History Of Breast Biopsy Research Articles

Population Attributable Risk of Advanced-Stage Breast Cancer by Race and Ethnicity

Advanced-stage breast cancer rates vary by race and ethnicity, with Black women having a 2-fold higher rate than White women among regular screeners. Clinical risk factors that explain a large proportion of advanced breast cancers by race and ethnicity are unknown. To evaluate the population attributable risk proportions (PARPs) for advanced-stage breast cancer (prognostic pathologic stage IIA or higher) associated with clinical risk factors among routinely screened premenopausal and postmenopausal women by race and ethnicity. This cohort study used data collected prospectively from Breast Cancer Surveillance Consortium community-based breast imaging facilities from January 2005 to June 2018. Participants were women aged 40 to 74 years undergoing 3 331 740 annual (prior screening within 11-18 months) or biennial (prior screening within 19-30 months) screening mammograms associated with 1815 advanced breast cancers diagnosed within 2 years of screening examinations. Data analysis was performed from September 2022 to August 2023. Heterogeneously or extremely dense breasts, first-degree family history of breast cancer, overweight/obesity (body mass index >25.0), history of benign breast biopsy, and screening interval (biennial vs annual) stratified by menopausal status and race and ethnicity (Asian or Pacific Islander, Black, Hispanic/Latinx, White, other/multiracial). PARPs for advanced breast cancer. Among 904 615 women, median (IQR) age was 57 (50-64) years. Of the 3 331 740 annual or biennial screening mammograms, 10.8% were for Asian or Pacific Islander women; 9.5% were for Black women; 5.3% were for Hispanic/Latinx women; 72.0% were for White women; and 2.0% were for women of other races and ethnicities, including those who were Alaska Native, American Indian, 2 or more reported races, or other. Body mass index PARPs were larger for postmenopausal vs premenopausal women (30% vs 22%) and highest for postmenopausal Black (38.6%; 95% CI, 32.0%-44.8%) and Hispanic/Latinx women (31.8%; 95% CI, 25.3%-38.0%) and premenopausal Black women (30.3%; 95% CI, 17.7%-42.0%), with overall prevalence of having overweight/obesity highest in premenopausal Black (84.4%) and postmenopausal Black (85.1%) and Hispanic/Latinx women (72.4%). Breast density PARPs were larger for premenopausal vs postmenopausal women (37% vs 24%, respectively) and highest among premenopausal Asian or Pacific Islander (46.6%; 95% CI, 37.9%-54.4%) and White women (39.8%; 95% CI, 31.7%-47.3%) whose prevalence of dense breasts was high (62%-79%). For premenopausal and postmenopausal women, PARPs were small for family history of breast cancer (5%-8%), history of breast biopsy (7%-12%), and screening interval (2.1%-2.3%). In this cohort study among routinely screened women, the proportion of advanced breast cancers attributed to biennial vs annual screening was small. To reduce the number of advanced breast cancer diagnoses, primary prevention should focus on interventions that shift patients with overweight and obesity to normal weight.

The effect of the use of the Gail model on breast cancer diagnosis in BIRADs 4a cases.

The BI-RADS classification system and the Gail Model are the scoring systems that contribute to the diagnosis of breast cancer. The aim of the study was to determine the contribution of Gail Model to the diagnosis of breast lesions that were radiologically categorized as BI-RADS 4A. We retrospectively examined the medical records of 320 patients between January 2011 and December 2020 whose lesions had been categorized as BI-RADS 4A. Radiological parameters of breast lesions and clinical parameters according to the Gail Model were collected. The relationship between malignant BI-RADS 4A lesions and radiological and clinical parameters was evaluated. In addition, the effect of the Gail Model on diagnosis in malignant BI-RADS 4A lesions was evaluated. Among radiological features, there were significant differences between lesion size, contour, microcalcification content, echogenicity, and presence of ectasia with respect to the pathological diagnosis (p <0.05). No significant difference was found between the lesions' pathological diagnosis and the patients' Gail score (p> 0.05). An analysis of the features of the Gail model revealed that there was no significant difference between the age of menarche, age at first live birth, presence of a first-degree relative with breast cancer, and a history of breast biopsy and the pathological diagnosis (p> 0.05). As a conclusion Gail Model does not contribute to the diagnosis of BC, especially in patients with BI-RADS 4A lesions.

The impact of mandatory mammographic breast density notification on supplemental screening practice in the United States: a systematic review.

Dense breast tissue is an independent risk factor for breast cancer and lowers the sensitivity of screening mammography. Supplemental screening with ultrasound or MRI improves breast cancer detection rate but has potential harms. Breast density notification (BDN) legislation has been introduced in the United States (US) and its impact on supplemental screening practice is unclear. This study systematically reviewed current evidence to explore the impact of BDN on supplemental screening practice in the US. Medline, PubMed, Embase, Cochrane and the Cinhal Library databases were searched (2009-August 2020). Studies were assessed for eligibility, data were extracted and summarised, and study quality was evaluated. Evidence from the included studies (n = 14) predominantly showed that BDN legislation increased the overall utilisation of supplemental screening by 0.5-143%. This effect was amplified if the notification included a follow-up telephone call informing women about additional screening benefits, and if the state's law mandated insurance cover for supplemental screening. Likelihood of supplemental screening was also influenced by history of breast biopsy and family history of breast cancer, race, age, socioeconomic status, density category, and physician's specialty and region. Some studies reported increases in biopsy rate (up to 4%) and cancer detection rate (up to 11%) after implementation of BDN legislation. BDN leads to increased use of supplemental screening. This has implications for women and the health system. These findings can help inform current and future screening programs, where breast density notification is currently implemented or being considered.

