Tyrer-Cuzick Model Research Articles

Defining the Need for Services for Patients at High Risk of Breast Cancer at a Safety-Net Hospital: An Approach to Narrowing the Disparities Gap.

The National Accreditation Program for Breast Cancer (NAPBC) standards were recently revised to promote breast cancer (BC) risk assessment and subsequent referral for high-risk services. This project sought to estimate the proportion of patients at high risk for BC in the authors' safety-net hospital system, gauge patient interest in high-risk services, and define resources for program development. Women presenting for breast imaging during 2 weeks in 2023 were surveyed. Thirty-five patients with a history or diagnosis of BC were excluded. The Tyrer-Cuzick (TC) model version 8 was used to calculate BC risk. High/intermediate risk was defined as a 10-year risk of 5% or more, a lifetime risk of 15% or more, or both. The criteria for genetic counseling and testing referral were based on National Comprehensive Cancer Network guidelines. A total of 257 patients had a TC risk assessment showing 14.8% (n = 38) with a 10-year BC risk of 5% or more (consideration of endocrine therapy), 6.2% (n = 16) with a lifetime BC risk of 20% or more (qualifying for annual screening MRI), and 10.5% (n = 27) with a lifetime BC risk of 15% or more (consideration of high-risk screening). The criteria for genetic counseling/testing were met by 61 (23.7%) of the 257 patients. Overall, 31.5% (n = 81) qualified for high/intermediate-risk screening, risk reduction, and/or genetic assessment/testing, 92.8% of whom were interested in referrals for additional information and care. In the authors' community, almost one third of patients undergoing breast imaging qualify for BC high-risk assessment and services. The majority of the patients expressed interest in pursuing such services. These data will be used in financial planning and resource allocation to develop a high-risk program at the authors' institution in line with NAPBC guidelines. They are hopeful that these efforts will improve oncologic outcomes and survival from BC in their community.

Assessing the Correlation between Allostatic Load and False-Positive Image-Guided Breast Biopsies.

Background: Allostatic load (AL) is the accumulation of physiological dysregulation attributed to repeated activation of the stress response over a lifetime. We assessed the utility of AL as a prognostic measure for high-risk benign breast biopsy pathology results. Method: Eligible patients were women 18 years or older, with a false-positive outpatient breast biopsy between January and December 2022 at a tertiary academic health center. AL was calculated using 12 variables representing four physiological systems: cardiovascular (pulse rate, systolic and diastolic blood pressures, total cholesterol, high-density lipoprotein, and low-density lipoprotein); metabolic (body mass index, albumin, and hemoglobin A1C); renal (creatinine and estimated glomerular filtration rate); and immune (white blood cell count). Multivariable logistic regression was used to assess the association between AL before biopsy and breast biopsy outcomes controlling for patients' sociodemographics. Results: In total, 170 women were included (mean age, 54.1 ± 12.9 years): 89.4% had benign and 10.6% had high-risk pathologies (radial scar/complex sclerosing lesion, atypical ductal or lobular hyperplasia, flat epithelial atypia, intraductal papilloma, or lobular carcinoma in-situ). A total of 56.5% were White, 24.7% Asian, and 17.1% other races. A total of 32.5% identified as Hispanic. The mean breast cancer risk score using the Tyrer-Cuzick model was 11.9 ± 7.0. In multivariable analysis, with every one unit increase in AL, the probability of high-risk pathology increased by 37% (odds ratio, 1.37; 95% confidence interval, 1.03, 1.81; p = 0.03). No significant association was seen between high-risk pathology and age, ethnicity, breast cancer risk, or area deprivation index. Conclusion: Our findings support that increased AL, a biological marker of stress, is associated with high-risk pathology among patients with false-positive breast biopsy results.

Artificial Intelligence-Powered Imaging Biomarker Based on Mammography for Breast Cancer Risk Prediction.

The purposes of this study were to develop an artificial intelligence (AI) model for future breast cancer risk prediction based on mammographic images, investigate the feasibility of the AI model, and compare the AI model, clinical statistical risk models, and Mirai, a state of-the art deep learning algorithm based on screening mammograms for 1-5-year breast cancer risk prediction. We trained and developed a deep learning model using a total of 36,995 serial mammographic examinations from 21,438 women (cancer-enriched mammograms, 17.5%). To determine the feasibility of the AI prediction model, mammograms and detailed clinical information were collected. C-indices and area under the receiver operating characteristic curves (AUCs) for 1-5-year outcomes were obtained. We compared the AUCs of our AI prediction model, Mirai, and clinical statistical risk models, including the Tyrer-Cuzick (TC) model and Gail model, using DeLong's test. A total of 16,894 mammograms were independently collected for external validation, of which 4002 were followed by a cancer diagnosis within 5 years. Our AI prediction model obtained a C-index of 0.76, with AUCs of 0.90, 0.84, 0.81, 0.78, and 0.81, to predict the 1-5-year risks. Our AI prediction model showed significantly higher AUCs than those of the TC model (AUC: 0.57; p < 0.001) and Gail model (AUC: 0.52; p < 0.001), and achieved similar performance to Mirai. The deep learning AI model using mammograms and AI-powered imaging biomarkers has substantial potential to advance accurate breast cancer risk prediction.

