10-year performance of four models of breast cancer risk: a validation study

Risk-based breast cancer screening: What are the challenges?

Population-based screening programs aim to detect the disease at an early stage, so less treatment will be needed as well as having better oncological outcomes when diagnosed earlier. In the majority of European countries, breast cancer screening programs are designed based on women age.Meta-analysis of randomized clinical trial data demonstrates a reduction in the relative risk of breast cancer mortality due to screening, which has been estimated to be approximately 20%.One of the controversies about the population breast screening programs is that age-based screening ignores women's individual breast cancer risk. Identification of high-risk women may intensify the screening measures and will optimize the population screening programs to align them to individual risks.Family history of breast cancer is one of the risk factors to consider along with the recently developed polygenic risk scores to stratify women into a risk group. Other factors to assess risk include: mammographic breast density; B3 lesions with atypia in breast biopsy specimens; hormonal and lyfestyle and, potentially, epigenetic markers. Still, there are some difficulties in validating these factors and reflecting the interaction between risk factors in the models.Ongoing screening trials (e.g., WISDOM and MyPebs) are currently evaluating the clinical acceptability and utility of risk-stratified screening programs in the general population, and should provide valuable information for the possible implementation of such programs.Communication of complex risk information to the women, as well as assessing ethical concerns need to be addressed before implementation of risk stratified programs.

The "Latines Lideres En Salud (LaLiSa)" study: Rationale and design

BACKGROUND

Latines suffer from breast cancer (BC), due to elevated biological and social determinants of health (SDOH) risks. This study compares the effects of different strategies on uptake of cancer genetic services, specifically hereditary cancer risk assessment, genetic counseling, and genetic testing, and risk-based BC care.

DESIGN/METHODS

In Chicago, Illinois, Aim 1 participants are recruited from a federally qualified health center (FQHC) and community venues. For Aim 1, eligible participants: (1) are female; (2) are Latine; (3) are 30+ years old; (4) have personal or family history of BC or cancers with shared hereditary mutations; (5) have at least one SDOH risk; and (6) have not received any cancer genetic services. Participants are randomly assigned to different study arms. Both arms include phone-based sessions, FQHC-based navigation for SDOH, and low- or no-cost cancer genetic services. The educate sessions focus on risk assessment and prevention. The empower sessions focus on risk assessment and equip participants with the skills to share information about FQHC-based cancer genetic services. For Aim 2, eligible participants are: (1) female; (2) network members of Aim 1 participants; and (3) eligible for BC screening based on guidelines recommended by the American Cancer Society (ACS). Primary outcomes include uptake of any cancer genetic services. Analyses will also explore intervention differences by neighborhood context.

DISCUSSION

This is one of the first trials focused on Latines' participation in cancer genetic services and risk-based BC care within the context of SDOH - which has major implications for equity in precision cancer prevention.

Strategies for Identifying and Recruiting Women at High Risk for Breast Cancer for Research Outside of Clinical Settings: Observational Study

BACKGROUND

Research is needed to understand and address barriers to risk management for women at high (≥20% lifetime) risk for breast cancer, but recruiting this population for research studies is challenging.

OBJECTIVE

This paper compares a variety of recruitment strategies used for a cross-sectional, observational study of high-risk women.

METHODS

Eligible participants were assigned female at birth, aged 25-85 years, English-speaking, living in the United States, and at high risk for breast cancer as defined by the American College of Radiology. Individuals were excluded if they had a personal history of breast cancer, prior bilateral mastectomy, medical contraindications for magnetic resonance imaging, or were not up-to-date on screening mammography per American College of Radiology guidelines. Participants were recruited from August 2020 to January 2021 using the following mechanisms: targeted Facebook advertisements, Twitter posts, ResearchMatch (a web-based research recruitment database), community partner promotions, paper flyers, and community outreach events. Interested individuals were directed to a secure website with eligibility screening questions. Participants self-reported method of recruitment during the eligibility screening. For each recruitment strategy, we calculated the rate of eligible respondents and completed surveys, costs per eligible participant, and participant demographics.

RESULTS

We received 1566 unique responses to the eligibility screener. Participants most often reported recruitment via Facebook advertisements (724/1566, 46%) and ResearchMatch (646/1566, 41%). Community partner promotions resulted in the highest proportion of eligible respondents (24/46, 52%), while ResearchMatch had the lowest proportion of eligible respondents (73/646, 11%). Word of mouth was the most cost-effective recruitment strategy (US $4.66 per completed survey response) and paper flyers were the least cost-effective (US $1448.13 per completed survey response). The demographic characteristics of eligible respondents varied by recruitment strategy: Twitter posts and community outreach events resulted in the highest proportion of Hispanic or Latina women (1/4, 25% and 2/6, 33%, respectively), and community partner promotions resulted in the highest proportion of non-Hispanic Black women (4/24, 17%).

CONCLUSIONS

Although recruitment strategies varied in their yield of study participants, results overall support the feasibility of identifying and recruiting women at high risk for breast cancer outside of clinical settings. Researchers must balance the associated costs and participant yield of various recruitment strategies in planning future studies focused on high-risk women.

Breast Cancer Screening in the Intermediate-Risk Population: Falling Through the Cracks?

Breast cancer screening guidelines vary for women at intermediate risk (15%-20% lifetime risk) for developing breast cancer across jurisdictions. Currently available risk assessment models have differing strengths and weaknesses, creating difficulty and ambiguity in selecting the most appropriate model to utilize. Clarifying which model to utilize in individual circumstances may help determine the best screening guidelines to use for each individual.

Pregnancy-Related Factors and Breast Cancer Risk for Women Across a Range of Familial Risk

Importance

Few studies have investigated whether the associations between pregnancy-related factors and breast cancer (BC) risk differ by underlying BC susceptibility. Evidence regarding variation in BC risk is critical to understanding BC causes and for developing effective risk-based screening guidelines.

Objective

To examine the association between pregnancy-related factors and BC risk, including modification by a of BC where scores are based on age and BC family history.

Design, Setting, and Participants

This cohort study included participants from the prospective Family Study Cohort (ProF-SC), which includes the 6 sites of the Breast Cancer Family Registry (US, Canada, and Australia) and the Kathleen Cuningham Foundation Consortium (Australia). Analyses were performed in a cohort of women enrolled from 1992 to 2011 without any personal history of BC who were followed up through 2017 with a median (range) follow-up of 10 (1-23) years. Data were analyzed from March 1992 to March 2017.

Exposures

Parity, number of full-term pregnancies (FTP), age at first FTP, years since last FTP, and breastfeeding.

Main Outcomes and Measures

BC diagnoses were obtained through self-report or report by a first-degree relative and confirmed through pathology and data linkages. Cox proportional hazards regression models estimated hazard ratios (HR) and 95% CIs for each exposure, examining modification by PARS of BC. Differences were assessed by estrogen receptor (ER) subtype.

