Breast cancer risk models: a comprehensive overview of existing models, validation, and clinical applications

Comparison of Machine Learning Models for Classification of Breast Cancer Risk Based on Clinical Data

BACKGROUND

Breast cancer (BC) is a major global health concern with rising incidence and mortality rates in many developing countries. Effective BC risk assessment models are crucial for prevention and early detection. While the Gail model, a traditional logistic regression-based model, has been broadly used, its predictive performance may be limited by its linear assumptions. With the rapid advancement of artificial intelligence (AI) in medical sciences, various complex machine learning algorithms have been developed for risk prediction, including for BC.

AIMS

This study aims to compare the quality of AI-based models with the traditional Gail model in assessing BC risk using a population dataset. It also evaluates the performance of these models in predicting BC risk.

METHODS AND RESULTS

This study involved 942 newly diagnosed BC patients and 975 healthy controls at the Cancer Institute in IKH hospital Complex, Tehran. Ten classification algorithms were applied to the dataset. The accuracy, sensitivity, precision, and feature importance in the machine learning algorithms were assessed and compared to previous studies for evaluation. The study found that AI algorithms alone did not significantly improve predictability compared to the Gail model. However, the importance of variables varied significantly among the AI algorithms. Understanding feature importance and interactions is crucial in AI modeling in order to enhance accuracy and identify critical risk factors.

CONCLUSION

This study concluded that, in BC risk prediction, incorporating specific risk factors, such as genetic and image-related variables, may be necessary to further enhance accuracy in BC risk prediction models. Furthermore, it is crucial to address modeling issues in models with a restricted number of features for future research.

Short-term Patient-Reported Outcomes Following Bilateral Risk-Reducing Mastectomy for Patients at a High Risk for Breast Cancer: A Systematic Review

BACKGROUND

Bilateral risk-reducing mastectomies (RRMs) have been proven to decrease the risk of breast cancer in patients at high risk owing to family history or having pathogenic genetic mutations. However, few resources with consolidated data have detailed the patient experience following surgery. This systematic review features patient-reported outcomes for patients with no breast cancer history in the year after their bilateral RRM.

METHODS

The databases MEDLINE, Embase, and Scopus were used to identify studies. Studies were then evaluated by multiple authors, and their quality was assessed by using the Methodological Index for Non-Randomized Studies score.

RESULTS

Our search identified 1858 unique studies, of which 11 met our inclusion criteria. Only two of these studies included patients who did not receive postmastectomy reconstruction. The included studies were either retrospective cohort studies or prospective studies. General satisfaction with the outcome of RRM and the decision to undergo RRM was high across many of the studies, with low levels of regret. There was also a noticeable trend of improved psychosocial outcomes following RRM. For postoperative sexual well-being, body image, aesthetic satisfactions, and somatosensory function, there were a mix of positive and negative outcomes.

CONCLUSIONS

The patients who elected to manage their breast cancer risk with bilateral RRM (mostly with reconstruction) tend to be satisfied with their decision and the surgical outcomes. This may be related to decreased cancer-related anxiety. Postmastectomy psychosocial well-being tends to improve while physical health after surgery varies by patient.

Hereditary Breast Cancer: Comprehensive Risk Assessment and Prevention Strategies

Women carrying pathogenic/likely pathogenic (P/LP) variants in moderate- or high-penetrance genes have an increased risk of developing breast cancer. However, most P/LP variants associated with breast cancer risk show incomplete penetrance. Age, gender, family history, polygenic risk, lifestyle, reproductive, hormonal, and environmental factors can affect the expressivity and penetrance of the disease. However, there are gaps in translating how individual genomic variation affects phenotypic presentation. The expansion of criteria for genetic testing and the increasing utilization of comprehensive genetic panels may enhance the identification of individuals carrying P/LP variants linked to hereditary breast cancer. Individualized risk assessment could facilitate the implementation of personalized risk-reduction strategies for these individuals. Preventive interventions encompass lifestyle modifications, chemoprevention, enhanced surveillance through breast imaging, and risk-reducing surgeries. This review addresses the current literature's inconsistencies and limitations, particularly regarding risk factors and the intensity of preventive strategies for women with P/LP variants in moderate- and high-penetrance genes. In addition, it synthesizes the latest evidence on risk assessment and primary and secondary prevention in women at high risk of breast cancer.

Germline genetic mutations in a multi-center cohort of 248 phyllodes tumors

PURPOSE

Germline genetic mutations in women with phyllodes tumors (PT) are understudied, although some describe associations of PT with various mutations. We sought to determine the prevalence of pathogenic/likely pathogenic (P/LP) variants in women with PT.

METHODS

A 6-site multi-center study of women with a PT was initiated, then expanded nationally through an online "Phyllodes Support Group." All women underwent 84-gene panel testing. We defined eligibility for testing based on select NCCN (National Comprehensive Cancer Network) criteria (v1.2022). Logistic regression was used to estimate the association of covariates with the likelihood of a P/LP variant.

RESULTS

274 women were enrolled: 164 (59.9%) through multi-center recruitment and 110 (40.1%) via online recruitment. 248 women completed testing; overall 14.1% (N = 35) had a P/LP variant, and over half (N = 19) of these individuals had a mutation in genes associated with autosomal dominant (AD) cancer conditions. The most common AD genes with a P/LP variant included CHEK2, ATM, and RAD51D. A quarter of participants (23.8%) met NCCN criteria for testing, but we found no difference in prevalence of a P/LP variant based on eligibility (p = 0.54). After adjustment, the presence of P/LP variants was not associated with age, NCCN testing eligibility, or PT type (all p > 0.05).

CONCLUSION

Our study demonstrates that 7.7% of women with PT harbor germline P/LP variants in genes associated with AD cancer conditions. Early identification of these variants has implications for screening, risk reduction, and/or treatment. National guidelines for women with PT do not currently address germline genetic testing, which could be considered.

Clinical genetics in breast cancer

As genetic testing becomes increasingly more accessible and more applicable with a broader range of clinical implications, it may also become more challenging for breast cancer providers to remain up-to-date. This review outlines some of the current clinical guidelines and recent literature surrounding germline genetic testing, as well as genomic testing, in the screening, prevention, diagnosis, and treatment of breast cancer, while identifying potential areas of further research.

Association Between Polymorphisms in DNA Damage Repair Pathway Genes and Female Breast Cancer Risk

Breast cancer risk have been discussed to be associated with polymorphisms in genes as well as abnormal DNA damage repair function. This study aims to assess the relationship between genes single nucleotide polymorphisms (SNPs) related to DNA damage repair and female breast cancer risk in Chinese population. A case-control study containing 400 patients and 400 healthy controls was conducted. Genotype was identified using the sequence MassARRAY method and expression of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor-2 (HER-2) in tumor tissues was analyzed by immunohistochemistry assay. The results revealed that ATR rs13091637 decreased breast cancer risk influenced by ER, PR (CT/TT vs. CC: adjusted odds ratio [OR] = 1.54, 95% confidence interval [CI]: 1.04-2.27, p = 0.032; CT/TT vs. CC: adjusted OR = 1.63, 95%CI: 1.14-2.35, p = 0.008) expression. Stratified analysis revealed that PALB2 rs16940342 increased breast cancer risk in response to menstrual status (AG/GG vs. AA: adjusted OR = 1.72, 95%CI: 1.13-2.62, p = 0.011) and age of menarche (AG/GG vs. AA: adjusted OR = 1.54, 95%CI: 1.03-2.31, p = 0.037), whereas ATM rs611646 and Ku70 rs132793 were associated with reduced breast cancer risk influenced by menarche (GA/AA vs. GG: adjusted OR = 0.50, 95%CI: 0.30-0.95, p = 0.033). In a summary, PALB2 rs16940342, ATR rs13091637, ATM rs611646, and Ku70 rs132793 were associated with breast cancer risk.

