Personalized Breast Cancer Screening Research Articles

Risk estimation, anxiety, and breast cancer worry in women at risk for breast cancer: A single-arm trial of personalized risk communication.

Elevated anxiety and breast cancer worry can impede mammographic screening and early breast cancer detection. Genetic advances and risk models make personalized breast cancer risk assessment and communication feasible, but it is unknown whether such communication of risk affects anxiety and disease-specific worry. We studied the effect of a personalized breast cancer screening intervention on risk perception, anxiety, and breast cancer worry. Women with a normal mammogram but elevated risk for breast cancer (N = 122) enrolled in the Athena Breast Health risk communication program were surveyed before and after receiving a letter conveying their breast cancer risk and a breast health genetic counselor consultation. We compared breast cancer risk estimation, anxiety, and breast cancer worry before and after risk communication and evaluated the relationship of anxiety and breast cancer worry to risk estimation accuracy. Women substantially overestimated their lifetime breast cancer risk, and risk communication somewhat mitigated this overestimation (49% pre-intervention, 42% post-intervention, 13% Gail model risk estimate, P < .001). Both general anxiety and breast cancer worry declined significantly after risk communication in women with high baseline anxiety. Baseline anxiety and breast cancer worry were essentially unrelated to risk estimation accuracy, but risk communication increased alignment of worry with accuracy of risk assessment. Personalized communication about breast cancer risk was associated with modestly improved risk estimation accuracy in women with relatively low anxiety and less anxiety and breast cancer worry in women with higher anxiety. We detected no negative consequences of informing women about elevated breast cancer risk.

Is Background Parenchymal Enhancement an Important Risk Factor for Breast Cancer Development in Women with Increased Risk?

Is Background Parenchymal Enhancement an Important Risk Factor for Breast Cancer Development in Women with Increased Risk?

Abstract OT2-08-01: Personalized breast cancer screening in a population based study: Women Informed to Screen Depending On Measures of risk (WISDOM)

Abstract Background: WISDOM is a 100,000 healthy women preference-tolerant, pragmatic study comparing annual to personalized risk-based breast screening. The novelty of WISDOM personalized screening is the integration of previously validated genetic and clinical risk factors (age, family history, breast biopsy results, ethnicity, mammographic density) into a single risk assessment model that directs the starting age, timing, and frequency of screening. The goal of WISDOM is to determine if personalized screening, compared to annual screening, is as safe, less morbid, enables prevention, and is preferred by women. The study is registered on ClinicalTrials.gov, NCT02620852. Methods: Women aged 40-74 years with no history of breast cancer or DCIS, and no previous double mastectomy can join the study online at wisdomstudy.org. Participants can elect randomization or self-select a study arm, and provide electronic consent and Release for Medical Information using DocuSign. For all participants, 5-year risk of developing breast cancer is calculated according to the Breast Cancer Screening Consortium (BCSC) model. Participants in the personalized arm undergo panel-based mutation testing, and their 5-year risk is calculated using the BCSC score combined with a Polygenic Risk Score (BCSC-PRS) that includes 75 single nucleotide polymorphisms (SNPs, increase to 229) known to increase breast cancer risk. SNPs and mutations (BRCA1, BRCA2, TP53, PTEN, STK11, CDH1, ATM, PALB2, and CHEK2) are assessed by saliva-based testing through Color Genomics. 5-year risk level thresholds are used to stratify for low-, moderate- and high risk. Risk stratification determines age to start, stop, and frequency of screening. Enrollment: As of July 2018, the WISDOM study is open to all eligible women in California, North Dakota, South Dakota, Minnesota and Iowa. To date, 23,329 eligible women have registered and 14,393 women have consented to participate in the trial. We analyzed 3,255 participants who have completed risk assessment in the personalized arm. The median age was 56 years. 82% were Caucasian, 1% African-American, and 6% Asian. 9% self-reported as Hispanic. We are partnering with health insurers and self-insured companies using coverage with evidence progression. To strengthen generalizability, we are expanding to other states. WISDOM enrollment will continue past 2019. Feasibility: To evaluate the addition of PRS, we used paired statistical tests (McNemar) to compare the distributions of BCSC, and BCSC-PRS risk estimates around low-risk (&amp;lt;1.3%), and very-high risk (&amp;gt;6%) thresholds, the latter corresponding to 5-year risk of a BRCA mutation carrier. The median 5-year risk was 1.5% (IQR 1.0-2.1%) using the BCSC model, and 1.4% (IQR 0.8-2.5%) using the BCSC-PRS model. The BCSC-PRS model classified more women into the low (&amp;lt;1%) and very high (≥6%) risk categories compared to the BCSC model (p &amp;lt; 0.001). Conclusions: Our findings demonstrate that incorporating genetic variants into a validated clinical model is feasible and impacts risk classification compared to a model without genetic risk factors. Results at 5 years will reveal if this classification improves healthcare value by reducing screen volumes and costs without jeopardizing outcomes. Citation Format: Acerbi I, Shieh Y, Madlensky L, Tice J, Ziv E, Eklund M, Blanco A, DeRosa D, Tong B, Goodman D, Nassereddine L, Anderson N, Harvey H, Layton T, Park HL, Petruse A, Stewart S, Wernisch J, Risty L, Koenig B, Sarrafan S, Firouzian R, Kaplan C, Hiatt R, Parker BA, Wenger N, Lee V, Heditsian D, Brain S, Stover Fiscalini A, Borowsky AD, Anton-Culver H, Naeim A, Kaster A, Talley M, van 't Veer LJ, LaCroix A, Wisdom Study and Athena Breast Health Network Investigators and Advocate Partners, Esserman LJ. Personalized breast cancer screening in a population based study: Women Informed to Screen Depending On Measures of risk (WISDOM) [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr OT2-08-01.