Study of breast cancer surgeries at a tertiary hospital

Background: Breast cancer is commonly treated by various combinations of surgery, chemotherapy, radiation therapy, hormone therapy and targeted therapy via a multimodality approach. Surgery is the mainstay of treatment of the early stages of breast cancer, and it ranges from lumpectomy to modified radical mastectomy. Surgery typically includes sentinel lymph nodes (LN) dissection for staging the extent of spread into the axilla. In present study we aimed to study surgeries done in patients with breast cancer at our tertiary hospital. Material and Methods: Present study was a prospective, observational study conducted in patients who underwent surgery for breast cancer, followed by histopathological report positive for breast malignancy. Results: Total 86 biopsy proven breast cancer patients underwent surgery during study period. All patients were females, with mean age 47.4 ± 10.7 years, from rural areas (74%), 9% had positive family history, 13% received Hormone Replacement Therapy/Oral Contraceptive Pills (for more than 5 years), 5% were smokers, 64% were premenopausal and 7% had history of breast biopsy. Modified radical mastectomy with axillary node dissection (69%) was most common surgery performed followed by breast conservation surgery (26%). Most patients were from stage II (64%) followed by stage I (15%). Infiltrating ductal carcinoma was most common histopathological variant (81%). Histopathologically most cancers were grade II (52%). Lymph nodal involvement (48%) and positive surgical margins (7%) were noted. Conclusion: Surgery is mainstay in treatment of breast cancer With introduction of BCS cosmetic and other concerns are tackled effectively.

Psychosocial consequences of false-positive results in screening mammography.

Introduction:To evaluate the psychosocial impact of benign breast biopsies on Lebanese women after a screening mammography and the effect of these biopsies on patients’ attitudes toward subsequent screening.Methods:In this retrospective study (January 2005 till April 2011), 109 consecutive patients with a history of breast biopsy without cancer were asked to answer a phone questionnaire. The response rate was 91.7% (100 women accepted to participate). A questionnaire about sociodemographic characteristics, biopsy characteristics, and patients’ attitudes as measured by the negative Psychosocial Consequences Questionnaire (PCQ) and other independent questions was filled by phone call by one interviewer.Results:The negative PCQ score was low for most women (only 9% have a negative PCQ score ≥18/36) and is statistically dependent on the result of the last mammography (P = 0.01) and the number of previous benign breast biopsies (P = 0.01). A total of 10% of women increased their medical visits after this biopsy, 8% were treated for psychiatric problems after this biopsy, and 19% self-examine their breasts more than once per week. The benign breast biopsy experience increases the willingness to adhere to the screening mammography in 71% of the patients, this reported adherence depends positively on the score of the negative PCQ (P = 0.043).Conclusions:The negative psychosocial effect of the biopsy is minimal in general and is positively correlated to the adherence to future mammographies. Interventions are necessary to decrease the anxiety in most susceptible women and to raise the awareness of women at risk of nonadherence to the screening mammography.

Combining quantitative and qualitative breast density measures to assess breast cancer risk

BackgroundAccurately identifying women with dense breasts (Breast Imaging Reporting and Data System [BI-RADS] heterogeneously or extremely dense) who are at high breast cancer risk will facilitate discussions of supplemental imaging and primary prevention. We examined the independent contribution of dense breast volume and BI-RADS breast density to predict invasive breast cancer and whether dense breast volume combined with Breast Cancer Surveillance Consortium (BCSC) risk model factors (age, race/ethnicity, family history of breast cancer, history of breast biopsy, and BI-RADS breast density) improves identifying women with dense breasts at high breast cancer risk.MethodsWe conducted a case-control study of 1720 women with invasive cancer and 3686 control subjects. We calculated ORs and 95% CIs for the effect of BI-RADS breast density and Volpara™ automated dense breast volume on invasive cancer risk, adjusting for other BCSC risk model factors plus body mass index (BMI), and we compared C-statistics between models. We calculated BCSC 5-year breast cancer risk, incorporating the adjusted ORs associated with dense breast volume.ResultsCompared with women with BI-RADS scattered fibroglandular densities and second-quartile dense breast volume, women with BI-RADS extremely dense breasts and third- or fourth-quartile dense breast volume (75% of women with extremely dense breasts) had high breast cancer risk (OR 2.87, 95% CI 1.84–4.47, and OR 2.56, 95% CI 1.87–3.52, respectively), whereas women with extremely dense breasts and first- or second-quartile dense breast volume were not at significantly increased breast cancer risk (OR 1.53, 95% CI 0.75–3.09, and OR 1.50, 95% CI 0.82–2.73, respectively). Adding continuous dense breast volume to a model with BCSC risk model factors and BMI increased discriminatory accuracy compared with a model with only BCSC risk model factors (C-statistic 0.639, 95% CI 0.623–0.654, vs. C-statistic 0.614, 95% CI 0.598–0.630, respectively; P < 0.001). Women with dense breasts and fourth-quartile dense breast volume had a BCSC 5-year risk of 2.5%, whereas women with dense breasts and first-quartile dense breast volume had a 5-year risk ≤ 1.8%.ConclusionsRisk models with automated dense breast volume combined with BI-RADS breast density may better identify women with dense breasts at high breast cancer risk than risk models with either measure alone.