Results

The study included 17 274 women (mean [SD] age, 46.7 [15.1] years; 791 African American or Black participants [4.6%], 1399 Hispanic or Latinx participants [8.2%], and 13 790 White participants [80.7%]) with 943 prospectively ascertained BC cases. Compared with nulliparous women, BC risk was higher after a recent pregnancy for those women with higher PARS (last FTP 0-5 years HR for interaction, 1.53; 95% CI, 1.13-2.07; P for interaction < .001). Associations between other exposures were limited to ER-negative disease. ER-negative BC was positively associated with increasing PARS and increasing years since last FTP (P for interaction < .001) with higher risk for recent pregnancy vs nulliparous women (last FTP 0-5 years HR for interaction, 1.54; 95% CI, 1.03-2.31). ER-negative BC was positively associated with increasing PARS and being aged 20 years or older vs less than 20 years at first FTP (P for interaction = .002) and inversely associated with multiparity vs nulliparity (P for interaction = .01).

Conclusions and Relevance

In this cohort study of women with no prior BC diagnoses, associations between pregnancy-related factors and BC risk were modified by PARS, with greater associations observed for ER-negative BC.

Validation of the BOADICEA model in a prospective cohort of BRCA1/2 pathogenic variant carriers

BACKGROUND

No validation has been conducted for the BOADICEA multifactorial breast cancer risk prediction model specifically in BRCA1/2 pathogenic variant (PV) carriers to date. Here, we evaluated the performance of BOADICEA in predicting 5-year breast cancer risks in a prospective cohort of BRCA1/2 PV carriers ascertained through clinical genetic centres.

METHODS

We evaluated the model calibration and discriminatory ability in the prospective TRANsIBCCS cohort study comprising 1614 BRCA1 and 1365 BRCA2 PV carriers (209 incident cases). Study participants had lifestyle, reproductive, hormonal, anthropometric risk factor information, a polygenic risk score based on 313 SNPs and family history information.

RESULTS

The full multifactorial model considering family history together with all other risk factors was well calibrated overall (E/O=1.07, 95% CI: 0.92 to 1.24) and in quintiles of predicted risk. Discrimination was maximised when all risk factors were considered (Harrell's C-index=0.70, 95% CI: 0.67 to 0.74; area under the curve=0.79, 95% CI: 0.76 to 0.82). The model performance was similar when evaluated separately in BRCA1 or BRCA2 PV carriers. The full model identified 5.8%, 12.9% and 24.0% of BRCA1/2 PV carriers with 5-year breast cancer risks of <1.65%, <3% and <5%, respectively, risk thresholds commonly used for different management and risk-reduction options.

CONCLUSION

BOADICEA may be used to aid personalised cancer risk management and decision-making for BRCA1 and BRCA2 PV carriers. It is implemented in the free-access CanRisk tool (https://www.canrisk.org/).

Joint ABS-UKCGG-CanGene-CanVar consensus regarding the use of CanRisk in clinical practice

BACKGROUND

The CanRisk tool, which operationalises the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) is used by Clinical Geneticists, Genetic Counsellors, Breast Oncologists, Surgeons and Family History Nurses for breast cancer risk assessments both nationally and internationally. There are currently no guidelines with respect to the day-to-day clinical application of CanRisk and differing inputs to the model can result in different recommendations for practice.

METHODS

To address this gap, the UK Cancer Genetics Group in collaboration with the Association of Breast Surgery and the CanGene-CanVar programme held a workshop on 16th of May 2023, with the aim of establishing best practice guidelines.

RESULTS

Using a pre-workshop survey followed by structured discussion and in-meeting polling, we achieved consensus for UK best practice in use of CanRisk in making recommendations for breast cancer surveillance, eligibility for genetic testing and the input of available information to undertake an individualised risk assessment.

CONCLUSIONS

Whilst consensus recommendations were achieved, the meeting highlighted some of the barriers limiting the use of CanRisk in clinical practice and identified areas that require further work and collaboration with relevant national bodies and policy makers to incorporate wider use of CanRisk into routine breast cancer risk assessments.

Using clinical and genetic risk factors for risk prediction of 8 cancers in the UK Biobank

BACKGROUND

Models with polygenic risk scores and clinical factors to predict risk of different cancers have been developed, but these models have been limited by the polygenic risk score-derivation methods and the incomplete selection of clinical variables.

METHODS

We used UK Biobank to train the best polygenic risk scores for 8 cancers (bladder, breast, colorectal, kidney, lung, ovarian, pancreatic, and prostate cancers) and select relevant clinical variables from 733 baseline traits through extreme gradient boosting (XGBoost). Combining polygenic risk scores and clinical variables, we developed Cox proportional hazards models for risk prediction in these cancers.

RESULTS

Our models achieved high prediction accuracy for 8 cancers, with areas under the curve ranging from 0.618 (95% confidence interval = 0.581 to 0.655) for ovarian cancer to 0.831 (95% confidence interval = 0.817 to 0.845) for lung cancer. Additionally, our models could identify individuals at a high risk for developing cancer. For example, the risk of breast cancer for individuals in the top 5% score quantile was nearly 13 times greater than for individuals in the lowest 10%. Furthermore, we observed a higher proportion of individuals with high polygenic risk scores in the early-onset group but a higher proportion of individuals at high clinical risk in the late-onset group.

CONCLUSION

Our models demonstrated the potential to predict cancer risk and identify high-risk individuals with great generalizability to different cancers. Our findings suggested that the polygenic risk score model is more predictive for the cancer risk of early-onset patients than for late-onset patients, while the clinical risk model is more predictive for late-onset patients. Meanwhile, combining polygenic risk scores and clinical risk factors has overall better predictive performance than using polygenic risk scores or clinical risk factors alone.

Awareness and Candidacy for Endocrine Prevention and Risk Reducing Mastectomy in Unaffected High-Risk Women Referred for Breast Cancer Risk Assessment

INTRODUCTION

Primary prevention of breast cancer in women at elevated risk includes several strategies such as endocrine prevention and risk-reducing mastectomy (RRM). The objective of this study was to evaluate awareness of different preventive strategies across high-risk subgroups.

PATIENTS AND METHODS

Women referred for high risk evaluation between 2020 and 2023 completed an initial risk-assessment questionnaire that included questions around perceived lifetime risk and consideration of preventive strategies. One-way analysis of variance (ANOVA) and chi-squared tests were used to compare differences across different high-risk subgroups.

RESULTS

482 women with a median age of 43 years (20-79 years) met inclusion criteria; 183 (38.0%) germline pathogenic variant carriers (GPV), 90 (18.7%) with high-risk lesions (HRL) on breast biopsy, and 209 (43.4%) with strong family history (FH) without a known genetic predisposition. Most high-risk women reported that they had considered increased screening and surveillance (83.7%) and lifestyle strategies (80.6%), while fewer patients had considered RRM (39.8%) and endocrine prevention (27.0%). Prior to initial consultation, RRM was more commonly considered in GPV carriers (59.4%) relative to those with HRL (33.3%) or strong FH (26.3%, p < 0.001). Based on current guidelines, 206 (43%) patients were deemed eligible for endocrine prevention, including 80.5% with HRL and 39.0% with strong FH. Prior consideration of endocrine prevention was highest in patients with HRL and significantly lower in those with strong FH (47.2% HRL versus 31.1% GPV versus 18.7% FH, p = 0.001).

CONCLUSIONS

Endocrine prevention is the least considered preventive option for high-risk women, despite eligibility in a significant proportion of those presenting with HRL or strong FH.

