Breast Cancer Risk Assessment Tool Research Articles

Risk Assessment Tool of Breast Cancer and Barriers against Breast Self-Examination among Nurses: An Educational Program

Purpose. Breast cancer is considered a huge health problem among women all over the world due to its increased mortality rate. Breast cancer deaths are decreased by 28–34% due to improvement in treatment and early detection. In order to detect breast cancer early and provide the best possible therapy, knowledge and awareness are essential. So, this study aimed to assess risk of breast cancer among nurses, the knowledge and practices of breast self-examination (BSE) and determine its barriers, and implement an educational program for them. Design. A quasiexperimental research design was used. Methods. This study was conducted at two university hospitals (main university hospital and woman’s health hospital). A purposive sample of 280 nurses with age more than thirty-five years were included in the study using three tools, namely, structure interview questionnaire, breast cancer risk assessment tool, and a structured breast cancer knowledge and practice questionnaire. Nurses’ knowledge and practices regarding breast cancer and BSE were first assessed and then they were provided with two educational courses covering theoretical and practical information. One month after the program ended, the nurses’ knowledge and practices were assessed again. Results. About 3.2% of the participants have five years high risk of developing breast cancer according the risk assessment tool, the main barriers of do not do BSE did not find any symptoms and scared from finding any abnormality. There was a significant increase in knowledge and practice of breast self-examination posteducation compared to pre-education (73.2 and 98.2%, respectively). Also, there was a statistically significant difference between pre- and postprogram with a p value of 0.0001. Conclusion. This study concluded that 3.2% of the nurses with high risk of developing breast cancer needed a follow-up; more than half of them do not perform breast self-examination because they think that do not have any symptoms in the breast and the educational program with a significant effect on knowledge and practices of nurses. So, the researchers recommend the nurses need for an empowerment program and support by providing assistance in places of work as done free clinical examination and mammography by female specialist for privacy.

Abstract PO3-08-01: Decision curve analysis to compare breast cancer risk predictions for a polygenic integrated clinical risk model with those of a gold standard

Abstract Risk-based guidelines have been developed to provide advice on options that are available to women who are at increased risk of breast cancer. We and others are focused on improving risk prediction models for general population use. For breast cancer, polygenic risk has been identified as important for risk prediction, especially when combined with clinical risk factors such as family history, anthropometric measures, breast imaging measures, and hormonal and reproductive risk factors. When comparing the performance of risk prediction models, the AUC is most often used but it focuses on performance in terms of distinguishing between affected and unaffected individuals and does not take into account differences in the specificity (true negative rate) and sensitivity (true positive rate) of the models being compared. In contrast, decision curve analysis takes sensitivity and specificity into account and enables the comparison of the net benefit of risk prediction tools at the clinical risk thresholds that are used to guide clinical management decisions. Herein, we use decision curve analysis to compare breast cancer risk prediction guidelines for BRISK and the Breast Cancer Risk Assessment Tool (BCRAT) in the UK Biobank, and for BRISK and IBIS version 7 in the Nurses’ Health Study. We evaluated the net benefit at the 5-year risk thresholds (1.67% and 3%) that are used to guide clinical management decisions around risk-reducing medication and at the remaining lifetime risk of 20% that is used to guide supplemental MRI surveillance. In the UK Biobank, BRISK showed a net benefit over BCRAT at the 5-year risk thresholds of 1.67% and 3.0%. The AUCs were 0.649 (95% CI = 0.640, 0.695) for BRISK and 0.567 (95% CI = 0.556, 0.577) for BCRAT and showed that, overall, BRISK discriminated between affected and unaffected women better than BCRAT (P < 0.001). In the Nurses’ Health Study, BRISK showed a net benefit over IBIS at the remaining lifetime risk threshold of 20%. The AUCs were 0.647; 95% CI = 0.627, 0.668 for BRISK and 0.571; 95% CI = 0.546, 0.595 for IBIS, a statistically significant improvement for BRISK over IBIS (P < 0.0001). We also looked at sensitivity, specificity, positive predictive value and negative predictive value even though these risk models are one-step removed from breast cancer diagnosis by screening/risk-reduction options. The positive predictive values are higher for the BRISK (27.1%) model compared to BCRAT (5.5%) in the UK Biobank dataset and higher for BRISK (15.4%) compared to IBISv7 (14.0%) in the Nurses’ dataset. We have shown the application of a statistical tool that can be used to directly compare risk models at clinically relevant thresholds. We have shown that BRISK, which comprises polygenic risk and clinical risk factors, shows higher net benefit at both 1.67% and 3% thresholds compared to BCRAT and at 20% remaining lifetime risk for IBIS version 7. In breast cancer risk prediction, a tool with high specificity (and therefore a necessary trade-off for low sensitivity) is preferred because we do not want to incorrectly classify women as not being at high risk and deny them the opportunity for intervention in the form of more frequent screening, alternative modes of screening or risk-reducing medication. This improvement in general population risk stratification that BRISK can provide has the potential to have a clinically significant effect on women’s health. Citation Format: Erika Spaeth, Bernard A Rosner, Gill Dite. Decision curve analysis to compare breast cancer risk predictions for a polygenic integrated clinical risk model with those of a gold standard [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 PO3-08-01.

