Performance of Breast Cancer Risk-Assessment Models in a Large Mammography Cohort

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.

Performance of Supplemental US Screening in Women with Dense Breasts and Varying Breast Cancer Risk: Results from the Breast Cancer Surveillance Consortium

Background It is unclear whether breast US screening outcomes for women with dense breasts vary with levels of breast cancer risk. Purpose To evaluate US screening outcomes for female patients with dense breasts and different estimated breast cancer risk levels. Materials and Methods This retrospective observational study used data from US screening examinations in female patients with heterogeneously or extremely dense breasts conducted from January 2014 to October 2020 at 24 radiology facilities within three Breast Cancer Surveillance Consortium (BCSC) registries. The primary outcomes were the cancer detection rate, false-positive biopsy recommendation rate, and positive predictive value of biopsies performed (PPV3). Risk classification of participants was performed using established BCSC risk prediction models of estimated 6-year advanced breast cancer risk and 5-year invasive breast cancer risk. Differences in high- versus low- or average-risk categories were assessed using a generalized linear model. Results In total, 34 791 US screening examinations from 26 489 female patients (mean age at screening, 53.9 years ± 9.0 [SD]) were included. The overall cancer detection rate per 1000 examinations was 2.0 (95% CI: 1.6, 2.4) and was higher in patients with high versus low or average risk of 6-year advanced breast cancer (5.5 [95% CI: 3.5, 8.6] vs 1.3 [95% CI: 1.0, 1.8], respectively; P = .003). The overall false-positive biopsy recommendation rate per 1000 examinations was 29.6 (95% CI: 22.6, 38.6) and was higher in patients with high versus low or average 6-year advanced breast cancer risk (37.0 [95% CI: 28.2, 48.4] vs 28.1 [95% CI: 20.9, 37.8], respectively; P = .04). The overall PPV3 was 6.9% (67 of 975; 95% CI: 5.3, 8.9) and was higher in patients with high versus low or average 6-year advanced cancer risk (15.0% [15 of 100; 95% CI: 9.9, 22.2] vs 4.9% [30 of 615; 95% CI: 3.3, 7.2]; P = .01). Similar patterns in outcomes were observed by 5-year invasive breast cancer risk. Conclusion The cancer detection rate and PPV3 of supplemental US screening increased with the estimated risk of advanced and invasive breast cancer. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Helbich and Kapetas in this issue.

Artificial Intelligence for Breast Cancer Risk Assessment

Breast cancer risk prediction models based on common clinical risk factors are used to identify women eligible for high-risk screening and prevention. Unfortunately, these models have only modest discriminatory accuracy with disparities in performance in underrepresented race and ethnicity groups. The field of artificial intelligence (AI) and deep learning are rapidly advancing the field of breast cancer risk prediction with the development of mammography-based AI breast cancer risk models. Early studies suggest mammography-based AI risk models may perform better than traditional risk factor-based models with more equitable performance.

Breast Density: Where Are We Now?

Breast density refers to the amount of fibroglandular tissue relative to fat on mammography and is determined either qualitatively through visual assessment or quantitatively. It is a heritable and dynamic trait associated with age, race/ethnicity, body mass index, and hormonal factors. Increased breast density has important clinical implications including the potential to mask malignancy and as an independent risk factor for the development of breast cancer. Breast density has been incorporated into breast cancer risk models. Given the impact of dense breasts on the interpretation of mammography, supplemental screening may be indicated.

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.

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.

Artificial Intelligence for Image-Based Breast Cancer Risk Prediction Using Attention

Accurate prediction of individual breast cancer risk paves the way for personalised prevention and early detection. The incorporation of genetic information and breast density has been shown to improve predictions for existing models, but detailed image-based features are yet to be included despite correlating with risk. Complex information can be extracted from mammograms using deep-learning algorithms, however, this is a challenging area of research, partly due to the lack of data within the field, and partly due to the computational burden. We propose an attention-based Multiple Instance Learning (MIL) model that can make accurate, short-term risk predictions from mammograms taken prior to the detection of cancer at full resolution. Current screen-detected cancers are mixed in with priors during model development to promote the detection of features associated with risk specifically and features associated with cancer formation, in addition to alleviating data scarcity issues. MAI-risk achieves an AUC of 0.747 [0.711, 0.783] in cancer-free screening mammograms of women who went on to develop a screen-detected or interval cancer between 5 and 55 months, outperforming both IBIS (AUC 0.594 [0.557, 0.633]) and VAS (AUC 0.649 [0.614, 0.683]) alone when accounting for established clinical risk factors.