Artificial Intelligence-Driven Mammography-Based Future Breast Cancer Risk Prediction: A Systematic Review

PURPOSE

To summarize the literature regarding the performance of mammography-image based artificial intelligence (AI) algorithms, with and without additional clinical data, for future breast cancer risk prediction.

MATERIALS AND METHODS

A systematic literature review was performed using six databases (medRixiv, bioRxiv, Embase, Engineer Village, IEEE Xplore, and PubMed) from 2012 through September 30, 2022. Studies were included if they used real-world screening mammography examinations to validate AI algorithms for future risk prediction based on images alone or in combination with clinical risk factors. The quality of studies was assessed, and predictive accuracy was recorded as the area under the receiver operating characteristic curve (AUC).

RESULTS

Sixteen studies met inclusion and exclusion criteria, of which 14 studies provided AUC values. The median AUC performance of AI image-only models was 0.72 (range 0.62-0.90) compared with 0.61 for breast density or clinical risk factor-based tools (range 0.54-0.69). Of the seven studies that compared AI image-only performance directly to combined image + clinical risk factor performance, six demonstrated no significant improvement, and one study demonstrated increased improvement.

CONCLUSIONS

Early efforts for predicting future breast cancer risk based on mammography images alone demonstrate comparable or better accuracy to traditional risk tools with little or no improvement when adding clinical risk factor data. Transitioning from clinical risk factor-based to AI image-based risk models may lead to more accurate, personalized risk-based screening approaches.

A Systematic Review and Critical Assessment of Breast Cancer Risk Prediction Tools Incorporating a Polygenic Risk Score for the General Population

Single nucleotide polymorphisms (SNPs) in the form of a polygenic risk score (PRS) have emerged as a promising factor that could improve the predictive performance of breast cancer (BC) risk prediction tools. This study aims to appraise and critically assess the current evidence on these tools. Studies were identified using Medline, EMBASE and the Cochrane Library up to November 2022 and were included if they described the development and/ or validation of a BC risk prediction model using a PRS for women of the general population and if they reported a measure of predictive performance. We identified 37 articles, of which 29 combined genetic and non-genetic risk factors using seven different risk prediction tools. Most models (55.0%) were developed on populations from European ancestry and performed better than those developed on populations from other ancestry groups. Regardless of the number of SNPs in each PRS, models combining a PRS with genetic and non-genetic risk factors generally had better discriminatory accuracy (AUC from 0.52 to 0.77) than those using a PRS alone (AUC from 0.48 to 0.68). The overall risk of bias was considered low in most studies. BC risk prediction tools combining a PRS with genetic and non-genetic risk factors provided better discriminative accuracy than either used alone. Further studies are needed to cross-compare their clinical utility and readiness for implementation in public health practices.

Self-reported barriers to screening breast MRI among women at high risk for breast cancer

BACKGROUND

Annual screening breast MRI is recommended for women at high (≥ 20% lifetime) breast cancer risk, but is underutilized. Guided by the Health Services Utilization Model (HSUM), we assessed factors associated with screening breast MRI among high-risk women.

METHODS

From August 2020-January 2021, we recruited an online convenience sample of high-risk women ages 25-85 (N = 232). High-risk was defined as: pathogenic genetic mutation in self or first-degree relative; history of lobular carcinoma in situ; history of thoracic radiation; or estimated lifetime risk ≥ 20%. Participants self-reported predisposing factors (breast cancer knowledge, health locus of control), enabling factors (health insurance type, social support), need factors (perceived risk, screening-supportive social norms, provider recommendation), and prior receipt of screening breast MRI. Multivariable logistic regression analysis with backward selection identified HSUM factors associated with receipt of screening breast MRI.

RESULTS

About half (51%) of participants had received a provider recommendation for screening breast MRI; only 32% had ever received a breast MRI. Breast cancer knowledge (OR = 1.15, 95% CI = 1.04-1.27) and screening-supportive social norms (OR = 2.21, 95% CI = 1.64-2.97) were positively related to breast MRI receipt. No other HSUM variables were associated with breast MRI receipt (all p's > 0.1).

CONCLUSIONS

High-risk women reported low uptake of screening breast MRI, indicating a gap in guideline-concordant care. Breast cancer knowledge and screening-supportive social norms are two key areas to target in future interventions. Data were collected during the COVID-19 pandemic and generalizability of results is unclear. Future studies with larger, more heterogeneous samples are needed to replicate these findings.

Breast Cancer Screening for Women at Higher-Than-Average Risk: Updated Recommendations From the ACR

Early detection decreases breast cancer death. The ACR recommends annual screening beginning at age 40 for women of average risk and earlier and/or more intensive screening for women at higher-than-average risk. For most women at higher-than-average risk, the supplemental screening method of choice is breast MRI. Women with genetics-based increased risk, those with a calculated lifetime risk of 20% or more, and those exposed to chest radiation at young ages are recommended to undergo MRI surveillance starting at ages 25 to 30 and annual mammography (with a variable starting age between 25 and 40, depending on the type of risk). Mutation carriers can delay mammographic screening until age 40 if annual screening breast MRI is performed as recommended. Women diagnosed with breast cancer before age 50 or with personal histories of breast cancer and dense breasts should undergo annual supplemental breast MRI. Others with personal histories, and those with atypia at biopsy, should strongly consider MRI screening, especially if other risk factors are present. For women with dense breasts who desire supplemental screening, breast MRI is recommended. For those who qualify for but cannot undergo breast MRI, contrast-enhanced mammography or ultrasound could be considered. All women should undergo risk assessment by age 25, especially Black women and women of Ashkenazi Jewish heritage, so that those at higher-than-average risk can be identified and appropriate screening initiated.

Combining Molecular and Radiomic Features for Risk Assessment in Breast Cancer

Breast cancer risk is highly variable within the population and current research is leading the shift toward personalized medicine. By accurately assessing an individual woman's risk, we can reduce the risk of over/undertreatment by preventing unnecessary procedures or by elevating screening procedures. Breast density measured from conventional mammography has been established as one of the most dominant risk factors for breast cancer; however, it is currently limited by its ability to characterize more complex breast parenchymal patterns that have been shown to provide additional information to strengthen cancer risk models. Molecular factors ranging from high penetrance, or high likelihood that a mutation will show signs and symptoms of the disease, to combinations of gene mutations with low penetrance have shown promise for augmenting risk assessment. Although imaging biomarkers and molecular biomarkers have both individually demonstrated improved performance in risk assessment, few studies have evaluated them together. This review aims to highlight the current state of the art in breast cancer risk assessment using imaging and genetic biomarkers.

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

BACKGROUND

Breast cancer risk models guide screening and chemoprevention decisions, but the extent and effect of variability among models, particularly at the individual level, is uncertain.

OBJECTIVE

To quantify the accuracy and disagreement between commonly used risk models in categorizing individual women as average vs. high risk for developing invasive breast cancer.

DESIGN

Comparison of three risk prediction models: Breast Cancer Risk Assessment Tool (BCRAT), Breast Cancer Surveillance Consortium (BCSC) model, and International Breast Intervention Study (IBIS) model.

SUBJECTS

Women 40 to 74 years of age presenting for screening mammography at a multisite health system between 2011 and 2015, with 5-year follow-up for cancer outcome.