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.

Personalized survivorship care: Routine breast cancer risk assessment in the gynecologic oncology clinic

IntroductionGynecologic and breast cancers share several risk factors. Breast cancer risk assessment tools can identify those at elevated risk and allow for enhanced breast surveillance and chemoprevention, however such tools are underutilized. We aim to evaluate the use of routine breast cancer risk assessment in a gynecologic oncology clinic. MethodsA patient-facing web-based tool was used to collect personal and family history and run four validated breast cancer risk assessment models (Tyrer-Cuzick (TC), Gail, BRCAPRO, and Claus) in a gynecologic oncology clinic. We evaluated completion of the tools and identification of patients at elevated risk for breast cancer using the four validated models. ResultsA total of 99 patients were included in this analysis. The BRCAPRO model had the highest completion rate (84.8%), followed by the TC model (74.7%), Gail model (74.7%), and the Claus model (52.1%). The TC model identified 21.6% of patients completing the model as having ≥20% lifetime risk of breast cancer, compared to 6.8% by the Gail model, and 0% for both the BRCAPRO and Claus models. The Gail model identified 52.5% of patients as having ≥1.67% 5-year risk of breast cancer. Among patients identified as high-risk for breast cancer and eligible for screening, 9/9 (100%) were referred to a high-risk breast clinic. ConclusionAmong patients that completed the TC breast cancer risk assessment in a gynecologic oncology clinic, approximately 1 in 5 were identified to be at significantly elevated lifetime risk for breast cancer. The gynecologic oncologist's office might offer a convenient and feasible setting to incorporate this risk assessment into routine patient care, as gynecologic oncologists often have long-term patient relationships and participate in survivorship care.

Abstract B085: Increasing utilization of breast cancer risk assessment tools: Identifying and adapting implementation strategies using multiple stakeholder groups

Abstract Background: Each year ~12,000 female breast cancers are diagnosed in US women ages 18-45 with overrepresentation of aggressive cancers, advanced stage disease at diagnosis, and inferior outcomes. The American College of Radiology and American Society of Breast Surgeons recommend that breast cancer risk assessment (RA) start no later than age 30 to identify high-risk individuals in whom to initiate screening earlier. However, RA tools are currently utilized in as few as 3-26% of eligible women. Methods: Our study aims are to assess feasibility, reach, acceptability, and appropriateness of customized strategies to increase uptake of breast cancer RA. We identified barriers to increasing utilization of RA tools through discussions with long-term community partners in the Tennessee Breast and Cervical Cancer Screening Program (TBCSP), a three-phase adapted Feasibility of Intervention Measure (FIM) in five health clinics, and one-on-one interviews guided by the Consolidated Framework for Implementation Research with 24 providers in public health settings and 37 interviews guided by the Health Belief Model with Black women age 30-50 years with no personal history of breast cancer. Interview data contributed to the development of a conceptual framework to depict influences of provider behavior in the utilization of breast cancer risk assessment. Using the CFIR-ERIC Matching Tool and working with the study’s Community Advisory Board (CAB), the research team identified three potential strategies to increase the use of breast cancer RA in public health settings. Findings: Selected strategies included having a site champion for each clinic, offering a short series of education modules, and distributing print educational materials including a provider education sheet and patient-facing information cards. These strategies were further revised in collaboration with the CAB and patients and providers in Community Engagement Studios. Implications for D&I Research: This project demonstrates the contributions of a long-standing relationship with a state-wide public health agency, implementation science tools, and collaboration with community advisors for identifying and addressing barriers to breast cancer RA. The study has generated replicable scripts for educating providers and community-informed reproducible patient education materials to increase breast cancer RA. Evaluation of the impact of these strategies on provider behavior in six clinical settings is currently underway. Citation Format: Rebecca Selove, Lucy Spalluto, Maureen Sanderson, Laura Ernst, Carolyn Audet. Increasing utilization of breast cancer risk assessment tools: Identifying and adapting implementation strategies using multiple stakeholder groups [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 B085.

