Individual Breast Cancer Risk Research Articles

Comparing Personal Risk Communication with Generic Information on Breast Cancer Screening Attendance: A Randomized Controlled Trial

Objective: This study sought to discern the differential impacts of communicating individualized breast cancer risk versus disseminating generic breast cancer information on women's proclivity towards breast cancer screenings. Method: A structured experimental design was used, including 300 female volunteers aged between 40-69, who had not previously been diagnosed with breast cancer, in Erzincan. Data collection entailed a face-to-face administration of a 22-item questionnaire complemented by the ASSISTS scale. Participants were randomized into two conditions: one receiving a standardized briefing on breast cancer screening modalities, and the other being apprised of their lifetime risk of breast cancer development based on the modified Gail model. Results: Post-intervention, 55.8% (n=72) of the risk-informed cohort and 40.5% (n=41) of the generic information cohort expressed a willingness to undergo screening (p=0.022). Notably, the group informed of their personalized risk exhibited a more pronounced uptick in requests for breast self-examination, clinical breast examination, and mammography screening compared to their counterparts. Conclusion: The communication of personalized breast cancer risk proves to be a more efficacious and time-efficient strategy than generic information dissemination in bolstering screening participation rates.

Abstract PO4-19-02: Genetic risk estimation in breast cancer and assessing health disparities

Abstract Background: Breast Cancer (BC) incidence and the distribution of BC risk factors vary between racial and ethnic groups within the United States. It has been demonstrated that providing education on individualized breast cancer risk will improve patient uptake to recommended BC screening and prevention strategies. Most clinical risk assessment tools, including the validated IBIS model were derived from data using non-Hispanic white women. Consortia have also now identified over 300 common genetic susceptibility loci for BC, summarized by the polygenic risk score (PRS). When combined with clinical risk assessment tools, such as the IBIS or BCRAT models, the PRS can further refine risk estimation for patients. To date, most studies on the use of PRS have been done in non-Hispanic white women. While these PRS still perform well in racial minorities, there has been a recognized need to improve racial and ethnic diversity in genomic research cohorts. Herein, we aim to combine clinical risk assessment models that are already used in routine clinical practice with information derived from PRS testing in women of racial minorities to determine if this can improve risk estimation, patient understanding, and uptake to recommended breast cancer screening and prevention strategies. Design: This is a minimal risk prospective study with a single arm incorporating the PRS into a standard breast cancer risk reduction consultation, followed by annual surveys over 10 years to determine if and how the information provided by the PRS influenced patient decisions regarding recommended BC screening and prevention. Patients will have IBIS and BCRAT risks calculated at baseline visit. A survey of patient perceptions/understanding of risk, intention to undergo screening, and use of preventive medicine is completed after baseline visit prior to receiving the PRS results. Blood sample is obtained at baseline and DNA samples are analyzed for approximately 300 SNPs on a custom-designed, targeted SNP panel from ThermoFisher Scientific by the Mayo Clinic Genomics Laboratory. This PRS result is then combined with the clinical risk assessments (IBIS and BCRAT scores) using the R package Individualized Coherent Absolute Risk Estimators (iCare) tool to provide a new estimate of breast cancer risk for 5year, 10year, and lifetime risk. Patients are seen for follow-up to review results and then complete a second survey to assess their understanding of the results, BC risk, and how the PRS impacted their decision to undergo screening/prevention strategies. Eligibility Criteria: Women who self-identify as African American/Black or Hispanic/Latinx between 30-75 years old with any of the following: 1.)IBIS score of ≥5% for the 10 year risk OR BCRAT score of ≥ 3 % for the 5 year risk. 2.) History of biopsy proven atypical hyperplasia 3.) History of biopsy proven lobular carcinoma in situ. Specific Aims: Aim 1: The primary aim of this study is to explore if the addition of PRS to the BCRAT and IBIS score will improve intentions to undergo recommended breast cancer screening strategies such as mammography, MRI, or molecular breast imaging in women of underserved racial minorities Aim 2: To explore if the addition of the PRS to the BCRAT and IBIS risk score will aid women of racial minorities in deciding whether to take preventative endocrine therapy. Aim 3: To understand how individualized risk assessment and information on PRS may alter perceived risk of breast cancer. Statistical Methods Analysis will be mostly descriptive. Continuous variables will be summarized as mean (standard deviation) or median (range) and categorical variables will be reported as frequency (percentage). Kaplan-Meier method will be used to estimate the long-term cumulative risk of cancer. Current Accrual: 3. Target Accrual: 50 Citation Format: Lauren Cornell, Sandhya Pruthi, Linda Hasadsri, Sarah McLaughlin. Genetic risk estimation in breast cancer and assessing health disparities [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-19-02.