The Prognostic Quality of Risk Prediction Models to Assess the Individual Breast Cancer Risk in Women: An Overview of Reviews

Purpose

Breast cancer is the most common cancer among women globally, with an incidence of approximately two million cases in 2018. Organised age-based breast cancer screening programs were established worldwide to detect breast cancer earlier and to reduce mortality. Currently, there is substantial anticipation regarding risk-adjusted screening programs, considering various risk factors in addition to age. The present study investigated the discriminatory accuracy of breast cancer risk prediction models and whether they suit risk-based screening programs.

Methods

Following the PICO scheme, we conducted an overview of reviews and systematically searched four databases. All methodological steps, including the literature selection, data extraction and synthesis, and the quality appraisal were conducted following the 4-eyes principle. For the quality assessment, the AMSTAR 2 tool was used.

Results

We included eight systematic reviews out of 833 hits based on the prespecified inclusion criteria. The eight systematic reviews comprised ninety-nine primary studies that were also considered for the data analysis. Three systematic reviews were assessed as having a high risk of bias, while the others were rated with a moderate or low risk of bias. Most identified breast cancer risk prediction models showed a low prognostic quality. Adding breast density and genetic information as risk factors only moderately improved the models' discriminatory accuracy.

Conclusion

All breast cancer risk prediction models published to date show a limited ability to predict the individual breast cancer risk in women. Hence, it is too early to implement them in national breast cancer screening programs. Relevant randomised controlled trials about the benefit-harm ratio of risk-adjusted breast cancer screening programs compared to conventional age-based programs need to be awaited.

Extending the Breast Cancer Surveillance Consortium Model of Invasive Breast Cancer

PURPOSE

We extended the Breast Cancer Surveillance Consortium (BCSC) version 2 (v2) model of invasive breast cancer risk to include BMI, extended family history of breast cancer, and age at first live birth (version 3 [v3]) to better inform appropriate breast cancer prevention therapies and risk-based screening.

METHODS

We used Cox proportional hazards regression to estimate the age- and race- and ethnicity-specific relative hazards for family history of breast cancer, breast density, history of benign breast biopsy, BMI, and age at first live birth for invasive breast cancer in the BCSC cohort. We evaluated calibration using the ratio of expected-to-observed (E/O) invasive breast cancers in the cohort and discrimination using the area under the receiver operating characteristic curve (AUROC).

RESULTS

We analyzed data from 1,455,493 women age 35-79 years without a history of breast cancer. During a mean follow-up of 7.3 years, 30,266 women were diagnosed with invasive breast cancer. The BCSC v3 model had an E/O of 1.03 (95% CI, 1.01 to 1.04) and an AUROC of 0.646 for 5-year risk. Compared with the v2 model, discrimination of the v3 model improved most in Asian, White, and Black women. Among women with a BMI of 30.0-34.9 kg/m2, the true-positive rate in women with an estimated 5-year risk of 3% or higher increased from 10.0% (v2) to 19.8% (v3) and the improvement was greater among women with a BMI of ≥35 kg/m2 (7.6%-19.8%).

CONCLUSION

The BCSC v3 model updates an already well-calibrated and validated breast cancer risk assessment tool to include additional important risk factors. The inclusion of BMI was associated with the largest improvement in estimated risk for individual women.

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

BACKGROUND

To support mammography screening decision making, we developed a competing-risk model to estimate 5-year breast cancer risk and 10-year nonbreast cancer death for women aged 55 years and older using Nurses' Health Study data and examined model performance in the Black Women's Health Study (BWHS). Here, we examine model performance in predicting 10-year outcomes in the BWHS, Women's Health Initiative-Extension Study (WHI-ES), and Multiethnic Cohort (MEC) and compare model performance to existing breast cancer prediction models.

METHODS

We used competing-risk regression and Royston and Altman methods for validating survival models to calculate our model's calibration and discrimination (C index) in BWHS (n = 17 380), WHI-ES (n = 106 894), and MEC (n = 49 668). The Nurses' Health Study development cohort (n = 48 102) regression coefficients were applied to the validation cohorts. We compared our model's performance with breast cancer risk assessment tool (Gail) and International Breast Cancer Intervention Study (IBIS) models by computing breast cancer risk estimates and C statistics.

RESULTS

When predicting 10-year breast cancer risk, our model's C index was 0.569 in BWHS, 0.572 in WHI-ES, and 0.576 in MEC. The Gail model's C statistic was 0.554 in BWHS, 0.564 in WHI-ES, and 0.551 in MEC; IBIS's C statistic was 0.547 in BWHS, 0.552 in WHI-ES, and 0.562 in MEC. The Gail model underpredicted breast cancer risk in WHI-ES; IBIS underpredicted breast cancer risk in WHI-ES and in MEC but overpredicted breast cancer risk in BWHS. Our model calibrated well. Our model's C index for predicting 10-year nonbreast cancer death was 0.760 in WHI-ES and 0.763 in MEC.

CONCLUSIONS

Our competing-risk model performs as well as existing breast cancer prediction models in diverse cohorts and predicts nonbreast cancer death. We are developing a website to disseminate our model.

Development, Usability Testing, and Implementation Assessment of Cancer Related Infertility Score Predictor, an Online Cancer Related Infertility Risk Counseling Tool

Purpose: Oncofertility counseling of female cancer patients lacks efficient access to tailored and valid infertility risk estimates to support shared decision-making on fertility preservation treatments. The objective was to develop, conduct user-centered design, and plan clinic-based implementation of the Cancer Related Infertility Score Predictor (CRISP), a web-based tool to support infertility risk counseling. Methods: Using a mixed methods design, literature review was undertaken to abstract data on infertility, primary ovarian insufficiency, and amenorrhea risks of common cancer treatments. The CRISP website was programmed to take user input about patient ages and cancer treatments and generate a risk summary. Using user experience methodology and semistructured interviews, usability testing and implementation assessment were conducted with 12 providers recruited from 5 medical centers in Southern California. Results: The web-based CRISP tool encompasses infertility risk data for 60 treatment regimens among 10 cancer types. Usability testing demonstrated that the tool is intuitive and informed minor modifications, including adding crowd-sourced submission of additional cancer treatments. Participants rated the tool as credible, advantageous over current provider methods to ascertain infertility risks, and useful for tailoring treatment planning and counseling patients. A key barrier was lack of information on some cancer treatments. Fit within clinical workflow was feasible, particularly with electronic health record integration. Conclusions: The novel, web-based CRISP tool is a feasible, acceptable, and appropriate tool to address provider knowledge gap about cancer related infertility risks and use for patient counseling. CRISP has significant potential to support tailored oncofertility counseling in the heterogeneous young cancer patient population.