Employing a Risk Assessment Tool for Shared Decision-Making in Breast Cancer Screening.

Background: The existing guidelines for breast cancer screening incorporate assessing individual risk but contain no universally recommended risk assessment tool in the primary care setting. Local Problem: A family planning program utilizing nurses and nurse practitioners for assessment, education, and referral lacked a risk assessment tool for assessing breast cancer risk. Methods: A breast cancer risk assessment tool was implemented in practice. Interventions: The interventions involved assessing the patient's risks, providing education on risk reduction strategies, and determining the need for clinical breast exams and referrals for mammography. Results: The study demonstrated a clear correlation between the number of breast cancer risks identified and the extent of education provided to patients based on their specific risk profiles (0 vs. 1.5 risks; 2 vs. 4.6 educational components). Among patients under age 35, 80% opted for a clinical breast exam, while 100% of patients age 40 and above agreed to both clinical breast exams and referrals for mammography after discussing individual risks. Conclusions: The use of a breast cancer risk assessment tool provided an evidence-based foundation for the shared decision-making conversation between patient and nurse/provider on risk reduction. The discussion included current evidence on the risk versus benefit of age-based screening options for breast cancer, allowing for an informed decision made jointly between the patient and provider.

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.

Extending the Breast Cancer Surveillance Consortium Model of Invasive Breast Cancer.

We extended the Breast Cancer Surveillance Consortium (BCSC) version 2 (v2) model of invasive breast cancer risk to include BMI, extended family history of breast cancer, and age at first live birth (version 3 [v3]) to better inform appropriate breast cancer prevention therapies and risk-based screening. We used Cox proportional hazards regression to estimate the age- and race- and ethnicity-specific relative hazards for family history of breast cancer, breast density, history of benign breast biopsy, BMI, and age at first live birth for invasive breast cancer in the BCSC cohort. We evaluated calibration using the ratio of expected-to-observed (E/O) invasive breast cancers in the cohort and discrimination using the area under the receiver operating characteristic curve (AUROC). We analyzed data from 1,455,493 women age 35-79 years without a history of breast cancer. During a mean follow-up of 7.3 years, 30,266 women were diagnosed with invasive breast cancer. The BCSC v3 model had an E/O of 1.03 (95% CI, 1.01 to 1.04) and an AUROC of 0.646 for 5-year risk. Compared with the v2 model, discrimination of the v3 model improved most in Asian, White, and Black women. Among women with a BMI of 30.0-34.9 kg/m2, the true-positive rate in women with an estimated 5-year risk of 3% or higher increased from 10.0% (v2) to 19.8% (v3) and the improvement was greater among women with a BMI of ≥35 kg/m2 (7.6%-19.8%). The BCSC v3 model updates an already well-calibrated and validated breast cancer risk assessment tool to include additional important risk factors. The inclusion of BMI was associated with the largest improvement in estimated risk for individual women.

Uptake of screening MRI in high risk patients without a personal history of breast cancer.

133 Background: The American Cancer Society (ACS) recommends breast MRI in addition to annual screening mammogram for women who are high risk for breast cancer starting at age 30, regardless of breast density. This includes women with a lifetime risk of breast cancer of 20% or greater, according to breast cancer risk assessment tools. In this study, we evaluated the uptake of supplemental MRI screening in patients ages 25-75 classified as high risk (Tyrer Cuzick (TC) score greater than 20%), without a personal history of breast cancer (PHBC). Methods: Cancer risk assessment software was used to calculate a TC score. Data was extracted from the EMR of an academic center for patients ages 25-75 years old between 07/20/2020 and 07/29/2021 with a TC score of ≥ 20%. Factors such as age, ethnicity, race, insurance type, breast density, and whether patients had access to a High-Risk Breast Cancer (HRBC) provider were analyzed and a chi-squared test was used to determine whether there were significant differences in MRI supplemental uptake based on these subsets. Results: 1144 patients were identified as high risk, having a TC ≥ 20%. 27% (n=307) underwent supplemental MRI screening, while 73% (n=837) did not. 842 patients were classified as having dense breasts and 28% (n=237) of these patients underwent MRI. Of 635 patients with an MRI order, 36% (n=227) had access to a HRBC provider, while 64% (n=408) did not. Patients with a HRBC provider were more likely to fulfill their MRI order (88%, n=200), compared to patients without a HRBC provider (33%, n=135) (p<0.001). Conclusions: Various factors may be influencing whether high risk patients complete supplemental screening MRI. Patients with dense breasts and those with access to HRBC were more likely to undergo supplemental MRI screening. Patients who have not completed their recommended breast MRI may be faced with various challenges, such as, not having access to a HRBC provider. Identifying barriers to screening can help increase early rates of breast cancer detection in this high risk population of women.