Abstract PO1-20-01: A Prospective Single-Arm Pilot Interventional Trial to Assess Individual Breast Cancer Risk Prior to Gender-Affirming Chest Masculinization Surgery

Abstract Background: The number of individuals who identify as transgender and/or gender-diverse (TGD) is steadily increasing and the number of individuals seeking chest masculinization surgery, commonly called “top-surgery,” is also increasing. In persons assigned female at birth, top surgery removes most breast tissue to allow the chest to appear masculine. However, top surgery is not equivalent to oncologic risk-reducing mastectomies (RRM), which aim to remove all breast tissue to reduce future breast cancer (BC) risk. While top surgery has significant psychological and health benefits, its impact on future BC risk is unclear, especially for those individuals who are at high-risk for future BC development (defined as ≥17% by international consensus guidelines) and/or have a pathogenic germline mutation for BC. The aims of this study are to 1) determine the percentage of TGD persons assigned female at birth who are considering undergoing top surgery and are at elevated BC risk using validated risk models, 2) determine the percentage of TGD persons at elevated BC risk who choose to undergo RRM as part of top surgery, and 3) assess and compare self-perceived BC risk compared to calculated risk. Methods: A single-institution prospective single-arm pilot study is currently underway from March 2023 through January 2024. Individuals assigned female at birth, who are age ≥18 years, and considering undergoing top surgery, but have not yet undergone top surgery, are eligible to enroll (goal accrual N=35). Participants are being actively recruited form our institution’s Comprehensive LGBTQIA+ Inclusion Health Clinic and Plastic Surgery Clinic. Consented participants undergo a personalized BC risk assessment the validated IBIS and Gail (for those ≥35 years old) BC risk models. Those with an average lifetime BC risk (< 17%) are recommended to continue top surgery as planned/no intervention, participants with a moderate lifetime BC risk (17-30%) are counseled they may consider RRM as part of their top surgery operation given that sparse data exists in this space, while those with a high lifetime BC risk ( >30% or pathogenic germline variant) are counseled on the potential benefits of RRM. Participants with a family history suggestive of a hereditary cancer syndrome meet with a certified Genetics counselor for consideration of genetic testing, per NCCN guidelines. Results: We anticipate reporting 1) general demographic description of the cohort, 2) the percentage who have an elevated lifetime BC risk and/or a pathogenic genetic mutation, 3) the percentage of those with an elevated risk who choose to undergo RRM as part of top surgery, and 4) report the differences between self-perceived vs calculated lifetime BC risk. Conclusion: Our findings will inform TGD persons and surgeons on the utility of a personalized BC risk assessment prior to top surgery to assist in surgical decision-making, report the accuracy of self-perceived BC risk in TGD persons, and potentially change the standard pre-operative evaluation for TGD persons considering top surgery to include a formal BC risk assessment to ensure high-risk individuals have the option to undergo RRM to appropriately reduce their future BC risk. Citation Format: Chandler Cortina, Anna Purdy, Samantha Stachowiak, Kristen Klement, Sarah Sasor, Kate Krucoff, Andrew Petroll, Kevin Robertson, Annie Lakatos, Erin Doren. A Prospective Single-Arm Pilot Interventional Trial to Assess Individual Breast Cancer Risk Prior to Gender-Affirming Chest Masculinization Surgery [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-20-01.