MAIN MEASURES

Comparison of model discrimination and calibration at the population level and inter-model agreement for 5-year breast cancer risk at the individual level using two cutoffs (≥ 1.67% and ≥ 3.0%).

KEY RESULTS

A total of 31,115 women were included. When using the ≥ 1.67% threshold, more than 21% of women were classified as high risk for developing breast cancer in the next 5 years by one model, but average risk by another model. When using the ≥ 3.0% threshold, more than 5% of women had disagreements in risk severity between models. Almost half of the women (46.6%) were classified as high risk by at least one of the three models (e.g., if all three models were applied) for the threshold of ≥ 1.67%, and 11.1% were classified as high risk for ≥ 3.0%. All three models had similar accuracy at the population level.

CONCLUSIONS

Breast cancer risk estimates for individual women vary substantially, depending on which risk assessment model is used. The choice of cutoff used to define high risk can lead to adverse effects for screening, preventive care, and quality of life for misidentified individuals. Clinicians need to be aware of the high false-positive and false-negative rates and variation between models when talking with patients.

Artificial Intelligence Applications in Breast Imaging: Current Status and Future Directions

Attempts to use computers to aid in the detection of breast malignancies date back more than 20 years. Despite significant interest and investment, this has historically led to minimal or no significant improvement in performance and outcomes with traditional computer-aided detection. However, recent advances in artificial intelligence and machine learning are now starting to deliver on the promise of improved performance. There are at present more than 20 FDA-approved AI applications for breast imaging, but adoption and utilization are widely variable and low overall. Breast imaging is unique and has aspects that create both opportunities and challenges for AI development and implementation. Breast cancer screening programs worldwide rely on screening mammography to reduce the morbidity and mortality of breast cancer, and many of the most exciting research projects and available AI applications focus on cancer detection for mammography. There are, however, multiple additional potential applications for AI in breast imaging, including decision support, risk assessment, breast density quantitation, workflow and triage, quality evaluation, response to neoadjuvant chemotherapy assessment, and image enhancement. In this review the current status, availability, and future directions of investigation of these applications are discussed, as well as the opportunities and barriers to more widespread utilization.

A Review of the Literature for Individualizing Women's Care Through Breast Cancer Risk Assessment

Breast cancer is well recognized as a leading type of cancer affecting women in the United States. Although breast cancer screening is well supported in the literature, there is a lack of clear agreement regarding which breast cancer risk calculating tools should be used to develop personalized screening regimens. In this review of 11 primary articles published from 2017 to 2022, we assess current evidence on breast cancer risk assessment in outpatient clinic and mammography settings and the pivotal role of health care providers in influencing patients' choices regarding individualized screenings. Risk assessment is strongly recommended by multiple clinical practice guidelines, yet there is inadequate evidence to endorse one risk assessment tool as best practice. Further research is needed to integrate risk assessment within the clinic workflow and screening encounters. Patient-centered communication and shared decision-making are critical components for managing each woman's perceived risk and objective risk for breast cancer.

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

PREVENTION RELEVANCE

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

Challenges and Changes of the Breast Cancer Screening Paradigm

Since four decades mammography is used for early breast cancer detection in asymptomatic women and still remains the gold standard imaging modality. However, population screening programs can be personalized and women can be divided into different groups based on risk factors and personal preferences. The availability of new and evolving imaging modalities, for example, digital breast tomosynthesis, dynamic-contrast-enhanced magnetic resonance imaging (MRI), abbreviated MRI protocols, diffusion-weighted MRI, and contrast-enhanced mammography leads to new challenges and perspectives regarding the feasibility and potential harms of breast cancer screening. The aim of this review is to discuss the current guidelines for different risk groups, to analyze the recent published studies about the diagnostic performance of the imaging modalities and to discuss new developments and future perspectives. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 6.

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

BACKGROUND

The benefits and harms of breast screening may be better balanced through a risk-stratified approach. We conducted a systematic review assessing the accuracy of questionnaire-based risk assessment tools for this purpose.

METHODS

Population: asymptomatic women aged ≥40 years; Intervention: questionnaire-based risk assessment tool (incorporating breast density and polygenic risk where available); Comparison: different tool applied to the same population; Primary outcome: breast cancer incidence; Scope: external validation studies identified from databases including Medline and Embase (period 1 January 2008-20 July 2021). We assessed calibration (goodness-of-fit) between expected and observed cancers and compared observed cancer rates by risk group. Risk of bias was assessed with PROBAST.

RESULTS

Of 5124 records, 13 were included examining 11 tools across 15 cohorts. The Gail tool was most represented (n = 11), followed by Tyrer-Cuzick (n = 5), BRCAPRO and iCARE-Lit (n = 3). No tool was consistently well-calibrated across multiple studies and breast density or polygenic risk scores did not improve calibration. Most tools identified a risk group with higher rates of observed cancers, but few tools identified lower-risk groups across different settings. All tools demonstrated a high risk of bias.

CONCLUSION

Some risk tools can identify groups of women at higher or lower breast cancer risk, but this is highly dependent on the setting and population.

Combining Breast Cancer Risk Prediction Models

Accurate risk stratification is key to reducing cancer morbidity through targeted screening and preventative interventions. Multiple breast cancer risk prediction models are used in clinical practice, and often provide a range of different predictions for the same patient. Integrating information from different models may improve the accuracy of predictions, which would be valuable for both clinicians and patients. BRCAPRO is a widely used model that predicts breast cancer risk based on detailed family history information. A major limitation of this model is that it does not consider non-genetic risk factors. To address this limitation, we expand BRCAPRO by combining it with another popular existing model, BCRAT (i.e., Gail), which uses a largely complementary set of risk factors, most of them non-genetic. We consider two approaches for combining BRCAPRO and BCRAT: (1) modifying the penetrance (age-specific probability of developing cancer given genotype) functions in BRCAPRO using relative hazard estimates from BCRAT, and (2) training an ensemble model that takes BRCAPRO and BCRAT predictions as input. Using both simulated data and data from Newton-Wellesley Hospital and the Cancer Genetics Network, we show that the combination models are able to achieve performance gains over both BRCAPRO and BCRAT. In the Cancer Genetics Network cohort, we show that the proposed BRCAPRO + BCRAT penetrance modification model performs comparably to IBIS, an existing model that combines detailed family history with non-genetic risk factors.