Localized mammographic density is associated with interval cancer and large breast cancer: a nested case-control study

BackgroundHigh mammographic density is associated with breast cancer and with delayed detection. We have examined whether localized density, at the site of the subsequent cancer, is independently associated with being diagnosed with a large-sized or interval breast cancer.MethodsWithin a prospective cohort of 63,130 women, we examined 891 women who were diagnosed with incident breast cancer. For 386 women, retrospective localized density assessment was possible. The main outcomes were interval cancer vs. screen-detected cancer and large (> 2 cm) vs. small cancer. In negative screening mammograms, overall and localized density were classified reflecting the BI-RADS standard. Density concordance probabilities were estimated through multinomial regression. The associations between localized density and the two outcomes were modeled through logistic regression, adjusted for overall density, age, body mass index, and other characteristics.ResultsThe probabilities of concordant localized density were 0.35, 0.60, 0.38, and 0.32 for overall categories “A,” “B,” “C,” and “D.” Overall density was associated with large cancer, comparing density category D to A with OR 4.6 (95%CI 1.8–11.6) and with interval cancer OR 31.5 (95%CI 10.9–92) among all women. Localized density was associated with large cancer at diagnosis with OR 11.8 (95%CI 2.7–51.8) among all women and associated with first-year interval cancer with OR 6.4 (0.7 to 58.7) with a significant linear trend p = 0.027.ConclusionsOverall density often misrepresents localized density at the site where cancer subsequently arises. High localized density is associated with interval cancer and with large cancer. Our findings support the continued effort to develop and examine computer-based measures of localized density for use in personalized breast cancer screening.

Effective Preventive Care Management of Multiple Chronic Conditions

Clinical practice guidelines do not sufficiently address the needs of patients with multiple chronic conditions (MCC) as these guidelines focus on single disease management and ignore unique patient-specific conditions. As a result, a non-personalized approach for the management of the patients with MCC leads to adverse events and increases the financial burden on the health care system as over 150 million Americans experience MCC. To this end, we develop a stochastic modeling framework to personalize the management of MCC and provide an exact solution algorithm. We consider the optimal management of preventive care for an index disease (e.g., breast cancer, colorectal cancer, HIV, etc.) while accounting for the existence of a chronic condition (e.g., hypertension, diabetes, Alzheimer's disease, etc.) Our modeling framework is particularly useful for the cases where the chronic condition affects the risk of the index disease. In a case study using real breast cancer epidemiology data, we demonstrate how our modeling framework can be used to personalize breast cancer screening for women with type 2 diabetes. In addition to providing a personalized breast cancer screening schedule for diabetic women, we find some important policy insights that were not previously recognized by the medical community. More specifically, we find that compared to non-diabetic women, diabetic women should be screened less aggressively but screening should end at similar ages. We also find that adherence to the optimal screening policy is more crucial for diabetic women compared to non-diabetic women. Our main insight on screening recommendations also has important resource implications as it leads to fewer screening mammograms. That is, compared to the current national breast cancer screening guidelines, the optimal breast cancer screening policy for diabetic women could save the health care system approximately 2.6 million mammograms annually which translates to $405 million of annual cost savings.

