancer Surveillance Consortium registries from 2012 to 2017. Women self-reported social and financial barriers to receiving health care in the 12 months before their screening mammogram. We evaluated woman- and census-based community-level factors associated with reporting a barrier using multivariate logistic regression. We assessed interaction with urban versus nonurban residence using Wald tests. Results: Among 393,430 women, 3.6% reported a barrier with a higher proportion in urban versus nonurban settings (3.9% [n = 11,977] vs. 2.2% [n = 1,655], respectively; p

Background Established mammography screening performance metrics use the initial screening mammography assessment because they were developed for radiologist performance auditing, yet these metrics are frequently used to inform health policy and screening decision making. The authors have developed new performance metrics based on the final assessment that consider the entire screening episode, including diagnostic workup. Methods The authors used data from 2,512,577 screening episodes during 2005-2017 at 146 facilities in the United States participating in the Breast Cancer Surveillance Consortium. Screening performance metrics based on the final assessment of the screening episode were compared with conventional metrics defined with the initial assessment. Results were also stratified by breast density and breast cancer risk. Results The cancer detection rates were similar for the final assessment (4.1 per 1000; 95% confidence interval [CI], 3.8-4.3 per 1000) and the initial assessment (4.1 per 1000; 95% CI, 3.9-4.3 per 1000). The interval cancer rate was 12% higher when it was based on the final assessment (0.77 per 1000; 95% CI, 0.71-0.83 per 1000) versus the initial assessment (0.69 per 1000; 95% CI, 0.64-0.74 per 1000), and this resulted in a modest difference in sensitivity (84.1% [95% CI, 83.0%-85.1%] vs 85.7% [95% CI, 84.8%-86.6%], respectively). Absolute differences in the interval cancer rate between final and initial assessments increased with breast density and breast cancer risk (eg, a difference of 0.29 per 1000 for women with extremely dense breasts and a 5-year risk >2.49%). Conclusions Established screening performance metrics underestimate the interval cancer rate of a mammography screening episode, particularly for women with dense breasts or an elevated breast cancer risk. Women, clinicians, policymakers, and researchers should use final-assessment performance metrics to support informed screening decisions.

Breast cancer screening with digital breast tomosynthesis may decrease false-positive results compared with digital mammography.To estimate the probability of receiving at least 1 false-positive result after 10 years of screening with digital breast tomosynthesis vs digital mammography in the US.An observational comparative effectiveness study with data collected prospectively for screening examinations was performed between January 1, 2005, and December 31, 2018, at 126 radiology facilities in the Breast Cancer Surveillance Consortium. Analysis included 903 495 individuals aged 40 to 79 years. Data analysis was conducted from February 9 to September 7, 2021.Screening modality, screening interval, age, and Breast Imaging Reporting and Data System breast density.Cumulative risk of at least 1 false-positive recall for further imaging, short-interval follow-up recommendation, and biopsy recommendation after 10 years of annual or biennial screening with digital breast tomosynthesis vs digital mammography, accounting for competing risks of breast cancer diagnosis and death.In this study of 903 495 women, 2 969 055 nonbaseline screening examinations were performed with interpretation by 699 radiologists. Mean (SD) age of the women at the time of the screening examinations was 57.6 (9.9) years, and 58% of the examinations were in individuals younger than 60 years and 46% were performed in women with dense breasts. A total of 15% of examinations used tomosynthesis. For annual screening, the 10-year cumulative probability of at least 1 false-positive result was significantly lower with tomosynthesis vs digital mammography for all outcomes: 49.6% vs 56.3% (difference, -6.7; 95% CI, -7.4 to -6.1) for recall, 16.6% vs 17.8% (difference, -1.1; 95% CI, -1.7 to -0.6) for short-interval follow-up recommendation, and 11.2% vs 11.7% (difference, -0.5; 95% CI, -1.0 to -0.1) for biopsy recommendation. For biennial screening, the cumulative probability of a false-positive recall was significantly lower for tomosynthesis vs digital mammography (35.7% vs 38.1%; difference, -2.4; 95% CI, -3.4 to -1.5), but cumulative probabilities did not differ significantly by modality for short-interval follow-up recommendation (10.3% vs 10.5%; difference, -0.1; 95% CI, -0.7 to 0.5) or biopsy recommendation (6.6% vs 6.7%; difference, -0.1; 95% CI, -0.5 to 0.4). Decreases in cumulative probabilities of false-positive results with tomosynthesis vs digital mammography were largest for annual screening in women with nondense breasts (differences for recall, -6.5 to -12.8; short-interval follow-up, 0.1 to -5.2; and biopsy recommendation, -0.5 to -3.1). Regardless of modality, cumulative probabilities of false-positive results were substantially lower for biennial vs annual screening (overall recall, 35.7 to 38.1 vs 49.6 to 56.3; short-interval follow-up, 10.3 to 10.5 vs 16.6 to 17.8; and biopsy recommendation, 6.6 to 6.7 vs 11.2 to 11.7); older vs younger age groups (eg, among annual screening in women ages 70-79 vs 40-49, recall, 39.8 to 47.0 vs 60.8 to 68.0; short-interval follow-up, 13.3 to 14.2 vs 20.7 to 20.9; and biopsy recommendation, 9.1 to 9.3 vs 13.2 to 13.4); and women with entirely fatty vs extremely dense breasts (eg, among annual screening in women aged 50-59 years, recall, 29.1 to 36.3 vs 58.8 to 60.4; short-interval follow-up, 8.9 to 11.6 vs 19.5 to 19.8; and biopsy recommendation, 4.9 to 8.0 vs 15.1 to 15.3).In this comparative effectiveness study, 10-year cumulative probabilities of false-positive results were lower on digital breast tomosynthesis vs digital mammography. Biennial screening interval, older age, and nondense breasts were associated with larger reductions in false-positive probabilities than screening modality.