Association of Longitudinal Mammographic Breast Density Changes with Subsequent Breast Cancer Risk

Background Although Breast Imaging Reporting and Data System (BI-RADS) density classification has been used to assess future breast cancer risk, its reliability and validity are still debated in literature. Purpose To determine the association between overall longitudinal changes in mammographic breast density and breast cancer risk stratified by menopausal status. Materials and Methods In a retrospective cohort study using the Korean National Health Insurance Service database, women aged at least 40 years without a history of cancer who underwent three consecutive biennial mammographic screenings in 2009-2014 were followed up through December 2020. Participants were divided according to baseline breast density: fatty (BI-RADS categories a, b) versus dense (BI-RADS categories c, d) and then into subgroups on the basis of changes from the first to second and from second to third screenings. Women without change in breast density were used as the reference group. Main outcomes were incident breast cancer events, both invasive breast cancer and ductal carcinoma in situ. Cox proportion hazard regression was used to calculate the hazard ratio (HR) with adjustment for other covariables. Results Among 2 253 963 women (mean age, 59 years ± 9) there were 22 439 detected breast cancers. Premenopausal women with fatty breasts at the first screening had a higher risk of breast cancer as density increased in the second and third screenings (fatty-to-dense HR, 1.45 [95% CI: 1.27, 1.65]; dense-to-fatty HR, 1.53 [95% CI: 1.34, 1.74]; dense-to-dense HR, 1.93 [95% CI: 1.75, 2.13]). In premenopausal women with dense breasts at baseline, those in whom density continuously decreased had a 0.62-fold lower risk (95% CI: 0.56, 0.69). Similar results were observed in postmenopausal women, remaining significant after adjustment for baseline breast density or changes in body mass index (fatty-to-dense HR, 1.50 [95% CI: 1.39, 1.62]; dense-to-fatty HR, 1.42 [95% CI: 1.31, 1.53]; dense-to-dense HR, 1.62 [95% CI: 1.51, 1.75]). Conclusion In both premenopausal and postmenopausal women undergoing three consecutive biennial mammographic screenings, a consecutive increase in breast density augmented the future breast cancer risk whereas a continuous decrease was associated with a lower risk. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Kataoka et al in this issue.

Screening Programs for Breast Cancer: Toward Individualized, Risk-Adapted Strategies of Early Detection

Early detection of breast cancer (BC) comprises two approaches: screening of asymptomatic women in a specified target population at risk (usually a target age range for women at average risk), and early diagnosis for women with BC signs and symptoms. Screening for BC is a key health intervention for early detection. While population-based screening programs have been implemented for age-selected women, the pivotal clinical trials have not addressed the global utility nor the improvement of screening performance by utilizing more refined parameters for patient eligibility, such as individualized risk stratification. In addition, with the exception of the subset of women known to carry germline pathogenetic mutations in (high- or moderately-penetrant) cancer predisposition genes, such as BRCA1 and BRCA2, there has been less success in outreach and service provision for the unaffected relatives of women found to carry a high-risk mutation (i.e., "cascade testing") as it is in these individuals for whom such actionable information can result in cancers (and/or cancer deaths) being averted. Moreover, even in the absence of clinical cancer genetics services, as is the case for the immediate and at least near-term in most countries globally, the capacity to stratify the risk of an individual to develop BC has existed for many years, is available for free online at various sites/platforms, and is increasingly being validated for non-Caucasian populations. Ultimately, a precision approach to BC screening is largely missing. In the present chapter, we aim to address the concept of risk-adapted screening of BC, in multiple facets, and understand if there is a value in the implementation of adapted screening strategies in selected women, outside the established screening prescriptions, in the terms of age-range, screening modality and schedules of imaging.

Prospective validation of the BOADICEA multifactorial breast cancer risk prediction model in a large prospective cohort study

BACKGROUND

The multifactorial Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) breast cancer risk prediction model has been recently extended to consider all established breast cancer risk factors. We assessed the clinical validity of the model in a large independent prospective cohort.

METHODS

We validated BOADICEA (V.6) in the Swedish KARolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) cohort including 66 415 women of European ancestry (median age 54 years, IQR 45-63; 816 incident breast cancers) without previous cancer diagnosis. We calculated 5-year risks on the basis of questionnaire-based risk factors, pedigree-structured first-degree family history, mammographic density (BI-RADS), a validated breast cancer polygenic risk score (PRS) based on 313-SNPs, and pathogenic variant status in 8 breast cancer susceptibility genes: BRCA1, BRCA2, PALB2, CHEK2, ATM, RAD51C, RAD51D and BARD1. Calibration was assessed by comparing observed and expected risks in deciles of predicted risk and the calibration slope. The discriminatory ability was assessed using the area under the curve (AUC).

RESULTS

Among the individual model components, the PRS contributed most to breast cancer risk stratification. BOADICEA was well calibrated in predicting the risks for low-risk and high-risk women when all, or subsets of risk factors are included in the risk prediction. Discrimination was maximised when all risk factors are considered (AUC=0.70, 95% CI: 0.66 to 0.73; expected-to-observed ratio=0.88, 95% CI: 0.75 to 1.04; calibration slope=0.97, 95% CI: 0.95 to 0.99). The full multifactorial model classified 3.6% women as high risk (5-year risk ≥3%) and 11.1% as very low risk (5-year risk <0.33%).

CONCLUSION

The multifactorial BOADICEA model provides valid breast cancer risk predictions and a basis for personalised decision-making on disease prevention and screening.

Breast Cancer Screening: An Overview of Risk-specific Screening and Risk Assessment

Breast cancer screening decreases stage at diagnosis, treatment morbidity, and disease mortality. A comprehensive risk assessment is critical to determine an individual's most appropriate screening regimen. Multiple guidelines exist for screening mammography in average-risk individuals, which differ on age and frequency of screening. Annual mammography starting at age 40 is associated with the greatest reduction in breast cancer mortality and greatest number of life-years saved. A formal risk calculator is helpful to assess one's lifetime risk of breast cancer and assess eligibility for high-risk screening. Screening guidelines exist for genetic mutations that increase breast cancer risk.

Awareness of non-communicable diseases in women: a cross-sectional study

Chronic non-communicable diseases (NCD) are the major reason for death, morbidity, loss of independency and public health cost. NCD prevalence could be significantly reduced by adopting a healthy lifestyle. This cross-sectional cohort study (online survey) in 221 women aimed to assess NCD awareness, knowledge about NCD prevention and willingness to adopt a healthier lifestyle in women. Overall, NCD awareness level was quite high with, however, information mainly originating from lay media, probably being one reason for false estimations of age groups mainly affected by NCD, impact of NCD on quality of life, NCD mortalities, and the extent of NCD prevention by lifestyle interventions, respectively. Furthermore, also due to mainly lay media, half of women knew online NCD risk calculators, most of them would like to know their NCD risk, but only few had been offered NCD risk calculation by their physician. The mean threshold for willing to adopt a healthier lifestyle was a roughly calculated 37% 5-10 years risk to develop a certain NCD. Acceptance of non-pharmacological interventions for NCD prevention was high, however, major barriers for not implementing a healthier lifestyle were lack of expert information and lack of time. In conclusion, future public health strategies should focus on distributing better understandable and correct information about NCD as well as meeting the individuals' request for personalized NCD risk calculation. Furthermore, physicians should be better trained for personalized NCD prevention counseling.