The WISDOM Personalized Breast Cancer Screening Trial: Simulation Study to Assess Potential Bias and Analytic Approaches

BackgroundWISDOM (Women Informed to Screen Depending on Measures of Risk) is a randomized trial to assess whether personalized breast cancer screening—where women are screened biannually, annually, biennially, or not at all depending on risk and age—can prevent as many advanced (stage IIB or higher) cancers as annual screening in women ages 40–74 years across 5 years of trial time. The short study time in combination with design choices of not requiring study entry and exit mammograms for all participants may introduce different sources of bias in favor of either the personalized or the annual arm.MethodsWe designed a simulation model and performed 5000 virtual WISDOM trials to assess potential biases. Each virtual trial simulated 65 000 randomly assigned participants who were each assigned a risk stratum and a time to stage of at least IIB cancer sampled from an exponential distribution with the hazard rate based on the risk stratum. Results from the virtual trials were used to evaluate two candidate analysis strategies with respect to susceptibility for introducing bias: 1) difference between arms in total number of events over total trial time, and 2) difference in number of events within complete screening cycles.ResultsBased on the simulations, about 86 stage IIB or higher cancers will be detected within the trial and the total exposure time will be about 74 000 years in each arm. Potential ascertainment bias is introduced at study entry and exit. Analysis strategy 1 works better for the nonscreened stratum, whereas method 2 is considerably more unbiased for the strata of women screened biennially or every 6 months.ConclusionCombining the two candidate analysis approaches gives a reasonably unbiased analysis based on the simulations and is the method we will use for the primary analysis in WISDOM. Publishing the WISDOM analysis approach provides transparency and can aid the design and analysis of other individualized screening trials.

Women's preferences, willingness-to-pay, and predicted uptake for single-nucleotide polymorphism gene testing to guide personalized breast cancer screening strategies: a discrete choice experiment.

BackgroundSingle-nucleotide polymorphism (SNP) gene test is a potential tool for improving the accuracy of breast cancer risk prediction. We seek to measure women’s preferences and marginal willingness-to-pay (mWTP) for this new technology.Materials and methodsWe administered a discrete choice experiment (DCE) to English-speaking Singaporean women aged 40–69 years without any history of breast cancer, enrolled via door-to-door recruitment with quota sampling by age and ethnicity. DCE attributes comprise: 1) sample type (buccal swab and dried blood spot), 2) person conducting pretest discussion (specialist doctor, non-specialist doctor, and nurse educator), 3) test location (private family clinic, public primary-care clinic, and hospital), and 4) out-of-pocket cost (S$50, S$175, and S$300). Mixed logit model was used to estimate the effect of attribute levels on women’s preferences and mWTP. Interactions between significant attributes and respondent characteristics were investigated. Predicted uptake rates for various gene testing scenarios were studied.ResultsA total of 300 women aged 52.6±7.6 years completed the survey (100 Chinese, Malay, and Indian women, respectively). Sample type (P=0.046), person conducting pretest discussion, and out-of-pocket cost (P<0.001) are significantly associated with going for SNP gene testing. Women with higher income and education levels are more willing to pay higher prices for the test. Preferences in terms of mWTP across ethnic groups appear similar, but Chinese women have greater preference heterogeneity for the attributes. Predicted uptake for a feasible scenario consisting of buccal swab, pretest discussion with nurse educator at the hospital costing S$50 is 60.5%. Only 3.3% of women always opted out of the SNP gene test in real life. Reasons include high cost, poor awareness, and indifference toward test results.ConclusionSNP gene testing may be tailored according to individual preferences to encourage uptake. Future research should focus on outcomes and cost-effectiveness of personalized breast cancer screening using SNP gene testing.

Women's decision-making regarding risk-stratified breast cancer screening and prevention from the perspective of international healthcare professionals.