Validation of a clinical breast cancer risk assessment tool combining a polygenic score for all ancestries with traditional risk factors

PurposeWe previously described a combined risk score (CRS) that integrates a multiple-ancestry polygenic risk score (MA-PRS) with the Tyrer-Cuzick (TC) model to assess breast cancer (BC) risk. Here, we present a longitudinal validation of CRS in a real-world cohort. MethodsThis study included 130,058 patients referred for hereditary cancer genetic testing and negative for germline pathogenic variants in BC-associated genes. Data were obtained by linking genetic test results to medical claims (median follow-up 12.1 months). CRS calibration was evaluated by the ratio of observed to expected BCs. ResultsThree hundred forty BCs were observed over 148,349 patient-years. CRS was well-calibrated and demonstrated superior calibration compared with TC in high-risk deciles. MA-PRS alone had greater discriminatory accuracy than TC, and CRS had approximately 2-fold greater discriminatory accuracy than MA-PRS or TC. Among those classified as high risk by TC, 32.6% were low risk by CRS, and of those classified as low risk by TC, 4.3% were high risk by CRS. In cases where CRS and TC classifications disagreed, CRS was more accurate in predicting incident BC. ConclusionCRS was well-calibrated and significantly improved BC risk stratification. Short-term follow-up suggests that clinical implementation of CRS should improve outcomes for patients of all ancestries through personalized risk-based screening and prevention.

Feasibility of universal breast cancer risk assessment in a diverse, publicly insured obstetrician gynecology clinic.

e13759 Background: Breast cancer is the most diagnosed cancer among women in the United States. Established breast cancer risk assessment (BCRA) tools can estimate a woman’s lifetime risk and inform the need for enhanced breast screening which can improve cancer-associated morbidity and mortality. Currently, critical disparities exist both in breast cancer prognosis and screening uptake. We aimed to evaluate the feasibility of routine BCRA in a diverse, publicly insured obstetrician gynecology clinic. Methods: All patients presenting to the obstetrician gynecology clinic were offered a BCRA using the Tyrer Cuzick (TC) risk model. Patients completed paper surveys and the information was input into the TC model to generate lifetime risk estimates. Printed evidence-based management guidelines were reviewed with and provided to those at ≥ 20% lifetime breast cancer risk and shared with the patient’s provider. Results: Between June 2023 and February 2024, 300 patients were approached and 208 (69.3%) completed the survey. One hundred seven (51.4%) survey respondents were presenting for obstetrical care and 101 (48.6%) for gynecologic care. Respondents had a median age of 33 years (IQR: 12). Ninety-six (46.2%) responding patients self-identified as Hispanic, 45 (21.6%) as Black, 25 (12.0%) as Asian, 28 (13.5%) as Non-Hispanic White, and 14 (6.7%) did not identify in these categories. One hundred eighty-nine (90.9%) survey respondents were publicly-insured. Ten (4.8%) patients were assisted by an interpreter. Overall, 12 (5.8%) patients had an elevated lifetime breast of breast cancer, defined as ≥ 20% based on the TC model (6 (50.0%) among obstetrical patients and 6 (50.0%) among gynecology patients). All patients at elevated risk were referred to a genetics clinic for counseling on options for enhanced breast surveillance. Conclusions: As leaders in women’s cancer care, gynecologic oncologists can lead and support programs to identify those women at highest risk for cancer, especially among historically underserved populations. Our feasibility study demonstrated that 69.3% of patients were willing to complete a BRCA and this tool identified 5.8% of patients to have a significantly elevated lifetime breast cancer risk. Providers should consider incorporating such tools in gynecologic oncology and obstetrical/gynecology clinics.

Improving a polygenic risk score (PRS) for breast cancer (BC) risk assessment in diverse ancestries.

