Breast Cancer Risk Estimation Research Articles

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 PO5-08-10: Breast cancer risk prediction performance of polygenic risk score in Taiwanese female with dense breast: A nested case-control study

Abstract Introduction: Breast cancer is the most common cancer among women in Taiwan and the incidence rate of breast cancer has been increasing steadily over the past 30 years. To address this, a nationwide screening program with biennial mammography for women aged 40-69 was implemented in 2004, which has contributed to a significant reduction in breast cancer mortality. Asian women are more likely to have dense breast tissue, and studies have shown a correlation between breast density and the risk of developing breast cancer, with higher breast density values associated with increased risk. Polygenic risk scores (PRS) are calculated based on an individual's genetic information and can estimate their risk of developing a particular disease. Although PRS can identify low-penetrance risk variants, its use in breast cancer risk estimation is typically limited to gene-only models, and integration with breast cancer screening databases is rare. Hence, the aim of this specific study was to evaluate the predictive capacity of PRS in women with dense breast tissue by exploring potential genetic markers and constructing a PRS to investigate the genetic factors associated with clinical characteristics and the risk of developing breast cancer. Methods: The PRS was developed using genetic information from the Taiwan Precision Medicine Initiative genotyping data. A total of 6335 patients were enrolled, and 101 single nucleotide polymorphisms (SNPs) were selected as candidate markers based on the PGS Catalog (PGS000001). Clinical characteristics of the study cohort were summarised using median and range, or frequency and percentage. The distribution of characteristics between breast cancer and controls was estimated using an independent two-sample t-test, chi-square, and Fisher’s exact test. The association of the PRS and related characteristics with breast cancer risk in the study cohort was estimated using univariate and multivariate binomial logistic regression. Harrel’s C-index was reported to demonstrate the predictive performance of PRS in both univariate and multivariate models. All p-values were two-sided, and a p less than .05 is considered statistically significant. All analyses were performed using R 4.1.2 (R core team, 2023) Results: The results showed that breast cancer patients constituted a higher proportion of individuals in PRS Q4 (37.8% vs 24.8% in controls). The model's predictive performance for breast cancer risk increased from 0.565 (95% CI = 0.520-0.611) to 0.699 (95% CI = 0.644-0.755) by adding related clinical characteristics compared to the PRS-only model. Patients characterized with benign breast disease (OR = 0.50, 95% CI = 0.31-0.79, P = 0.004) showed a decreased breast cancer risk, and patients with mastalgia or palpable breast lesion (OR = 6.87, 95% CI = 4.29-10.8, P < 0.001) predicted significant greater breast cancer risk. Subgroup analysis of patients without breast symptoms was conducted as they may be overlooked or underestimated for breast cancer screening. For dense breast patients without symptoms, the high PRS group (Q4) consistently showed a significantly elevated breast cancer risk compared to the low PRS group (Q1-Q3) in both univariate (OR = 2.25, 95% CI = 1.43-3.50, P < 0.001) and multivariate analyses (OR = 2.22, 95% CI = 1.41-3.46, P < 0.001). Conclusion: Breast cancer screening has undergone a shift from a general approach to a personalized, risk-based approach. Besides breast cancer screening using family history as the only risk factor, our proposed model identifies both genetic and clinical risk factors using big data analyses including the EHR, cancer screening, and registry from a single institute. The study suggests that integrating PRS into personalized screening strategies could improve risk prediction for Taiwanese females with dense breasts without prominent symptoms. Table 1. Clinical characteristics of study cohort (n=6335). Table 2. Association of the PRS quartiles in breast cancer risk. Table 3. Predictive performance of PRS for breast cancer risk. Citation Format: Chih Yean Lum, Chih Chiang Hung, Chi-Cheng Huang, Tzu-Hung Hsiao, Sin-Hua Moi. Breast cancer risk prediction performance of polygenic risk score in Taiwanese female with dense breast: A nested case-control study [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 PO5-08-10.