Abstract 4355: Altered DNA methylation in breast milk from women with breast cancer

Abstract Prior candidate gene studies have shown that methylation-induced silencing of tumor suppressor genes in breast milk is related with history of breast biopsy, an established risk factor for breast cancer. To further establish the utility of breast milk as a tissue-specific biospecimen for investigations of breast carcinogenesis we measured genome-wide DNA methylation in breast milk from women with and without a diagnosis of breast cancer. Breast milk provides a source of cells without invasive procedure that is available during a window of increased breast cancer risk. Actively lactating women were enrolled and consented through the University of Massachusetts Amherst breast milk study targeting high risk subjects. Participants completed a questionnaire to provide demographic and breast cancer risk factor data. Genome-wide DNA methylation levels were quantified using the Illumina 450K HumanMethylation array. Data was processed in R using RnBeads. Beta-values were normalized (FUNnorm), low quality probes were filtered and transformed to M-values. Linear mixed effects models were fit including a random effect for subject and adjusted for history of breast biopsy, age, body mass index, age of the baby, and estimated cell type proportions, an interaction term for time of diagnosis was included. DNA methylation in women with breast cancer (n=9), was compared with women without a history of breast cancer (n=22). Among women with a diagnosis of breast cancer there were two groups: those diagnosed within one year of donating breast milk, and those diagnosed more than one year before sample collection. Four women in the control and five women in the cancer group provided bilateral breast milk samples at recruitment and both samples were included in the analyses. CpGs with significant differential methylation were tested for enrichment in gene pathways using the KEGG database. We identified 215 differentially methylated CpG sites in women diagnosed within one year of sample donation compared with women without a diagnosis of breast cancer (Q&amp;lt;0.10). The top sites were in ATF3 (an adaptive-response gene linked to epithelial mesenchymal transition), and in COL5A3 (an extracellular matrix gene related to metastasis potential). In addition, among women with a breast cancer diagnosis more than one year before sample donation we identified 8148 differentially methylated CpG sites compared with women without a breast cancer diagnosis (Q&amp;lt;0.05). Five sites were in OTOF a calcium-sensing protein in cancer. These differentially methylated sites were significantly enriched in relevant pathways such metabolic (Q= 3.12E-116), and cancer pathways (Q= 2.08E-58), PI3K-Akt signaling pathway (Q=7.05E-40), and transcriptional misregulation in cancer (Q=1.41E-29). Future studies are needed to validate our findings in independent subjects and to investigate the utility of breast milk as a biospecimen for understanding the molecular basis of disease risk and prospective risk assessment. Citation Format: Lucas A. Salas, Sara N. Lundgren, Eva P. Browne, Elizabeth C. Punska, Douglas L. Anderton, Kathleen F. Arcaro, Brock C. Christensen. Altered DNA methylation in breast milk from women with breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4355. doi:10.1158/1538-7445.AM2017-4355

Mammographic breast density and breast cancer risk in a Mediterranean population: a nested case-control study in the EPIC Florence cohort.

Mammographic breast density (MBD) has been consistently associated with breast cancer (BC) risk, and at the same time it is modulated by established BC risk factors related to reproductive and hormonal history and to lifestyle. We aimed to evaluate the association between the clinical breast imaging reporting and data system (BI-RADS), a qualitative MBD classification used in clinical setting, and BC risk through a case-control nested in the EPIC Florence cohort where baseline information on reproductive history, lifestyle and anthropometry were collected. The study includes 136 newly diagnosed BC cases and 635 controls from the 10,083 healthy women enroled in the cohort between 1993 and 1998 and followed for 6years on average. MBD was assessed on a negative mammogram performed at least one year before diagnosis in cases and on a mammogram performed in the same period for controls matched for age, enrolment date and menopausal status. Multivariate analyses adjusted for education, body mass index, parity, number of children, breastfeeding, BC family history, history of breast biopsies and Hormone Replacement Therapy use were performed. An increase in BC risk across BI-RADS categories emerged with adjusted odds ratios (OR) 1.79 (95% CI 1.06-3.01), OR 2.09 (95% CI 1.17-3.74) and OR 2.67 (95% CI 1.08-6.62) for categories 2, 3 and 4 in comparison with the reference category (p for trend=0.008). We confirm in this Mediterranean population the association of increasing MBD, classified according to BI-RADS with BC risk also taking into account other well-known risk factors for this neoplasm.