Evaluating DNA Methylation in Random Fine Needle Aspirates from the Breast to Inform Cancer Risk

Introduction. Critical regulatory genes are functionally silenced by DNA hypermethylation in breast cancer and premalignant lesions. The objective of this study was to examine whether DNA methylation assessed in random fine needle aspirates (rFNA) can be used to inform breast cancer risk. Methods. In 20 women with invasive breast cancer scheduled for surgery at Johns Hopkins Hospital, cumulative methylation status was assessed in a comprehensive manner. rFNA was performed on tumors, adjacent normal tissues, and all remaining quadrants. Pathology review was conducted on blocks from all excised tissue. The cumulative methylation index (CMI) for 12 genes was assessed by a highly sensitive QM-MSP assay in 280 aspirates and tissue from 11 incidental premalignant lesions. Mann–Whitney and Kruskal Wallis tests were used to compare median CMI by patient, location, and tumor characteristics. Results. The median age of participants was 49 years (interquartile range [IQR]: 44–58). DNA methylation was detectable at high levels in all tumor aspirates (median CMI = 252, IQR: 75–111). Methylation was zero or low in aspirates from adjacent tissue (median CMI = 11, IQR: 0–13), and other quadrants (median CMI = 2, IQR: 1–5). Nineteen incidental lesions were identified in 13 women (4 malignant and 15 premalignant). Median CMI levels were not significantly different in aspirates from quadrants ( $p=0.43$ ) or adjacent tissue ( $p=0.93$ ) in which 11 methylated incidental lesions were identified. Conclusions. The diagnostic accuracy of methylation based on rFNA alone to detect premalignant lesions or at-risk quadrants is poor and therefore should not be used to evaluate cancer risk. A more targeted approach needs to be evaluated.

Deep Learning vs Traditional Breast Cancer Risk Models to Support Risk-Based Mammography Screening

BACKGROUND

Deep learning breast cancer risk models demonstrate improved accuracy compared with traditional risk models but have not been prospectively tested. We compared the accuracy of a deep learning risk score derived from the patient's prior mammogram to traditional risk scores to prospectively identify patients with cancer in a cohort due for screening.

METHODS

We collected data on 119 139 bilateral screening mammograms in 57 617 consecutive patients screened at 5 facilities between September 18, 2017, and February 1, 2021. Patient demographics were retrieved from electronic medical records, cancer outcomes determined through regional tumor registry linkage, and comparisons made across risk models using Wilcoxon and Pearson χ2 2-sided tests. Deep learning, Tyrer-Cuzick, and National Cancer Institute Breast Cancer Risk Assessment Tool (NCI BCRAT) risk models were compared with respect to performance metrics and area under the receiver operating characteristic curves.

RESULTS

Cancers detected per thousand patients screened were higher in patients at increased risk by the deep learning model (8.6, 95% confidence interval [CI] = 7.9 to 9.4) compared with Tyrer-Cuzick (4.4, 95% CI = 3.9 to 4.9) and NCI BCRAT (3.8, 95% CI = 3.3 to 4.3) models (P &lt; .001). Area under the receiver operating characteristic curves of the deep learning model (0.68, 95% CI = 0.66 to 0.70) was higher compared with Tyrer-Cuzick (0.57, 95% CI = 0.54 to 0.60) and NCI BCRAT (0.57, 95% CI = 0.54 to 0.60) models. Simulated screening of the top 50th percentile risk by the deep learning model captured statistically significantly more patients with cancer compared with Tyrer-Cuzick and NCI BCRAT models (P &lt; .001).

CONCLUSIONS

A deep learning model to assess breast cancer risk can support feasible and effective risk-based screening and is superior to traditional models to identify patients destined to develop cancer in large screening cohorts.

Adherence to the 2020 American Cancer Society Guideline for Cancer Prevention and risk of breast cancer for women at increased familial and genetic risk in the Breast Cancer Family Registry: an evaluation of the weight, physical activity, and alcohol consumption recommendations

PURPOSE

The American Cancer Society (ACS) published an updated Guideline for Cancer Prevention (ACS Guideline) in 2020. Research suggests that adherence to the 2012 ACS Guideline might lower breast cancer risk, but there is limited evidence that this applies to women at increased familial and genetic risk of breast cancer.

METHODS

Using the Breast Cancer Family Registry (BCFR), a cohort enriched for increased familial and genetic risk of breast cancer, we examined adherence to three 2020 ACS Guideline recommendations (weight management (body mass index), physical activity, and alcohol consumption) with breast cancer risk in 9615 women. We used Cox proportional hazard regression modeling to calculate hazard ratios (HRs) and 95% confidence intervals (CI) overall and stratified by BRCA1 and BRCA2 pathogenic variant status, family history of breast cancer, menopausal status, and estrogen receptor-positive (ER +) breast cancer.

RESULTS

We observed 618 incident invasive or in situ breast cancers over a median 12.9 years. Compared with being adherent to none (n = 55 cancers), being adherent to any ACS recommendation (n = 563 cancers) was associated with a 27% lower breast cancer risk (HR = 0.73, 95% CI: 0.55-0.97). This was evident for women with a first-degree family history of breast cancer (HR = 0.68, 95% CI: 0.50-0.93), women without BRCA1 or BRCA2 pathogenic variants (HR = 0.71, 95% CI: 0.53-0.95), postmenopausal women (HR = 0.63, 95% CI: 0.44-0.89), and for risk of ER+ breast cancer (HR = 0.63, 95% CI: 0.40-0.98).

DISCUSSION

Adherence to the 2020 ACS Guideline recommendations for BMI, physical activity, and alcohol consumption could reduce breast cancer risk for postmenopausal women and women at increased familial risk.

Comprehensive epithelial tubo-ovarian cancer risk prediction model incorporating genetic and epidemiological risk factors

BACKGROUND

Epithelial tubo-ovarian cancer (EOC) has high mortality partly due to late diagnosis. Prevention is available but may be associated with adverse effects. A multifactorial risk model based on known genetic and epidemiological risk factors (RFs) for EOC can help identify women at higher risk who could benefit from targeted screening and prevention.

METHODS

We developed a multifactorial EOC risk model for women of European ancestry incorporating the effects of pathogenic variants (PVs) in BRCA1, BRCA2, RAD51C, RAD51D and BRIP1, a Polygenic Risk Score (PRS) of arbitrary size, the effects of RFs and explicit family history (FH) using a synthetic model approach. The PRS, PV and RFs were assumed to act multiplicatively.

RESULTS

Based on a currently available PRS for EOC that explains 5% of the EOC polygenic variance, the estimated lifetime risks under the multifactorial model in the general population vary from 0.5% to 4.6% for the first to 99th percentiles of the EOC risk distribution. The corresponding range for women with an affected first-degree relative is 1.9%-10.3%. Based on the combined risk distribution, 33% of RAD51D PV carriers are expected to have a lifetime EOC risk of less than 10%. RFs provided the widest distribution, followed by the PRS. In an independent partial model validation, absolute and relative 5-year risks were well calibrated in quintiles of predicted risk.

CONCLUSION

This multifactorial risk model can facilitate stratification, in particular among women with FH of cancer and/or moderate-risk and high-risk PVs. The model is available via the CanRisk Tool (www.canrisk.org).

Risk-Adjusted Cancer Screening and Prevention (RiskAP): Complementing Screening for Early Disease Detection by a Learning Screening Based on Risk Factors

BACKGROUND

Risk-adjusted cancer screening and prevention is a promising and continuously emerging option for improving cancer prevention. It is driven by increasing knowledge of risk factors and the ability to determine them for individual risk prediction. However, there is a knowledge gap between evidence of increased risk and evidence of the effectiveness and efficiency of clinical preventive interventions based on increased risk. This gap is, in particular, aggravated by the extensive availability of genetic risk factor diagnostics, since the question of appropriate preventive measures immediately arises when an increased risk is identified. However, collecting proof of effective preventive measures, ideally by prospective randomized preventive studies, typically requires very long periods of time, while the knowledge about an increased risk immediately creates a high demand for action.