We evaluated self-report of decision quality and regret with breast cancer surgical treatment by pre-operative breast MRI use in women recently diagnosed with breast cancer.We conducted a survey with 957 women aged 18 + with stage 0-III breast cancer identified in the Breast Cancer Surveillance Consortium. Participants self-reported receipt of pre-operative breast MRI. Primary outcomes were process measures in the Breast Cancer Surgery Decision Quality Instrument (BCS-DQI) (continuous outcome) and Decision Regret Scale (dichotomized outcome as any/none). Generalized estimating equations with linear and logit link were used to estimate adjusted associations between breast MRI and primary outcomes. All analyses were also stratified by breast density.Survey participation rate was 27.9% (957/3430). Study population was primarily 60 years, White, college educated, and diagnosed with early-stage breast cancer. Pre-operative breast MRI was reported in 46% of women. A higher proportion of women who were younger age ( 50 years), commercially insured, and self-detected their breast cancer reported pre-operative breast MRI use. In adjusted analysis, pre-operative breast MRI use compared with no use was associated with a small but statistically significantly higher decision quality scores (69.5 vs 64.7, p-value = 0.043). Decision regret did not significantly differ in women who reported pre-operative breast MRI use compared with no use (54.2% v. 48.7%, respectively, p-value = 0.11). Study results did not vary when stratified by breast density for either primary outcome.Breast MRI use in the diagnostic work-up of breast cancer does not negatively alter women's perceptions of surgical treatment decisions in early survivorship.NCT03029286.

BACKGROUND: Consistency in reporting Breast Imaging Reporting and Data System (BI-RADS) breast density on mammograms is important because breast density is used for breast cancer risk assessment and is reported directly to women and clinicians to inform decisions about supplemental screening. PURPOSE: To assess the consistency of BI-RADS density reporting between digital breast tomosynthesis (DBT) and digital mammography (DM) and evaluate density as a breast cancer risk factor when assessed using DM versus DBT. MATERIALS AND METHODS: The Breast Cancer Surveillance Consortium is a prospective cohort study of women undergoing mammography with DM or DBT. This secondary analysis included women aged 40–79 years who underwent at least two screening mammography examinations less than 36 months apart. Percentage agreement and κ statistic were estimated for pairs of BI-RADS density assessments. Cox proportional hazards regression was used to calculate hazard ratios (HRs) of breast density as a risk factor for invasive breast cancer. RESULTS: A total of 403 326 pairs of mammograms from 342 149 women were evaluated. There were no significant differences in breast density assessment in pairs consisting of one DM and one DBT examination (57 516 of 74 729 [77%]; κ = 0.64), two DM examinations (238 678 of 301 743 [79%]; κ = 0.67), and two DBT examinations (20 763 of 26 854 [77%]; κ = 0.65). Results were similar when restricting the analyses to pairs read by the same radiologist. The breast cancer HRs for breast density were similar for DM and DBT (P = .45 for interaction). The HRs for density acquired using DM and DBT, respectively, were 0.55 (95% CI: 0.49, 0.63) and 0.37 (95% CI: 0.21, 0.66) for almost entirely fat, 1.47 (95% CI: 1.37, 1.58) and 1.36 (95% CI: 1.02, 1.82) for heterogeneously dense, and 1.72 (95% CI: 1.54, 1.93) and 2.05 (95% CI: 1.25, 3.36) for extremely dense breasts. CONCLUSION: Radiologist reporting of Breast Imaging Reporting and Data System density obtained with digital breast tomosynthesis did not differ from that obtained with digital mammography. © RSNA, 2021 Online supplemental material is available for this article.