Abstract PO2-09-04: Changes in an artificial intelligence model score for breast cancer detection (Transpara) with tamoxifen and aromatase inhibitor treatment

Abstract Artificial intelligence (AI) models applied to mammography have been shown to improve breast cancer (BC) detection and risk estimation. The Transpara AI detection system has been associated with short and longer-term risk of invasive BC. Whether Transpara can identify changes in the breast associated with tamoxifen (TAM) and aromatase inhibitor (AIs) treatment is unknown. We compare changes in the Transpara malignancy score in the unaffected breast of BC cases using TAM or AIs to those of untreated women without cancer. Cases and controls were sampled from the Mayo Clinic Rochester Breast Screening Practice. Eligible cases were women with breast cancer treated with TAM and/or AIs for at least 8 months who had full field digital (FFDM) mammograms within two years prior to start of treatment (pretreatment mammogram). Controls did not have BC, were not using TAM, AIs, or postmenopausal hormones, and had at least two FFDM screening mammograms. Transpara scores (1-10) were calculated for the contralateral breast in cases and a randomly selected side for controls. Two controls were matched to each case on menopausal status at both mammograms, time between mammograms (< 6 months) and baseline Transpara score (±2 units). Conditional logistic regression was used to assess the association of treatment (case (TAM/AIs) vs. control) with change in Transpara score between the pretreatment mammogram and the mammogram taken closest to 13 months later. Change was classified as having a decrease in Transpara score between the two mammograms vs. no change or an increase. Models were adjusted for age and Transpara score at pretreatment mammogram, and time between the two mammograms. We performed analyses on all women combined and stratified by time on treatment, menopausal status, and treatment type (TAM vs. AIs). A total of 134 cases were identified, with 53 on TAM (21 premenopausal and 32 postmenopausal) and 81 on AIs (postmenopausal); 268 controls were matched to the cases. Characteristics of the study population are summarized (Table). Most case mammograms were obtained within six months prior to treatment start [median 2.63 months (interquartile range (IQR), 1.62, 5.72)] and median time on TAM/AIs was 10.9 months (IQR, 8.03, 16.6) at time of second mammogram. The median absolute change in Transpara score between the two mammograms was similar in cases [0 (IQR, -2, 1)] vs. controls [0 (IQR, -1.25, 1)]; 40.3% of cases and 39.2% of controls had any decrease. Conditional logistic models found no evidence for an association between TAM/AIs and decrease in Transpara score [odds ratio (OR) = 1.07; 95% confidence interval (CI), 0.65, 1.78]. When stratified by median time on treatment (10.9 months), there was suggestion of a stronger association for those on treatment for a longer duration (OR = 1.21; 95% CI, 0.61, 2.40 for >10.9 months vs. OR = 0.96; 95% CI, 0.43, 2.13 for < 10.9 months) but these results were not statistically significant. Similarly, odds ratios were stronger in postmenopausal (OR = 1.18; 95% CI, 0.67, 2.07) compared to premenopausal (OR = 0.91; 95% CI, 0.24, 3.45) women, and for AIs (OR = 1.21, 95% CI, 0.64, 2.29) vs. TAM (OR = 0.93; 95% CI, 0.39, 2.22) but none of these results reached statistical significance. In summary, we found no association between treatment with TAM/AIs and change in Transpara score. However, our study had limited power. Also, longer time on treatment as well as investigation of AI-based scores trained directly to assess BC risk may be necessary to identify relevant changes. Citation Format: Yvonne Wang, Christopher Scott, Matthew Jensen, Dan Hursh, Kathleen Brandt, Aaron Norman, Fang Fang Wu, Stacey Winham, Karla Kerlikowske, Celine Vachon. Changes in an artificial intelligence model score for breast cancer detection (Transpara) with tamoxifen and aromatase inhibitor treatment [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 PO2-09-04.

Abstract PS07-04: Improved uptake and adherence to prevention medication with use of baby tamoxifen in patients at high risk for breast cancer