SUMMARY

Therefore, we propose a risk-adjusted prevention concept that is based on the best current evidence making needed and appropriate preventive measures available, and which is constantly evaluated through outcome evaluation, and continuously improved based on these results. We further discuss the structural and procedural requirements as well as legal and socioeconomical aspects relevant for the implementation of this concept.

Development of a risk-stratification scoring system for predicting risk of breast cancer based on non-alcoholic fatty liver disease, non-alcoholic fatty pancreas disease, and uric acid

Many breast cancer patients have both non-alcoholic fatty liver disease (NAFLD) and non-alcoholic fatty pancreas disease (NAFPD). Consequently, we hypothesized that NAFPD and NAFLD were associated with breast cancer, and aimed to build a novel risk-stratification scoring system based on it. In this study, a total of 961 patients with breast cancer and 1,006 non-cancer patients were recruited. The clinical characteristics were collected and analyzed using logistic analysis. Risk factors were assessed by a risk rating system. Univariate analysis showed that body mass index, triglyceride, total cholesterol, NAFLD, NAFPD, low-density lipoprotein, and uric acid (UA) were significantly related to breast cancer. Among them, NAFLD, NAFPD, and UA were independent risk factors related to breast cancer identified by multivariate analysis. The risk assessment model was established based on these factors and demonstrated that the odds ratio sharply increased with the rising scores. Compared with the low-risk group, the odds ratio in the intermediate- and high-risk groups were 1.662 (1.380–2.001) and 3.185 (2.145–4.728), respectively. In conclusion, the risk-stratification scoring system combining NAFLD, NAFPD, and UA can accurately predict the occurrence of breast cancer.

The current status of risk-stratified breast screening

Apart from high-risk scenarios such as the presence of highly penetrant genetic mutations, breast screening typically comprises mammography or tomosynthesis strategies defined by age. However, age-based screening ignores the range of breast cancer risks that individual women may possess and is antithetical to the ambitions of personalised early detection. Whilst screening mammography reduces breast cancer mortality, this is at the risk of potentially significant harms including overdiagnosis with overtreatment, and psychological morbidity associated with false positives. In risk-stratified screening, individualised risk assessment may inform screening intensity/interval, starting age, imaging modality used, or even decisions not to screen. However, clear evidence for its benefits and harms needs to be established. In this scoping review, the authors summarise the established and emerging evidence regarding several critical dependencies for successful risk-stratified breast screening: risk prediction model performance, epidemiological studies, retrospective clinical evaluations, health economic evaluations and qualitative research on feasibility and acceptability. Family history, breast density or reproductive factors are not on their own suitable for precisely estimating risk and risk prediction models increasingly incorporate combinations of demographic, clinical, genetic and imaging-related parameters. Clinical evaluations of risk-stratified screening are currently limited. Epidemiological evidence is sparse, and randomised trials only began in recent years.

[Non-genetic indications for risk reducing mastectomies: Guidelines of the National College of French Gynecologists and Obstetricians (CNGOF)]

OBJECTIVE

To determine the value of performing a risk-reducting mastectomy (RRM) in the absence of a deleterious variant of a breast cancer susceptibility gene, in 4 clinical situations at risk of breast cancer.

DESIGN

The CNGOF Commission of Senology, composed of 26 experts, developed these recommendations. A policy of declaration and monitoring of links of interest was applied throughout the process of making the recommendations. Similarly, the development of these recommendations did not benefit from any funding from a company marketing a health product. The Commission of Senology adhered to the AGREE II (Advancing guideline development, reporting and evaluation in healthcare) criteria and followed the Grading of Recommendations Assessment, Development and Evaluation (GRADE) method to assess the quality of the evidence on which the recommendations were based. The potential drawbacks of making recommendations in the presence of poor quality or insufficient evidence were highlighted.

METHODS

The Commission of Senology considered 8 questions on 4 topics, focusing on histological, familial (no identified genetic abnormality), radiological (of unrecognized cancer), and radiation (history of Hodgkin's disease) risk. For each situation, it was determined whether performing RRM compared with surveillance would decrease the risk of developing breast cancer and/or increase survival.

RESULTS

The Commission of Senology synthesis and application of the GRADE method resulted in 11 recommendations, 6 with a high level of evidence (GRADE 1±) and 5 with a low level of evidence (GRADE 2±).

CONCLUSION

There was significant agreement among the Commission of Senology members on recommendations to improve practice for performing or not performing RRM in the clinical setting.

Prospective evaluation of a breast-cancer risk model integrating classical risk factors and polygenic risk in 15 cohorts from six countries

BACKGROUND

Rigorous evaluation of the calibration and discrimination of breast-cancer risk-prediction models in prospective cohorts is critical for applications under clinical guidelines. We comprehensively evaluated an integrated model incorporating classical risk factors and a 313-variant polygenic risk score (PRS) to predict breast-cancer risk.

METHODS

Fifteen prospective cohorts from six countries with 239 340 women (7646 incident breast-cancer cases) of European ancestry aged 19-75 years were included. Calibration of 5-year risk was assessed by comparing expected and observed proportions of cases overall and within risk categories. Risk stratification for women of European ancestry aged 50-70 years in those countries was evaluated by the proportion of women and future cases crossing clinically relevant risk thresholds.

RESULTS

Among women <50 years old, the median (range) expected-to-observed ratio for the integrated model across 15 cohorts was 0.9 (0.7-1.0) overall and 0.9 (0.7-1.4) at the highest-risk decile; among women ≥50 years old, these were 1.0 (0.7-1.3) and 1.2 (0.7-1.6), respectively. The proportion of women identified above a 3% 5-year risk threshold (used for recommending risk-reducing medications in the USA) ranged from 7.0% in Germany (∼841 000 of 12 million) to 17.7% in the USA (∼5.3 of 30 million). At this threshold, 14.7% of US women were reclassified by adding the PRS to classical risk factors, with identification of 12.2% of additional future cases.

CONCLUSION

Integrating a 313-variant PRS with classical risk factors can improve the identification of European-ancestry women at elevated risk who could benefit from targeted risk-reducing strategies under current clinical guidelines.

Validation of Breast Cancer Risk Models by Race/Ethnicity, Family History and Molecular Subtypes

(1) Background: The purpose of this study is to compare the performance of four breast cancer risk prediction models by race, molecular subtype, family history of breast cancer, age, and BMI. (2) Methods: Using a cohort of women aged 40-84 without prior history of breast cancer who underwent screening mammography from 2006 to 2015, we generated breast cancer risk estimates using the Breast Cancer Risk Assessment tool (BCRAT), BRCAPRO, Breast Cancer Surveillance Consortium (BCSC) and combined BRCAPRO+BCRAT models. Model calibration and discrimination were compared using observed-to-expected ratios (O/E) and the area under the receiver operator curve (AUC) among patients with at least five years of follow-up. (3) Results: We observed comparable discrimination and calibration across models. There was no significant difference in model performance between Black and White women. Model discrimination was poorer for HER2+ and triple-negative subtypes compared with ER/PR+HER2-. The BRCAPRO+BCRAT model displayed improved calibration and discrimination compared to BRCAPRO among women with a family history of breast cancer. Across models, discriminatory accuracy was greater among obese than non-obese women. When defining high risk as a 5-year risk of 1.67% or greater, models demonstrated discordance in 2.9% to 19.7% of patients. (4) Conclusions: Our results can inform the implementation of risk assessment and risk-based screening among women undergoing screening mammography.