ImportanceDetection of ductal carcinoma in situ (DCIS) by mammography screening is a controversial outcome with potential benefits and harms. The association of mammography screening interval and woman’s risk factors with the likelihood of DCIS detection after multiple screening rounds is poorly understood.ObjectiveTo develop a 6-year risk prediction model for screen-detected DCIS according to mammography screening interval and women’s risk factors.Design, Setting, and ParticipantsThis Breast Cancer Surveillance Consortium cohort study assessed women aged 40 to 74 years undergoing mammography screening (digital mammography or digital breast tomosynthesis) from January 1, 2005, to December 31, 2020, at breast imaging facilities within 6 geographically diverse registries of the consortium. Data were analyzed between February and June 2022.ExposuresScreening interval (annual, biennial, or triennial), age, menopausal status, race and ethnicity, family history of breast cancer, benign breast biopsy history, breast density, body mass index, age at first birth, and false-positive mammography history.Main Outcomes and MeasuresScreen-detected DCIS defined as a DCIS diagnosis within 12 months after a positive screening mammography result, with no concurrent invasive disease.ResultsA total of 916 931 women (median [IQR] age at baseline, 54 [46-62] years; 12% Asian, 9% Black, 5% Hispanic/Latina, 69% White, 2% other or multiple races, and 4% missing) met the eligibility criteria, with 3757 screen-detected DCIS diagnoses. Screening round–specific risk estimates from multivariable logistic regression were well calibrated (expected-observed ratio, 1.00; 95% CI, 0.97-1.03) with a cross-validated area under the receiver operating characteristic curve of 0.639 (95% CI, 0.630-0.648). Cumulative 6-year risk of screen-detected DCIS estimated from screening round–specific risk estimates, accounting for competing risks of death and invasive cancer, varied widely by all included risk factors. Cumulative 6-year screen-detected DCIS risk increased with age and shorter screening interval. Among women aged 40 to 49 years, the mean 6-year screen-detected DCIS risk was 0.30% (IQR, 0.21%-0.37%) for annual screening, 0.21% (IQR, 0.14%-0.26%) for biennial screening, and 0.17% (IQR, 0.12%-0.22%) for triennial screening. Among women aged 70 to 74 years, the mean cumulative risks were 0.58% (IQR, 0.41%-0.69%) after 6 annual screens, 0.40% (IQR, 0.28%-0.48%) for 3 biennial screens, and 0.33% (IQR, 0.23%-0.39%) after 2 triennial screens.Conclusions and RelevanceIn this cohort study, 6-year screen-detected DCIS risk was higher with annual screening compared with biennial or triennial screening intervals. Estimates from the prediction model, along with risk estimates of other screening benefits and harms, could help inform policy makers’ discussions of screening strategies.

358 Background: In a primary analysis of the STAMPEDE protocol, the addition of Abiraterone acetate to androgen deprivation therapy (ADT) in men with castration sensitive, high-risk, non-metastatic prostate cancer was associated with significantly higher rates of metastasis-free survival compared with ADT alone. However, the cost-effectiveness of Abiraterone in this population remains unaddressed. Methods: A decision model based on partitioned survival simulations estimated costs and quality-adjusted life years (QALY), and incremental cost-effectiveness ratios (ICERs) associated with ADT with or without Abiraterone. This analysis was performed from a United States healthcare sector perspective. We estimated drug acquisition costs using the Federal Supply Schedule. Given current heterogeneity in prescription drug coverage, we used both brand-name and generic drug prices. The willingness-to-pay (WTP) threshold was $100,000 per QALY. Analyses were performed over the published STAMPEDE trial with median follow-up duration of 72 months. Model input parameters were based on the STAMPEDE trial data as well as best available and most recent data provided in the published literature. Sensitivity analyses explored uncertainty with regard to the model assumptions. Results: Using brand-name pricing, the addition of Abiraterone to ADT was associated with an increase in net costs by $217,616, and an increase in QALYS by 0.79. Therefore, the ICER associated with brand-name Abiraterone was $277,156 per QALY. Sensitivity analyses revealed that the monthly price of Abiraterone would need to be less than or equal to $2,581 in order to be cost-effective at a WTP threshold of $100,000 per QALY. In contrast, generic Abiraterone was associated with an increase in net costs by $45,325, and a cost-effective ICER of $57,726 per QALY. Conclusions: Abiraterone for high-risk non-metastatic prostate cancer is cost-effective using generic pricing. However, current brand-name pricing is roughly four times the threshold of cost-effectiveness, underscoring the need to increase access to generic drugs in the United States.