Abstract Background: Women at increased risk for breast cancer (BC) may benefit from taking prevention medication (PM) with tamoxifen (tam). Historical uptake to PMs for women who qualify has been low. Recent studies have shown baby tam (5mg/day) to have similar efficacy to standard dosing (20mg/day) with lower risk for adverse events (AEs) in women with DCIS or high risk intraepithelial lesions. Herein, we aimed to evaluate uptake, adherence, and tolerability of baby tam in women at increased risk for BC and those with DCIS. Methods: Women seen at the Mayo Clinic Breast Center in Minnesota or Florida who qualified for PM due to DCIS, high risk intraepithelial lesions (IELs) including LCIS and/or AH, or with increased risk based on validated BRCAT or IBIS model calculation (BCRAT 5 year risk ≥ 3% or IBIS 10 year risk ≥ 8%) were offered participation. All women received consultation with a breast specialist for discussion of PM rationale, benefits, side effects, and risks. Patients received baseline survey to assess understanding of their risk and role for PM and then 1 year follow-up survey to assess adherence and tolerability. Results: 41 patients consented for participation and 31 of those completed follow-up at 1 year.Median age of enrollment was 49 years. After initial consultation, 90% (n=37) reported good or complete understanding of BC risk. Of the 31 patients included in 1 year follow-up, 5 patients had DCIS, 13 had high-risk IELs, and 13 qualified based on BCRAT/IBIS calculation. Seventy-four percent (n= 23) of patients reported they took baby tam after consultation. No differences in age (p=0.89), education (p=0.13), or menopause status (p=0.11) between those who did and did not take medication were seen. Those who initiated baby tam were more likely to have DCIS or high risk IEL compared to those who did not (p < 0.001). Seventy-eight percent (n=18) of those who initiated baby tam were still taking medication at 1 year follow-up. Of patients who discontinued baby tam, all cited side effects as primary reason for discontinuation, with hot flashes (n=2), night sweats (n=2), and fatigue (n=2) being most common. Patients who continued medication had higher estimated BC risk compared to those who discontinued baby tam (IBIS 10 yr risk 12.7% vs 7.6%, p = 0.027). Patients with DCIS or high risk IEL were more likely to continue medication at 1 year compared to those patients who qualified for PM based on calculated BCRAT/IBIS score (p=0.05). Only 1 patient with DCIS and 1 patient with high-risk IEL discontinued baby tam at 1 year. Conclusions: Uptake to baby tam after informed discussion in patients who qualify is high, especially in those patients with high risk IELs or DCIS. Adherence and tolerability at 1 year follow-up is improved compared with traditional dosing of tam. Qualifying criteria for patients who did and did not take baby tamoxifen after informed consultation with breast specialist Citation Format: Lauren Cornell, Sandhya Pruthi, Karthik Ghosh, Klassen Christine, Pooja Advani. Improved uptake and adherence to prevention medication with use of baby tamoxifen in patients at high risk for breast cancer [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 PS07-04.

Abstract PO2-28-06: An on-line deep learning decision support tool, iBRISK, aimed at improving breast cancer risk estimation and reducing unnecessary biopsies for BI-RADS 4 patients

Abstract Probability of malignancy (POM) for Breast Imaging Reporting and Data System (BI-RADS) category 4 designated breast lesions ranges from 2% – 95% and contributes to a high unnecessary biopsy rate. This is as most clinicians often stick to the biopsy option to rule in or out breast cancer early; withholding biopsy could be risky, and biopsies of BI-RADS 4 lesions serve as a quality metric and performance standard. At 21.1%, biopsy-proven positive predictive value (PPV3) rates for BI-RADS 4 have not improved for decades, translating to high false-positive rates of mammography. Unnecessary biopsies are a big issue in the management of BI-RADS 4 lesions with negative implications including increased medical costs, healthcare wastes, unnecessary psychological burdens to the patients, and potential complications and risks. Objectives 1. Optimize the precision breast cancer risk assessment tool, iBRISK, that utilizes artificial intelligence (AI) technologies, including natural language processing (NLP), image processing, and deep learning, with clinical risk factors and imaging features. 2. Develop a user-friendly web interface for iBRISK to facilitate clinicians or insurers in estimating cancer risk and making informed biopsy decisions for BI-RADS 4 lesions. Methods Our intelligent-augmented breast cancer risk calculator (iBRISK) model was trained on multimodal data collected from 10,778 patients, including demographic factors, historical and clinical characteristics, mammographic features, and pathologic signatures. We validated iBRISK using 4,200 patients from multiple leading hospitals, including Houston Methodist Neal Cancer Center, the University of Texas MD Anderson Cancer Center, and the University of Texas Health San Antonio MD Anderson Mays Cancer Center. The iBRISK was connected to a backend server which is linked to a frontend web user interface using technologies like Hypertext Preprocessor (PHP) for the backend application running on an APACHE HTTP server and React-JavaScript for the front-end web application that communicates with the backend using Representational State Transfer Application Programming Interface (RESTful API) and JavaScript Object Notation (JSON) format. The communication was secured using Secure Sockets Layer (SSL) encryption. Results The iBRISK model demonstrated high sensitivity in malignancy prediction and achieved an accuracy of 89.5%, area under the receiver operating characteristic curve of 0.93 (95% CI: 0.92-0.95), sensitivity of 100%, and specificity of 81%. Only 0.16% of lesions determined to have low POM by the model were malignant. Our multi-center study shows that iBRISK achieves at least 50% reduction in unnecessary biopsies of BI-RADS 4 cases. Data elements for the required 20 features are entered into the interface using a variety of imputation methods including direct text, dropdown menus and radio button selections. The user-friendly web interface provides risk scores (0 – 1), risk levels (low, medium, and high), and associated biopsy recommendations. Conclusion The user-friendly iBRISK web interface is proposed as an adjunct to the BI-RADS system, enhancing the precision of BI-RADS 4 lesion cancer risk stratification. It is expected to reduce unnecessary biopsies, lower health costs, and enhance the quality of health care. This approach aims to tackle a critical issue in breast cancer diagnosis by leveraging advanced AI technologies and big data and providing clinicians with a tool to make more informed and precise biopsy decisions for BI-RADS 4 lesions. Citation Format: Chika Ezeana, Xiaohui Yu, Zhihao Wan, Tiancheng He, Tejal Patel, Virginia Kaklamani, Maryam Elmi, Erika Brigmon, Pamela Otto, Kenneth Kist, Lin Wang, Joe Ensor, Heather Speck, Ya-Chen Shih, Bumyang Kim, I-Wen Pan, David Spak, Wei Yang, Jenny Chang, Stephen Wong. An on-line deep learning decision support tool, iBRISK, aimed at improving breast cancer risk estimation and reducing unnecessary biopsies for BI-RADS 4 patients [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 PO2-28-06.