IntroductionIncreased knowledge of breast cancer risk factors may enable a paradigm shift from one-size-fits-all breast cancer screening to screening and subsequent prevention guided by a woman’s individual risk of breast cancer. Professionals will play a key role in informing women about this new personalised screening and prevention programme. Therefore, it is essential to explore professionals’ views of the acceptability of this new programme, since this may affect shared decision-making.MethodsProfessionals from three European countries (the Netherlands, United Kingdom, and Sweden) participated in digital concept mapping, a systematic mixed methods approach used to explore complex multidimensional constructs.ResultsAcross the three countries, professionals prioritised the following five themes which may impact decision-making from the perspective of eligible women: (1) Anxiety/worry; (2) Proactive approach; (3) Reassurance; (4) Lack of knowledge; and (5) Organisation of risk assessment and feedback. Furthermore, Dutch and British professionals expressed concerns regarding the acceptability of a heterogeneous screening policy, suggesting women will question their risk feedback and assigned pathway of care. Swedish professionals emphasised the potential impact of the programme on family relations.ConclusionsThe perspectives of Dutch, British, and Swedish professionals of women’s decision-making regarding personalised breast cancer screening and prevention generally appear in line with women’s own views of acceptability as previously reported. This will facilitate shared decision-making. However, concerns regarding potential consequences of this new programme for screening outcomes and organisation need to be addressed, since this may affect how professionals communicate the programme to eligible women.

Abstract OT3-03-01: Preference-Tolerant randomized trial of risk-based vs. annual breast cancer screening: WISDOM study in progress

Abstract Purpose: Women Informed to Screen Depending on Measures of risk (WISDOM) trial is a pragmatic study comparing two real world approaches to clinical care for breast screening: annual screening versus personalized screening. The novelty of the personalized arm of the study is that we are combining known risk factors (age, family history, history of breast disease, ethnicity, BIRADS breast density, and genetics) into a single risk assessment model. All components of the model have been tested and established, but have never been used jointly. The goal of the WISDOM study is to examine the effectiveness of personalized breast cancer screening and to bring objective recommendations to the current mammography screening debate. Methods: The WISDOM trial will enroll 100,000 women with a preference-tolerant design that will determine if risk-based screening vs. annual screening, is as safe, less morbid, enables prevention, and is preferred by women. Women 40 - 74 years of age with no history of breast cancer or DCIS, and no previous double mastectomy can join the study from the WISDOM Study website (wisdomstudy.org). All participants sign up, elect randomization or self-select the study arm, provide electronic consent using DocuSign (eConsent), and sign a Medical Release Form. For all participants, 5-year risk of developing breast cancer is calculated according to the Breast Cancer Screening Consortium (BCSC) model. For participants in the personalized arm, the overall 5-year risk BCSC score is combined with a Polygenic Risk Score, based on a genetic test including mutations in 9 genes (BRCA1, BRCA2, TP53, PTEN, STK11, CDH1, ATM, PALB2, and CHEK2) and a panel of 75 common single nucleotide polymorphisms known to increase breast cancer risk. Risk stratification will determine frequency of screening. The study is registered on ClinicalTrials.gov as NCT02620852. Results: As of June 12th 2017, the WISDOM study is live at all UC medical centers and recruitment is open to all eligible women in California. Up to date 4,769 eligible women registered at all sites. 2,823 women have consented in the trial. 64% were randomized and 36% chose their screening arm. A pilot was conducted to test the logistics of online participation and examine the acceptance of the study design and approach. We are partnering with health insurance companies and self-insured companies to reach our recruitment goal. Conclusions: Enrollment will be completed by end of 2018. Acknowledgment: support by the Patient-Centered Outcomes Research Institute (PCORI), PCS-1402-10749 to L.J.E. (*) Authors equally contributed to this work. Citation Format: Acerbi I, Abihider K, Ling J, Layton T, DeRosa D, Madlensky L, Tice J, Shieh Y, Ziv E, Sarrafan S, Firouzian R, Tong B, Blanco A, Lee V, Heditsian D, Brain S, Kaplan C, Borowsky A, Anton-Culver H, Naeim A, Cink T, Stover Fiscalini A, Parker B, van 't Veer L, Wisdom Study and Athena Breast Health Network Investigators and Advocate Partners, LaCroix A, Esserman L. Preference-Tolerant randomized trial of risk-based vs. annual breast cancer screening: WISDOM study in progress [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr OT3-03-01.