Abstract P5-03-03: A blood-based proteomic Videssa® breast assay performs comparably in women with dense and non-dense breasts

Abstract Breast density is associated with reduced imaging sensitivity and specificity for breast cancer (BC). Women with dense breasts are at a four- to six-fold increased risk of developing BC. A biochemical approach that is not affected by density would provide an additional tool to health-care professionals who are managing women with dense breasts and suspicious imaging findings. Videssa® Breast, a combinatorial proteomic biomarker assay, comprised of Serum Protein Biomarkers (SPBs) and Tumor –Associated Autoantibodies (TAAbs) integrated with clinical characteristic data to produce one diagnostic score that reliably detects BC was recently developed as an adjunctive tool to imaging. The goal of this study was to determine whether the diagnostic performance of Videssa® Breast was impacted by breast density. Provista-001 enrolled 351 participants under the age of 50 years with no prior history of breast biopsy, and Provista-002 cohort one enrolled 210 participants under the age of 50 years with no history of breast biopsy within six months; all participants were assessed as BI-RADS 3 or 4. Breast density status was retrospectively obtained for participants; the four American College of Radiology breast density categories (a, b, c, and d) used for clinical reporting were applied. Serum was collected and tested with Videssa® Breast. Women were categorized into Dense, which included categories c and d, and Non-dense, which included categories a and b, groups. To understand the performance of Videssa® Breast in women with dense breasts, the clinical sensitivity, specificity, NPV and PPV were evaluated in the dense and non-dense groups from the comprehensive Provista-001 and Provista-002 set (n=545). Of these 545, breast density information was available for 454; 62.6% (n=284) were categorized as having dense breasts and 37.4% (n=170) were categorized as having non-dense breasts. The sensitivity of Videssa® Breast in the non-dense and dense groups was 92.3% and 88.9%, respectively, and the specificity in the non-dense and dense groups was 86.6% and 81.2%, respectively. No significant differences were observed in the sensitivity (p=1.0) or specificity (p=0.1783) of Videssa® Breast in detecting BC in participants with non-dense breasts compared to those with dense breasts. The NPV in both groups exceeded 99%; the PPV was similar across groups. In summary, this study demonstrates that Videssa® Breast has comparable performance in women with dense and non-dense breasts. Videssa® Breast demonstrates high sensitivity and specificity for detecting BC (Grades I through III), irrespective of density status. Videssa® breast provides an additional tool for health-care providers when women with dense breasts present with challenging imaging findings. In addition, Videssa® breast provides assurance to a woman with dense breasts that she does not have BC, potentially reducing further anxiety in this higher risk patient population. Citation Format: Silver M, Tran Q, Gordon K, Benson KL, Henderson MC, Letsios E, Mulpuri R, Reese DE. A blood-based proteomic Videssa® breast assay performs comparably in women with dense and non-dense breasts [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P5-03-03.

Abstract 2585: Mammography screening adherence after a false positive test result

Abstract Mammography screening has been shown to be effective in reducing breast cancer mortality, but it also generates a significant number of false positive test results. We sought to compare mammography guideline adherence after a false positive versus a true negative screening mammogram, looking for differences by age, race, family history of breast cancer, time since prior mammogram, and mammographic breast density. Linked data from the Carolina Mammography Registry and privately insured beneficiaries in North Carolina from 2004-2009 were used to evaluate screening adherence among 14,071 women aged 40 to 64 years who had a screening mammogram with a false positive or true negative result. Women were classified as being adherent to mammography screening guidelines if they obtained a screening mammogram in the 9 to 24 months after their index screening mammogram. We compared characteristics of women with a false positive versus true negative screening mammogram result using the chi-square test. To determine if the index mammogram result (false positive or true negative) was predictive of subsequent adherence to screening mammography, we employed a random effects logistic regression, adjusting for age, race, menopausal status, first-degree family history of breast cancer, personal history of breast biopsy, mammographic breast density, time since prior mammogram, and comorbidities. We tested for interactions of these characteristics with the index mammogram result using backwards selection and considered interaction terms with p-values &amp;lt;0.05 significant. Of the 14,071 screening mammograms 92.1% were true negative and 7.9% were false positive. Women with a false positive were more likely to be younger, pre-menopausal, have dense breasts, and have had their last mammogram 3 or more years ago. Women with a false positive result were also less adherent to screening mammography in the subsequent 24 months compared to women with a true negative result (81.8% vs. 84.2%, p-value = 0.031). Women with a prior mammogram within 1 to 3 years and a false positive result were less likely to be adherent (adjusted odds ratio (OR) = 0.77, 95% CI: 0.64-0.94) than women with a true negative result. In contrast, for women who had a prior mammogram 3 or more years ago, those with a false positive result were more likely to be adherent (adjusted OR = 1.80, 95% CI: 1.13-2.87) than those with a true negative result. Our findings suggest that a false positive screening mammogram may reduce the likelihood of a woman returning for her next planned screening mammogram if her prior mammogram was within 1 to 3 years, but it may increase her likelihood to return if her prior mammogram was more than 3 years prior. Future studies should expand beyond observational data to incorporate qualitative interview data with women to better understand the psychological distress experienced after a false positive screening mammogram result. Citation Format: Mary W. Marsh, Thad Benefield, Mikael Anne Greenwood-Hickman, Laura Jones, Anne Marie Meyer, Louise M. Henderson. Mammography screening adherence after a false positive test result. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2585.