Meta-analysis of breast cancer risk for individuals with PALB2 pathogenic variants.

Multigene panel testing now allows efficient testing of many cancer susceptibility genes leading to a larger number of mutation carriers being identified. They need to be counseled about their cancer risk conferred by the specific gene mutation. An important cancer susceptibility gene is PALB2. Multiple studies reported risk estimates for breast cancer (BC) conferred by pathogenic variants in PALB2. Due to the diverse modalities of reported risk estimates (age-specific risk, odds ratio, relative risk, and standardized incidence ratio) and effect sizes, a meta-analysis combining these estimates is necessary to accurately counsel patients with this mutation. However, this is not trivial due to heterogeneity of studies in terms of study design and risk measure. We utilized a recently proposed Bayesian random-effects meta-analysis method that can synthesize estimates from such heterogeneous studies. We applied this method to combine estimates from 12 studies on BC risk for carriers of pathogenic PALB2 mutations. The estimated overall (meta-analysis-based) risk of BC is 12.80% (6.11%-22.59%) by age 50 and 48.47% (36.05%-61.74%) by age 80. Pathogenic mutations in PALB2 makes women more susceptible to BC. Our risk estimates can help clinically manage patients carrying pathogenic variants in PALB2.

Exposure to outdoor ambient air toxics and risk of breast cancer: The multiethnic cohort

BackgroundA growing literature has reported associations between traffic-related air pollution and breast cancer, however there are fewer investigations into specific ambient agents and any putative risk of breast cancer development, particularly studies occurring in populations residing in higher pollution areas such as Los Angeles. ObjectivesTo estimate breast cancer risks related to ambient air toxics exposure at residential addresses. MethodsWe examined the relationships between ambient air toxics and breast cancer risk in the Multiethnic Cohort among 48,665 California female participants followed for cancer from 2003 through 2013. We obtained exposure data on chemicals acting as endocrine disruptors or mammary gland carcinogens from the National-Scale Air Toxics Assessment. Cox proportional hazards models were used to estimate breast cancer risk per one interquartile range (IQR) increase in air toxics exposure lagged by 5-years. Stratified analyses were conducted by race, ethnicity, and hormone receptor types. ResultsAmong all women, increased risks of invasive breast cancer were observed with toxicants related to industries [1,1,2,2-tetrachloroethane (hazard ratio [HR] = 4.22, 95% confidence interval [95% CI] 3.18–5.60), ethylene dichloride (HR = 2.81, 95% CI 2.20–3.59), and vinyl chloride (HR = 2.27, 95% CI 1.81, 2.85); these 3 agents were correlated (r2 = 0.45–0.77)]. Agents related to gasoline production or combustion were related to increased breast cancer risk [benzene (HR = 1.32, 95% CI 1.24, 1.41), ethylbenzene (HR = 1.20, 95% CI 1.13–1.28), toluene (HR = 1.29, 95% CI 1.20–1.38), naphthalene (HR = 1.11, 95% CI 1.02–2.22), acrolein (HR = 2.26, 95% CI 1.92, 2.65)]. Higher hazard ratios were observed in African Americans and Whites compared to other racial and ethnic groups (p-heterogeneity <0.05 for traffic-related air toxics, acrolein, and vinyl acetate). ConclusionsOur findings suggest that specific toxic air pollutants may be associated with increase breast cancer risk.