Pearls and Pitfalls of Interpretation of Automated Breast US

Dense breast tissue is an independent risk factor for breast cancer and reduces the sensitivity of mammography. Patients with dense breast tissue are more likely to present with interval cancers and higher-stage disease. Successful breast cancer screening outcomes rely on detection of early-stage breast cancers; therefore, several supplemental screening modalities have been developed to improve cancer detection in dense breast tissue. US is the most widely used supplemental screening modality worldwide and has been proven to demonstrate additional mammographically occult cancers that are predominantly invasive and node negative. According to the American College of Radiology, intermediate-risk women with dense breast tissue may benefit from adjunctive screening US due to the limitations of mammography. Several studies have demonstrated handheld US (HHUS) and automated breast US (AUS) to be comparable in the screening setting. The advantages of AUS over HHUS include lack of operator dependence and a formal training requirement, image reproducibility, and ability for temporal comparison. However, AUS exhibits unique features that can result in high false-positive rates and long interpretation times for new users. Familiarity with the common appearance of benign mammographic findings and artifacts, technical challenges, and unique AUS features is essential for fast, efficient, and accurate interpretation. The goals of this article are to (a) examine the role of AUS as a supplemental screening modality and (b) review the pearls and pitfalls of AUS interpretation. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Genetic counseling and testing for females at elevated risk for breast cancer: Protocol for the randomized controlled trial of the Know Your Risk intervention

BACKGROUND

Genetic counseling and testing have an important role in the care of patients at elevated risk for breast cancer. However, conventional pre- and post-test genetic counseling is labor and time intensive, less accessible for patients living outside major urban centers, and impractical on a large scale. A patient-driven approach to genetic counseling and testing may increase access, improve patients' experiences, affect efficiency of clinical practice, and help meet workforce demand. The objective of this 2-arm randomized controlled trial is to determine the efficacy of Know Your Risk (KYR), a genetic counseling patient preference intervention.

METHODS

Females (n = 1000) at elevated risk (>20% lifetime) for breast cancer will be randomized to the KYR intervention or conventional genetic counseling. The study will provide comprehensive assessment of breast cancer risk by multigene panel testing and validated polygenic risk score. Primary outcome is adherence to National Comprehensive Cancer Network guidelines for a clinical encounter every 6-12 months and an annual mammogram (breast MRI if recommended) determined by medical record review. Secondary outcomes include adherence to other recommended cancer screening tests determined by medical record review and changes in breast cancer knowledge, perception of risk, post-test/counseling distress, and satisfaction with counseling by completion of three surveys during the study. Study aims will be evaluated for non-inferiority of the KYR intervention compared to conventional genetic counseling.

CONCLUSION

If efficacious, the KYR intervention has the potential to improve patients' experience and may change how genetic counseling is delivered, inform best practices, and reduce workforce burden.

TRIAL REGISTRATION

ClinicalTrials.govNCT05325151.

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.

Robust cross-vendor mammographic texture models using augmentation-based domain adaptation for long-term breast cancer risk

Purpose

Risk-stratified breast cancer screening might improve early detection and efficiency without comprising quality. However, modern mammography-based risk models do not ensure adaptation across vendor-domains and rely on cancer precursors, associated with short-term risk, which might limit long-term risk assessment. We report a cross-vendor mammographic texture model for long-term risk.

Approach

The texture model was robustly trained using two systematically designed case-control datasets. Textural features, indicative of future breast cancer, were learned by excluding samples with diagnosed/potential malignancies from training. An augmentation-based domain adaption technique, based on flavorization of mammographic views, ensured generalization across vendor-domains. The model was validated in 66,607 consecutively screened Danish women with flavorized Siemens views and 25,706 Dutch women with Hologic-processed views. Performances were evaluated for interval cancers (IC) within 2 years from screening and long-term cancers (LTC) from 2 years after screening. The texture model was combined with established risk factors to flag 10% of women with the highest risk.

Results

In Danish women, the texture model achieved an area under the receiver operating characteristic curve (AUC) of 0.71 and 0.65 for ICs and LTCs, respectively. In Dutch women with Hologic-processed views, the AUCs were not different from AUCs in Danish women with flavorized views. The AUC for texture combined with established risk factors increased to 0.68 for LTCs. The 10% of women flagged as high-risk accounted for 25.5% of ICs and 24.8% of LTCs.

Conclusions

The texture model robustly estimated long-term breast cancer risk while adapting to an unseen processed vendor-domain and identified a clinically relevant high-risk subgroup.

Predicting 10-year breast cancer mortality risk in the general female population in England: a model development and validation study

BACKGROUND

Identifying female individuals at highest risk of developing life-threatening breast cancers could inform novel stratified early detection and prevention strategies to reduce breast cancer mortality, rather than only considering cancer incidence. We aimed to develop a prognostic model that accurately predicts the 10-year risk of breast cancer mortality in female individuals without breast cancer at baseline.

METHODS

In this model development and validation study, we used an open cohort study from the QResearch primary care database, which was linked to secondary care and national cancer and mortality registers in England, UK. The data extracted were from female individuals aged 20-90 years without previous breast cancer or ductal carcinoma in situ who entered the cohort between Jan 1, 2000, and Dec 31, 2020. The primary outcome was breast cancer-related death, which was assessed in the full dataset. Cox proportional hazards, competing risks regression, XGBoost, and neural network modelling approaches were used to predict the risk of breast cancer death within 10 years using routinely collected health-care data. Death due to causes other than breast cancer was the competing risk. Internal-external validation was used to evaluate prognostic model performance (using Harrell's C, calibration slope, and calibration in the large), performance heterogeneity, and transportability. Internal-external validation involved dataset partitioning by time period and geographical region. Decision curve analysis was used to assess clinical utility.