Background: We evaluated differences in diagnostic mammography performance based on women's race/ethnicity. Methods: This cohort study included 267,868 diagnostic mammograms performed to evaluate screening mammogram findings at 98 facilities in the Breast Cancer Surveillance Consortium between 2005 and 2017. Mammogram assessments were recorded prospectively and breast cancers occurring within one year were ascertained. Performance statistics were calculated with 95% confidence intervals (CI) for each racial/ethnic group. Multivariable regression was used to control for personal characteristics and imaging facility. Results: Among non-Hispanic White (70%), non-Hispanic Black (13%), Asian/Pacific Islander (10%), and Hispanic (7%) women, the invasive cancer detection rate (iCDR, per 1,000 mammograms) and positive predictive value (PPV2) were highest among non-Hispanic White women (iCDR, 35.8; 95% CI, 35.0–36.7; PPV2, 27.8; 95% CI, 27.3–28.3) and lowest among Hispanic women (iCDR, 22.3; 95% CI, 20.2–24.6; PPV2, 19.4; 95% CI, 18.0–20.9). Short interval follow-up recommendations were most common among non-Hispanic Black women [(31.0%; 95% CI, 30.6%–31.5%) vs. other groups, range, 16.6%–23.6%]. False-positive biopsy recommendations were most common among Asian/Pacific Islander women [per 1,000 mammograms: 169.2; 95% CI, 164.8–173.7) vs. other groups, range, 126.5–136.1]. Some differences were explained by adjusting for receipt of diagnostic ultrasound or MRI for iCDR and imaging facility for short-interval follow-up. Other differences changed little after adjustment. Conclusions: Diagnostic mammography performance varied across racial/ethnic groups. Addressing characteristics related to imaging facility and access, rather than personal characteristics, may help reduce some of these disparities. Impact: Diagnostic mammography performance studies should include racially and ethnically diverse populations to provide an accurate view of the population-level effects.

BACKGROUND The cost-effectiveness of screening mammography beyond age 75 years remains unclear. OBJECTIVE To estimate benefits, harms, and cost-effectiveness of extending mammography to age 80, 85, or 90 years according to comorbidity burden. DESIGN Markov microsimulation model. DATA SOURCES SEER (Surveillance, Epidemiology, and End Results) program and Breast Cancer Surveillance Consortium. TARGET POPULATION U.S. women aged 65 to 90 years in groups defined by Charlson comorbidity score (CCS). TIME HORIZON Lifetime. PERSPECTIVE National health payer. INTERVENTION Screening mammography to age 75, 80, 85, or 90 years. OUTCOME MEASURES Breast cancer death, survival, and costs. RESULTS OF BASE-CASE ANALYSIS Extending biennial mammography from age 75 to 80 years averted 1.7, 1.4, and 1.0 breast cancer deaths and increased days of life gained by 5.8, 4.2, and 2.7 days per 1000 women for comorbidity scores of 0, 1, and 2, respectively. Annual mammography beyond age 75 years was not cost-effective, but extending biennial mammography to age 80 years was ($54 000, $65 000, and $85 000 per quality-adjusted life-year [QALY] gained for women with CCSs of 0, 1, and ≥2, respectively). Overdiagnosis cases were double the number of deaths averted from breast cancer. RESULTS OF SENSITIVITY ANALYSIS Costs per QALY gained were sensitive to changes in invasive cancer incidence and shift of breast cancer stage with screening mammography. LIMITATION No randomized controlled trials of screening mammography beyond age 75 years are available to provide model parameter inputs. CONCLUSION Although annual mammography is not cost-effective, biennial screening mammography to age 80 years is; however, the absolute number of deaths averted is small, especially for women with comorbidities. Women considering screening beyond age 75 years should weigh the potential harms of overdiagnosis versus the potential benefit of averting death from breast cancer. PRIMARY FUNDING SOURCE National Cancer Institute and National Institutes of Health.