Abstract P2-09-16: Comparison of prediction models of breast cancer in high risk populations?

Abstract Breast cancer is the most frequent female cancer with 48 800 new cases each year in France. A French national screening program in women without genetic risk factors has been initiated in 2004. Women with BRCA 1 or 2 mutations are included in surveillance programs. Women without such mutations can be assessed for breast cancer risk using risk prediction models. The aim of this study is to compare the performance of three breast cancer risk prediction models: GAIL2, BODICEA and IBIS in a population of patients (pts) who developed breast cancer. Patients and Methods: The GAIL1 model was performed to 188 pts at high risk of breast cancer according to their family history and who developed breast cancer. Personal and familial data was regenerated at the day before the diagnosis of breast cancer and the three models were applied. From this population, 60 pts were selected: for whom all information necessary to use the 3 models was available and for whom the GAIL1 model provided sufficient variability in the relative risk of breast cancer. The GAIL2 model takes into account individual risk factors: age, menarches, parity, age at first birth, history of breast biopsy, atypical hyperplasia or in situ lobular carcinoma. The BODICEA model considers age and familial risk factors: number of related affected by breast ovarian, prostate and pancreas cancer and age at diagnosis. Both personal and familial risk factors are used for the IBIS model. We estimated lifetime breast cancer risk for each patient with the three models from the available software packages. We assessed Pearson's correlation coefficient between the three models. Results Median (range) age was 45 years (25-74). Risk prediction could not be evalauted by the GAIL2 model for 14 pts since they were less than 35 years of age. Lifetime breast cancer risk was 16.1% (4.4-38.7) for GAIL2, 11.6% (2.2-39.5) for BODICEA and 16.5% (3.5-36.3) for IBIS. Pearson's correlation coefficient between BODICEA and GAIL2, IBIS and GAIL2 and between BODICEA and IBIS were 0.36, 0.38 and 0.69 respectively. In most cases, IBIS risk predictions were higher than GAIL2's which were higher than BODICEA's as soon as lifetime risk was at least 20%. When the three models were applicable (46pts), IBIS estimated a higher risk in 31 cases (67%) versus 10 for GAIL2 (21.74%) and 5 for BODICEA (10.86%). The median (range) time for use of the tools per patient was 30 seconds (16-80) for GAIL2, 588 (198-1804) for BODICEA and 86 (46-135) for IBIS. Discussion Results in this selected population of pts who developed breast cancer show that IBIS seems to be the better performer to predict breast cancer risk:. Results are obtained faster and the risk predictions provide higher estimates. The GAIL2 model is quick and easy to use but with a limited number of items. Conversely, BODICEA requires a very large number of items, not always available, and does not consider incomplete data. In conclusion, IBIS model seems to be the most suitable for practical use in the evaluation of breast cancer risk. Citation Format: Mailliez A, Kramar A, Peyrat J-P, Revillion F, Bonneterre J. Comparison of prediction models of breast cancer in high risk populations?. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P2-09-16.

Abstract P6-02-08: Breast cancer screening in the precision medicine era

Abstract We are entering the era of precision medicine in which cancer screening, prevention and treatment will be tailored to each individual. The progress made in this field is due, in part, to advances in our understanding of cancer risk and tumor biology. The challenge before us is to harness this knowledge and apply it in the clinical setting. Breast cancer screening provides an excellent opportunity to test the value of precision medicine in the real world. In this report we describe the process of designing a model of personalized breast cancer screening. Methods Risk factors were selected that have the greatest impact, have been validated and can be measured across a population. A risk model was selected that is highly calibrated, has been validated in a large screening cohort and is easy to apply in a large population of women. An expert committee was convened that set risk thresholds for stratifying women into groups that will be recommended to undergo biennial, annual or every six month screening. Risk thresholds and screening schedules are in accordance with the United States Preventive Services Task Force breast cancer screening recommendations. Results Risk factors: Age, race/ethnicity, personal history of breast biopsies and benign breast disease, family history, breast density and breast cancer-associated genetic mutations and single nucleotide polymorphisms (SNPs) were chosen as the risk factors that will be used to determine breast cancer risk. Risk model: The Breast Cancer Surveillance Consortium risk model will be used to calculate a woman's 5-year risk and will be modified by a polygenic risk score based on 81 SNPs. Risk thresholds: Women will be recommended to undergo biennial screening mammography when they reach the age of 50 or have the risk of an average 50 year-old woman (1.3% 5-year risk). Women will be advised to undergo annual screening if they are at increased risk of developing an interval cancer (women in their forties with extremely dense breasts and women at increased risk of developing estrogen receptor negative breast cancer based on their SNPs). Women will be recommended to undergo annual mammography and annual MRI if they are found to be gene mutation positive, have the risk of a BRCA1 mutation carrier (6% 5-year risk) or have a history of mantle radiation. Discussion Selecting the appropriate risk factors and risk model and determining risk thresholds are key components of designing a personalized breast cancer screening model. Personalized screening may be the way forward, but this can only be determined within the setting of a randomized controlled trial. We will conduct such a trial to determine if personalized screening is as safe as, less morbid than, more preferred by women than and enables prevention when compared to annual screening. The WISDOM (Women Informed to Screen Depending on Measures of risk) study will compare risk-based screening to annual screening within the Athena Breast Health Network with support from the Patient-Centered Outcomes Research Institute. Our intent is that this trial will provide us with the data that we need to determine the safest and most effective way to screen women for breast cancer in the era of precision medicine. Citation Format: Thompson CK, Fiscalini AS, Donnellan P, Kaplan CP, Madlensky L, Eklund M, Ziv E, van't Veer LJ, Tice JA, Esserman LJ. Breast cancer screening in the precision medicine era. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P6-02-08.