Role of Breast Cancer Risk Estimation Models to Identify Women Eligible for Genetic Testing and Risk-Reducing Surgery.

Hereditary breast and ovarian cancer (HBOC) syndrome is responsible for approximately 10% of breast cancers (BCs). The HBOC gene panel includes both high-risk genes, i.e., a four times higher risk of BC (BRCA1, BRCA2, PALB2, CDH1, PTEN, STK11 and TP53), and moderate-risk genes, i.e., a two to four times higher risk of BC (BARD1, CHEK2, RAD51C, RAD51D and ATM). Pathogenic germline variants (PGVs) in HBOC genes confer an absolute risk of BC that changes according to the gene considered. We illustrate and compare different BC risk estimation models, also describing their limitations. These models allow us to identify women eligible for genetic testing and possibly to offer surgical strategies for primary prevention, i.e., risk-reducing mastectomies and salpingo-oophorectomies.

An advanced machine learning method for simultaneous breast cancer risk prediction and risk ranking in Chinese population: A prospective cohort and modeling study.

Breast cancer (BC) risk-stratification tools for Asian women that are highly accurate and can provide improved interpretation ability are lacking. We aimed to develop risk-stratification models to predict long- and short-term BC risk among Chinese women and to simultaneously rank potential non-experimental risk factors. The Breast Cancer Cohort Study in Chinese Women, a large ongoing prospective dynamic cohort study, includes 122,058 women aged 25-70 years from the eastern part of China. We developed multiple machine-learning risk prediction models using parametric models (penalized logistic regression, bootstrap, and ensemble learning), which were the short-term ensemble penalized logistic regression (EPLR) risk prediction model and the ensemble penalized long-term (EPLT) risk prediction model to estimate BC risk. The models were assessed based on calibration and discrimination, and following this assessment, they were externally validated in new study participants from 2017 to 2020. The AUC values of the short-term EPLR risk prediction model were 0.800 for the internal validation and 0.751 for the external validation set. For the long-term EPLT risk prediction model, the area under the receiver operating characteristic curve was 0.692 and 0.760 in internal and external validations, respectively. The net reclassification improvement index of the EPLT relative to the Gail and the Han Chinese Breast Cancer Prediction Model (HCBCP) models for external validation was 0.193 and 0.233, respectively, indicating that the EPLT model has higher classification accuracy. We developed the EPLR and EPLT models to screen populations with a high risk of developing BC. These can serve as useful tools to aid in risk-stratified screening and BC prevention.

Making sense of breast cancer risk estimates.

Individual probabilistic assessments on the risk of cancer, primary or secondary, will not be understood by most patients. That is the essence of our arguments in this paper. Greater understanding can be achieved by extensive, intensive, and detailed counseling. But since probability itself is a concept that easily escapes our everyday intuition-consider the famous Monte Hall paradox-then it would also be wise to advise patients and potential patients, to not put undue weight on any probabilistic assessment. Such assessments can be of value to the epidemiologist in the investigation of different potential etiologies describing cancer evolution or to the clinical trialist as a way to maximize design efficiency. But to an ordinary individual we cannot anticipate that these assessments will be correctly interpreted.

Validity of self-reported night shift work among women with and without breast cancer.

This study aimed to estimate the validity of self-reported information on ever-night shift work among women with and without breast cancer and illustrate the consequences for breast cancer risk estimates. During 2015-2016, 225 women diagnosed with breast cancer and 1800 matched controls without breast cancer employed within the Danish hospital regions during 2007-2016 participated in a questionnaire-based survey. Their reported night shift work status was linked with objective payroll register day-by-day working hour data from the Danish Working Hour Database and the Danish Cancer Registry. For the breast cancer patients and their matched controls, we estimated sensitivity and specificity for ever-working night shifts using the payroll data as the gold standard. We also used quantitative bias analysis to estimate the impact on relative risk estimates for a hypothetical population. For breast cancer patients, we observed a sensitivity of ever-night shifts of 86.2% and a specificity of never-night shifts of 82.6%. For controls, the sensitivity was 80.6% and the specificity 83.7%. Odds ratio for breast cancer in a hypothetical population decreased from 1.12 [95% confidence interval (CI) 1.03-1.21] to 1.05 (95% CI 0.95-1.16) when corrected by the sensitivity and specificity estimates. This study shows that female breast cancer patients had slightly better recall of previous night shift work than controls. Additionally, both breast cancer patients and controls recalled previous never-night shift work with low specificity. The net effect of this misclassification is a small over-estimation of the relative breast cancer risk due to night shift work.