FINDINGS

We identified data for 11 626 969 female individuals, with 70 095 574 person-years of follow-up. There were 142 712 (1·2%) diagnoses of breast cancer, 24 043 (0·2%) breast cancer-related deaths, and 696 106 (6·0%) deaths from other causes. Meta-analysis pooled estimates of Harrell's C were highest for the competing risks model (0·932, 95% CI 0·917-0·946). The competing risks model was well calibrated overall (slope 1·011, 95% CI 0·978-1·044), and across different ethnic groups. Decision curve analysis suggested favourable clinical utility across all age groups. The XGBoost and neural network models had variable performance across age and ethnic groups.

INTERPRETATION

A model that predicts the combined risk of developing and then dying from breast cancer at the population level could inform stratified screening or chemoprevention strategies. Further evaluation of the competing risks model should comprise effect and health economic assessment of model-informed strategies.

FUNDING

Cancer Research UK.

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.

Breast cancer risk characteristics of women undergoing whole-breast ultrasound screening versus mammography alone

BACKGROUND

There are no consensus guidelines for supplemental breast cancer screening with whole-breast ultrasound. However, criteria for women at high risk of mammography screening failures (interval invasive cancer or advanced cancer) have been identified. Mammography screening failure risk was evaluated among women undergoing supplemental ultrasound screening in clinical practice compared with women undergoing mammography alone.

METHODS

A total of 38,166 screening ultrasounds and 825,360 screening mammograms without supplemental screening were identified during 2014-2020 within three Breast Cancer Surveillance Consortium (BCSC) registries. Risk of interval invasive cancer and advanced cancer were determined using BCSC prediction models. High interval invasive breast cancer risk was defined as heterogeneously dense breasts and BCSC 5-year breast cancer risk ≥2.5% or extremely dense breasts and BCSC 5-year breast cancer risk ≥1.67%. Intermediate/high advanced cancer risk was defined as BCSC 6-year advanced breast cancer risk ≥0.38%.

RESULTS

A total of 95.3% of 38,166 ultrasounds were among women with heterogeneously or extremely dense breasts, compared with 41.8% of 825,360 screening mammograms without supplemental screening (p < .0001). Among women with dense breasts, high interval invasive breast cancer risk was prevalent in 23.7% of screening ultrasounds compared with 18.5% of screening mammograms without supplemental imaging (adjusted odds ratio, 1.35; 95% CI, 1.30-1.39); intermediate/high advanced cancer risk was prevalent in 32.0% of screening ultrasounds versus 30.5% of screening mammograms without supplemental screening (adjusted odds ratio, 0.91; 95% CI, 0.89-0.94).

CONCLUSIONS

Ultrasound screening was highly targeted to women with dense breasts, but only a modest proportion were at high mammography screening failure risk. A clinically significant proportion of women undergoing mammography screening alone were at high mammography screening failure risk.

Assessing Breast Cancer Risk by Combining AI for Lesion Detection and Mammographic Texture

Background Recent mammography-based risk models can estimate short-term or long-term breast cancer risk, but whether risk assessment may improve by combining these models has not been evaluated. Purpose To determine whether breast cancer risk assessment improves when combining a diagnostic artificial intelligence (AI) system for lesion detection and a mammographic texture model. Materials and Methods This retrospective study included Danish women consecutively screened for breast cancer at mammography from November 2012 to December 2015 who had at least 5 years of follow-up data. Examinations were evaluated for short-term risk using a commercially available diagnostic AI system for lesion detection, which produced a score to indicate the probability of cancer. A mammographic texture model, trained on a separate data set, assessed textures associated with long-term cancer risk. Area under the receiver operating characteristic curve (AUC) analysis was used to evaluate both the individual and combined performance of the AI and texture models for the prediction of future cancers in women with a negative screening mammogram, including those with interval cancers diagnosed within 2 years of screening and long-term cancers diagnosed 2 years or more after screening. AUCs were compared using the DeLong test. Results The Danish screening cohort included 119 650 women (median age, 59 years [IQR, 53-64 years]), of whom 320 developed interval cancers and 1401 developed long-term cancers. The combination model achieved a higher AUC for interval and long-term cancers grouped together than either the diagnostic AI (AUC, 0.73 vs 0.70; P < .001) or the texture risk (AUC, 0.73 vs 0.66; P < .001) models. The 10% of women with the highest combined risk identified by the combination model accounted for 44.1% (141 of 320) of interval cancers and 33.7% (472 of 1401) of long-term cancers. Conclusion Combining a diagnostic AI system and mammographic texture model resulted in improved risk assessment for interval cancers and long-term cancers and enabled identification of women at high risk. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Poynton and Slanetz in this issue.

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.

Evaluation of a genetic risk score computed using human chromosomal-scale length variation to predict breast cancer

INTRODUCTION

The ability to accurately predict whether a woman will develop breast cancer later in her life, should reduce the number of breast cancer deaths. Different predictive models exist for breast cancer based on family history, BRCA status, and SNP analysis. The best of these models has an accuracy (area under the receiver operating characteristic curve, AUC) of about 0.65. We have developed computational methods to characterize a genome by a small set of numbers that represent the length of segments of the chromosomes, called chromosomal-scale length variation (CSLV).

METHODS

We built machine learning models to differentiate between women who had breast cancer and women who did not based on their CSLV characterization. We applied this procedure to two different datasets: the UK Biobank (1534 women with breast cancer and 4391 women who did not) and the Cancer Genome Atlas (TCGA) 874 with breast cancer and 3381 without.