The Prognostic Quality of Risk Prediction Models to Assess the Individual Breast Cancer Risk in Women: An Overview of Reviews

Purpose. Breast cancer is the most common cancer among women globally, with an incidence of approximately two million cases in 2018. Organised age-based breast cancer screening programs were established worldwide to detect breast cancer earlier and to reduce mortality. Currently, there is substantial anticipation regarding risk-adjusted screening programs, considering various risk factors in addition to age. The present study investigated the discriminatory accuracy of breast cancer risk prediction models and whether they suit risk-based screening programs. Methods. Following the PICO scheme, we conducted an overview of reviews and systematically searched four databases. All methodological steps, including the literature selection, data extraction and synthesis, and the quality appraisal were conducted following the 4-eyes principle. For the quality assessment, the AMSTAR 2 tool was used. Results. We included eight systematic reviews out of 833 hits based on the prespecified inclusion criteria. The eight systematic reviews comprised ninety-nine primary studies that were also considered for the data analysis. Three systematic reviews were assessed as having a high risk of bias, while the others were rated with a moderate or low risk of bias. Most identified breast cancer risk prediction models showed a low prognostic quality. Adding breast density and genetic information as risk factors only moderately improved the models’ discriminatory accuracy. Conclusion. All breast cancer risk prediction models published to date show a limited ability to predict the individual breast cancer risk in women. Hence, it is too early to implement them in national breast cancer screening programs. Relevant randomised controlled trials about the benefit-harm ratio of risk-adjusted breast cancer screening programs compared to conventional age-based programs need to be awaited.

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

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

Breast Cancer Risk Assessment among Rural Women in Turkey

Background: Breast cancer is the most prevalent cancer type in women both Turkey and worldwide. In order to construct screening programs correctly, to identify breast cancer at the early stage and to reduce the burden of disease after diagnosis; first, women’s risk levels should be determined. Aim: The aim of this study is to identify women’s breast cancer risk levels, the frequency of BSE, and affecting factors in rural women in Turkey. Method and Material: This cross-sectional study was included 280 adult women. As data collection tools were used “The Sociodemographic Characteristics Questionnaire” and “Breast Cancer Risk Assessment Tool”. Results: Women’s mean breast cancer risk level was found as 137.69±67.56. 91.1 % of women were found to be in a low-risk group. When women’s mean scores related to Breast Cancer Risk Assessment Tool and sociodemographic variables were compared and a statistically significant difference was detected between vocational status, education level and smoking status. The vast majority of women take place in the low-risk group in terms of breast cancer. Conclusions: The effects of community-based and opportunistic national breast cancer screenings on mortality rates and their cost-effectiveness must be assessed. Primarily, individual breast cancer risk evaluations must be done instead of community-based screenings, useful tools must be selected for risk evaluation, screening programs must be performed according to the risk and women should be invited to health organizations for screenings.

Association between prediabetes and breast cancer: a comprehensive meta-analysis.

Breast cancer accounts for up to 30% of cancer cases in women in the US. Diabetes mellitus has been recognized as a risk factor for breast cancer. Some studies have suggested that prediabetes may also be associated with breast cancer whereas other studies have shown no or an inverse association; thus, we conducted a meta-analysis to assess the risk of breast cancer in prediabetes. We searched PubMed/Medline, EMBASE, Google Scholar, and Scopus to identify studies that reported breast cancer risks in patients having prediabetes compared to normoglycemic patients. Binary random-effects model was used to calculate a pooled odds ratio (OR) with 95% confidence intervals. I2 statistics were used to assess heterogeneity. Leave-one-out sensitivity analysis and subgroup analyses were performed. We analyzed 7 studies with 24,586 prediabetic and 224,314 normoglycemic individuals (783 and 5739 breast cancer cases, respectively). Unadjusted odds ratio (OR) for breast cancer was 1.45 (95% CI = 1.14, 1.83); adjusted OR was 1.19 (95% CI = 1.07, 1.34) in prediabetes. Subgroup analysis revealed a higher breast cancer risk in individuals aged less than 60years (OR = 1.86, 95% CI = 1.39, 2.49) than in those aged 60years or more (OR = 1.07, 95% CI = 0.97, 1.18). Subgroup analysis by median follow-up length indicated a higher risk of breast cancer for follow-ups of less than or equal to 2years (OR = 2.34, 95% CI = 1.85, 2.95) than in those of over 10years (OR = 1.1, 95% CI = 0.99, 1.23) and 6 to 10years (OR = 1.03, 95% CI = 0.88, 1.21). In conclusion, individuals with prediabetes have higher risk of developing breast cancer than those with normoglycemia, especially younger prediabetes patients. These individuals may benefit from early identification, monitoring, and interventions to reverse prediabetes.