Talking numbers: how women and providers use risk scores during and after risk counseling – a qualitative investigation from the NRG Oncology/NSABP DMP-1 study

ObjectivesLittle research exists on how risk scores are used in counselling. We examined (a) how Breast Cancer Risk Assessment Tool (BCRAT) scores are presented during counselling; (b) how women react...

Turning the Page on Breast Cancer in Ohio: Lessons learned from implementing a multilevel intervention to reduce breast cancer mortality among Black women.

Turning the Page on Breast Cancer (TPBC) uses a multilevel approach to reduce breast cancer (BC) mortality among Black women. TPBC intervenes by (1) improving health care facilities' ability to conduct effective BC screening, follow-up, and treatment; (2) involving community-based organizations; and (3) providing education and personal risk information through a culturally relevant website. Ohio has among the worst BC mortality rates in the United States for Black women. TPBC is in its third year of providing targeted interventions in 12 Ohio counties with particularly high BC rates among Black women. TPBC enrolls health care facilities, collects organizational and patient data, and conducts key informant interviews to inform the provision of appropriate evidence-based interventions. TPBC engages Black communities through community-based organizations and social media advertising. The TPBC website offers BC information, connects Black women to community BC resources, and provides access to a risk-assessment tool. TPBC has provided tailored information packets, evidence-based interventions, and systematic support for improving the tracking and follow-up of breast health care among patients in 10 clinical partnerships. The project has provided education at community events monthly since mid-2021. The TPBC website (http://endbreastcancerohio.org) is promoted through social media (primarily Facebook) and community events to reach Black women aged 25-70 years. To date, 4108 unique users have visited the website, of whom 15.9% completed the risk assessment. Novel strategies are needed to address persistent disparities in BC outcomes among Black women. TPBC demonstrates the potential effectiveness of multiple methods of community-based, clinic-based, and web-based engagement. Turning the Page on Breast Cancer (TPBC) aims to reduce breast cancer mortality among Black women in Ohio by conducting multilevel, community-engaged interventions in 12 counties. Women are provided risk information and education at virtual and in-person community events and through a community-friendly website that was launched in November 2020. Almost 4000 women have visited the website, which offers community-targeted information, urges screening for individuals at elevated risk, and offers access to patient navigation services; 655 users have used a breast cancer risk-assessment tool on the site. Community-based organizations conduct educational efforts. TPBC partners with health care facilities, which are taught to improve their ability to conduct effective breast cancer screening, follow-up, and treatment. So far, TPBC has provided educational information, evidence-based intervention lists, tailored information packets, and ongoing quarterly support to partners in 10 counties. Evaluation will focus on aggregated data for screening and genetic testing referral at the clinic level.

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.

Comparison of the Diagnostic Accuracy of Mammogram-based Deep Learning and Traditional Breast Cancer Risk Models in Patients Who Underwent Supplemental Screening with MRI.