RESULTS

We found a machine learning model that could predict breast cancer with an AUC of 0.836 95% CI (0.830.0.843) in the UK Biobank data. Using a similar approach with the TCGA data, we obtained a model with an AUC of 0.704 95% CI (0.702, 0.706). Variable importance analysis indicated that no single chromosomal region was responsible for significant fraction of the model results.

CONCLUSION

In this retrospective study, chromosomal-scale length variation could effectively predict whether or not a woman enrolled in the UK Biobank study developed breast cancer.

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 Clinical Prediction Model for Breast Cancer in Women Having Their First Mammogram

Background: Digital mammography is the most efficient screening and diagnostic modality for breast cancer (BC). However, the technology is not widely available in rural areas. This study aimed to construct a prediction model for BC in women scheduled for their first mammography at a breast center to prioritize patients on waiting lists. Methods: This retrospective cohort study analyzed breast clinic data from January 2013 to December 2017. Clinical parameters that were significantly associated with a BC diagnosis were used to construct predictive models using stepwise multiple logistic regression. The models’ discriminative capabilities were compared using receiver operating characteristic curves (AUCs). Results: Data from 822 women were selected for analysis using an inverse probability weighting method. Significant risk factors were age, body mass index (BMI), family history of BC, and indicated symptoms (mass and/or nipple discharge). When these factors were used to construct a model, the model performance according to the Akaike criterion was 1387.9, and the AUC was 0.82 (95% confidence interval: 0.76–0.87). Conclusion: In a resource-limited setting, the priority for a first mammogram should be patients with mass and/or nipple discharge, asymptomatic patients who are older or have high BMI, and women with a family history of BC.

EBCC-13 manifesto: Balancing pros and cons for contralateral prophylactic mastectomy

After a diagnosis of unilateral breast cancer, increasing numbers of patients are requesting contralateral prophylactic mastectomy (CPM), the surgical removal of the healthy breast after diagnosis of unilateral breast cancer. It is important for the community of breast cancer specialists to provide meaningful guidance to women considering CPM. This manifesto discusses the issues and challenges of CPM and provides recommendations to improve oncological, surgical, physical and psychological outcomes for women presenting with unilateral breast cancer: (1) Communicate best available risks in manageable timeframes to prioritise actions; better risk stratification and implementation of risk-assessment tools combining family history, genetic and genomic information, and treatment and prognosis of the first breast cancer are required; (2) Reserve CPM for specific situations; in women not at high risk of contralateral breast cancer (CBC), ipsilateral breast-conserving surgery is the recommended option; (3) Encourage patients at low or intermediate risk of CBC to delay decisions on CPM until treatment for the primary cancer is complete, to focus on treating the existing disease first; (4) Provide patients with personalised information about the risk:benefit balance of CPM in manageable timeframes; (5) Ensure patients have an informed understanding of the competing risks for CBC and that there is a realistic plan for the patient; (6) Ensure patients understand the short- and long-term physical effects of CPM; (7) In patients considering CPM, offer psychological and surgical counselling before surgery; anxiety alone is not an indication for CPM; (8) Eliminate inequality between countries in reimbursement strategies; CPM should be reimbursed if it is considered a reasonable option resulting from multidisciplinary tumour board assessment; (9) Treat breast cancer patients at specialist breast units providing the entire patient-centred pathway.

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.

Breast Cancer Among Transgender and Nonbinary Patients: Paradigms for Improving Data Collection and Inclusion in Breast Imaging Settings

Breast cancer incidence among transgender and nonbinary (TGNB) individuals is not well characterized owing to the absence of robust data collection among this patient population. Consequently, breast cancer risks are largely unknown, and screening guidelines are not based on robust evidence. Additionally, TGNB patients experience barriers to access health care. A first step in improving data collection, research, and ultimately care of TGNB individuals is the identification of group members and demonstration to patients that our breast imaging centers are champions of LGBTQ+ health. At our institution, patients who present for breast imaging complete an iPad-administered breast imaging history and breast cancer risk assessment survey. Using the modified Tyrer-Cuzick model, the lifetime risk of developing breast cancer is estimated, and additional key history that may impact breast care and future breast imaging is collected. Under the previous clinic workflow, patients are identified as either "male" or "female" and complete a corresponding gender-specific survey. To improve care, we revised the survey using gender-inclusive language and developed four versions to allow patients to separately self-report their sex assigned at birth and gender identity. Relevant queries relating to hormone use and gender-affirming chest/breast surgery that are concordant with six gender-identity groups were added. Long-term collection of these inclusive data by imaging centers has the potential to enhance the data set available to improve breast care and better understand breast cancer risk and outcomes among TGNB populations.

Digital Breast Tomosynthesis Plus Ultrasound Versus Digital Mammography Plus Ultrasound for Screening Breast Cancer in Women With Dense Breasts

OBJECTIVE

To compare the outcomes of digital breast tomosynthesis (DBT) screening combined with ultrasound (US) with those of digital mammography (DM) combined with US in women with dense breasts.