Abstract PR009: Effect of testosterone therapy on quantitative breast lobular atrophy in transmasculine individuals

Abstract Background. Most transmasculine (TM) individuals pursue gender-affirming testosterone therapy (TT). The health outcomes associated with long-term TT use remains unclear. We previously reported that TT is associated with breast lobular atrophy (i.e., shrinking) when assessed by pathologists. Breast lobules are the main sites whereby cancer develops, and there are ongoing research to clarify the association between lobular atrophy and breast cancer risk. As such, TT may influence breast cancer risk in TM individuals. This study investigated the quantitative relationship between length of TT use and lobular atrophy. Materials and Methods. Breast surgical specimens were obtained from 417 TM subjects who had gender affirming chest-contouring surgery. Clinical data were retrieved from medical records. Each subject had one to six H&amp;E slides that were reviewed by pathologists and digitized at 20× (n=1073 whole slide images (WSIs)). We applied our deep-learning computational pathology method that detects and quantifies acini and breast lobules to the WSIs. For each WSI, our method extracted total tissue area (mm2), fat area (mm2), number of breast lobules, area (mm2) and span (mm) of each lobule, and the number of acini in each lobule. Multiple WSIs for each subject were combined to compute four measures of lobular atrophy—1) median lobule size (mm2), 2) median acini counts per lobule, 3) number of lobules per mm2 of tissue area, and 4) % lobular area. We evaluated the relationship between quantitative measures of lobular atrophy for every six months of TT use with linear regression models, adjusting for age, year of surgery, race/ethnicity, BMI, chest binding, whether the subject removed their ovaries, and TT dose. Patients who did not use TT prior to surgery were assigned as zero months of TT. In order to improve the interpretability of the beta coefficient, we back transformed the natural log beta coefficients. Results. The size of breast lobules in TM individuals shrank by 3.9 % for every six months of TT use (adjusted p&amp;lt;0.001). The results of the other three measures were similar: the median number of acini in each breast lobule decreased by 5.5%, the number of lobules per mm2 of breast tissue decreased by 4.9%, and the % of lobular area in the breast was reduced by 10.0%, for every six months that a patient received TT (all adjusted p≤0.001). Conclusions. This is the first study to investigate the quantitative relationship between duration of TT and lobular atrophy. For every six months of TT, the breast lobules of TM subjects decreased in size and numbers at an approximate rate of 5%, after accounting for potential confounders. This work will support the understanding of breast cancer risk in the TM population, and contributes to reducing healthcare disparities in the transgender community. Citation Format: Gabrielle M. Baker, Yaileen D. Guzman-Arocho, Vanessa C. Bret-Mounet, Mitko Veta, Suzanne C. Wetstein, Brittany M. Charlton, Sarah S. Jackson, Gerburg M. Wulf, Yujing Jan Heng. Effect of testosterone therapy on quantitative breast lobular atrophy in transmasculine individuals [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr PR009.

Artificial Intelligence for Image-Based Breast Cancer Risk Prediction Using Attention.

Accurate prediction of individual breast cancer risk paves the way for personalised prevention and early detection. The incorporation of genetic information and breast density has been shown to improve predictions for existing models, but detailed image-based features are yet to be included despite correlating with risk. Complex information can be extracted from mammograms using deep-learning algorithms, however, this is a challenging area of research, partly due to the lack of data within the field, and partly due to the computational burden. We propose an attention-based Multiple Instance Learning (MIL) model that can make accurate, short-term risk predictions from mammograms taken prior to the detection of cancer at full resolution. Current screen-detected cancers are mixed in with priors during model development to promote the detection of features associated with risk specifically and features associated with cancer formation, in addition to alleviating data scarcity issues. MAI-risk achieves an AUC of 0.747 [0.711, 0.783] in cancer-free screening mammograms of women who went on to develop a screen-detected or interval cancer between 5 and 55 months, outperforming both IBIS (AUC 0.594 [0.557, 0.633]) and VAS (AUC 0.649 [0.614, 0.683]) alone when accounting for established clinical risk factors.