Individualizing Breast Cancer Risk Assessment in Clinical Practice

Multiple tools exist to assess a patient's breast cancer risk. The choice of risk model depends on the patient's risk factors and how the calculation will impact care. High-risk patients-those with a lifetime breast cancer risk of ≥20%-are, for instance, eligible for supplemental screening with breast magnetic resonance imaging. Those with an elevated short-term breast cancer risk (frequently defined as a 5-year risk ≥1.66%) should be offered endocrine prophylaxis. High-risk patients should also receive guidance on modification of lifestyle factors that affect breast cancer risk.

Early breast cancer risk detection: a novel framework leveraging polygenic risk scores and machine learning

BACKGROUND

Breast cancer (BC) is the most common cancer and the second leading cause of cancer death in women; an estimated one in eight women in the USA will develop BC during her lifetime. However, current methods of BC screening, including clinical breast exams, mammograms, biopsies and others, are often underused due to limited access, expense and a lack of risk awareness, causing 30% (up to 80% in low-income and middle-income countries) of patients with BC to miss the precious early detection phase.

METHODS

This study creates a key step to supplement the current BC diagnostic pipeline: a prescreening platform, prior to traditional detection and diagnostic steps. We have developed BREast CAncer Risk Detection Application (BRECARDA), a novel framework that personalises BC risk assessment using artificial intelligence neural networks to incorporate relevant genetic and non-genetic risk factors. A polygenic risk score (PRS) was enhanced by employing AnnoPred and validated by fivefolds cross-validation, outperforming three existing state-of-the-art PRS methods.

RESULTS

We used data from 97 597 female participants of the UK BioBank to train our algorithm. Using the enhanced PRS thus trained together with non-genetic information, BRECARDA was evaluated in a testing dataset with 48 074 UK Biobank female participants and achieved a high accuracy of 94.28% and area under the curve of 0.7861. Our optimised AnnoPred outperformed other state-of-the-art methods on quantifying genetic risk, indicating its potential for supplementing the current BC detection tests, population screening and risk evaluation.

CONCLUSION

BRECARDA can enhance disease risk prediction, identify high-risk individuals for BC screening, facilitate disease diagnosis and improve population-level screening efficiency. It can serve as a valuable and supplemental platform to assist doctors in BC diagnosis and evaluation.

Breast Cancer in Dense Breasts: Detection Challenges and Supplemental Screening Opportunities

Dense breast tissue at mammography is associated with higher breast cancer incidence and mortality rates, which have prompted new considerations for breast cancer screening in women with dense breasts. The authors review the definition and classification of breast density, density assessment methods, breast cancer risk, current legislation, and future efforts and summarize trials and key studies that have affected the existing guidelines for supplemental screening. Cases of breast cancer in dense breasts are presented, highlighting a variety of modalities and specific imaging findings that can aid in cancer detection and staging. Understanding the current state of breast cancer screening in patients with dense breasts and its challenges is important to shape future considerations for care. Shifting the paradigm of breast cancer detection toward early diagnosis for women with dense breasts may be the answer to reducing the number of deaths from this common disease. ©RSNA, 2023 Online supplemental material is available for this article. Quiz questions for this article are available through the Online Learning Center. See the invited commentary by Yeh in this issue.

Breast cancer risk stratification using genetic and non-genetic risk assessment tools for 246,142 women in the UK Biobank

PURPOSE

The benefit of using individual risk prediction tools to identify high-risk individuals for breast cancer (BC) screening is uncertain, despite the personalized approach of risk-based screening.

METHODS

We studied the overlap of predicted high-risk individuals among 246,142 women enrolled in the UK Biobank. Risk predictors assessed include the Gail model (Gail), BC family history (FH, binary), BC polygenic risk score (PRS), and presence of loss-of-function (LoF) variants in BC predisposition genes. Youden J-index was used to select optimal thresholds for defining high-risk.

RESULTS

In total, 147,399 were considered at high risk for developing BC within the next 2 years by at least 1 of the 4 risk prediction tools examined (Gail2-year > 0.5%: 47%, PRS2-yea r > 0.7%: 30%, FH: 6%, and LoF: 1%); 92,851 (38%) were flagged by only 1 risk predictor. The overlap between individuals flagged as high-risk because of genetic (PRS) and Gail model risk factors was 30%. The best-performing combinatorial model comprises a union of high-risk women identified by PRS, FH, and, LoF (AUC2-year [95% CI]: 62.2 [60.8 to 63.6]). Assigning individual weights to each risk prediction tool increased discriminatory ability.

CONCLUSION

Risk-based BC screening may require a multipronged approach that includes PRS, predisposition genes, FH, and other recognized risk factors.

Risk prediction models for antineoplastic-associated cardiotoxicity in treatment of breast cancer: A systematic review

PURPOSE

The objective of this systematic review is to assess methodology of published models to predict the risk of antineoplastic-associated cardiotoxicity in patients with breast cancer.

METHODS

We searched PubMed and Embase for studies that developed or validated a multivariable risk prediction model. Data extraction and quality assessments were performed according to the Prediction Model Risk of Bias Assessment Tool (PROBAST).

RESULTS

We identified 2,816 unique publications and included 8 eligible studies (7 new risk models and 1 validation of a risk stratification tool) that modeled risk with trastuzumab (n = 5), anthracyclines (n = 2), and anthracyclines with or without trastuzumab (n = 1). The most common final predictors were previous or concomitant chemotherapy (n = 5) and age (n = 4). Three studies incorporated measures of myocardial mechanics that may not be frequently available. Model discrimination was reported in 7 studies (range of area under the receiver operating characteristic curve, 0.56-0.88), while calibration was reported in 1 study. Internal and external validation were performed in 4 studies and 1 study, respectively. Using the PROBAST methodology, we rated the overall risk of bias as high for 7 of 8 studies and unclear for 1 study. Concerns for applicability were low for all studies.

CONCLUSION

Among 8 models to predict the risk of cardiotoxicity of antineoplastic agents for breast cancer, 7 were rated as having a high risk of bias and all had low concerns for clinical applicability. Most evaluated studies reported positive measures of model performance but did not perform external validation. Efforts to improve development and reporting of these models to facilitate their use in practice are warranted.

Unawareness of breast cancer family history among African women

Introduction

comprehensive cancer risk assessment services are lacking in most sub-Saharan African countries and the use of accurate family history (FH) information could serve as a cheap strategy for risk evaluation. The aim of this study is to determine the proportion of women unaware of family history of cancer among female relatives and associated socio-demographic characteristics.