Abstract PD2-14: Participation in a personalized breast cancer screening trial does not increase anxiety at baseline

Abstract Introduction: The purpose of this study is to examine whether participation in a personalized screening trial is associated with anxiety or breast cancer worry. The Patient Centered Outcomes Research Institute recently funded WISDOM (Women Informed to Screen Depending On Measures of risk), which is a randomized trial that tests the safety and efficacy of basing starting age, stopping age, frequency and modality of breast cancer screening on individual risk (Clinical Trials Identifier NCT02620852). Methods: In WISDOM, participants can be randomized to annual screening or personalized screening arm, or self-select an arm an observational cohort. This interim analysis examined the first 1817 participants to determine if the personalized risk arm is acceptable and to explore whether baseline anxiety was associated with study arm. For acceptability our target was to have &amp;gt;60% of participants agree to randomization. Participants completed questions about their Risk Perception, the PROMIS Anxiety short form 8a (total scores 8-40 with higher scores indicating more anxiety), and Breast Cancer Risk Worry (BCRW) survey (total scores 5-20) with higher scores indicating more worry) at baseline and before they were given information on their personal risk or study assignment. For the purposes of these analyses, we defined high anxiety to be the percentage of participants scoring =&amp;gt;22 on the PROMIS and &amp;gt;8 on the BCRW. Results: The participants were recruited from three sites (UCSD, UCSF, Sanford Health). Of the 1817 initial participants, 1643 completed the baseline questionnaire. Participants has a mean age of 57 years (SD 9). 15.8% felt their chances of developing breast cancer was high, 19.5% felt their chance of developing breast cancer was greater than the average women, and 56.6% felt their lifetime risk of developing breast cancer was &amp;gt;25. Risk perception was not significantly different between women who opted to be randomized versus the observational arm. The majority of participants were willing to be randomly assigned to an arm (1071/1643, 65.1%). Of those who joined the observational cohort, the majority selected personalized risk arm (474/572, 82.9%). Overall, PROMIS anxiety scores were low at baseline (14.0 MEAN (SD 4.6)) as were the Breast Cancer Risk Worry scores (5.7 MEAN (SD 1.05)). Less than 8% of participants had PROMIS scores &amp;gt;22 and that did not vary across the randomized or observational groups (P=0.2)). About 2% of participants had a BCRW scores &amp;gt;8. Women who worried with breast cancer were more likely to select to be in the observational (3.5%) than randomized (1.7%) arm of the study (P=0.02). Conclusions: For the women approached to participate in Wisdom, personalized screening was acceptable alternative to annual mammography. Participants in general overestimated their lifetime risk of breast cancer, had very low anxiety and low breast cancer worry. Those who were worried about breast cancer opted more often for the observational arm of the study to allow them to choose between the personalized versus annual arm. Future analyses will follow participants prospectively to determine adherence to assigned or selected arm, and whether anxiety changes after receipt of their personalized risk information. Citation Format: Naeim A, Sepucha K, Wenger N, Eklund M, Annette S, Madlensky L, van't Veer L, Parker B, Yau C, Cink T, Anton-Culver H, Borowsky A, Petruse A, Sarrafan S, Stover-Fiscalini A, LaCroix A, Adduci K, Wisdom Advocate Partners, Laura E. Participation in a personalized breast cancer screening trial does not increase anxiety at baseline [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr PD2-14.

Beyond BI-RADS Density: A Call for Quantification in the Breast Imaging Clinic.

Ultimately, the incorporation of automated quantitative measures of breast density will lead to more effective clinical care and more robust outcomes research than the current, subjective assignment of Breast Imaging and Reporting Data System density categories, by providing reproducible estimates of both the risk of masking a cancer as well as the risk of developing breast cancer—two important factors in determining personalized breast cancer screening algorithms.

Simulation modeling for stratified breast cancer screening \u2013 a systematic review of cost and quality of life assumptions

BackgroundThe economic evaluation of stratified breast cancer screening gains momentum, but produces also very diverse results. Systematic reviews so far focused on modeling techniques and epidemiologic assumptions. However, cost and utility parameters received only little attention. This systematic review assesses simulation models for stratified breast cancer screening based on their cost and utility parameters in each phase of breast cancer screening and care.MethodsA literature review was conducted to compare economic evaluations with simulation models of personalized breast cancer screening. Study quality was assessed using reporting guidelines. Cost and utility inputs were extracted, standardized and structured using a care delivery framework. Studies were then clustered according to their study aim and parameters were compared within the clusters.ResultsEighteen studies were identified within three study clusters. Reporting quality was very diverse in all three clusters. Only two studies in cluster 1, four studies in cluster 2 and one study in cluster 3 scored high in the quality appraisal. In addition to the quality appraisal, this review assessed if the simulation models were consistent in integrating all relevant phases of care, if utility parameters were consistent and methodological sound and if cost were compatible and consistent in the actual parameters used for screening, diagnostic work up and treatment. Of 18 studies, only three studies did not show signs of potential bias.ConclusionThis systematic review shows that a closer look into the cost and utility parameter can help to identify potential bias. Future simulation models should focus on integrating all relevant phases of care, using methodologically sound utility parameters and avoiding inconsistent cost parameters.