MATERIALS AND METHODS

A retrospective database search identified consecutive asymptomatic women with dense breasts who underwent breast cancer screening with DBT or DM and whole-breast US simultaneously between June 2016 and July 2019. Women who underwent DBT + US (DBT cohort) and DM + US (DM cohort) were matched using 1:2 ratio according to mammographic density, age, menopausal status, hormone replacement therapy, and a family history of breast cancer. The cancer detection rate (CDR) per 1000 screening examinations, abnormal interpretation rate (AIR), sensitivity, and specificity were compared.

RESULTS

A total of 863 women in the DBT cohort were matched with 1726 women in the DM cohort (median age, 53 years; interquartile range, 40-78 years) and 26 breast cancers (9 in the DBT cohort and 17 in the DM cohort) were identified. The DBT and DM cohorts showed comparable CDR (10.4 [9 of 863; 95% confidence interval {CI}: 4.8-19.7] vs. 9.8 [17 of 1726; 95% CI: 5.7-15.7] per 1000 examinations, respectively; P = 0.889). DBT cohort showed a higher AIR than the DM cohort (31.6% [273 of 863; 95% CI: 28.5%-34.9%] vs. 22.4% [387 of 1726; 95% CI: 20.5%-24.5%]; P < 0.001). The sensitivity for both cohorts was 100%. In women with negative findings on DBT or DM, supplemental US yielded similar CDRs in both DBT and DM cohorts (4.0 vs. 3.3 per 1000 examinations, respectively; P = 0.803) and higher AIR in the DBT cohort (24.8% [188 of 758; 95% CI: 21.8%-28.0%] vs. 16.9% [257 of 1516; 95% CI: 15.1%-18.9%; P < 0.001).

CONCLUSION

DBT screening combined with US showed comparable CDR but lower specificity than DM screening combined with US in women with dense breasts.

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.

An Assessment of the Predictive Performance of Current Machine Learning-Based Breast Cancer Risk Prediction Models: Systematic Review

BACKGROUND

Several studies have explored the predictive performance of machine learning-based breast cancer risk prediction models and have shown controversial conclusions. Thus, the performance of the current machine learning-based breast cancer risk prediction models and their benefits and weakness need to be evaluated for the future development of feasible and efficient risk prediction models.

OBJECTIVE

The aim of this review was to assess the performance and the clinical feasibility of the currently available machine learning-based breast cancer risk prediction models.

METHODS

We searched for papers published until June 9, 2021, on machine learning-based breast cancer risk prediction models in PubMed, Embase, and Web of Science. Studies describing the development or validation models for predicting future breast cancer risk were included. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias and the clinical applicability of the included studies. The pooled area under the curve (AUC) was calculated using the DerSimonian and Laird random-effects model.

RESULTS

A total of 8 studies with 10 data sets were included. Neural network was the most common machine learning method for the development of breast cancer risk prediction models. The pooled AUC of the machine learning-based optimal risk prediction model reported in each study was 0.73 (95% CI 0.66-0.80; approximate 95% prediction interval 0.56-0.96), with a high level of heterogeneity between studies (Q=576.07, I²=98.44%; P<.001). The results of head-to-head comparison of the performance difference between the 2 types of models trained by the same data set showed that machine learning models had a slightly higher advantage than traditional risk factor-based models in predicting future breast cancer risk. The pooled AUC of the neural network-based risk prediction model was higher than that of the nonneural network-based optimal risk prediction model (0.71 vs 0.68, respectively). Subgroup analysis showed that the incorporation of imaging features in risk models resulted in a higher pooled AUC than the nonincorporation of imaging features in risk models (0.73 vs 0.61; P_{heterogeneity}=.001, respectively). The PROBAST analysis indicated that many machine learning models had high risk of bias and poorly reported calibration analysis.

CONCLUSIONS

Our review shows that the current machine learning-based breast cancer risk prediction models have some technical pitfalls and that their clinical feasibility and reliability are unsatisfactory.

Addressing Misinformation About the Canadian Breast Screening Guidelines

Screening mammography has been shown to reduce breast cancer mortality by 41% in screened women ages 40-69 years. There is misinformation about breast screening and the Canadian breast screening guidelines. This can decrease confidence in screening mammography and can lead to suboptimal recommendations. We review some of this misinformation to help radiologists and referring physicians navigate the varied international and provincial guidelines. We address the ages to start and stop breast screening. We explore how these recommendations may vary for specific populations such as patients who are at increased risk, transgender patients and minorities. We identify who would benefit from supplemental screening and review the available supplemental screening modalities including ultrasound, MRI, contrast-enhanced mammography and others. We describe emerging technologies including the potential use of artificial intelligence for breast screening. We provide background on why screening policies vary across the country between provinces and territories. This review is intended to help radiologists and referring physicians understand and navigate the varied international and provincial recommendations and guidelines and make the best recommendations for their patients.

Breast Density: Current Knowledge, Assessment Methods, and Clinical Implications

Breast density is an accepted independent risk factor for the future development of breast cancer, and greater breast density has the potential to mask malignancies on mammography, thus lowering the sensitivity of screening mammography. The risk associated with dense breast tissue has been shown to be modifiable with changes in breast density. Numerous studies have sought to identify factors that influence breast density, including age, genetic, racial/ethnic, prepubertal, adolescent, lifestyle, environmental, hormonal, and reproductive history factors. Qualitative, semiquantitative, and quantitative methods of breast density assessment have been developed, but to date there is no consensus assessment method or reference standard for breast density. Breast density has been incorporated into breast cancer risk models, and there is growing consciousness of the clinical implications of dense breast tissue in both the medical community and public arena. Efforts to improve breast cancer screening sensitivity for women with dense breasts have led to increased attention to supplemental screening methods in recent years, prompting the American College of Radiology to publish Appropriateness Criteria for supplemental screening based on breast density.