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

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

Optimizing the Patient Experience in Breast Imaging Facilities: AJR Expert Panel Narrative Review.

Breast imaging studies are complex examinations for patients and providers. Breast imaging providers and organizations invest significant resources in educating patients and referring providers to address variability in changing breast cancer screening recommendations, cultural biases, and socioeconomic barriers for patients. The breast imaging examination frequently involves multiple imaging modalities including interventional procedures, thus requiring multiple room types. Practices need to consider variables that affect workflow efficiency throughout the process of scheduling, examination performance, interpretation, and results delivery, as well as options in facilities design to create inviting yet functional environments for patients. Breast imaging appointments provide opportunity to capture individual breast cancer risk and to engage patients in health education and breast screening awareness. This AJR Expert Panel Narrative Review discusses ways in which breast imaging facilities can optimize patient experience throughout the complex process of a breast imaging examination, based on the authors' observations and opinions that include private and academic breast imaging experience.

Dietary Diabetes Risk Reduction Score (DDRRS) and Breast Cancer Risk: A Case-Control Study in Iran

Introduction Given the role of type 2 diabetes and insulin resistance in tumor initiation, we hypothesized that following a diet that reduces the risk of type 2 diabetes could also reduce the risk of breast cancer. Herein, we conducted a case-control study to investigate the association between dietary diabetes risk reduction score and breast cancer risk in Iranian women. Method We recruited 136 newly diagnosed cases and 272 age-matched hospitalized controls from referral hospitals. A valid and reliable 168-item food frequency questionnaire (FFQ) was used to collect the data on dietary intake. We used Multivariate Logistic regression to assess the odds ratio (OR) with 95% confidence interval (CI) of breast cancer by the dietary diabetes risk reduction score tertiles. Results After adjusting for confounding variables, no association was seen between dietary diabetes risk reduction score and breast cancer risk (OR = 0.65, 95% CI: 0.37–1.14). However, after stratification by menopausal status, a decreased risk was observed between adherence to dietary diabetes risk reduction score and breast cancer risk in postmenopausal individuals (OR = 0.43, 95% CI: 0.19–0.99). Conclusion The present study states that there is no significant relationship between the dietary diabetes risk reduction score and the risk of breast cancer. However, adherence to dietary diabetes risk reduction score could have a preventive role for breast cancer among postmenopausal women.

RS17817449 FTO gene variation associated with familial disease burden rather than individual risk for breast cancer

Introduction: Breast cancer (BC) is the most common malignancy in the female population globally. Obesity is associated with an increased risk of postmenopausal BC, BC recurrence, and mortality. Fat mass and obesity-associated (FTO) gene polymorphisms have attracted the most attention due to several single-nucleotide polymorphisms (SNPs) that may have an impact on obesity and different types of cancer. The primary goal of our work was to assess the association of the SNP rs17817449 FTO, physical status/metabolic changes, and dietary habits with the occurrence of BC. Methods: We conducted research as a population-genetic study including 93 women with a diagnosis of BC during their lifetime. Genomic DNA was extracted from the swabs of the buccal mucosa. Genotyping was achieved by polymerase chain reaction-restriction fragment length polymorphism. The IBM SPSS Statistics program v. 23.0 was used for statistical analysis. All values of p &lt; 0.05 were considered statistically significant. Results: The risk genotype of the FTO gene (rs17817449) GG was detected in 16 subjects (17.2%), the heterozygous TG in 46 subjects (49.5%), while the normal genotype TT was recorded in 29 subjects (31.2%). We found no statistically significant difference in the body mass index values of the three genotype groups, p = 0.72, χ2 = 2.1 and no significant relationship between the allelic or genotypic frequencies of the rs17817449 FTO gene polymorphism and other variables examined in our study. Analysis of the distribution of hereditary diseases in the family according to the molecular subtype of BC showed statistically significant p-values, p = 0.02. Conclusion: While previous research has suggested a potential link between FTO gene polymorphism, obesity, and BC, our study did not find a statistically significant association between the aforementioned variables. Future studies with a larger number of subjects in different populations should confirm the role of the FTO genotype in the risk of BC.