10533 Background: Accurate BC risk assessment is essential to identify women for whom screening and preventive interventions may be lifesaving. Incorporation of PRS into clinical models can improve risk prediction, but most PRS have shown suboptimal performance among non-Europeans. We previously described a multiple-ancestry PRS (MA-149) based on 56 ancestry-informative and 93 BC-associated single-nucleotide polymorphisms (SNPs). MA-149 achieved accuracy for diverse populations by characterizing genetic ancestry at each SNP in terms of fractions attributable to reference ancestries (African, East Asian, European) and applying ancestry-specific risks and frequencies. MA-149 significantly outperformed the Tyrer-Cuzick (TC) model, and integration of MA-149 with TC improved predictive accuracy by roughly two-fold over TC alone. Here, we aimed to improve MA-149 by expanding the set of BC SNPs and refining ancestry-specific risks. Methods: We developed a novel stepwise regression methodology accounting for linkage disequilibrium to select an optimal set of BC SNPs. Women referred for hereditary cancer testing and negative for pathogenic variants in BC genes were divided into consecutive cohorts to (1) refine ancestry-specific SNP risks ( N=58,191 Black/African; N=27,160 East Asian), (2) develop the PRS ( N=184,322), and (3) conduct independent validation ( N=146,110). Predictive accuracy and calibration of the new PRS were evaluated in the full cohort and subpopulations of different ancestries. Odds ratios (OR) from multivariable regression are reported per standard deviation. Results: A set of 385 SNPs (56 ancestry, 329 BC) was selected for the new PRS (MA-385). MA-385 improved upon clinical factors and outperformed MA-149 within each ancestry (Table). Among non-Europeans, MA-385 was a better BC risk predictor (OR=1.47, 95% CI: 1.42-1.52) than MA-149 (OR=1.40, 95% CI: 1.35-1.45). The strongest associations were observed in Ashkenazi Jewish and Hispanic women (Table). MA-385 identified more women at &gt;2-fold increased risk than MA-149 (6.5 % vs 2%). Goodness-of-fit tests showed that MA-385 was well-calibrated while a 385-SNP PRS with European weights was miscalibrated for non-Europeans. Conclusions: MA-385 was well-calibrated, improved upon clinical factors, and outperformed existing PRS in all tested ancestries. Incorporation of MA-385 into risk assessment could improve the early detection and prevention of BC. [Table: see text]

Abstract PS10-06: Contribution of a breast cancer polygenic risk score to mammography artificial intelligence models and breast density for long term breast cancer risk prediction

Abstract Artificial intelligence (AI) models based on deep learning of mammogram images have been developed for lesion detection and diagnosis as well as breast cancer (BC) risk. We and others have shown that these AI models, coupled with breast density, predict long-term breast cancer risk. Common germline genetic variation, in particular a polygenic risk score (PRS) that captures the cumulative impact of hundreds of common risk variants, has been shown associated with 3-fold increased BC risk. The BC-PRS may complement imaging risk factors to improve identification of women at high BC risk and better target prevention strategies. We evaluated the contribution of a BC-PRS to two imaging-based AI models (Transpara AI cancer detection system and Mirai 5-year risk) and breast density measures to long-term BC risk. We hypothesized that the BC-PRS will improve long-term risk prediction above AI and density measures. We examined this hypothesis within a cohort of 10,271 women enrolled to the Mayo Clinic Biobank between 2009 and 2015. Eligible women had no prior cancer, were ages 35 to 85, had a full-field digital screening mammogram within two years of enrollment and genotyping by Regeneron. Incident invasive and DCIS BC was identified through the Mayo tumor registry and follow-up questionnaires, and person years were calculated from date of mammogram through date of BC, last mammogram at Mayo or last questionnaire. We estimated Transpara AI malignancy score (1-10), 5-year Mirai risk and Volpara volumetric density measures (volumetric percent density, dense volume) on screening mammograms at enrollment and obtained clinical BI-RADS density. We calculated the BC-PRS using 268 SNPs (of the 313-SNP score) and the weighted sum of the SNPs using odds ratios from the largest BC GWAS of European ancestry to date. We performed cox proportional hazards regression adjusting for age and BMI to estimate hazard ratios (HRs), 95% confidence intervals (CI), and C-statistics (AUC) to describe the contribution of BC-PRS (per standard deviation (SD)) to the association of AI scores and breast density with BC risk. Likelihood ratio tests (LRT) and bootstrapping were used to compare model performance with vs. without inclusion of BC-PRS, at 10-years follow-up. Comparisons to the Tyrer-Cuzick model are ongoing. Over a median of 9.1 years (Interquartile range, 6.8 to 10.6), 446 incident BC were identified of which 285 invasive and 103 DCIS were confirmed by pathology report (60 were self-reported); 399 (256 invasive) occurred within 10 years and were used in primary analyses. Transpara score [HR per 1 unit=1.18 (1.14, 1.23), AUC=0.655 (0.628, 0.682)] and Mirai risk [HR per SD log risk=1.54 (1.43, 1.65), AUC=0.681 (0.655, 0.706)] were both associated with BC. All three breast density measures were also significantly associated with BC, including in models with AI scores (Table). Addition of the BC-PRS to models with AI scores and breast density improved both model fit (All PLRT &amp;lt; 0.001; Table) and discrimination, with increases in AUC ranging from 0.033 to 0.034 for models with Transpara and density and 0.025 to 0.026 for models with Mirai and density (all P-values &amp;lt; 0.001; Table). Analyses restricted to invasive BC showed identical results. But, the BC-PRS contribution at 5-year follow-up was attenuated. In this cohort study, we found BC-PRS contributed to long-term risk prediction of BC above imaging AI models and breast density. Imaging and genetic risk factors are complementary and may outperform existing risk models and provide more accurate risk assessment to aid in precision prevention strategies. Citation Format: Celine Vachon, Christopher Scott, Imon Banerjee, Ramon Correa Moderno, Dan Hursh, Matthew Jensen, Yvonne Wang, Kathleen Brandt, Karla Kerlikowske, Sandhya Pruthi, Fergus Couch, Stacey Winham. Contribution of a breast cancer polygenic risk score to mammography artificial intelligence models and breast density for long term breast cancer risk prediction [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 PS10-06.