Cumulative Advanced Breast Cancer Risk Prediction Model Developed in a Screening Mammography Population

BACKGROUND

Estimating advanced breast cancer risk in women undergoing annual or biennial mammography could identify women who may benefit from less or more intensive screening. We developed an actionable model to predict cumulative 6-year advanced cancer (prognostic pathologic stage II or higher) risk according to screening interval.

METHODS

We included 931 186 women aged 40-74 years in the Breast Cancer Surveillance Consortium undergoing 2 542 382 annual (prior mammogram within 11-18 months) or 752 049 biennial (prior within 19-30 months) screening mammograms. The prediction model includes age, race and ethnicity, body mass index, breast density, family history of breast cancer, and prior breast biopsy subdivided by menopausal status and screening interval. We used fivefold cross-validation to internally validate model performance. We defined higher than 95th percentile as high risk (>0.658%), higher than 75th percentile to 95th or less percentile as intermediate risk (0.380%-0.658%), and 75th or less percentile as low to average risk (<0.380%).

RESULTS

Obesity, high breast density, and proliferative disease with atypia were strongly associated with advanced cancer. The model is well calibrated and has an area under the receiver operating characteristics curve of 0.682 (95% confidence interval = 0.670 to 0.694). Based on women's predicted advanced cancer risk under annual and biennial screening, 69.1% had low or average risk regardless of screening interval, 12.4% intermediate risk with biennial screening and average risk with annual screening, and 17.4% intermediate or high risk regardless of screening interval.

CONCLUSION

Most women have low or average advanced cancer risk and can undergo biennial screening. Intermediate-risk women may consider annual screening, and high-risk women may consider supplemental imaging in addition to annual screening.

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.

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.

Are English-language online patient education materials related to breast cancer risk assessment understandable, readable, and actionable?

PURPOSE

To evaluate the readability, understandability, and actionability of online patient education materials (OPEM) related to breast cancer risk assessment.

MATERIAL AND METHODS

We queried seven English-language search terms related to breast cancer risk assessment: breast cancer high-risk, breast cancer risk factors, breast cancer family history, BRCA, breast cancer risk assessment, Tyrer-Cuzick, and Gail model. Websites were categorized as: academic/hospital-based, commercial, government, non-profit or academic based on the organization hosting the site. Grade-level readability of qualifying websites and categories was determined using readability metrics and generalized estimating equations based on written content only. Readability scores were compared to the recommended parameters set by the American Medical Association (AMA). Understandability and actionability of OPEM related to breast cancer high-risk were evaluated using the Patient Education Materials Assessment Tool (PEMAT) and compared to criteria set at ≥70%. Descriptive statistics and inter-rater reliability analysis were utilized.

RESULTS

343 websites were identified, of which 162 met study inclusion criteria. The average grade readability score was 12.1 across all websites (range 10.8-13.4). No website met the AMA recommendation. Commercial websites demonstrated the highest overall average readability of 13.1. Of the 26 websites related to the search term breast cancer high-risk, the average understandability and actionability scores were 62% and 34% respectively, both below criteria.

CONCLUSIONS

OPEM on breast cancer risk assessment available to the general public do not meet criteria for readability, understandability, or actionability. To ensure patient comprehension of medical information online, future information should be published in simpler, more appropriate terms.

The Value of Tyrer-Cuzick Versus Gail Risk Modeling in Predicting Benefit from Screening MRI in Breast Cancer

OBJECTIVE

Breast cancer is the most commonly diagnosed malignancy in US women. Risk assessment tools such as the Gail and Tyrer-Cuzick (TC) models calculate risk for breast cancer based on modifiable and non-modifiable factors in order to guide screening and prevention for high-risk patients. Screening with magnetic resonance imaging (MRI) in addition to mammography is recommended in high-risk patients (>20% lifetime risk on TC or other familial based models). Currently, no published data indicate these recommendations improve cancer detection.

MATERIALS AND METHODS

With the aim to determine what percentage lifetime risk (LR%) is associated with a statistically significant increase in cancer detection, the Virginia Commonwealth University (VCU) breast imaging database was reviewed to identify patients who received screening MRI.

RESULTS

The receiver operating characteristics (ROC) curves for the Gail and TC models and the rate of cancer detection correlated to 20% LR% were calculated. The Gail model was considered the control model as it is NOT considered a validated screening tool for MRI. TC is not more accurate than Gail when predicting benefit of breast MRI screening. (area under the curve (AUC): 0.6841, 0.6543 respectively, p = 0.828). Univariate analysis failed to demonstrate a statistically significant relationship between the Gail or TC LR % and diagnosis of breast cancer when using 20% as the cutoff for high-risk classification (p = 1.0, 0.369 respectively). Neither the TC nor the Gail risk calculators demonstrated a significant correlation between risk and the likelihood of diagnosis of breast cancer when screened with MRI.