Personalized Care: A Clinical Decision Support System for Breast Cancer Screening Using Clustering and Classification

In recent years, Computer-Aided Diagnosis (CADx) is popularly used in the healthcare system for the clinical guidelines. Breast cancer is the most leading cause of cancer death in women both in developed and developing countries. The lack of personalized care, early diagnosis and treatment of breast cancer is the major challenge for medical practitioners. To address this issue, personalized breast cancer screening is developed by considering the patient personal features as well as the screening features. This is to suggest the treatment and regular follow up. Therefore, a clinical decision support system is mandatory for the quality decision making and timely diagnosis for the women. The system entails the Partitioning around Medoids (PAM) clustering of personal features and active classifier on each cluster with the screening features. Breast Imaging Reporting and Data System (BI-RADS) scheme is used to place the findings from the screening into well defined categories. This system helps in reducing the cost of screening and providing the guidelines for the medical practitioners.

Feasibility Test of Quantitative Assessment of Breast Density Based on Dielectric Impedance Spectroscopy

Breast density is a well-recognized important breast cancer risk factor. However, accurately assessing breast density is difficult. Although the mammographic density is the most popular method to assess breast density in current clinical practice, it is not accurate and reliable due to the overlap of breast tissues and inter-reader variability. In order to more accurately assess breast density, we proposed a new non-invasive assessment technology based on the measurement of dielectric impedance spectrums of the breast regions. The objective of this study is to test the feasibility of this new technology through a unique phantom study. The phantom design is based on mixing two types of agar saline materials with two different levels of electrical conductivity corresponding to fat and fibro-glandular tissue conductivities. In the phantom design considerations, we assembled four breast phantoms to mimic four breast density categories defined by the Breast Imaging Reporting and Data System. The testing results showed that as the increase of the simulated “fibro-glandular” tissues inside the phantom, the measured electrical impedance values monotonically decreased. The testing results were consistent and reproducible at different positions between detection probes and breast phantom surface when considering and calibrating the systematic errors. Thus, this study demonstrated the feasibility of developing and applying a new dielectric impedance measurement technology and device to assess breast density, which is low-cost, non-invasive, non-hazard and easy-to-use. If successful in future tests, this new method has potential to assist better assessing breast cancer risk for developing an optimal personalized breast cancer screening paradigm.

Abstract P5-02-03: Evaluating the feasibility of a web-based preference-tolerant randomized trial of risk-based vs. annual breast cancer screening: WISDOM study pilot