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.

Rates of high-risk screening prior to a breast cancer diagnosis in patients under age 40

BackgroundNCCN Guidelines recommend screening young women with an increased breast cancer risk (>20 ​% lifetime risk). We sought to evaluate our institutional rates of high-risk screening in young breast cancer patients prior to their diagnoses." MethodsA single-institution retrospective review (2013–2018) was performed investigating risk scores (Tyrer-Cuzick model) and characteristics of breast cancer patients (age <40 ​y) prior to diagnosis. Results92 breast cancer patients age <40 ​y were identified (average age 34.5). Only 3.3 ​% (n ​= ​3) underwent appropriate screening, despite 35.8 ​% meeting high-risk criteria. Nearly all patients underwent genetic testing (98.9 ​%) with pathogenic mutations identified in 36.5 ​%, including 15.3 ​% with BRCA1/2 mutations. ConclusionsThis analysis highlights a significant discrepancy between those meeting criteria for high-risk screening and those who underwent appropriate screening. We identified that this cohort carries significant genetic burden. Future analysis should investigate these findings on a broader scale and strategies to improve screening.

Clinical Significance of Background Parenchymal Enhancement in Breast Cancer Risk Stratification.

Background parenchymal enhancement (BPE) is an established breast cancer risk factor. However, the relationship between BPE levels and breast cancer risk stratification remains unclear. To evaluate the clinical relationship between BPE levels and breast cancer risk with covariate adjustments for age, ethnicity, and hormonal status. Retrospective. 954 screening breast MRI datasets representing 721 women divided into four cohorts: women with pathogenic germline breast cancer (BRCA) mutations (Group 1, N = 211), women with non-BRCA germline mutations (Group 2, N = 60), women without high-risk germline mutations but with a lifetime breast cancer risk of ≥20% using the Tyrer-Cuzick model (Group 3, N = 362), and women with <20% lifetime risk (Group 4, N = 88). 3 T/axial non-fat-saturated T1, short tau inversion recovery, fat-saturated pre-contrast, and post-contrast T1-weighted images. Data on age, body mass index, ethnicity, menopausal status, genetic predisposition, and hormonal therapy use were collected. BPE levels were evaluated by two breast fellowship-trained radiologists independently in accordance with BI-RADS, with a third breast fellowship-trained radiologist resolving any discordance. Propensity score matching (PSM) was utilized to adjust covariates, including age, ethnicity, menopausal status, hormonal treatments, and prior bilateral oophorectomy. The Mann-Whitney U test, chi-squared test, and univariate and multiple logistic regression analysis were performed, with an odds ratio (OR) and corresponding 95% confidence interval. Weighted Kappa statistic was used to assess inter-reader variation. A P value <0.05 indicated a significant result. In the assessment of BPE, there was substantial agreement between the two interpreting radiologists (κ = 0.74). Patient demographics were not significantly different between patient groups after PSM. The BPE of Group 1 was significantly lower than that of Group 4 and Group 3 among premenopausal women. In estimating the BPE level, the OR of gene mutations was 0.35. Adjusting for potential confounders, the BPE level of premenopausal women with BRCA mutations was significantly lower than that of non-high-risk women. 3 TECHNICAL EFFICACY: Stage 3.

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.

Early detection in cases of familial breast cancer predisposition: What is appropriate and beneficial for the individual seeking advice?

A "high-risk situation" is present when the lifetime risk of developing breast cancer is ≥ 30%. Currently, the most accurate risk assessment is provided by the Tyrer-Cuzick model. This takes into account several factors including the presence of certain risk genes, age, family history of breast and ovarian cancer, as well as mammographic breast density. In addition to BRCA1 and BRCA2 several other risk genes are known that can be tested using gene panels. However specific familial risk constellations are prerequisites for indicating a genetic test. Prior to conducting a genetic test comprehensive counselling should take place and the individual seeking advice should be given time to consider. The individual seeking advice faces a series of questions regarding the potential implications of a genetic test which not only affect herself but also her environment. In high-risk situations prophylactic mastectomy is an established surgical measure and intensified surveillance is a conservative approach. The latter includes semi-annual clinical breast examination with ultrasound starting from age 25 as well as an annual MRI mammography which exhibits the highest reliability compared to other imaging methods. Medical consultation aims to enable a participatory decision-making process for the individual seeking advice. This requires comprehensive information for the individual seeking advice and incorporation of her preferences. Evidence-based decision aids from professional societies can improve the decisions of the individual seeking advice.

A Clinical Risk Model for Personalized Screening and Prevention of Breast Cancer.