Methods

using case-control data on breast cancer among 4294 women in Nigeria, Uganda and Cameroon, we investigated the proportion of women unaware of family history of cancer among their female relatives. The association between participants' response to their awareness of female relatives' cancer history and socio-demographic characteristics was analysed according to case-control status, family side and distance of relation. Results: the proportion of women unaware if any relative had cancer was 33%, and was significantly higher among controls (43.2%) compared to 23.9% among cases (p<0.001) (Adjusted Odds Ratio (OR) = 2.51, 95% CI = 2.14 - 2.95). Age, education and marital status remained significantly associated with being unaware of FH among controls on multiple regression.

Conclusion

about a third of women interviewed did not know about cancer history in at least one of their female relatives. Efforts aimed at improving cancer awareness in sub-Saharan Africa (SSA) are needed. Our findings could be useful for future studies of cancer risk assessment in SSA.

Automatic Breast Volume Scanner and B-Ultrasound-Based Radiomics Nomogram for Clinician Management of BI-RADS 4A Lesions

RATIONALE AND OBJECTIVES

To develop and validate a nomogram for predicting the risk of malignancy of breast imaging reporting and data system (BI-RADS) 4A lesions to reduce unnecessary invasive examinations.

MATERIALS AND METHODS

From January 2017 to July 2021, 190 cases of 4A lesions included in this study were divided into training and validation sets in a ratio of 8:2. Radiomics features were extracted from sonograms by Automatic Breast Volume Scanner (ABVS) and B-ultrasound. We constructed the radiomics model and calculated the rad-scores. Univariate and multivariate logistic regressions were used to assess demographics and lesion elastography values (virtual touch tissue image, shear wave velocity) and to develop clinical model. A clinical radiomics model was developed using rad-score and independent clinical factors, and a nomogram was plotted. Nomogram performance was evaluated using discrimination, calibration, and clinical utility.

RESULTS

The nomogram included rad-score, age, and elastography, and showed good calibration. In the training set, the area under the receiver operating characteristic curve (AUC) of the clinical radiomics model (0.900, 95% confidence interval (CI): 0.843-0.958) was superior to that of the radiomics model (0.860, 95% CI: 0.799-0.921) and clinical model (0.816, 95% CI: 0.735-0.958) (p = 0.024 and 0.008, respectively). The decision curve analysis showed that the clinical radiomics model had the highest net benefit in most threshold probability ranges.

CONCLUSION

ABVS and B-ultrasound-based radiomics nomograms have satisfactory performance in differentiating benign and malignant 4A lesions. This can help clinicians make an accurate diagnosis of 4A lesions and reduce unnecessary biopsy.

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.

Understanding the contribution of lifestyle in breast cancer risk prediction: a systematic review of models applicable to Europe

BACKGROUND

Breast cancer (BC) is a significant health concern among European women, with the highest prevalence rates among all cancers. Existing BC prediction models account for major risks such as hereditary, hormonal and reproductive factors, but research suggests that adherence to a healthy lifestyle can reduce the risk of developing BC to some extent. Understanding the influence and predictive role of lifestyle variables in current risk prediction models could help identify actionable, modifiable, targets among high-risk population groups.

PURPOSE

To systematically review population-based BC risk prediction models applicable to European populations and identify lifestyle predictors and their corresponding parameter values for a better understanding of their relative contribution to the prediction of incident BC.

METHODS

A systematic review was conducted in PubMed, Embase and Web of Science from January 2000 to August 2021. Risk prediction models were included if (i) developed and/or validated in adult cancer-free women in Europe, (ii) based on easily ascertained information, and (iii) reported models' final predictors. To investigate further the comparability of lifestyle predictors across models, estimates were standardised into risk ratios and visualised using forest plots.

RESULTS

From a total of 49 studies, 33 models were developed and 22 different existing models, mostly from Gail (22 studies) and Tyrer-Cuzick and co-workers (12 studies) were validated or modified for European populations. Family history of BC was the most frequently included predictor (31 models), while body mass index (BMI) and alcohol consumption (26 and 21 models, respectively) were the lifestyle predictors most often included, followed by smoking and physical activity (7 and 6 models respectively). Overall, for lifestyle predictors, their modest predictive contribution was greater for riskier lifestyle levels, though highly variable model estimates across different models.

CONCLUSIONS

Given the increasing BC incidence rates in Europe, risk models utilising readily available risk factors could greatly aid in widening the population coverage of screening efforts, while the addition of lifestyle factors could help improving model performance and serve as intervention targets of prevention programmes.

Genetic and lifestyle factors for breast cancer risk assessment in Southeast China

BACKGROUND

Despite the rising incidence and mortality of breast cancer among women in China, there are currently few predictive models for breast cancer in the Chinese population and with low accuracy. This study aimed to identify major genetic and life-style risk factors in a Chinese population for potential application in risk assessment models.

METHODS

A case-control study in southeast China was conducted including 1321 breast cancer patients and 2045 controls during 2013-2016, in which the data were randomly divided into a training set and a test set on a 7:3 scale. The association between genetic and life-style factors and breast cancer was examined using logistic regression models. Using AUC curves, we also compared the performance of the logistic model to machine learning models, namely LASSO regression model and support vector machine (SVM), and the scores calculated from CKB, Gail and Tyrer-Cuzick models in the test set.

RESULTS

Among all factors considered, the best model was achieved when polygenetic risk score, lifestyle, and reproductive factors were considered jointly in the logistic regression model (AUC = 0.73; 95% CI: 0.70-0.77). The models created in this study performed better than those using scores calculated from the CKB, Gail, and Tyrer-Cuzick models. However, the logistic model and machine learning models did not significantly differ from one another.

CONCLUSION

In summary, we have found genetic and lifestyle risk predictors for breast cancer with moderate discrimination, which might provide reference for breast cancer screening in southeast China. Further population-based studies are needed to validate the model for future applications in personalized breast cancer screening programs.

Validation of the BOADICEA model for predicting the likelihood of carrying pathogenic variants in eight breast and ovarian cancer susceptibility genes

BOADICEA is a comprehensive risk prediction model for breast and/or ovarian cancer (BC/OC) and for carrying pathogenic variants (PVs) in cancer susceptibility genes. In addition to BRCA1 and BRCA2, BOADICEA version 6 includes PALB2, CHEK2, ATM, BARD1, RAD51C and RAD51D. To validate its predictions for these genes, we conducted a retrospective study including 2033 individuals counselled at clinical genetics departments in Denmark. All counselees underwent comprehensive genetic testing by next generation sequencing on suspicion of hereditary susceptibility to BC/OC. Likelihoods of PVs were predicted from information about diagnosis, family history and tumour pathology. Calibration was examined using the observed-to-expected ratio (O/E) and discrimination using the area under the receiver operating characteristics curve (AUC). The O/E was 1.11 (95% CI 0.97-1.26) for all genes combined. At sub-categories of predicted likelihood, the model performed well with limited misestimation at the extremes of predicted likelihood. Discrimination was acceptable with an AUC of 0.70 (95% CI 0.66-0.74), although discrimination was better for BRCA1 and BRCA2 than for the other genes in the model. This suggests that BOADICEA remains a valid decision-making aid for determining which individuals to offer comprehensive genetic testing for hereditary susceptibility to BC/OC despite suboptimal calibration for individual genes in this population.