Breast Density and Benign Breast Disease: Risk Assessment to Identify Women at High Risk of Breast Cancer.

Women with proliferative breast lesions are candidates for primary prevention, but few risk models incorporate benign findings to assess breast cancer risk. We incorporated benign breast disease (BBD) diagnoses into the Breast Cancer Surveillance Consortium (BCSC) risk model, the only breast cancer risk assessment tool that uses breast density. We developed and validated a competing-risk model using 2000 to 2010 SEER data for breast cancer incidence and 2010 vital statistics to adjust for the competing risk of death. We used Cox proportional hazards regression to estimate the relative hazards for age, race/ethnicity, family history of breast cancer, history of breast biopsy, BBD diagnoses, and breast density in the BCSC. We included 1,135,977 women age 35 to 74 years undergoing mammography with no history of breast cancer; 17% of the women had a prior breast biopsy. During a mean follow-up of 6.9 years, 17,908 women were diagnosed with invasive breast cancer. The BCSC BBD model slightly overpredicted risk (expected-to-observed ratio, 1.04; 95% CI, 1.03 to 1.06) and had modest discriminatory accuracy (area under the receiver operator characteristic curve, 0.665). Among women with proliferative findings, adding BBD to the model increased the proportion of women with an estimated 5-year risk of 3% or higher from 9.3% to 27.8% (P<.001). The BCSC BBD model accurately estimates women's risk for breast cancer using breast density and BBD diagnoses. Greater numbers of high-risk women eligible for primary prevention after BBD diagnosis are identified using the BCSC BBD model.

One versus Two Breast Density Measures to Predict 5- and 10-Year Breast Cancer Risk.

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. 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. 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 <1.67% with the one-density model to ≥1.67% with the two-density model. 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. A two-density model should be considered for women whose density decreases when calculating breast cancer risk.

Selected breast cancer risk factors and early detection of the neoplasm in women from Lublin region attending screening program inSt. John's Cancer Center, years 2005-2006.

In Poland, consulting is offered to women about risk factors of breast cancer, breast symptoms and early cancer detection. Study aims were to evaluate the correlation between some risk factors and women's attendance to breast checkups, and to assess links between risk factors and detection of early breast cancer. The study involved 8014 women aged 50-69 years, participating in breast cancer screening program. The frequency of breast checkups (mammography, CBE and BSE) and occurrence of breast cancer risk factors in studied women were evaluated as well as clinical stage of detected tumors. Odds ratios were used to compare relative odds of breast cancer with exposure to risk factors. 47.11% studied women had regularly undergone MMG, 30.82% had CBE within the previous year, 14.26% regularly performed BSE. The incidence of risk factors varied from 2.94% (menarche at ≤11 years) to 12.38% (current use of HRT). In 47.82% women, no evidence of studied risk factors was found. Women with history of breast biopsies and current users of HRT had MMG, CBE and BSE significantly more often (p<0.0001). 29.27% early breast cancers (pTis, pT1abN0) were found among 82 detected tumors. Relative odds of breast cancer occurrence in women without the risk factors were significantly lower (OR=0.55, 95% CI [0.35; 0.86]). Only nulliparous women had significantly higher odds of early breast cancer (OR=7.37, 95% CI [1.32; 41.17]). Women using HRT and women after breast biopsy were significantly more likely to attend breast checkups. There were no significant links between most risk factors and odds of early stages of breast cancer. Women should have preventive checkups irrespective of their breast cancer risk factors.

Abstract 3249: Differential impact of body mass index (BMI) on absolute and percent breast density; implications regarding their use as breast cancer risk (BCR) biomarkers