Abstract Background: The WISDOM Study (Women Informed to Screen Depending on Measures of risk) aims to examine the effectiveness of personalized breast cancer screening and to bring objective recommendations to the current mammography screening debate. The WISDOM Study is a 100,000 woman randomized trial with a preference-tolerant design that will determine if risk-based screening (RBS) vs. annual screening, is as safe, less morbid, enables prevention and is preferred by women. A pilot was conducted to test the logistics of online participation and examine the acceptance of the study design and approach. Methods: Women were recruited from the UCSF site of the Athena Breast Health Network, a clinical care-research cohort of 110,000 women from the 5 University of California Medical Centers and Sanford Health. The pilot recruited women via email who were 40 -74 years of age with no history of breast cancer and a normal mammogram in the past year. Those interested visited the WISDOM Study website (wisdomstudy.org), signed up, elected randomization or self-selection, provided electronic consent using DocuSign (eConsent), and completed genetic testing (RBS arm). The Breast Cancer Surveillance Consortium (BCSC) model (standard risk factors, ethnicity, and breast density) in addition to genetic testing (9 genes and 75 SNPs) was used to calculate breast cancer risks that informed the start and frequency of screening for women in the RBS arm. BCSC was also used in the annual screening arm but did not inform mammography screening recommendations. The pilot used a mixed method approach (using enrollment data, Exit Survey data, individual interviews and focus groups) to assess enrollment preferences, randomization acceptance and overall study workflow. Results: The online electronic enrollment process and patient engagement portal was successfully implemented. In total, 639 women were invited, 235 registered (34%), and 171 (27%) consented to the pilot. Of these, 74% (127) elected to be randomized, and 26% chose to self-assign (66% chose annual screening (29)). Mean age was 56 years and the ethnic breakdown of the cohort was: 79% White, 10% Asian, 7% Latino, 3% Black, 1% other. 92% of those in the risk-based arm of the study completed genetic testing and were given results; only one genetic mutation was identified and occurred in CHEK2. Within the RBS arm (78), mammography recommendations were: 61% no further mammography until the age of 50, 22% biennial, 11% annual, and 6% every 6 month alternating MRI and mammogram. Exit Survey data illuminated confusion in study arm names (risk-based vs. annual), randomization acceptance (74%), annual arm preference in the self-selection group (66%), eConsent satisfaction (90%), enrollment process ease of use (88%), and website content, navigation and appearance satisfaction (66%). The pilot concluded in May 2016 to allow for refinements prior to the full trial. Conclusion: Our pilot demonstrates that the majority of women are willing to be randomized and participate in an online screening study to answer the important question on optimal breast cancer screening. The pilot study results will inform implementation of the 100,000 women WISDOM Study which launches in fall of 2016. Citation Format: Stover Fiscalini A, Theiner S, Kaplan C, Sarrafan S, Sawyer S, Liang A, Rosenberg-Wohl S, Gordon D, Frick M, Borowsky A, Anton-Culver H, Naeim A, LaCroix A, Cink T, Collaboration Athena Breast Health Network and Advocate Partners, Esserman L, van 't Veer L. Evaluating the feasibility of a web-based preference-tolerant randomized trial of risk-based vs. annual breast cancer screening: WISDOM study pilot [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P5-02-03.

ConfidentCare: A Clinical Decision Support System for Personalized Breast Cancer Screening

Breast cancer screening policies attempt to achieve timely diagnosis by regularly screening healthy women via various imaging tests. Various clinical decisions are needed to manage the screening process: selecting initial screening tests, interpreting test results, and deciding if further diagnostic tests are required. Current screening policies are guided by clinical practice guidelines (CPGs), which represent a “one-size-fits-all” approach, designed to work well (on average) for a population, and can only offer coarse expert-based patient stratification that is not rigorously validated through data. Since the risks and benefits of screening tests are functions of each patient's features, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">personalized screening policies</i> tailored to the features of individuals are desirable. To address this issue, we developed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ConfidentCare</i> : a computer-aided clinical decision support system that learns a personalized screening policy from electronic health record (EHR) data. By a “personalized screening policy,” we mean a clustering of women's features, and a set of customized screening guidelines for each cluster. ConfidentCare operates by computing clusters of patients with similar features, then learning the “best” screening procedure for each cluster using a supervised learning algorithm. The algorithm ensures that the learned screening policy satisfies a predefined accuracy requirement with a high level of confidence for every cluster. By applying ConfidentCare to real-world data, we show that it outperforms the current CPGs in terms of cost efficiency and false positive rates: a reduction of 31 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> in the false positive rate can be achieved.

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

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

Automated measurements for personalized breast cancer screening

Automated measurements for personalized breast cancer screening

Abstract P2-09-03: Got patient advocates? The value of patient advocate participation in a large research study to develop personalized risk-based breast cancer screening strategies