Image-derived artificial intelligence (AI) risk models have shown promise in identifying high-risk women in the short term. The long-term performance of image-derived risk models expanded with clinical factors has not been investigated. We performed a case-cohort study of 8110 women aged 40-74 randomly selected from a Swedish mammography screening cohort initiated in 2010 together with 1661 incident BCs diagnosed before January 2022. The imaging-only AI risk model extracted mammographic features and age at screening. Additional lifestyle/familial risk factors were incorporated into the lifestyle/familial-expanded AI model. Absolute risks were calculated using the two models and the clinical Tyrer-Cuzick v8 model. Age-adjusted model performances were compared across the 10-year follow-up. The AUCs of the lifestyle/familial-expanded AI risk model ranged from 0.75 (95%CI: 0.70-0.80) to 0.68 (95%CI: 0.66-0.69) 1-10 years after study entry. Corresponding AUCs were 0.72 (95%CI: 0.66-0.78) to 0.65 (95%CI: 0.63-0.66) for the imaging-only model and 0.62 (95%CI: 0.55-0.68) to 0.60 (95%CI: 0.58-0.61) for Tyrer-Cuzick v8. The increased performances were observed in multiple risk subgroups and cancer subtypes. Among the 5% of women at highest risk, the PPV was 5.8% using the lifestyle/familial-expanded model compared with 5.3% using the imaging-only model, p < 0.01, and 4.6% for Tyrer-Cuzick, p < 0.01. The lifestyle/familial-expanded AI risk model showed higher performance for both long-term and short-term risk assessment compared with imaging-only and Tyrer-Cuzick models.

Polygenic risk score calibration and association with breast cancer in diverse ancestries.

10505 Background: Polygenic risk scores (PRS) have been shown to substantially improve breast cancer (BC) risk prediction when incorporated into traditional risk assessment models. However, PRS developed primarily in women of European ancestry have shown poor performance when applied to non-European populations. We previously described development and validation of a multiple-ancestry PRS (MA-PRS) that is based on individual ancestral genetic composition. MA-PRS has been incorporated into the Tyrer-Cuzick model for use in clinical practice and has been shown to provide independent value to the model. Here, we describe performance of the MA-PRS in a large cohort of women from diverse ancestral backgrounds. Methods: The study cohort included 203,586 patients who were referred for hereditary cancer testing and negative for pathogenic variants in genes associated with BC. MA-PRS was calculated as previously described based on 149 SNPs (93 BC and 56 ancestry-informative). Association of MA-PRS with invasive BC status (affected vs unaffected) was analyzed using logistic regression adjusted for personal and family cancer history, age, and ancestry. Calibration was assessed by examining the MA-PRS distribution within unaffected patients, and through goodness-of-fit tests. All analyses were conducted within the full cohort and within ancestral subpopulations. Odds ratios (ORs) are reported per standard deviation. Results: One-fifth (43,782/203,586, 21.5%) of patients were diagnosed with invasive BC, and 60,657 (29.8%) of patients reported BC in a first degree relative. The MA-PRS distribution was centered around zero among unaffected patients overall and within each subpopulation. MA-PRS significantly improved BC risk prediction over clinical factors in the full cohort (OR = 1.43, 95% CI:1.41-1.44) and within each ancestral subpopulation (Table). The strongest associations were observed in Ashkenazi Jewish (OR = 1.56, 95% CI:1.39-1.76) and South Asian (OR = 1.49,95% CI: 1.23-1.81) populations. The top decile of the MA-PRS consistently identified patients with two-fold increased risk of developing BC. Goodness-of-fit tests showed that the MA-PRS was well calibrated and predicted BC risk accurately in the tails of the distribution for both European and non-European women. Conclusions: The breast cancer MA-PRS based on genetic ancestral composition is well-calibrated and strongly associated with BC in all tested ancestral populations. Incorporation of PRS adds independent information to risk models and supports equitable access to personalized BC risk management. [Table: see text]

Abstract 3029: Barriers to optimal breast cancer screening modalities for high-risk patients in the primary care setting

Abstract Background: Annual screening breast MRIs are endorsed by current breast cancer screening guidelines as an adjunctive screening modality for high-risk patients (defined by more than 20% lifetime risk) in addition to a screening mammogram. Recent studies showed screening breast MRI remained underutilized despite the current recommendations. On the other hand, validated breast cancer risk models for identifying high-risk patients also remained under-recognized among primary care providers. This study aims to identify the potential barriers for primary care providers (PCPs) to using breast cancer risk models and referring high-risk patients to breast MRI for screening. Methods: An anonymous survey was distributed via email to the PCPs and OB/GYN providers in the network of Mount Auburn Hospital, a community teaching hospital affiliated with Harvard Medical School in Cambridge, Massachusetts. The survey included questions regarding breast cancer screening in average-risk and high-risk patients, providers’ understanding of breast cancer risk models, and current referral status of breast MRIs and high-risk/genetic clinics. Results: The survey was sent to 104 providers, and 43 responses were received. When asked about performing breast cancer risk assessments, 58% reported “Always (91-100%)” and 33% said “Often (71-90%)”. For factors to consider when performing risk assessments, most of the providers considered personal medical history (98%), family history (100%), genetic syndrome history (86%), and previous breast biopsy results (90%). When asked about breast cancer risk models, 58% of the providers had not heard of or used one. 33% of the providers have used the Gail model, and 26% have used the Tyrer-Cuzick model. 51% of the providers correctly identified high-risk as lifetime risk &amp;gt;20%. 56% correctly answered annual mammograms with MRI for breast cancer screening for high-risk patients. The majority of the providers (84%) would prefer to refer patients to the high-risk clinic for risk assessment and screening management. For barriers to referring to MRI, the insurance approval process (49%) was reported to be the largest barrier. 72% of the providers prefer not ordering it and defer to the high-risk/genetic clinic for ordering the test. Conclusions: Our survey has shown different barriers to breast cancer screening in high-risk patients in the primary care setting, including knowledge and logistic barriers. Further education for PCPs is needed for correctly identifying high-risk patients, utilizing risk models, and referring high-risk patients for breast MRI screening. Citation Format: Yu Jen Jan, Prudence B. Lam. Barriers to optimal breast cancer screening modalities for high-risk patients in the primary care setting [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 3029.