Abstract Percent breast density (PBD) is a commonly used biomarker of BCR. However, its use is greatly confounded by the strong influence of non-dense breast tissue on its measurement and factors, such as BMI, which have a direct impact on this non-dense tissue. Consequently, BMI, a potent BCR factor, is actually negatively correlated with PBD. We propose that absolute breast density (ABD), a more direct determinant of BCR which can be easily and accurately measured on a routine basis, is a more accurate and valid biomarker of BCR. To address this issue, we compared the correlation between PBD and ABD with baseline demographics (e.g., age, BMI, waist:hip ratio, reproductive parameters, family history of BC and personal history of breast biopsies) and dietary and physical activity variables (measured using a modified version of the NCI Diet History Questionnaire and the International Physical Activity Questionnaire, respectively) in a group of 169 healthy postmenopausal women enrolled in a chemoprevention trial testing the individual and combined effects of the antiestrogen Raloxifene and the omega-3 preparation Lovaza on BD used as a biomarker of BCR (NCT00723398). A volumetric breast density software (Volpara, Matakina, Wellington, New Zealand) was used for determination of ABD and PBD since it may be superior to the two-dimensional method in predicting BCR. As observed previously, a strong negative correlation between PBD and BMI was observed (Rho = -0.5, P&amp;lt;5e-12) due to the strong positive correlation of BMI with non-dense breast. However, we also observed a strong positive correlation of BMI with ABD (Rho = 0.41, P&amp;lt;2.5e-8), a relationship which is not well established in the literature and supports the use of ABD as a more accurate indicator of BCR. We also observed that correction of PBD by BMI did not frequently provide the same information as ABD, thus further indicating that the latter should be the gold standard for BD measurement. The strong positive association between BMI and ABD implies that even a modest change in BMI (as can be expected with dietary and physical activity manipulations) is likely to obscure the independent effect of an intervention on ABD. We indeed observed that many correlations between dietary variables and ABD did not emerge until adjustment was made for BMI. Our data support the following conclusions: 1) ABD instead of PBD (even after adjustment for BMI) should be used for assessment of BD; 2) based on our novel observation of the strong positive correlation between BMI and ABD, ABD should be adjusted for BMI when testing the influence of an intervention on BD as a biomarker of BCR. (Supported by grant KG081632 from Susan G. Komen for the Cure.) Citation Format: Susann Schetter, Terryl Hartman, Jason Liao, John P. Richie, Bogdan Prokopczyk, Cynthia Dubrock, Carina Signori, Christopher Hamilton, Laurence M. Demers, Karam El-Bayoumy, Andrea Manni. Differential impact of body mass index (BMI) on absolute and percent breast density; implications regarding their use as breast cancer risk (BCR) biomarkers. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3249. doi:10.1158/1538-7445.AM2014-3249

Cross-sectional study to assess the association of population density with predicted breast cancer risk.

The Gail and CARE models estimate breast cancer risk for white and African-American (AA) women, respectively. The aims of this study were to compare metropolitan and nonmetropolitan women with respect to predicted breast cancer risks based on known risk factors, and to determine if population density was an independent risk factor for breast cancer risk. A cross-sectional survey was completed by 15,582 women between 35 and 85years of age with no history of breast cancer. Metropolitan and nonmetropolitan women were compared with respect to risk factors, and breast cancer risk estimates, using general linear models adjusted for age. For both white and AA women, tisk factors used to estimate breast cancer risk included age at menarche, history of breast biopsies, and family history. For white women, age at first childbirth was an additional risk factor. In comparison to their nonmetropolitan counterparts, metropolitan white women were more likely to report having a breast biopsy, have family history of breast cancer, and delay childbirth. Among white metropolitan and nonmetropolitan women, mean estimated 5-year risks were 1.44% and 1.32% (p<0.001), and lifetime risks of breast cancer were 10.81% and 10.01% (p<0.001), respectively. AA metropolitan residents were more likely than those from nonmetropolitan areas to have had a breast biopsy. Among AA metropolitan and nonmetropolitan women, mean estimated 5-year risks were 1.16% and 1.12% (p=0.039) and lifetime risks were 8.94%, and 8.85% (p=0.344). Metropolitan residence was associated with higher predicted breast cancer risks for white women. Among AA women, metropolitan residence was associated with a higher predicted breast cancer risk at 5years, but not over a lifetime. Population density was not an independent risk factor for breast cancer.

Cohort Study of Risk Factors for Breast Cancer in Post Menopausal Women

OBJECTIVESThe present study assessed more than 800 potential risk factors to identify new predictors of breast cancer and compare the independence and relative importance of established risk factors.METHODSData were collected by the Women's Health Initiative and included 147,202 women ages 50 to 79 who were enrolled from 1993 to 1998 and followed for 8 years. Analyses performed in 2011 and 2012 used the Cox proportional hazard regression to test the association between more than 800 baseline risk factors and incident breast cancer.RESULTSBaseline factors independently associated with subsequent breast cancer at the p<0.001 level (in decreasing order of statistical significance) were breast aspiration, family history, age, weight, history of breast biopsies, estrogen and progestin use, fewer live births, greater age at menopause, history of thyroid cancer, breast tenderness, digitalis use, alcohol intake, white race, not restless, no vaginal dryness, relative with prostate cancer, colon polyps, smoking, no breast augmentation, and no osteoporosis. Risk factors previously reported that were not independently associated with breast cancer in the present study included socioeconomic status, months of breast feeding, age at first birth, adiposity measures, adult weight gain, timing of initiation of hormone therapy, and several dietary, psychological, and exercise variables. Family history was not found to alter the risk associated with other factors.CONCLUSIONSThese results suggest that some risk factors not commonly studied may be important for breast cancer and some frequently cited risk factors may be relatively unimportant or secondary.