Abstract Background: Over the last several years there has been confusion among women about breast cancer screening and patient advocates are increasingly used to help women understand the changes. In 2009, the U.S. Preventive Task Force (USPSTF) recommended that women under the age of 50 do not need routine screening. New state laws require breast density results to be given to women and their providers for making their screening choices. Women are not sure what to do with this information and are offered little guidance to personalize their screening recommendations. The goal of this study is to develop a risk model for improved personalized breast cancer screening recommendations. Patient advocates were incorporated throughout the design, recruitment, analysis, and dissemination phases of the study. Methods: The research team included three patient advocates who participated as full members in the bi-weekly and quarterly team meetings throughout the duration of the study. All advocates were breast cancer survivors. The primary components of the study included focus groups to understand women’s knowledge and views on breast density as well as personalized screening, a telephone survey to gain a broader view on these topics, and recruitment to a case:control study to build a breast cancer risk model that incorporates an automated measure of breast density. Results: Enrollment was completed over one year with 3,445 women; 839 cases and 2,606 controls. Study design and resulting recruitment strategies were reviewed early with regular feedback by the patient advocates. At the advice of the advocates, Facebook was chosen as primary social media, resulting in nearly 200 posts (stories) and 1583 likes for the project. Many of the posts were generated by or featured advocates. Regarding the focus groups, the advocates developed the questions. Women were informed about the study by the advocates and educated about breast density. The advocates were key in using the focus groups to find the right language for enrollment materials, obtain their perception of the importance of the study, and understand their views regarding a new model for personalized screening for women. The advocates were likewise key in developing questions for and analyzing results of the telephone survey. In the analysis phase, the advocates assisted the team in understanding the results of the risk questionnaires. For example, most women did not know the type of breast cancer that they had been diagnosed with or even if it was invasive. The advocates confirmed how and why even highly educated women would not necessarily retain this information. Finally, the advocates will have a strong role in the eventual dissemination of the study findings to women. Conclusions: The investigators have developed a breast cancer risk model that includes an automated measurement of breast density, with the goal of personalizing screening for women. The inclusion of patient advocates throughout all phases of the study improved knowledge and insight of the investigating team. Their role extended beyond community engagement and development of study materials. The advocates became integral members of the study team. Citation Format: Vernal Branch, Carolyn Achenbach, Kathleen G Ross, Wendy F Cohn, Martin D Yaffe, William A Knaus, Jennifer A Harvey. Got patient advocates? The value of patient advocate participation in a large research study to develop personalized risk-based breast cancer screening strategies [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P2-09-03.

Abstract P6-09-04: Volumetric breast density improves breast cancer risk prediction

Abstract Introduction: There is increasing interest in implementing personalized breast cancer screening strategies rather than relying on population based guidelines. Most risk models do not include breast density and two models that do rely on subjective BI-RADS categories; all have limited discriminatory ability (C-statistics ranging from 0.60-0.74). Our aim was to develop a model that includes an automated objective and numeric volumetric measurement of breast density with other known risk factors to improve risk prediction. Methods: A case-control study design was used to evaluate the association between risk factors and breast cancer diagnosis. All women diagnosed with breast cancer during 2003-13 with a digital contralateral mammogram at the University of Virginia at the time of diagnosis were eligible as cases. All women without a breast cancer diagnosis but with a digital mammogram at UVA during 2003-2008 were eligible as controls. Risk factor information was collected using a self-reported electronic questionnaire. Mean automated volumetric breast density (Volpara, NZ) was calculated for each patient as a percentage. Controls were matched to cases in a 2:1 ratio based on age group, race, and education, using the GREEDY algorithm. Case-control selections were made using the weighted sum of the absolute differences between the case and control matching factors. Conditional logistic regression using the partial likelihood function from Cox proportional hazard’s regression was used to fit risk prediction equations to the matched case-control study dataset, with stratification for each case matched set. A full model was estimated including all available covariates for use as a model performance reference standard. A reduced model was then estimated including covariates in the full model that had a Wald Chi-Square/degrees of freedom ratio &amp;gt; 1.0. The performance of the full and reduced models was measured using the C index and the maximum R-Square statistic. Results: The study enrolled 3,445 women; 839 cases and 2,606 controls. Multivariable analysis was conducted using 825 cases and 1,628 controls with 1 or more breast studies reported for the surveyed population. The matching process yielded balanced matching factor values between cases and controls, with no significant differences in age group (p = 0.95), race (p = 0.13), or education (p = 0.86). The full prediction model (with 97 df) yielded a C index of 0.88, and an R-Square of 0.53. The reduced model (with 15 df) had a C index of 0.83 and an R-Square of 0.54. Variables in the reduced model included: mean breast density; biopsy showing ADH, ALH/LCIS; BMI; use of HRT, contraceptives, NSAIDS; smoking; exercise; parity; diabetes; family history of breast cancer, HBOC, Li-Fraumeni, or Cowden Syndromes and/or BRCA mutation. Mean volumetric breast density was a leading independent predictor of case status in both the full (p&amp;lt;0.0001) and reduced models (p=0.0212). Conclusion: Volumetric breast density with other known risk factors may provide more accurate individual risk assessment, enabling clinicians to develop patient centered risk based screening protocols that better inform decision making while including patient preferences. The next steps require independent validation of the risk model in other populations. Citation Format: Jennifer A Harvey, George J Stukenborg, Wendy F Cohn, Kathy L Repich, Olivier Alonzo, Wendy M Novicoff, Martin D Yaffe, William A Knaus. Volumetric breast density improves breast cancer risk prediction [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P6-09-04.