Background Access to supplemental screening breast MRI is determined using traditional risk models, which are limited by modest predictive accuracy. Purpose To compare the diagnostic accuracy of a mammogram-based deep learning (DL) risk assessment model to that of traditional breast cancer risk models in patients who underwent supplemental screening with MRI. Materials and Methods This retrospective study included consecutive patients undergoing breast cancer screening MRI from September 2017 to September 2020 at four facilities. Risk was assessed using the Tyrer-Cuzick (TC) and National Cancer Institute Breast Cancer Risk Assessment Tool (BCRAT) 5-year and lifetime models as well as a DL 5-year model that generated a risk score based on the most recent screening mammogram. A risk score of 1.67% or higher defined increased risk for traditional 5-year models, a risk score of 20% or higher defined high risk for traditional lifetime models, and absolute scores of 2.3 or higher and 6.6 or higher defined increased and high risk, respectively, for the DL model. Model accuracy metrics including cancer detection rate (CDR) and positive predictive values (PPVs) (PPV of abnormal findings at screening [PPV1], PPV of biopsies recommended [PPV2], and PPV of biopsies performed [PPV3]) were compared using logistic regression models. Results This study included 2168 women who underwent 4247 high-risk screening MRI examinations (median age, 54 years [IQR, 48-60 years]). CDR (per 1000 examinations) was higher in patients at high risk according to the DL model (20.6 [95% CI: 11.8, 35.6]) than according to the TC (6.0 [95% CI: 2.9, 12.3]; P < .01) and BCRAT (6.8 [95% CI: 2.9, 15.8]; P = .04) lifetime models. PPV1, PPV2, and PPV3 were higher in patients identified as high risk by the DL model (PPV1, 14.6%; PPV2, 32.4%; PPV3, 36.4%) than those identified as high risk with the TC (PPV1, 5.0%; PPV2, 12.7%; PPV3, 13.5%; P value range, .02-.03) and BCRAT (PPV1, 5.5%; PPV2, 11.1%; PPV3, 12.5%; P value range, .02-.05) lifetime models. Conclusion Patients identified as high risk by a mammogram-based DL risk assessment model showed higher CDR at breast screening MRI than patients identified as high risk with traditional risk models. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Bae in this issue.

Breast density quantitative measures and breast cancer risk among screened Black women.

Mammographic density is a strong predictor of breast cancer but only slightly increased the discriminatory ability of existing risk prediction models in previous studies with limited racial diversity. We assessed discrimination and calibration of models consisting of the Breast Cancer Risk Assessment Tool (BCRAT), Breast Imaging-Reporting and Data System density and quantitative density measures. Patients were followed up from the date of first screening mammogram until invasive breast cancer diagnosis or 5-year follow-up. Areas under the curve for White women stayed consistently around 0.59 for all models, whereas the area under the curve increased slightly from 0.60 to 0.62 when adding dense area and area percent density to the BCRAT model for Black women. All women saw underprediction in all models, with Black women having less underprediction. Adding quantitative density to the BCRAT did not statistically significantly improve prediction for White or Black women. Future studies should evaluate whether volumetric breast density improves risk prediction.

Abstract 4175: Development of breast cancer risk assessment tool: The Korean decision aid for cancer screening (K-DACS) study

Abstract Purpose: Widely used breast cancer risk prediction tools are based on data from Western countries, but risk factors may differ for Asian women. Hence, we aimed to develop a risk assessment tool for breast cancer in Asian women, using a nationwide, population-based cohort in Korea. Methods: Women aged ≥ 40 years who participated in both breast cancer screening and general health examination in 2009 were eligible for this study. Age, body mass index (BMI), breast density, lifestyle and reproductive factors, and comorbidities were used to develop 5-year breast cancer risk prediction models in premenopausal (n=771,856) and postmenopausal (n=1,108,047) women at baseline. Backward stepwise selection in the Cox proportional hazards model was used to construct the best-fit risk prediction model, which was then converted into a risk score nomogram, representing an individual probability estimate of incident breast cancer. Model performance was evaluated by discrimination and calibration. Results: Among premenopausal women, high BMI, low parity, short breastfeeding period, early age at menarche, high breast density, history of benign breast mass, and family history of breast cancer contributed to breast cancer risk prediction. The appropriate predictors in postmenopausal women additionally included age, type 2 diabetes, dyslipidemia, late age at menopause, hormone replacement therapy use, except for number of parities. The concordant statistic of risk prediction model was 0.58 (95% confidence interval [CI] 0.57-0.59) for premenopausal women, and 0.64 (95% CI 0.63-0.65) for postmenopausal women. Our model correlated well in the calibration plot in both premenopausal and postmenopausal women.Conclusions: Our breast cancer risk prediction model showed good performance for postmenopausal women, providing the background for risk-based screening recommendations in Asian women. Citation Format: Wonyoung Jung, Yong-Moon Mark Park, Sang Hyun Park, Kyungdo Han, Junhee Park, Yohwan Yeo, Jung Kwon Lee, Dale P. Sandler, Dong Wook Shin. Development of breast cancer risk assessment tool: The Korean decision aid for cancer screening (K-DACS) study. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4175.