Abstract P5-04-05: Predictors of guideline-incongruent breast cancer screening in an urban comprehensive cancer center

Abstract Background: Guideline-congruent breast cancer (BC) screening is imperative to systematically curb BC mortality. This study was conducted to identify predictors of BC screening behaviors congruent with guidelines from various nationally recognized organizations (e.g, American Cancer Society, National Comprehensive Cancer Network, American College of Radiology) among high- and average-risk women, and to elucidate the alternative screening behaviors of women who were incongruently screened. Methods: Medical records of 6,090 women who received at least two screening mammograms from January 2016 to March 2018 at the Karmanos Cancer Institute were reviewed to determine breast cancer risk status (classified by the Tyrer-Cuzick model) and breast density status to determine whether breast cancer screening was concordant with risk-driven screening guidelines. Breast density was determined by BI-RADS density scoring, with non-dense breasts defined by a score of A or B, and dense breasts as C or D. For women at average-risk of breast cancer, incongruent screening was defined as receiving supplemental imaging in the interval between screening mammograms. For high-risk women, incongruent screening was defined as not having a recommended supplemental image in the interval between screening mammograms. Further, we examined BC risk, breast density, age, and race as predictors for guideline-concordant screening. Results: The screening cohort included 73.3% Black and 26.7% White women of whom 86.5% were classified as average-risk, 7.7% intermediate risk and 5.8% high risk. Further analyses focused on women with average and high-risk of breast cancer. Among both average- and high-risk women, 390 (6.9%) were incongruently screened, however the rate of incongruent screening was much higher among high-risk vs. average risk women (97.7 vs. 0.9%, p&amp;lt; 0.01). Among average-risk women, incongruent screening was more likely among women with dense vs. non-dense breasts (2.0% vs 0.1%, p&amp;lt; .01). High-risk women were more likely to be incongruently screened if they had non-dense compared to dense breasts (99.5% vs 95.2%, p &amp;lt; .01). Younger women more likely to be incongruently screened among average-risk women (55.11 [SD = 10.24] vs 62.20 [SD = 9.73]; t5267 = 4.87, p &amp;lt; .01, d = 0.73), whereas older women were more likely to be incongruently screened among high-risk women, although this difference was not statistically significant (52.38 [SD = 7.27] vs 47.50 [SD = 8.57]; t351 = 1.87, p = .06, d = 0.67).There was no significant impact of race and incongruent screening for individual risk-level categories. With the exception of rendering the age effect non-significant, preliminary multivariable analyses did not significantly change the results. Further analyses will be conducted to assess the relationship between predictive factors and incongruent screening. Conclusions: An apparent lack of adherence to evidence-based screening guidelines for BC has led to underutilization of supplementary breast imaging for women at high-risk for BC. Further interventions are needed to promote increased supplemental imaging for this group of women. Citation Format: Alexandra Wehbe, Madeleine Gonte, Suzanne O’Neill, Alit Amit-Yousif, Kristen Purrington, Mark Manning, Michael Simon. Predictors of guideline-incongruent breast cancer screening in an urban comprehensive cancer center [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 P5-04-05.

Abstract P5-04-06: Breast Imaging Recommendations for Females &amp;lt;40 Years of Age with ≥20% Lifetime Breast Cancer Risk: Practice Patterns at a Specialized Clinic