Perceptions and Usability of PREVENTION: A Breast Cancer Risk Assessment e-Platform

BACKGROUND

The PREVENTION e-platform was developed to provide accessible and evidence-based health information tailored to different Breast Cancer (BC) risk levels. The demonstration study objectives were to (1) assess the usability and perceived impact of PREVENTION on women with assigned hypothetical BC risk levels (i.e., near population, intermediate or high) and (2) explore perceptions and recommendations for e-platform improvement.

METHODS

Thirty women with no history of cancer were recruited through social media, commercial centers, health clinics, and community settings in Montreal, Qc, Canada. Participants accessed e-platform content tailored to their assigned hypothetical BC risk level, and then completed study e-questionnaires including the user Mobile Application Rating Scale (uMARS), an e-platform quality scale (i.e., in terms of engagement, functionality, aesthetics, and information). A subsample (n = 18) was randomly selected for an individual follow-up semi-structured interview.

RESULTS

The e-platform overall quality was high, with mean M = 4.01 (out of 5) and SD = 0.50. A total of 87% (n = 26) agreed or strongly agreed that PREVENTION increased their knowledge and awareness of BC risk, and 80% would recommend it to others while reporting likelihood of following lifestyle recommendations to decrease their BC risk. Follow up interviews indicated that participants perceived the e-platform as a trusted source of BC information and a promising means to connect with peers. They also reported that while the e-platform was easy to navigate, improvements were needed for connectivity, visuals, and the organization of scientific resources.

CONCLUSION

Preliminary findings support PREVENTION as a promising means to provide personalized BC information and support. Efforts are underway to further refine the platform, assess its impact in larger samples and gather feedback from BC specialists.

Clinical applicability of the Polygenic Risk Score for breast cancer risk prediction in familial cases

BACKGROUND

Common low-risk variants are presently not used to guide clinical management of familial breast cancer (BC). We explored the additive impact of a 313-variant-based Polygenic Risk Score (PRS₃₁₃) relative to standard gene testing in non-BRCA1/2 Dutch BC families.

METHODS

We included 3918 BC cases from 3492 Dutch non-BRCA1/2 BC families and 3474 Dutch population controls. The association of the standardised PRS₃₁₃ with BC was estimated using a logistic regression model, adjusted for pedigree-based family history. Family history of the controls was imputed for this analysis. SEs were corrected to account for relatedness of individuals. Using the BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) V.5 model, lifetime risks were retrospectively calculated with and without individual PRS₃₁₃. For 2586 cases and 2584 controls, the carrier status of pathogenic variants (PVs) in ATM, CHEK2 and PALB2 was known.

RESULTS

The family history-adjusted PRS₃₁₃ was significantly associated with BC (per SD OR=1.97, 95% CI 1.84 to 2.11). Including the PRS₃₁₃ in BOADICEA family-based risk prediction would have changed screening recommendations in up to 27%, 36% and 34% of cases according to BC screening guidelines from the USA, UK and the Netherlands (National Comprehensive Cancer Network, National Institute for Health and Care Excellence, and Netherlands Comprehensive Cancer Organisation), respectively. For the population controls, without information on family history, this was up to 39%, 44% and 58%, respectively. Among carriers of PVs in known moderate BC susceptibility genes, the PRS₃₁₃ had the largest impact for CHEK2 and ATM.

CONCLUSIONS

Our results support the application of the PRS₃₁₃ in risk prediction for genetically uninformative BC families and families with a PV in moderate BC risk genes.

Development and validation of a nomogram to predict Chinese breast cancer risk based on clinical serum biomarkers

Background: This study investigated and compared clinical serum biomarkers and developed a diagnostic nomogram for breast cancer. Methods: A total of 1224 breast cancer and 1280 healthy controls were enrolled. Univariate and multivariate analyses were performed to identify factors and a nomogram was developed. Discrimination, accuracy and clinical utility values were evaluated by receiver operating characteristic, Hosmer-Lemeshow, calibration plots, decision curve analysis and clinical impact plots. Results: carcinoembryonic antigen, CA125, CA153, lymphocyte-to-monocyte ratio, platelet-to-lymphocyte ratio, fibrinogen and platelet distributing width were effectively identified to predict breast cancer. The nomogram showed the area under the curve of 0.708 and 0.710 in the training and validation set. Calibration plots, Hosmer-Lemeshow, decision curve analysis and clinical impact plots confirmed great accuracy and clinical utility. Conclusion: We developed and validated a nomogram that is effectively used for risk prediction of Chinese breast cancer.

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.

Cumulative 6-Year Risk of Screen-Detected Ductal Carcinoma In Situ by Screening Frequency

Importance

Detection of ductal carcinoma in situ (DCIS) by mammography screening is a controversial outcome with potential benefits and harms. The association of mammography screening interval and woman's risk factors with the likelihood of DCIS detection after multiple screening rounds is poorly understood.

Objective

To develop a 6-year risk prediction model for screen-detected DCIS according to mammography screening interval and women's risk factors.

Design, Setting, and Participants

This Breast Cancer Surveillance Consortium cohort study assessed women aged 40 to 74 years undergoing mammography screening (digital mammography or digital breast tomosynthesis) from January 1, 2005, to December 31, 2020, at breast imaging facilities within 6 geographically diverse registries of the consortium. Data were analyzed between February and June 2022.

Exposures

Screening interval (annual, biennial, or triennial), age, menopausal status, race and ethnicity, family history of breast cancer, benign breast biopsy history, breast density, body mass index, age at first birth, and false-positive mammography history.

Main Outcomes and Measures

Screen-detected DCIS defined as a DCIS diagnosis within 12 months after a positive screening mammography result, with no concurrent invasive disease.

Results

A total of 916 931 women (median [IQR] age at baseline, 54 [46-62] years; 12% Asian, 9% Black, 5% Hispanic/Latina, 69% White, 2% other or multiple races, and 4% missing) met the eligibility criteria, with 3757 screen-detected DCIS diagnoses. Screening round-specific risk estimates from multivariable logistic regression were well calibrated (expected-observed ratio, 1.00; 95% CI, 0.97-1.03) with a cross-validated area under the receiver operating characteristic curve of 0.639 (95% CI, 0.630-0.648). Cumulative 6-year risk of screen-detected DCIS estimated from screening round-specific risk estimates, accounting for competing risks of death and invasive cancer, varied widely by all included risk factors. Cumulative 6-year screen-detected DCIS risk increased with age and shorter screening interval. Among women aged 40 to 49 years, the mean 6-year screen-detected DCIS risk was 0.30% (IQR, 0.21%-0.37%) for annual screening, 0.21% (IQR, 0.14%-0.26%) for biennial screening, and 0.17% (IQR, 0.12%-0.22%) for triennial screening. Among women aged 70 to 74 years, the mean cumulative risks were 0.58% (IQR, 0.41%-0.69%) after 6 annual screens, 0.40% (IQR, 0.28%-0.48%) for 3 biennial screens, and 0.33% (IQR, 0.23%-0.39%) after 2 triennial screens.