CONCLUSION

Larger cohort studies are necessary to determine the risk percentage most predictive of a breast cancer diagnosis using MRI as screening.

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

Simple Summary

Several statistical models exist to predict a person’s risk of breast cancer. Risk assessment models can guide cancer screening approaches by identifying individuals who would benefit from additional screening. In this study, we compared the performance of four models in predicting the 5-year risk of breast cancer in a cohort of women aged 40–84 years who underwent screening mammography at three large health systems. Models showed comparable discrimination (ability to distinguish between cases and non-cases) and calibration (ability to accurately predict risk) overall, with no difference by race. Model discrimination was poorer for some cancer subtypes, and better for women with high BMI. The combined BRCAPRO+BCRAT model had improved calibration and discrimination among women with a family history of breast cancer. Our results can inform risk-based screening approaches by identifying women at a high risk of breast cancer.

Abstract

(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.

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.

Supplemental Cancer Screening for Women With Dense Breasts: Guidance for Health Care Professionals

Mammography is the standard for breast cancer screening. The sensitivity of mammography in identifying breast cancer, however, is reduced for women with dense breasts. Thirty-eight states have passed laws requiring that all women be notified of breast tissue density results in their mammogram report. The notification includes a statement that differs by state, encouraging women to discuss supplemental screening options with their health care professionals (HCPs). Several supplemental screening tests are available for women with dense breast tissue, but no established guidelines exist to direct HCPs in their recommendation of preferred supplemental screening test. Tailored screening, which takes into consideration the patient's mammographic breast density and lifetime breast cancer risk, can guide breast cancer screening strategies that are more comprehensive. This review describes the benefits and limitations of the various available supplemental screening tests to guide HCPs and patients in choosing the appropriate breast cancer screening.

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.

Relationship of established risk factors with breast cancer subtypes

Background

Breast cancer is a heterogeneous disease, divided into subtypes based on the expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Subtypes have different biology and prognosis, with accumulating evidence of different risk factors. The purpose of this study was to compare breast cancer risk factors across tumor subtypes in a large, diverse mammography population.

Methods

Women aged 40–84 without a history of breast cancer with a screening mammogram at three United States health systems from 2006 to 2015 were included. Risk factor questionnaires were completed at mammogram visit, supplemented by electronic health records. Invasive tumor subtype was defined by immunohistochemistry as ER/PR+HER2−, ER/PR+HER2+, ER, and PR−HER2+, or triple‐negative breast cancer (TNBC). Cox proportional hazards models were run for each subtype. Associations of race, reproductive history, prior breast problems, family history, breast density, and body mass index (BMI) were assessed. The association of tumor subtypes with screen detection and interval cancer was assessed using logistic regression among invasive cases.

Results

The study population included 198,278 women with a median of 6.5 years of follow‐up (IQR 4.2–9.0 years). There were 4002 invasive cancers, including 3077 (77%) ER/PR+HER2−, 300 (8%) TNBC, 342 (9%) ER/PR+HER2+, and 126 (3%) ER/PR−HER2+ subtype. In multivariate models, Black women had 2.7 times higher risk of TNBC than white women (HR = 2.67, 95% CI 1.99–3.58). Breast density was associated with increased risk of all subtypes. BMI was more strongly associated with ER/PR+HER2− and HER2+ subtypes among postmenopausal women than premenopausal women. Breast density was more strongly associated with ER/PR+HER2− and TNBC among premenopausal than postmenopausal women. TNBC was more likely to be interval cancer than other subtypes.

Conclusions

These results have implications for risk assessment and understanding of the etiology of breast cancer subtypes. More research is needed to determine what factors explain the higher risk of TNBC for Black women.

Performance of the IBIS/Tyrer-Cuzick model of breast cancer risk by race and ethnicity in the Women's Health Initiative

BACKGROUND

The IBIS/Tyrer-Cuzick model is used clinically to guide breast cancer screening and prevention, but was developed primarily in non-Hispanic White women. Little is known about its long-term performance in a racially/ethnically diverse population.

METHODS

The Women's Health Initiative study enrolled postmenopausal women from 1993-1998. Women were included who were aged <80 years at enrollment with no prior breast cancer or mastectomy and with data required for IBIS/Tyrer-Cuzick calculation (weight; height; ages at menarche, first birth, and menopause; menopausal hormone therapy use; and family history of breast or ovarian cancer). Calibration was assessed by the ratio of observed breast cancer cases to the number expected by the IBIS/Tyrer-Cuzick model (O/E; calculated as the sum of cumulative hazards). Differential discrimination was tested for by self-reported race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, Asian or Pacific Islander, and American Indian or Alaskan Native) using Cox regression. Exploratory analyses, including simulation of a protective single-nucleotide polymorphism (SNP), rs140068132 at 6q25, were performed.

RESULTS

During follow-up (median 18.9 years, maximum 23.4 years), 6783 breast cancer cases occurred among 90,967 women. IBIS/Tyrer-Cuzick was well calibrated overall (O/E ratio = 0.95; 95% CI, 0.93-0.97) and in most racial/ethnic groups, but overestimated risk for Hispanic women (O/E ratio = 0.75; 95% CI, 0.62-0.90). Discrimination did not differ by race/ethnicity. Exploratory simulation of the protective SNP suggested improved IBIS/Tyrer-Cuzick calibration for Hispanic women (O/E ratio = 0.80; 95% CI, 0.66-0.96).