Abstract B080: Advancing Breast Cancer Risk Prediction: Introducing the Gabbi Risk Assessment Model (GRAM)

Abstract Introduction: Advanced breast cancer risk models hold promise in implementing more effective and accurate screening strategies, facilitating early detection while minimizing screening-related harm compared to current guidelines. However, integrating machine learning risk models into clinical practice requires essential steps to be taken, including continual refinement to enhance their accuracy, validation across diverse populations, and demonstrating their ability to optimize clinical workflows. Gabbi Risk Assessment Model (GRAM), an innovative machine learning model, is a binary classifier based on medical claims developed to provide personalized breast cancer risk prediction. GRAM efficiently estimates risk and utilizes potentially missing risk factor information. Furthermore, it offers easy integration into EMR systems, thus optimizing its practical application. Methods: Construction of the GRAM utilized a medical claims dataset of 500k members from IQVIA carefully sampled from the entire IQVIA population (IQVIA acquires ~94% of the US claims data) with 4 years of history. The information extracted from the dataset included demographics, and medical history in the form of diagnostic and treatment codes, laboratory tests, and medications. The dataset consisted of 250k women aged 18-70 with a breast cancer diagnosis and a random sample of 250k women aged 18-70 without a known breast cancer diagnosis. For the selected cohorts, a 4-year history before the first breast cancer diagnosis was obtained. The control group was carefully chosen to possess similar statistical properties as the group with breast cancer. All women had a minimum enrollment period of 4 years before diagnosis with the same insurance provider. The GRAM was developed with 4 main steps: 1) member selection - members with breast cancer based on selection criteria defined above were in the test group, members without in the control group; 2) feature engineering to ensure that GRAM learns from medical history before a first diagnosis; 3) model development &amp; testing - training/fine tuning using the ensemble methods and neural network, followed by hyperparameter tuning using a held out validation dataset; and 4) model performance on the held out validation set and identification of key risk factors contributing to the breast cancer risk. Results and Conclusion: By addressing these challenges and harnessing the potential of machine learning, GRAM has the capacity to revolutionize breast cancer risk estimation. Its integration into clinical practice can lead to more targeted and efficient screening strategies, resulting in earlier detection and improved patient outcomes. Preliminary results have shown that the GRAM surpasses the current standard of care models (GAIL and Tyrer Cusick) by at least 20% in sensitivity, and AUC ROC with no decline in specificity. GRAM achieved 91% AUC ROC (balanced data), 86% sensitivity, and 80% specificity on a held out dataset. As research progresses, continuous refinement and validation, the GRAM model holds promise to make significant contributions in the fight against breast cancer and identifying the correlated risk factors. Citation Format: Priyanshi Jain, Shaleen K Theiler, Kaitlin Christine. Advancing Breast Cancer Risk Prediction: Introducing the Gabbi Risk Assessment Model (GRAM) [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr B080.

Breast Cancer Polygenic-Risk Score Influence on Risk-Reducing Endocrine Therapy Use: Genetic Risk Estimate (GENRE) Trial 1-Year and 2-Year Follow-Up.

Risk-reducing medications for breast cancer are considerably underused. Informing women at risk with precise and individualized risk assessment tools may substantially affect the incidence of breast cancer. In our study, a risk assessment tool (IBIS-polygenic-risk score) yielded promising results, with 39% of women at highest risk starting preventive medication.

Germline Genetic Testing for Hereditary Breast and Ovarian Cancer: Current Concepts in Risk Evaluation.

Our understanding of hereditary breast and ovarian cancer has significantly improved over the past two decades. In addition to BRCA1/2, pathogenic variants in several other DNA-repair genes have been shown to increase the risks of breast and ovarian cancer. The magnitude of cancer risk is impacted not only by the gene involved, but also by family history of cancer, polygenic risk scores, and, in certain genes, pathogenic variant type or location. While estimates of breast and ovarian cancer risk associated with pathogenic variants are available, these are predominantly based on studies of high-risk populations with young age at diagnosis of cancer, multiple primary cancers, or family history of cancer. More recently, breast cancer risk for germline pathogenic variant carriers has been estimated from population-based studies. Here, we provide a review of the field of germline genetic testing and risk evaluation for hereditary breast and ovarian cancers in high-risk and population-based settings.