Conclusions and Relevance

In this cohort study, 6-year screen-detected DCIS risk was higher with annual screening compared with biennial or triennial screening intervals. Estimates from the prediction model, along with risk estimates of other screening benefits and harms, could help inform policy makers' discussions of screening strategies.

Validation of the Nigerian Breast Cancer Study Model for Predicting Individual Breast Cancer Risk in Cameroon and Uganda

BACKGROUND

The Nigerian Breast Cancer Study (NBCS) model is a new risk assessment tool developed for predicting risk of invasive breast cancer in Nigeria. Its applicability outside of Nigeria remains uncertain as it has not been validated in other sub-Saharan Africa populations.

METHODS

We conducted a case-control study among women with breast cancer and controls ascertained in Cameroon and Uganda from 2011 to 2016. Structured questionnaire interviews were performed to collect risk factor characteristics. The NBCS model, the Gail model, the Gail model for Black population, and the Black Women's Health Study model were applied to the Cameroon and Uganda samples separately. Nigerian as well as local incidence rates were incorporated into the models. Receiver-Operating Characteristic analyses were performed to indicate discriminating capacity.

RESULTS

The study included 550 cases (mean age 46.8 ± 11.9) and 509 controls (mean age 46.3 ± 11.7). Compared with the other three models, the NBCS model performed best in both countries. The discriminating accuracy of the NBCS model in Cameroon (age-adjusted C-index = 0.602; 95% CI, 0.542-0.661) was better than in Uganda (age-adjusted C-index = 0.531; 95% CI, 0.459-0.603).

CONCLUSIONS

These findings demonstrate the potential clinical utility of the NBCS model for risk assessment in Cameroon. All currently available models performed poorly in Uganda, which suggests that the NBCS model may need further calibration before use in other regions of Africa.

IMPACT

Differences in risk profiles across the African diaspora underscores the need for larger studies and may require development of region-specific risk assessment tools for breast cancer.

Promote Community Engagement in Participatory Research for Improving Breast Cancer Prevention: The P.I.N.K. Study Framework

Breast cancer (BC) has overtaken lung cancer as the most common cancer in the world and the projected incidence rates show a further increase. Early detection through population screening remains the cornerstone of BC control, but a progressive change from early diagnosis only-based to a personalized preventive and risk-reducing approach is widely debated. Risk-stratification models, which also include personal lifestyle risk factors, are under evaluation, although the documentation burden to gather population-based data is relevant and traditional data collection methods show some limitations. This paper provides the preliminary results from the analysis of clinical data provided by radiologists and lifestyle data collected using self-administered questionnaires from 5601 post-menopausal women. The weight of the combinations of women's personal features and lifestyle habits on the BC risk were estimated by combining a model-driven and a data-driven approach to analysis. The weight of each factor on cancer occurrence was assessed using a logistic model. Additionally, communities of women sharing common features were identified and combined in risk profiles using social network analysis techniques. Our results suggest that preventive programs focused on increasing physical activity should be widely promoted, in particular among the oldest women. Additionally, current findings suggest that pregnancy, breast-feeding, salt limitation, and oral contraception use could have different effects on cancer risk, based on the overall woman's risk profile. To overcome the limitations of our data, this work also introduces a mobile health tool, the Dress-PINK, designed to collect real patients' data in an innovative way for improving women's response rate, data accuracy, and completeness as well as the timeliness of data availability. Finally, the tool provides tailored prevention messages to promote critical consciousness, critical thinking, and increased health literacy among the general population.

Familial history and prevalence of BRCA1, BRCA2 and TP53 pathogenic variants in HBOC Brazilian patients from a public healthcare service

Several studies have demonstrated the cost-effectiveness of genetic testing for surveillance and treatment of carriers of germline pathogenic variants associated with hereditary breast/ovarian cancer syndrome (HBOC). In Brazil, seventy percent of the population is assisted by the public Unified Health System (SUS), where genetic testing is still unavailable. And few studies were performed regarding the prevalence of HBOC pathogenic variants in this context. Here, we estimated the prevalence of germline pathogenic variants in BRCA1, BRCA2 and TP53 genes in Brazilian patients suspected of HBOC and referred to public healthcare service. Predictive power of risk prediction models for detecting mutation carriers was also evaluated. We found that 41 out of 257 tested patients (15.9%) were carriers of pathogenic variants in the analyzed genes. Most frequent pathogenic variant was the founder Brazilian mutation TP53 c.1010G > A (p.Arg337His), adding to the accumulated evidence that supports inclusion of TP53 in routine testing of Brazilian HBOC patients. Surprisingly, BRCA1 c.5266dupC (p.Gln1756fs), a frequently reported pathogenic variant in Brazilian HBOC patients, was not observed. Regarding the use of predictive models, we found that familial history of cancer might be used to improve selection or prioritization of patients for genetic testing, especially in a context of limited resources.

Predicting breast cancer risk in a racially diverse, community-based sample of potentially high-risk women

BACKGROUND

Identifying women with high risk of breast cancer is necessary to study high-risk experiences and deliver risk-management care. Risk prediction models estimate individuals' lifetime risk but have rarely been applied in community-based settings among women not yet receiving specialized care. Therefore, we aimed: (1) to apply three breast cancer risk prediction models (i.e., Gail, Claus, and IBIS) to a racially diverse, community-based sample of women, and (2) to assess risk prediction estimates using survey data.

METHODS

An online survey was administered to women who were determined by a screening instrument to have potentially high risk for breast cancer. Risk prediction models were applied using their self-reported family and medical history information. Inclusion in the high-risk subsample required ≥20% lifetime risk per ≥1 model. Descriptive statistics were used to compare the proportions of women identified as high risk by each model.

RESULTS

N = 1053 women were initially eligible and completed the survey. All women, except one, self-reported the information necessary to run at least one model; 90% had sufficient information for >1 model. The high-risk subsample included 717 women, of which 75% were identified by one model only; 96% were identified by IBIS, 3% by Claus, <1% by Gail. In the high-risk subsample, 20% were identified by two models and 3% by all three models.

CONCLUSIONS

Assessing breast cancer risk using self-reported data in a community-based sample was feasible. Different models identify substantially different groups of women who may be at high risk for breast cancer; use of multiple models may be beneficial for research and clinical care.

Functions of Breast Cancer Predisposition Genes: Implications for Clinical Management

Approximately 5–10% of all breast cancer (BC) cases are caused by germline pathogenic variants (GPVs) in various cancer predisposition genes (CPGs). The most common contributors to hereditary BC are BRCA1 and BRCA2, which are associated with hereditary breast and ovarian cancer (HBOC). ATM, BARD1, CHEK2, PALB2, RAD51C, and RAD51D have also been recognized as CPGs with a high to moderate risk of BC. Primary and secondary cancer prevention strategies have been established for HBOC patients; however, optimal preventive strategies for most hereditary BCs have not yet been established. Most BC-associated CPGs participate in DNA damage repair pathways and cell cycle checkpoint mechanisms, and function jointly in such cascades; therefore, a fundamental understanding of the disease drivers in such cascades can facilitate the accurate estimation of the genetic risk of developing BC and the selection of appropriate preventive and therapeutic strategies to manage hereditary BCs. Herein, we review the functions of key BC-associated CPGs and strategies for the clinical management in individuals harboring the GPVs of such genes.