Recreational Physical Activity and Outcomes After Breast Cancer in Women at High Familial Risk

Background

Recreational physical activity (RPA) is associated with improved survival after breast cancer (BC) in average-risk women, but evidence is limited for women who are at increased familial risk because of a BC family history or BRCA1 and BRCA2 pathogenic variants (BRCA1/2 PVs).

Methods

We estimated associations of RPA (self-reported average hours per week within 3 years of BC diagnosis) with all-cause mortality and second BC events (recurrence or new primary) after first invasive BC in women in the Prospective Family Study Cohort (n = 4610, diagnosed 1993-2011, aged 22-79 years at diagnosis). We fitted Cox proportional hazards regression models adjusted for age at diagnosis, demographics, and lifestyle factors. We tested for multiplicative interactions (Wald test statistic for cross-product terms) and additive interactions (relative excess risk due to interaction) by age at diagnosis, body mass index, estrogen receptor status, stage at diagnosis, BRCA1/2 PVs, and familial risk score estimated from multigenerational pedigree data. Statistical tests were 2-sided.

Results

We observed 1212 deaths and 473 second BC events over a median follow-up from study enrollment of 11.0 and 10.5 years, respectively. After adjusting for covariates, RPA (any vs none) was associated with lower all-cause mortality of 16.1% (95% confidence interval [CI] = 2.4% to 27.9%) overall, 11.8% (95% CI = -3.6% to 24.9%) in women without BRCA1/2 PVs, and 47.5% (95% CI = 17.4% to 66.6%) in women with BRCA1/2 PVs (RPA*BRCA1/2 multiplicative interaction P = .005; relative excess risk due to interaction = 0.87, 95% CI = 0.01 to 1.74). RPA was not associated with risk of second BC events.

Conclusion

Findings support that RPA is associated with lower all-cause mortality in women with BC, particularly in women with BRCA1/2 PVs.

Breast cancer risk reduction: who, why, and what?

Women at increased risk of breast cancer have options to mitigate that risk. Understanding factors that increase risk and utilizing tools for quantitative estimates are important to be able to adequately counsel and target strategies for patients. On the basis of these estimates, patients may be able to engage in risk reduction interventions and increased screening, including chemoprevention or surgical risk reduction. Multiple organizations have published guidelines supporting risk assessment, genetic assessment, increased screening, and prevention measures for these women.

A Validated Risk Prediction Model for Breast Cancer in US Black Women

PURPOSE

Breast cancer risk prediction models are used to identify high-risk women for early detection, targeted interventions, and enrollment into prevention trials. We sought to develop and evaluate a risk prediction model for breast cancer in US Black women, suitable for use in primary care settings.

METHODS

Breast cancer relative risks and attributable risks were estimated using data from Black women in three US population-based case-control studies (3,468 breast cancer cases; 3,578 controls age 30-69 years) and combined with SEER age- and race-specific incidence rates, with incorporation of competing mortality, to develop an absolute risk model. The model was validated in prospective data among 51,798 participants of the Black Women's Health Study, including 1,515 who developed invasive breast cancer. A second risk prediction model was developed on the basis of estrogen receptor (ER)-specific relative risks and attributable risks. Model performance was assessed by calibration (expected/observed cases) and discriminatory accuracy (C-statistic).

RESULTS

The expected/observed ratio was 1.01 (95% CI, 0.95 to 1.07). Age-adjusted C-statistics were 0.58 (95% CI, 0.56 to 0.59) overall and 0.63 (95% CI, 0.58 to 0.68) among women younger than 40 years. These measures were almost identical in the model based on estrogen receptor-specific relative risks and attributable risks.

CONCLUSION

Discriminatory accuracy of the new model was similar to that of the most frequently used questionnaire-based breast cancer risk prediction models in White women, suggesting that effective risk stratification for Black women is now possible. This model may be especially valuable for risk stratification of young Black women, who are below the ages at which breast cancer screening is typically begun.

Prospective Evaluation over 15 Years of Six Breast Cancer Risk Models

Prospective validation of risk models is needed to assess their clinical utility, particularly over the longer term. We evaluated the performance of six commonly used breast cancer risk models (IBIS, BOADICEA, BRCAPRO, BRCAPRO-BCRAT, BCRAT, and iCARE-lit). 15-year risk scores were estimated using lifestyle factors and family history measures from 7608 women in the Melbourne Collaborative Cohort Study who were aged 50-65 years and unaffected at commencement of follow-up two (conducted in 2003-2007), of whom 351 subsequently developed breast cancer. Risk discrimination was assessed using the C-statistic and calibration using the expected/observed number of incident cases across the spectrum of risk by age group (50-54, 55-59, 60-65 years) and family history of breast cancer. C-statistics were higher for BOADICEA (0.59, 95% confidence interval (CI) 0.56-0.62) and IBIS (0.57, 95% CI 0.54-0.61) than the other models (p-difference ≤ 0.04). No model except BOADICEA calibrated well across the spectrum of 15-year risk (p-value < 0.03). The performance of BOADICEA and IBIS was similar across age groups and for women with or without a family history. For middle-aged Australian women, BOADICEA and IBIS had the highest discriminatory accuracy of the six risk models, but apart from BOADICEA, no model was well-calibrated across the risk spectrum.

Risk prediction models for esophageal cancer: A systematic review and critical appraisal

BACKGROUND AND AIMS

Esophageal cancer risk prediction models allow for risk-stratified endoscopic screening. We aimed to assess the quality of these models developed in the general population.

METHODS

A systematic search of the PubMed and Embase databases from January 2000 through May 2021 was performed. Studies that developed or validated a model of esophageal cancer in the general population were included. Screening, data extraction, and risk of bias (ROB) assessment by the Prediction model Risk Of Bias Assessment Tool (PROBAST) were performed independently by two reviewers.

RESULTS

Of the 13 models included in the qualitative analysis, 8 were developed for esophageal squamous cell carcinoma (ESCC) and the other 5 were developed for esophageal adenocarcinoma (EAC). Only two models conducted external validation. In the ESCC models, cigarette smoking was included in each model, followed by age, sex, and alcohol consumption. For EAC models, cigarette smoking and body mass index were included in each model, and gastroesophageal reflux disease, uses of acid-suppressant medicine, and nonsteroidal anti-inflammatory drug were exclusively included. The discriminative performance was reported in all studies, with C statistics ranging from 0.71 to 0.88, whereas only six models reported calibration. For ROB, all the models had a low risk in participant and outcome, but all models showed high risk in analysis, and 60% of models showed a high risk in predictors, which resulted in all models being classified as having overall high ROB. For model applicability, about 60% of these models had an overall low risk, with 30% of models of high risk and 10% of models of unclear risk, concerning the assessment of participants, predictors, and outcomes.

CONCLUSIONS

Most current risk prediction models of esophageal cancer have a high ROB. Prediction models need further improvement in their quality and applicability to benefit esophageal cancer screening.