Breast cancer: genetic personal risk factors: A review

Determination of cancer risk factors allow us to develop diagnostics tests that improved identification and reduced the rate of mortality of most frequent cancer diseases including breast cancer, prostate cancer, gastrointestinal tumors. Today individual risk of breast cancer considers personal genetics, medical history of patient, lifestyle, and a number of additional factors. Calculation of the first mathematical models for breast cancer risk assessment included anthropometric data, hormonal status, and family history of cancer. The discovery of BRCA1 and BRCA2 genes role in the development of breast cancer and the accumulation of data from population studies contributed to the introduction of the genetic component into mathematical models. The trend of the last decade is the integration of the polygenic component into the scheme for calculating the individual risk of breast cancer. In this review, we have analyzed existing models, assessed their relevance for certain groups of patients, studied the trends in the development of methods for molecular genetic diagnosis of breast cancer and determining the personal risk of developing the disease.

Association between diabetes status and breast cancer in US adults: findings from the US National Health and Nutrition Examination Survey.

The aim of this study was to investigate the association between diabetes status and the risk of breast cancer among adult Americans, exploring the impact of BMI, age, and race on this relationship. A cross-sectional analysis of 8,249 individuals from the National Health and Nutrition Examination Survey (NHANES) was conducted. Diabetes was categorized as type 2 diabetes and prediabetes, with both conditions diagnosed according to the ADA 2014 guidelines. The association between diabetes status and breast cancer risk was explored using multiple logistic regression analysis. Patients with diabetes had higher odds of breast cancer (OR: 1.51; 95% CI 1.00 to 2.28), Using the two-piecewise linear regression model, it was observed that there is a threshold effect in the risk of breast cancer occurrence at the age of 52 years. Specifically, the risk of breast cancer is relatively low before the age of 52 but increases significantly after this age. This study identified a significant association between diabetes status and breast cancer risk among adult Americans. We also found a threshold effect in breast cancer occurrence at the age of 52. Age was significantly associated with breast cancer risk in both Non-Hispanic White and Non-Hispanic Black individuals. These findings underscore the importance of diabetes management, maintaining a healthy BMI, and age-related risk considerations in reducing breast cancer risk.

A model for predicting both breast cancer risk and non-breast cancer death among women > 55 years old

BackgroundGuidelines recommend shared decision making (SDM) for mammography screening for women ≥ 75 and not screening women with < 10-year life expectancy. High-quality SDM requires consideration of women’s breast cancer (BC) risk, life expectancy, and values but is hard to implement because no models simultaneously estimate older women’s individualized BC risk and life expectancy.MethodsUsing competing risk regression and data from 83,330 women > 55 years who completed the 2004 Nurses’ Health Study (NHS) questionnaire, we developed (in 2/3 of the cohort, n = 55,533) a model to predict 10-year non-breast cancer (BC) death. We considered 60 mortality risk factors and used best-subsets regression, the Akaike information criterion, and c-index, to identify the best-fitting model. We examined model performance in the remaining 1/3 of the NHS cohort (n = 27,777) and among 17,380 Black Women’s Health Study (BWHS) participants, ≥ 55 years, who completed the 2009 questionnaire. We then included the identified mortality predictors in a previously developed competing risk BC prediction model and examined model performance for predicting BC risk.ResultsMean age of NHS development cohort participants was 70.1 years (± 7.0); over 10 years, 3.1% developed BC, 0.3% died of BC, and 20.1% died of other causes; NHS validation cohort participants were similar. BWHS participants were younger (mean age 63.7 years [± 6.7]); over 10-years 3.1% developed BC, 0.4% died of BC, and 11.1% died of other causes. The final non-BC death prediction model included 21 variables (age; body mass index [BMI]; physical function [3 measures]; comorbidities [12]; alcohol; smoking; age at menopause; and mammography use). The final BC prediction model included age, BMI, alcohol and hormone use, family history, age at menopause, age at first birth/parity, and breast biopsy history. When risk factor regression coefficients were applied in the validation cohorts, the c-index for predicting 10-year non-BC death was 0.790 (0.784–0.796) in NHS and 0.768 (0.757–0.780) in BWHS; for predicting 5-year BC risk, the c-index was 0.612 (0.538–0.641) in NHS and 0.573 (0.536–0.611) in BWHS.ConclusionsWe developed and validated a novel competing-risk model that predicts 10-year non-BC death and 5-year BC risk. Model risk estimates may help inform SDM around mammography screening.