Abstract Background— There are limited data to guide breast cancer screening recommendations among females &amp;lt; 40 yrs of age with elevated lifetime breast cancer risk not driven by a known germline mutation. The American Cancer Society recommends initiating screening at age 30, while the National Comprehensive Cancer Network (NCCN) recommends 10 yrs younger than the youngest affected relative (YAR). Both support screening MRI in addition to annual mammogram (MMG). This study describes practice patterns related to screening imaging recommendations and patient (pt) follow-through in young females with ≥20% lifetime breast cancer cared for in a specialized clinic. Methods— At the Brigham and Women’s Hospital high-risk breast clinic, specialized advanced practice providers, surgeons and oncologists perform risk assessment including use of the Tyrer-Cuzick (TC) risk model, and provide risk management recommendations. For this study, we identified pts age&amp;lt; 40 yrs with &amp;gt;20% lifetime breast cancer risk, no known genetic mutation or high-risk breast lesions, and ≥1 first or second-degree relatives (FDR or SDR) with breast cancer. We evaluated factors associated with recommendation for i) early screening initiation, defined as prior to age 40, and ii) use of supplemental imaging modalities. Results—335 pts met study criteria: 20% were age&amp;lt; 30, 36% were 30-34, and 44% were 35-39. Mean lifetime risk by the TC model was 32% (SD: 10%). Early screening was recommended in 75%; these pts were more likely to have an affected FDR (71% vs. 48%, p&amp;lt; 0.001) and younger affected relatives (median age of YAR: 44 vs. 55, p&amp;lt; 0.001). Among pts whose YARs were age&amp;lt; 50, early screening was recommended in-line with NCCN guidelines for 99% of pts with FDRs&amp;lt; 50 vs. 80% of pts with only SDRs&amp;lt; 50 (p&amp;lt; 0.001). Among pts whose YARs were age≥50, early screening was recommended contrary to NCCN guidelines in 51%. Factors associated with an early screening recommendation in this subgroup were having received a prior MMG (62% recommended early screening vs. 33% with no prior MMG) as well as being older at time of risk discussion (median age 37 in early screening group vs. 34 in routine screening group) and having younger affected relatives (median age of YAR: 53 vs. 56) (all p≤0.01). Regarding use of supplemental imaging, 35% were recommended screening MMG alone, while 65% were also offered screening MRI or ultrasound (US). Factors most strongly associated with offering MRI/US included having heterogeneously or extremely dense breasts, normal BMI, greater extent of family history, younger affected relatives and higher TC scores (Table). All except extent of family history remained statistically significant in multivariable analysis. Among those offered supplemental MRI/US who were eligible to initiate screening, 48% had pursued MRI, 7% US +/- MRI, 27% MMG alone, and 18% had no screening imaging at a median follow-up of 17 months. Conclusions— These data suggest that providers in our high-risk breast clinic are using nuanced clinical judgment related to screening recommendations in pts &amp;lt; 40 yrs with elevated lifetime risk. Those with affected FDRs at age&amp;lt; 50 were consistently recommended early screening initiation, while practice recommendations varied more for pts with only SDRs age&amp;lt; 50 or those with YAR age≥50, suggesting a need for consensus criteria as to when to initiate screening in these subgroups. Multiple factors impacted recommendations for screening MRI/US, most notably breast density. Factors associated with offering supplemental screening with MRI/US Citation Format: Alexandra Wehbe, Alison Laws, Fisher Katlin, Eshita Sharma, Marybeth Hans, Mary Graichen, Brittany Bychkovsky, Rochelle Scheib, Judy Garber, Lydia Pace, Tari King. Breast Imaging Recommendations for Females &amp;lt;40 Years of Age with ≥20% Lifetime Breast Cancer Risk: Practice Patterns at a Specialized Clinic [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 P5-04-06.

Abstract PD14-05: PD14-05 Prospective longitudinal validation of a breast cancer risk prediction model in a cohort of 130,058 women

Abstract Background: Personalized breast cancer (BC) risk assessment depends on known traditional risk factors, specific germline mutations, and genome-wide polygenic risk scores (PRS). PRS explains a substantial proportion of genetic BC susceptibility. Accuracy of BC risk prediction may be improved by combining a PRS with traditional risk factors. We recently developed and validated a 149-SNP PRS for women of diverse ancestries using ancestry-informative genetic markers and combined this with version 7 of the Tyrer-Cuzick (TC) model to generate a Combined Risk Score (CRS). Here, we describe a pre-specified prospective longitudinal clinical validation of CRS as a predictor of BC risk. Methods: Women in the U.S. who were referred for clinical genetic testing between January 2017 and February 2019 were matched to medical and hospital claims in an anonymized dataset. Women with a pathogenic mutation in a BC-related gene were excluded from analysis. Follow-up began 4 months after testing and extended to the earliest date of BC diagnosis, censoring at the time of BC preventive treatment, or November 1, 2019. Incident BC events were determined by an ICD10 code of C50.* and confirmed by relevant treatment codes. CRS calibration was evaluated by the ratio of observed (O) to expected (E) incident BCs for the full cohort, and for women split into event-based 5-year CRS risk deciles. Cox proportional hazards models were used to evaluate discriminatory accuracy in terms of hazard ratios (HR) with 95% confidence intervals (CI) and p-values from likelihood ratio chi-squared statistics. Kaplan-Meier analysis was used to examine risk for women split into high- or low-risk groups according to a 3% 5-year CRS risk threshold. Results: 130,058 women with 148,349 total patient years met study eligibility criteria and were matched to claims data. Over a median (range) follow-up of 12.1 (4.0-29.5) months, 340 incident BC events were observed. The CRS was well calibrated in the overall cohort with an O/E ratio of 1.11 (95% CI=0.99-1.23) and within deciles of predicted risk (Table). Importantly, in the highest risk decile, the O/E was 0.91 (95% CI=0.63-1.27) with CRS, but 0.67 (95% CI=0.46-0.94) with TC alone, illustrating the superior calibration of CRS. In a Cox model adjusted for age at testing, PRS had an HR per standard deviation (SD) of 1.48 (95% CI=1.33-1.64, p=2.55×10-13); the HR/SD was 1.43 (95% CI=1.29-1.59, p=1.61×10-11) after adjusting for family history. In a bivariate analysis using both CRS and TC to predict time to BC, CRS added significantly to the model after accounting for TC (HR/SD=2.89, 95% CI=2.12-3.94, p=1.20×10-11), whereas TC did not add significant information after accounting for CRS. 15,986 (12.3%) women were above the CRS high-risk threshold, including 123 with events. A total of 10,248 (7.9%) women were reclassified by the CRS model compared to the TC model. Among women who were classified as high-risk by TC, 32.6% were reclassified as low-risk by CRS; among those classified as low-risk by TC, 4.3% were reclassified as high-risk by CRS. The CRS high-risk group experienced events at over three times the rate of the low-risk group (HR=3.75, 95% CI=3.00-4.68, p=6.39×10-27). Conclusion: The CRS was well-calibrated in predicting BC and significantly improved upon a traditional risk factor model. Clinical use of the CRS may lead to improved BC prevention and screening strategies. Table: Absolute risk calibration by 5-year risk decile Incidence is reported per 1,000 women-years. 34 breast cancers were observed per decile. Citation Format: Brent Mabey, Elisha Hughes, Braden Probst, Holly J. Pederson, Timothy Simmons, Brian Morris, Brooke Hullinger, Susan Domchek, Charis Eng, Monique Gary, Jennifer Klemp, Semanti Mukherjee, Vijai Joseph, Kenneth Offit, Olufunmilayo I. Olopade, Sandhya Pruthi, Allison W. Kurian, Mark E. Robson, Pat Whitworth, Susanne Wagner, Jerry Lanchbury, Thomas Slavin, Alexander Gutin. PD14-05 Prospective longitudinal validation of a breast cancer risk prediction model in a cohort of 130,058 women [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 PD14-05.