Towards implementation of comprehensive breast cancer risk prediction tools in health care for personalised prevention

Advances in knowledge about breast cancer risk factors have led to the development of more comprehensive risk models. These integrate information on a variety of risk factors such as lifestyle, genetics, family history, and breast density. These risk models have the potential to deliver more personalised breast cancer prevention. This is through improving accuracy of risk estimates, enabling more effective targeting of preventive options and creating novel prevention pathways through enabling risk estimation in a wider variety of populations than currently possible. The systematic use of risk tools as part of population screening programmes is one such example. A clear understanding of how such tools can contribute to the goal of personalised prevention can aid in understanding and addressing barriers to implementation. In this paper we describe how emerging models, and their associated tools can contribute to the goal of personalised healthcare for breast cancer through health promotion, early disease detection (screening) and improved management of women at higher risk of disease. We outline how addressing specific challenges on the level of communication, evidence, evaluation, regulation, and acceptance, can facilitate implementation and uptake.

Interval Cancer Detection Using a Neural Network and Breast Density in Women with Negative Screening Mammograms

Background Inclusion of mammographic breast density (BD) in breast cancer risk models improves accuracy, but accuracy remains modest. Interval cancer (IC) risk prediction may be improved by combining assessments of BD and an artificial intelligence (AI) cancer detection system. Purpose To evaluate the performance of a neural network (NN)-based model that combines the assessments of BD and an AI system in the prediction of risk of developing IC among women with negative screening mammography results. Materials and Methods This retrospective nested case-control study performed with screening examinations included women who developed IC and women with normal follow-up findings (from January 2011 to January 2015). An AI cancer detection system analyzed all studies yielding a score of 1-10, representing increasing likelihood of malignancy. BD was automatically computed using publicly available software. An NN model was trained by combining the AI score and BD using 10-fold cross-validation. Bootstrap analysis was used to calculate the area under the receiver operating characteristic curve (AUC), sensitivity at 90% specificity, and 95% CIs of the AI, BD, and NN models. Results A total of 2222 women with IC and 4661 women in the control group were included (mean age, 61 years; age range, 49-76 years). AUC of the NN model was 0.79 (95% CI: 0.77,0.81), which was higher than AUC of the AI cancer detection system or BD alone (AUC, 0.73 [95% CI: 0.71, 0.76] and 0.69 [95% CI: 0.67, 0.71], respectively; P < .001 for both). At 90% specificity, the NN model had a sensitivity of 50.9% (339 of 666 women; 95% CI: 45.2, 56.3) for prediction of IC, which was higher than that of the AI system (37.5%; 250 of 666 women; 95% CI: 33.0, 43.7; P < .001) or BD percentage alone (22.4%; 149 of 666 women; 95% CI: 17.9, 28.5; P < .001). Conclusion The combined assessment of an artificial intelligence detection system and breast density measurements enabled identification of a larger proportion of women who would develop interval cancer compared with either method alone. Published under a CC BY 4.0 license.

Identifying and Managing Patients with Elevated Breast Cancer Risk Presenting for Screening Mammography

BACKGROUND

Identifying the prevalence and management of patients at high-risk for breast cancer can improve resource utilization and provide individualized screening strategies.

OBJECTIVE

The purpose of this study was to identify the prevalence of high-risk patients in our institution who presented for screening mammography and to understand how they utilized downstream resources offered to them.

MATERIALS AND METHODS

This single institution retrospective study utilized the Tyrer-Cuzick risk assessment model to provide lifetime risk of breast cancer of patients presenting for screening mammography over a one-year period. Their subsequent management and resource utilization were collated.

RESULTS

High-risk patients comprised 7.7% (701/9061) of our screening population. Of those high-risk women offered a Breast Center (BC) consultation, 75.2% (276/367) participated in the consultation, with 51.1% (141/276) of those patients completing MRI for supplemental screening. Risk reducing medication was adopted by 7.6% (6/79) of those offered. Of patients offered a genetics consultation, 66.3% (53/80) participated in the consultation, and 50.0% (40/80) completed genetic testing.

CONCLUSIONS

Identifying and understanding high-risk patient cohorts, whether locally or in a population-based context, is important for individualized patient care and practice efficiency.

Effective Surveillance of High-Risk Women

BACKGROUND

This study addresses the effectiveness of risk models and screening breast magnetic resonance imaging (MRI) in women who have atypical hyperplasia (AH), lobular carcinoma in situ (LCIS), or a family history of breast cancer, but not a genetic mutation.

PATIENTS AND METHODS

A retrospective review of 444 women who had 458 breast screening MRIs at a community teaching hospital over a 12-month period between March 25, 2014 and March 31, 2015 was performed. The patients underwent high risk screening with breast MRIs alternating with mammograms every 6 months. After excluding patients with prior breast or ovarian cancer, genetic mutations, and chest wall radiation, 200 remaining patients constituted the study cohort. Over the following 5 years, the patients were screened with MRIs alternating with mammograms every 6 months. A total of 961 total MRI screenings were performed over the entire 5-year period of the study.

RESULTS

A total of 200 women fit the study criteria. Of these 103 had a prior history of AH or LCIS. Over the 5-year period, 60 women dropped out of the screening regimen, 6 patients were diagnosed with breast cancer on screening MRIs, and 2 additional patients were diagnosed with breast cancer on screening mammograms. Surprisingly, the highest Tyrer-Cuzick (T-C) scores did not correlate with increased development of breast cancers in our population.

CONCLUSIONS

This study shows that there is wide variation in the results of risk assessment models. Risk models may overestimate breast cancer risk, suggesting that re-evaluation of current risk assessment and screening protocols is warranted.

Evaluation of the Dynamics of Breast Cancer Incidence in Kyrgyzstan: Component Analysis

BACKGROUND: In 2020, 19.2 million cases of all types of cancer were registered worldwide, of which 11.7%, that is, 2.3 million, related to breast cancer (BC). The global burden of cancer is increasing worldwide, with the majority of new cancer cases and related deaths occurring in low- and middle-income countries. OBJECTIVE: The study is to conduct a component analysis of the dynamics of the incidence of BC in Kyrgyzstan. METHODS: Primary data were for registered patients with BC (International Classification of Diseases – C50) in the whole country during the period of 2003–2017. Evaluation of changes in BC incidence in the population of Kyrgyzstan was performed using component analysis according to the methodological recommendations. RESULTS: The study period, 7850 new cases of BC were recorded. The incidence rate increased from 17.70°/0000 (2003) to 19.03°/0000 in 2017 and the overall growth was 1.34°/0000, including due to the age structure – ΣΔA=2.08°/0000, due to the risk of acquiring illness – ΣΔR=−0.55°/0000 and their combined effect – ΣΔRA=−0.19°/0000. The component analysis revealed that the increase in the number of patients with BC was mainly due to the growth of the population (ΔP=+71.8%), changes in its age structure (ΔA=+35.5%), and changes associated with the risk of acquiring illness (ΔR=+8.4%). The increase and, in some cases, the decrease in the number of patients in the regions of the republic is due to the influence of demographic factors and risk factors for getting sick. CONCLUSION: The component analysis assessed the role of the influence of demographic factors and the risk of acquiring the disease on the formation of the number of patients and the incidence of BC, while geographical variability was established. The implementation of the results of this study is recommended in the management of anticancer measures for BC.