Benign Breast Disease and Breast Cancer Risk in the Percutaneous Biopsy Era

Benign breast disease (BBD) comprises approximately 75% of breast biopsy diagnoses. Surgical biopsy specimens diagnosed as nonproliferative (NP), proliferative disease without atypia (PDWA), or atypical hyperplasia (AH) are associated with increasing breast cancer (BC) risk; however, knowledge is limited on risk associated with percutaneously diagnosed BBD. To estimate BC risk associated with BBD in the percutaneous biopsy era irrespective of surgical biopsy. In this retrospective cohort study, BBD biopsy specimens collected from January 1, 2002, to December 31, 2013, from patients with BBD at Mayo Clinic in Rochester, Minnesota, were reviewed by 2 pathologists masked to outcomes. Women were followed up from 6 months after biopsy until censoring, BC diagnosis, or December 31, 2021. Benign breast disease classification and multiplicity by pathology panel review. The main outcome was diagnosis of BC overall and stratified as ductal carcinoma in situ (DCIS) or invasive BC. Risk for presence vs absence of BBD lesions was assessed by Cox proportional hazards regression. Risk in patients with BBD compared with female breast cancer incidence rates from the Iowa Surveillance, Epidemiology, and End Results (SEER) program were estimated. Among 4819 female participants, median age was 51 years (IQR, 43-62 years). Median follow-up was 10.9 years (IQR, 7.7-14.2 years) for control individuals without BC vs 6.6 years (IQR, 3.7-10.1 years) for patients with BC. Risk was higher in the cohort with BBD than in SEER data: BC overall (standard incidence ratio [SIR], 1.95; 95% CI, 1.76-2.17), invasive BC (SIR, 1.56; 95% CI, 1.37-1.78), and DCIS (SIR, 3.10; 95% CI, 2.54-3.77). The SIRs increased with increasing BBD severity (1.42 [95% CI, 1.19-1.71] for NP, 2.19 [95% CI, 1.88-2.54] for PDWA, and 3.91 [95% CI, 2.97-5.14] for AH), comparable to surgical cohorts with BBD. Risk also increased with increasing lesion multiplicity (SIR: 2.40 [95% CI, 2.06-2.79] for ≥3 foci of NP, 3.72 [95% CI, 2.31-5.99] for ≥3 foci of PDWA, and 5.29 [95% CI, 3.37-8.29] for ≥3 foci of AH). Ten-year BC cumulative incidence was 4.3% for NP, 6.6% for PDWA, and 14.6% for AH vs an expected population cumulative incidence of 2.9%. In this contemporary cohort study of women diagnosed with BBD in the percutaneous biopsy era, overall risk of BC was increased vs the general population (DCIS and invasive cancer combined), similar to that in historical BBD cohorts. Development and validation of pathologic classifications including both BBD severity and multiplicity may enable improved BC risk stratification.

Clinician and patient perspectives on screening mammography among women age 75 and older: A pilot study of a novel decision aid

Supporting patient-clinician communication is key to implementing tailored, risk-based screening for older adults. Objectives of this multiphase mixed methods study were to identify factors that primary care clinicians consider influential when making screening mammography recommendations for women ≥ 75 years, develop a patient decision aid that incorporates these factors, and gather feasibility and acceptability from the patients' perspective. Clinicians from a Mid-Atlantic practice network completed online surveys. Women in the same network completed surveys before and after receiving a tailored booklet that included information about the benefits and harms of screening for women ≥ 75 years, a breast cancer risk-estimate, and a question prompt list to support patient-clinician communication. Clinicians (N = 21) were primarily women [57.1%] and practiced family medicine [81.0%]. They cited patients' age ≥ 75 years [95.4%], comorbidity [86.4%], functional status [77.3%], cancer family history [63.6%], U.S. Preventive Services Task Force guidelines [81.8%] and new research [77.3%] as factors influencing their recommendations. Fourteen women completed baseline surveys and received personalized decision aids (Mean age = 79.1 years). Eleven completed the post-intervention survey. All were satisfied with the booklet length, 81.8% found the booklet easy to understand and 72.7% helpful in decision-making Perceived lifetime breast cancer risk decreased significantly from pre- to post-intervention (p = 0.02). Results suggest this decision aid, which incorporates key decisional factors from the clinician's perspective, is feasible and acceptable to patients. A tailored decision aid booklet is innovative as it provides information on personalized risk and potential benefits and harms to older women considering screening.

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.