Validation of an Abridged Breast Cancer Risk Prediction Model for the General Population.

In this prospective population-based cohort study, we show the improved performance of a new risk assessment model compared with a gold-standard model (BCRAT). The classification of at-risk women using this new model highlights the opportunity to improve risk stratification and implement existing clinical risk-reduction interventions.

Barriers to Implementation of Breast Cancer Risk Assessment: The Health Care Team Perspective

PurposeTo assess health care professionals’ perceptions of barriers to the utilization of breast cancer risk assessment tools in the public health setting through a series of one-on-one interviews with health care team members. MethodsWe conducted a cross-sectional qualitative study consisting of one-on-one semistructured telephone interviews with health care team members in the public health setting in the state of Tennessee between May 2020 and October 2020. An iterative inductive-deductive approach was used for qualitative analysis of interview data, resulting in the development of a conceptual framework to depict influences of provider behavior in the utilization of breast cancer risk assessment. ResultsA total of 24 interviews were completed, and a framework of influences of provider behavior in the utilization of breast cancer risk assessment was developed. Participants identified barriers to the utilization of breast cancer risk assessment (knowledge and understanding of risk assessment tools, workflow challenges, and availability of personnel); patient-level barriers as perceived by health care team members (psychological, economic, educational, and environmental); and strategies to increase the utilization of breast cancer risk assessment at the provider level (leadership buy-in, training, supportive policies, and incentives) and patient level (improved communication and better understanding of patients’ perceived cancer risk and severity of cancer). ConclusionsUnderstanding barriers to implementation of breast cancer risk assessment and strategies to overcome these barriers as perceived by health care team members offers an opportunity to improve implementation of risk assessment and to identify a racially, geographically, and socioeconomically diverse population of young women at high risk for breast cancer.

Abstract P3-03-07: The Breast Cancer Prevention Clinic: A Single Institution’s Experience

Abstract The Breast Cancer Prevention Clinic: A Single Institution’s Experience Authors: Joe J. Stephenson MD1, LeAnn Perkins, FNP-BC, DipACLM1, Erinn Crowe, RN, BSN, OCN1, Regina Franco, MSN, NP-C, DipACLM1, Pamela Cloys, MSN, ANP-C1, Carmen Hancock, MSN, APRN, FNP-C, AOCNP1, Marie Smith, MS1 1Prisma Health Cancer Institute, Center for Integrative Oncology &amp; Survivorship Background: Roughly 1 in 8 women will be diagnosed with invasive breast cancer in their lifetime1. Some women have higher risk for developing breast cancer due to numerous factors such as elevated risk modeling scores, a history of chest radiation therapy, a history of atypical cells on a breast biopsy or a pathogenic genetic mutation. Enhanced surveillance to detect cancers early and addressing modifiable lifestyle risks are helpful to mitigate risks. Methods: The Prisma Health’s Breast Cancer Prevention Clinic (BCPC) approaches prevention by monitoring non-modifiable risks with regular surveillance and education on optimizing lifestyle habits to decrease modifiable risks. Established in 2017, the BCPC team now includes multiple medical oncologists and nurse practitioners, allowing the clinic to be offered any day of the week. During the visit, patients receive a personal risk assessment. Recommendations are given for surveillance imaging, risk-reducing endocrine therapy, and risk-reducing lifestyle modifications such as diet, alcohol consumption, and exercise routines. Family history is also reviewed to recommend Genetic Counseling if appropriate. In March 2022, a statement was added to patient mammograms, generating referrals for those with calculated elevated risk (a calculated lifetime risk &amp;gt;20% using the Tyrer-Cuzick or International Breast Cancer Intervention Study model and/or a 5-year risk &amp;gt;1.7% using the National Cancer Institute Breast Cancer Risk Assessment Tool or Gail model). Results: In a 12-month period from June 1, 2021 through May 31, 2022, 56.2% of patients were referred based on family history, 26.9% for higher-risk breast pathology, 8.1% for calculated elevated risk, 4.4 % for a pathogenic genetic variant that increases breast cancer risk and 4.1% for mammographic identification risk modeling. Following the BCPC visit, referrals for patients may include imaging such as mammograms, MRIs, genetic counseling or testing, or surgical evaluations. Referrals for lifestyle modifications may include nutrition, exercise navigation, mindfulness classes, or Sexual Health Evaluations. From June 2021 through May 2022, 41.4% of patients were referred for a breast MRI, 26.6% were referred to genetic counselors for genetic testing recommendations, 11.1% were referred to nutrition or exercise services, 8.42% were referred for a Bone Density Scan (DXA), 4% were referred to a Breast Surgeon and 3.7% were referred to the Genetic Management Clinic. Additionally, 79.5% of patients were referred to the High-Risk Breast Lifetime Clinic for continued monitoring and surveillance for early detection of breast cancer, continued reinforcement of lifestyle modifications, and management of risk-reduction endocrine therapy. Conclusion: The unique structure of this model allows for enhanced surveillance through greater use of evidence-based cancer prevention, early detection methods, and increased education for lifestyle medicine practices with the overall goals of reducing breast cancer risk, earlier breast cancer detection, and increasing quality of life. Reference List 1. DeSantis C, Ma J, Gaudet, M, et al. Breast cancer statistics, 2019. CA: A Cancer Journal for Clinicians. 2019;69(6), 438-451. https://doi.org/10.3322/caac.21583. Citation Format: Joe Stephenson, LeAnn Perkins, Erinn Crowe, Regina Franco, Carmen Hancock, Pamela Cloys, Marie Smith. The Breast Cancer Prevention Clinic: A Single Institution’s Experience [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-03-07.