Cost-Effectiveness Analysis of Imaging Modalities for Breast Cancer Surveillance Among BRCA1/2 Mutation Carriers: A Systematic Review

Background

BRCA1/2 mutation carriers are suggested with regular breast cancer surveillance screening strategies using mammography with supplementary MRI as an adjunct tool in Western countries. From a cost-effectiveness perspective, however, the benefits of screening modalities remain controversial among different mutated genes and screening schedules.

Methods

We searched the MEDLINE/PubMed, Embase, Cochrane Library, Scopus, and Web of Science databases to collect and compare the results of different cost-effectiveness analyses. A simulated model was used to predict the impact of screening strategies in the target group on cost, life-year gained, quality-adjusted life years, and incremental cost-effectiveness ratio (ICER).

Results

Nine cost-effectiveness studies were included. Combined mammography and MRI strategy is cost-effective in BRCA1 mutation carriers for the middle-aged group (age 35 to 54). BRCA2 mutation carriers are less likely to benefit from adjunct MRI screening, which implies that mammography alone would be sufficient from a cost-effectiveness perspective, regardless of dense breast cancer.

Conclusions

Precision screening strategies among BRCA1/2 mutation carriers should be conducted according to the acceptable ICER, i.e., a combination of mammography and MRI for BRCA1 mutation carriers and mammography alone for BRCA2 mutation carriers.

Systematic Review Registration

PROSPERO, identifier CRD42020205471.

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.

Disparities in Genetic Testing for Heritable Solid-Tumor Malignancies

Genetic testing offers providers a potentially life saving tool for identifying and intervening in high-risk individuals. However, disparities in receipt of genetic testing have been consistently demonstrated and undoubtedly have significant implications for the populations not receiving the standard of care. If correctly used, there is the potential for genetic testing to play a role in decreasing health disparities among individuals of different races and ethnicities. However, if genetic testing continues to revolutionize cancer care while being disproportionately distributed, it also has the potential to widen the existing mortality gap between various racial and ethnic populations.

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.

Reducing False-Positive Screening MRI Rate in Women with Extremely Dense Breasts Using Prediction Models Based on Data from the DENSE Trial

Background High breast density increases breast cancer risk and lowers mammographic sensitivity. Supplemental MRI screening improves cancer detection but increases the number of false-positive screenings. Thus, methods to distinguish true-positive MRI screening results from false-positive ones are needed. Purpose To build prediction models based on clinical characteristics and MRI findings to reduce the rate of false-positive screening MRI findings in women with extremely dense breasts. Materials and Methods Clinical characteristics and MRI findings in Dutch breast cancer screening participants (age range, 50-75 years) with positive first-round MRI screening results (Breast Imaging Reporting and Data System 3, 4, or 5) after a normal screening mammography with extremely dense breasts (Volpara density category 4) were prospectively collected within the randomized controlled Dense Tissue and Early Breast Neoplasm Screening (DENSE) trial from December 2011 through November 2015. In this secondary analysis, prediction models were built using multivariable logistic regression analysis to distinguish true-positive MRI screening findings from false-positive ones. Results Among 454 women (median age, 52 years; interquartile range, 50-57 years) with a positive MRI result in a first supplemental MRI screening round, 79 were diagnosed with breast cancer (true-positive findings), and 375 had false-positive MRI results. The full prediction model (area under the receiver operating characteristics curve [AUC], 0.88; 95% CI: 0.84, 0.92), based on all collected clinical characteristics and MRI findings, could have prevented 45.5% (95% CI: 39.6, 51.5) of false-positive recalls and 21.3% (95% CI: 15.7, 28.3) of benign biopsies without missing any cancers. The model solely based on readily available MRI findings and age had a comparable performance (AUC, 0.84; 95% CI: 0.79, 0.88; P = .15) and could have prevented 35.5% (95% CI: 30.4, 41.1) of false-positive MRI screening results and 13.0% (95% CI: 8.8, 18.6) of benign biopsies. Conclusion Prediction models based on clinical characteristics and MRI findings may be useful to reduce the false-positive first-round screening MRI rate and benign biopsy rate in women with extremely dense breasts. Clinical trial registration no. NCT01315015 © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Imbriaco in this issue.

Search Behavior Regarding Cancer Susceptibility Genes Using a Clinical Decision Support Tool for Gene-Specific Penetrance: Content Analysis

BACKGROUND

Genetic testing for germline cancer susceptibility genes is widely available. The Ask2Me.org (All Syndromes Known to Man Evaluator) tool is a clinical decision support tool that provides evidence-based risk predictions for individuals with pathogenic variants in cancer susceptibility genes.

OBJECTIVE

The aim of this study was to understand the search behavior of the Ask2Me.org tool users, identify the patterns of queries entered, and discuss how to further improve the tool.

METHODS

We analyzed the Ask2Me.org user-generated queries collected between December 12, 2018, and October 8, 2019. The gene frequencies of the user-generated queries were compared with previously published panel testing data to assess the correspondence between usage and prevalence of pathogenic variants. The frequencies of prior cancer in the user-generated queries were compared with the most recent US population-based cancer incidence.

RESULTS

A total of 10,085 search queries were evaluated. The average age submitted in the queries was 48.8 (SD 16.5) years, and 84.1% (8478/10,085) of the submitted queries were for females. BRCA2 (1671/10,085, 16.6%), BRCA1 (1627/10,085, 16.1%), CHEK2 (994/10,085, 9.9%), ATM (662/10,085, 6.6%), and APC (492/10,085, 4.9%) were the top 5 genes searched by users. There was a strong linear correlation between genes queried by users and the frequency of pathogenic variants reported in published panel testing data (r=0.95, r2=0.90, P<.001). Over half of the queries (5343/10,085, 53.0%) included a prior personal history of cancer. The frequencies of prior cancers in the queries on females were strongly correlated with US cancer incidences (r=0.97, r2=0.95, P<.001), while the same correlation was weaker among the queries on males (r=0.69, r2=0.47, P=.02).

CONCLUSIONS

The patients entered in the Ask2Me.org tool are a representative cohort of patients with pathogenic variants in cancer susceptibility genes in the United States. While a majority of the queries were on breast cancer susceptibility genes, users also queried susceptibility genes with lower prevalence, which may represent a transformation from single gene testing to multigene panel testing. Owing to these changing tides, more efforts are needed to improve evidence-based clinical decision support tools to better aid clinicians and their practice.