Beyond Breast Density: Risk Measures for Breast Cancer in Multiple Imaging Modalities.

Breast density is an independent risk factor for breast cancer. In digital mammography and digital breast tomosynthesis, breast density is assessed visually using the four-category scale developed by the American College of Radiology Breast Imaging Reporting and Data System (5th edition as of November 2022). Epidemiologically based risk models, such as the Tyrer-Cuzick model (version 8), demonstrate superior modeling performance when mammographic density is incorporated. Beyond just density, a separate mammographic measure of breast cancer risk is parenchymal textural complexity. With advancements in radiomics and deep learning, mammographic textural patterns can be assessed quantitatively and incorporated into risk models. Other supplemental screening modalities, such as breast US and MRI, offer independent risk measures complementary to those derived from mammography. Breast US allows the two components of fibroglandular tissue (stromal and glandular) to be visualized separately in a manner that is not possible with mammography. A higher glandular component at screening breast US is associated with higher risk. With MRI, a higher background parenchymal enhancement of the fibroglandular tissue has also emerged as an imaging marker for risk assessment. Imaging markers observed at mammography, US, and MRI are powerful tools in refining breast cancer risk prediction, beyond mammographic density alone.

Effect of an Educational Intervention on Women's Health Care Provider Knowledge Gaps About Breast Cancer Risk Model Use and High-risk Screening Recommendations.

To assess effectiveness of a web-based educational intervention on women's health care provider knowledge of breast cancer risk models and high-risk screening recommendations. A web-based pre- and post-test study including 177 U.S.-based women's health care providers was conducted in 2019. Knowledge gaps were defined as fewer than 75% of respondents answering correctly. Pre- and post-test knowledge differences (McNemar test) and associations of baseline characteristics with pre-test knowledge gaps (logistic regression) were evaluated. Respondents included 131/177 (74.0%) physicians; 127/177 (71.8%) practiced obstetrics/gynecology. Pre-test, 118/177 (66.7%) knew the Gail model predicts lifetime invasive breast cancer risk; this knowledge gap persisted post-test [(121/177, 68.4%); P = 0.77]. Just 39.0% (69/177) knew the Gail model identifies women eligible for risk-reducing medications; this knowledge gap resolved. Only 48.6% (86/177) knew the Gail model should not be used to identify women meeting high-risk MRI screening guidelines; this deficiency decreased to 66.1% (117/177) post-test (P = 0.001). Pre-test, 47.5% (84/177) knew the Tyrer-Cuzick model is used to identify women meeting high-risk screening MRI criteria, 42.9% (76/177) to predict BRCA1/2 pathogenic mutation risk, and 26.0% (46/177) to predict lifetime invasive breast cancer risk. These knowledge gaps persisted but improved. For a high-risk 30-year-old, 67.8% (120/177) and 54.2% (96/177) pre-test knew screening MRI and mammography/tomosynthesis are recommended, respectively; 19.2% (34/177) knew both are recommended; and 53% (94/177) knew US is not recommended. These knowledge gaps resolved or reduced. Web-based education can reduce important provider knowledge gaps about breast cancer risk models and high-risk screening recommendations.