Breast Cancer Risk Assessment Tools for Stratifying Women into Risk Groups: A Systematic Review

The benefits and harms of breast screening may be better balanced through a risk-stratified approach. We conducted a systematic review assessing the accuracy of questionnaire-based risk assessment tools for this purpose. Population: asymptomatic women aged ≥40 years; Intervention: questionnaire-based risk assessment tool (incorporating breast density and polygenic risk where available); Comparison: different tool applied to the same population; Primary outcome: breast cancer incidence; Scope: external validation studies identified from databases including Medline and Embase (period 1 January 2008-20 July 2021). We assessed calibration (goodness-of-fit) between expected and observed cancers and compared observed cancer rates by risk group. Risk of bias was assessed with PROBAST. Of 5124 records, 13 were included examining 11 tools across 15 cohorts. The Gail tool was most represented (n = 11), followed by Tyrer-Cuzick (n = 5), BRCAPRO and iCARE-Lit (n = 3). No tool was consistently well-calibrated across multiple studies and breast density or polygenic risk scores did not improve calibration. Most tools identified a risk group with higher rates of observed cancers, but few tools identified lower-risk groups across different settings. All tools demonstrated a high risk of bias. Some risk tools can identify groups of women at higher or lower breast cancer risk, but this is highly dependent on the setting and population.

Variability Among Breast Cancer Risk Classification Models When Applied at the Level of the Individual Woman

BackgroundBreast cancer risk models guide screening and chemoprevention decisions, but the extent and effect of variability among models, particularly at the individual level, is uncertain.ObjectiveTo quantify the accuracy and disagreement between commonly used risk models in categorizing individual women as average vs. high risk for developing invasive breast cancer.DesignComparison of three risk prediction models: Breast Cancer Risk Assessment Tool (BCRAT), Breast Cancer Surveillance Consortium (BCSC) model, and International Breast Intervention Study (IBIS) model.SubjectsWomen 40 to 74 years of age presenting for screening mammography at a multisite health system between 2011 and 2015, with 5-year follow-up for cancer outcome.Main MeasuresComparison of model discrimination and calibration at the population level and inter-model agreement for 5-year breast cancer risk at the individual level using two cutoffs (≥ 1.67% and ≥ 3.0%).Key ResultsA total of 31,115 women were included. When using the ≥ 1.67% threshold, more than 21% of women were classified as high risk for developing breast cancer in the next 5 years by one model, but average risk by another model. When using the ≥ 3.0% threshold, more than 5% of women had disagreements in risk severity between models. Almost half of the women (46.6%) were classified as high risk by at least one of the three models (e.g., if all three models were applied) for the threshold of ≥ 1.67%, and 11.1% were classified as high risk for ≥ 3.0%. All three models had similar accuracy at the population level.ConclusionsBreast cancer risk estimates for individual women vary substantially, depending on which risk assessment model is used. The choice of cutoff used to define high risk can lead to adverse effects for screening, preventive care, and quality of life for misidentified individuals. Clinicians need to be aware of the high false-positive and false-negative rates and variation between models when talking with patients.