Impact of family history on choosing risk-reducing surgery among BRCA mutation carriers

Despite substantial survival benefits of risk-reducing mastectomy (RRM) and risk-reducing bilateral salpingo-oophorectomy (RRBSO) among BRCA mutation carriers, only a minority elect to undergo these procedures. This study investigates factors that might influence decision making regarding prophylactic surgeries among women with BRCA mutations. Unaffected BRCA mutation carriers who were counseled at our center and either underwent prophylactic surgery or participated in a high-risk surveillance program at our institution from 1998 through 2010 were included in the study. Medical records were reviewed and data collected included age, family history, parity, mutation type, history of breast biopsy or cosmetic surgery, and uptake of prophylactic surgeries. Among 136 unaffected women with BRCA mutations, uptake of RRM was 42% and uptake of RRBSO was 52%. Family history of first- and second-degree relatives being deceased from breast cancer was predictive of uptake of RRM and of RRBSO (odds ratio [OR], 11.0; P = .005; and OR, 15.8; P = .023, respectively), and history of a mother lost to pelvic cancer was predictive of uptake of RRBSO (OR, 7.9; P = .001). Parity also predicted both RRM and RRBSO uptake (OR, 4.2; P = .001; and OR, 5.4; P = .003, respectively). Age at the time of genetic testing and history of breast biopsy or cosmetic surgery were not predictive of RRM uptake. Perceptions of cancer risk are heavily influenced by particular features of an individual's family history and may be motivators in preventive surgery more than actual cancer risk estimations themselves. Awareness of subtle factors beyond the statistical risk for cancers is relevant when counseling at-risk women.

Abstract B88: Breast cancer risk factors and Gail risk score distribution in a heavily Hispanic population.

Abstract Background: The Athena Breast Health Network is a collaborative University of California (UC) initiative, focused on providing evidence-based innovations in the screening, diagnosis and treatment of cancer, as well as changing the way patients and providers interact to prevent and manage the disease. An initial goal of the Athena project is to integrate standardized risk assessment into breast cancer screening. Women at highest cancer risk are identified and offered personalized counseling and referrals for prevention services. Previous studies have shown a difference in breast cancer risk factor distribution between Hispanics and non-Hispanic whites. This current study will describe these factors in the UC Irvine Athena cohort, a heavily Hispanic population. Methods: Between March, 2011 and July, 2012, 1,407 patients completed an electronic questionnaire at the time of their screening mammography and consented to enroll in the UC Irvine Athena Breast Health Network as a research participant. The questionnaire collected data on the woman's medical, reproductive, and family cancer history. Body mass index (BMI) was calculated using self-reported weight and height [BMI=weight (kg)/height (m)2]. NCI-Gail breast cancer scores were calculated using age, age at menarche, age at first full-term pregnancy, family history of breast cancer (mother, sister, daughter), number of biopsies, number of biopsies with atypia, and race/ethnicity. Elevated breast cancer risk was defined as a Gail score &amp;gt; 1.67. Our screening cohort was composed of 63.8% Hispanic (n=903), 19.6% non-Hispanic white (n=277), 10.5% Asian (n=148), 1.4% African American (n=20), and 4.2% who were unable to be classified (n=59). This analysis was limited to women who were Hispanic or non-Hispanic whites. Results: Mean ± SD ages of the Hispanic and non-Hispanic white groups were 53.9 ± 9.9 years and 60.6 ± 11.3 years (p&amp;lt;0.0001), respectively. The Hispanic group had a significantly higher rate of obesity (BMI &amp;gt; 29.9) compared to non-Hispanic whites (42.0% vs 29.9%; p&amp;lt;0.0001), diabetes (10.7% vs 2.5%; p&amp;lt;0.0001) and hypertension (26.7% vs 20.6%; p&amp;lt;0.04), and a lower rate of family cancer history (p&amp;lt;0.015). Hispanics had a significantly lower level of education, older age at menarche, less breast biopsies and biopsies with atypia. They were also less likely to have a mother, sister or daughter with a BRCA mutation or history of breast or ovarian cancer in a first- or second- degree relative compared to the non-Hispanic white group (p&amp;lt;0.01). In the Hispanic group, 4.5% had an elevated 5-year Gail score &amp;gt; 1.67 compared to 33.7% of non-Hispanic whites (p&amp;lt;0.0001). Overall, those with an elevated Gail score were more likely to have reported Jewish ancestry (p&amp;lt;0.002), have a higher educational level (p&amp;lt;0.0001), a history of breast biopsy and atypia (p&amp;lt;0.0001), or a history of a first- or second- degree relative with breast cancer (p&amp;lt;0.0001) and were less likely to be married (p&amp;lt;0.0001). Conclusion: In the UC Irvine Athena population, breast cancer risk factors and Gail scores differed significantly between Hispanics and non-Hispanic whites. Recognition of racial disparities is critical for implementing a personalized breast cancer screening and prevention program in the community. For example, our study suggests that in a heavily Hispanic population, screening and prevention programs should target obesity reduction, while greater risk reduction in a non-Hispanic white population may be achieved through genetic counseling. Citation Format: Deborah Goodman, Hannah Lui Park, Argyrios Ziogas, Chris Tannous, Kathryn Larsen, Hoda Anton-Culver. Breast cancer risk factors and Gail risk score distribution in a heavily Hispanic population. [abstract]. In: Proceedings of the Fifth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2012 Oct 27-30; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2012;21(10 Suppl):Abstract nr B88.