Breast radiation dose equivalent and cancer risk estimation during PET/CT examinations

Mammary glands or breasts are radiosensitive organs that makeup 12% of the body's overall radiation sensitivity. According to estimates, Saudi Arabia has a 27.3% overall cancer incidence rate for breast cancer. Recent studies reported that breasts get significant organ equivalent doses during diagnostic procedures with ionizing radiation, even though the breast is not the target organ. The objective of this work is to assess the effective and breast equivalent doses during positron emission tomography (PET)/Computed tomography (CT) imaging examinations. The effective and breast equivalent doses per PET/CT (GE PET/CT 710) examination were quantified using the amount of the injected radiopharmaceutical (MBq) (18F- fluorodeoxyglucose (FDG)), and external doses from CT examinations. The average, standard deviation (SD) and range of examined patients' age (years) and body mass index ((BMI), kg/m2) were 58 ± 11 (39–76) and 30 ± 5 (20.7–43.1), respectively. Constant administered activity (AA, 185 MBq) and tube potential (120 kVp) were used for all patients. The average, SD, and range of the patients effective dose (mSv) from PET/CT (18F- FDG) examination was 8.0 ± 0.93 (7.0–10.0) and 2.075 ± 0.11 (1.91–2.29) in that order. Breast doses are comparable with previous studies during the PET/CT and mammographic examination. Proper justification criteria and dose optimization are essential to lowering excessive exposure while preserving image quality, especially for young females because they have a higher risk of radiation-induced cancer.

Personalized Breast Cancer Screening: A Risk Prediction Model Based on Women Attending BreastScreen Norway.

We aimed to develop and validate a model predicting breast cancer risk for women targeted by breast cancer screening. This retrospective cohort study included 57,411 women screened at least once in BreastScreen Norway during the period from 2007 to 2019. The prediction model included information about age, mammographic density, family history of breast cancer, body mass index, age at menarche, alcohol consumption, exercise, pregnancy, hormone replacement therapy, and benign breast disease. We calculated a 4-year absolute breast cancer risk estimates for women and in risk groups by quartiles. The Bootstrap resampling method was used for internal validation of the model (E/O ratio). The area under the curve (AUC) was estimated with a 95% confidence interval (CI). The 4-year predicted risk of breast cancer ranged from 0.22-7.33%, while 95% of the population had a risk of 0.55-2.31%. The thresholds for the quartiles of the risk groups, with 25% of the population in each group, were 0.82%, 1.10%, and 1.47%. Overall, the model slightly overestimated the risk with an E/O ratio of 1.10 (95% CI: 1.09-1.11) and the AUC was 62.6% (95% CI: 60.5-65.0%). This 4-year risk prediction model showed differences in the risk of breast cancer, supporting personalized screening for breast cancer in women aged 50-69 years.

Validating a model for predicting breast cancer and nonbreast cancer death in women aged 55 years and older.

To support mammography screening decision making, we developed a competing-risk model to estimate 5-year breast cancer risk and 10-year nonbreast cancer death for women aged 55 years and older using Nurses' Health Study data and examined model performance in the Black Women's Health Study (BWHS). Here, we examine model performance in predicting 10-year outcomes in the BWHS, Women's Health Initiative-Extension Study (WHI-ES), and Multiethnic Cohort (MEC) and compare model performance to existing breast cancer prediction models. We used competing-risk regression and Royston and Altman methods for validating survival models to calculate our model's calibration and discrimination (C index) in BWHS (n = 17 380), WHI-ES (n = 106 894), and MEC (n = 49 668). The Nurses' Health Study development cohort (n = 48 102) regression coefficients were applied to the validation cohorts. We compared our model's performance with breast cancer risk assessment tool (Gail) and International Breast Cancer Intervention Study (IBIS) models by computing breast cancer risk estimates and C statistics. When predicting 10-year breast cancer risk, our model's C index was 0.569 in BWHS, 0.572 in WHI-ES, and 0.576 in MEC. The Gail model's C statistic was 0.554 in BWHS, 0.564 in WHI-ES, and 0.551 in MEC; IBIS's C statistic was 0.547 in BWHS, 0.552 in WHI-ES, and 0.562 in MEC. The Gail model underpredicted breast cancer risk in WHI-ES; IBIS underpredicted breast cancer risk in WHI-ES and in MEC but overpredicted breast cancer risk in BWHS. Our model calibrated well. Our model's C index for predicting 10-year nonbreast cancer death was 0.760 in WHI-ES and 0.763 in MEC. Our competing-risk model performs as well as existing breast cancer prediction models in diverse cohorts and predicts nonbreast cancer death. We are developing a website to disseminate our model.