: A quantitative approach to assess the correlation of mammographic breast density with selected affecting factors.

Breast density plays a significant role in increasing an individual's risk of breast cancer and its mortality rate. We aimed to assess the correlations of mammographic breast density with age, body mass index, weight, height and parity for the first time in Sri Lankan women. 52 participants who underwent diagnostic mammographic examinations at a tertiary care hospital in Sri Lanka were selected for the study. Demographic data and digital mammograms in DICOM format were collected. Mammographic breast density was quantitatively estimated using a validated, semi-automated computer programme devised by the authors using Java programming language. 65.4% of the participants were postmenopausal, and 34.6% were premenopausal. Mammographic breast density showed a significant negative correlation with age (r = -0.40, p < 0.05) and significant positive correlations with body mass index (r = 0.49, p< 0.05) and weight (r = 0.52, p< 0.05). The study did not find any correlation between mammographic breast density and height. Additionally, it did not find a significant difference between right and left breasts or between parous and nulliparous patients. Mammographic breast density was significantly higher among premenopausal patients compared to postmenopausal patients. Quantitative mammographic breast density demonstrated significant correlations with age, body mass index and weight. The findings of the study will be constructive in predicting breast density in the future and individualizing the breast cancer screening requirements based on the breast density without radiation exposure for females in Sri Lanka.

22 (PB-022) Poster Spotlight - An individualized breast cancer risk assessment model for women attending screening in BreastScreen Norway

Background: The results of the effectiveness of population-based screening are controversial in terms of the balance between mortality reduction and adverse effects. In order to improve it, studies have proposed personalized screening strategies based on woman’s individual breast cancer (BC) risk. There is, therefore, a need to create individual risk prediction models through the analysis of large population-based databases. We developed a model that could be used to classify women targeted for mammography screening according to individual BC risk.

Validation of the Nigerian Breast Cancer Study Model for Predicting Individual Breast Cancer Risk in Cameroon and Uganda.

The Nigerian Breast Cancer Study (NBCS) model is a new risk assessment tool developed for predicting risk of invasive breast cancer in Nigeria. Its applicability outside of Nigeria remains uncertain as it has not been validated in other sub-Saharan Africa populations. We conducted a case-control study among women with breast cancer and controls ascertained in Cameroon and Uganda from 2011 to 2016. Structured questionnaire interviews were performed to collect risk factor characteristics. The NBCS model, the Gail model, the Gail model for Black population, and the Black Women's Health Study model were applied to the Cameroon and Uganda samples separately. Nigerian as well as local incidence rates were incorporated into the models. Receiver-Operating Characteristic analyses were performed to indicate discriminating capacity. The study included 550 cases (mean age 46.8 ± 11.9) and 509 controls (mean age 46.3 ± 11.7). Compared with the other three models, the NBCS model performed best in both countries. The discriminating accuracy of the NBCS model in Cameroon (age-adjusted C-index = 0.602; 95% CI, 0.542-0.661) was better than in Uganda (age-adjusted C-index = 0.531; 95% CI, 0.459-0.603). These findings demonstrate the potential clinical utility of the NBCS model for risk assessment in Cameroon. All currently available models performed poorly in Uganda, which suggests that the NBCS model may need further calibration before use in other regions of Africa. Differences in risk profiles across the African diaspora underscores the need for larger studies and may require development of region-specific risk assessment tools for breast cancer.

149P Associations between the 313-SNP polygenic risk score and breast cancer risk in women from Crete

Polygenic risk scores (PRS) developed for women of European ancestry could be used for the prediction of individualized breast cancer risk, and thus, potentially have applications in guiding targeted preventative measures and population screening. However, the PRS performance across diverse populations within Europe has not been extensively evaluated. This, is crucial before the use of PRS for clinical decisions.