CONCLUSIONS

The IBIS/Tyrer-Cuzick model is well calibrated for several racial/ethnic groups over 2 decades of follow-up. Studies that incorporate genetic and other risk factors, particularly among Hispanic women, are essential to improve breast cancer-risk prediction.

Lifestyle, Behavioral, and Dietary Risk Factors in Relation to Mammographic Breast Density in Women at High Risk for Breast Cancer

BACKGROUND

Women at high risk for breast cancer due to genetics or risk factor profiles are counseled to adopt lifestyle, behavioral, and dietary changes to help reduce their risk. These recommendations are based on studies of women at average risk, so their effectiveness in high-risk women is unclear.

METHODS

We evaluated the impact of physical activity, smoking, alcohol consumption, and intake of folate and carotenoids on mammographic breast density-a proxy for breast cancer risk-among 387 high-risk women. Exposures were self-reported on questionnaires. Breast dense area, nondense area, and percent dense area were measured from screening mammograms with Library for Breast Radiodensity Assessment software. Cross-sectional associations were estimated with multivariable quantile regression models.

RESULTS

After adjusting for age, adiposity, reproductive history, and use of postmenopausal hormones, no breast density measure was associated with physical activity level, smoking status, alcohol consumption, or estimated intake of folate, alpha-carotene, beta-carotene, lutein/zeaxanthin, and beta-cryptoxanthin. Lycopene intake was associated with lower dense area when comparing the highest and lowest intake categories (adjusted difference in median = -14 cm², 95% confidence interval: -29 to 1.3 cm²). This association may be explained by incomplete adjustment for adiposity.

CONCLUSIONS

Recommended lifestyle, behavioral, and dietary changes to mitigate personal risk of breast cancer do not substantially impact mammographic breast density measures.

IMPACT

Alternative strategies, such as increased uptake of chemoprevention, may better serve risk reduction efforts in women at high risk for breast cancer.

Risk-predicted dual nomograms consisting of clinical and ultrasound factors for downgrading BI-RADS category 4a breast lesions - A multiple centre study

Purpose: To develop and to validate a risk-predicted nomogram for downgrading Breast Imaging Reporting and Data System (BI-RADS) category 4a breast lesions. Patients and Methods: We enrolled 680 patients with breast lesions that were diagnosed as BI-RADS category 4a by conventional ultrasound from December 2018 to June 2019. All 4a lesions were randomly divided into development and validation groups at the ratio of 3:1. In the development group consisting of 499 cases, the multiple clinical and ultrasound predicted factors were extracted, and dual-predicted nomograms were constructed by multivariable logistic regression analysis, named clinical nomogram and ultrasound nomogram, respectively. Patients were twice classified as either "high risk" or "low risk" in the two nomograms. The performance of these dual nomograms was assessed by an independent validation group of 181 cases. Receiver Operating Characteristic (ROC) curve and diagnostic value were calculated to evaluate the applicability of the new model. Results: After multiple logistic regression analysis, the clinical nomogram included 2 predictors: age and the first-degree family members with breast cancer. The area under the curve (AUC) value for the clinical nomogram was 0.661 and 0.712 for the development and validation groups, respectively. The ultrasound nomogram included 3 independent predictors (margins, calcification and strain ratio), and the AUC value in this nomogram was 0.782 and 0.747 in the development and validation groups, respectively. In the development group of 499 patients, approximately 50.90% (254/499) of patients were twice classified "low risk", with a malignancy rate of 1.18%. In the validation group of 181 patients, approximately 47.51% (86/181) of patients had been twice classified as "low risk", with a malignancy rate of 1.16%. Conclusions: A dual-predicted nomogram incorporating clinical factors and imaging characteristics is an applicable model for downgrading the low-risk lesions in BI-RADS category 4a and shows good stability and accuracy, which is useful for decreasing the rate of invasive examinations and surgery.

Differences in breast cancer risk after benign breast disease by type of screening diagnosis

Introduction

We aimed to assess differences in breast cancer risk across benign breast disease diagnosed at prevalent or incident screens.

Materials and methods

We conducted a retrospective cohort study with data from 629,087 women participating in a long-standing population-based breast cancer screening program in Spain. Each benign breast disease was classified as non-proliferative, proliferative without atypia, or proliferative with atypia, and whether it was diagnosed in a prevalent or incident screen. We used partly conditional Cox hazard regression to estimate the adjusted hazard ratios of the risk of breast cancer.

Results

Compared with women without benign breast disease, the risk of breast cancer was significantly higher (p-value = 0.005) in women with benign breast disease diagnosed in an incident screen (aHR, 2.67; 95%CI: 2.24–3.19) than in those with benign breast disease diagnosed in a prevalent screen (aHR, 1.87; 95%CI: 1.57–2.24). The highest risk was found in women with a proliferative benign breast disease with atypia (aHR, 4.35; 95%CI: 2.09–9.08, and 3.35; 95%CI: 1.51–7.40 for those diagnosed at incident and prevalent screens, respectively), while the lowest was found in women with non-proliferative benign breast disease (aHR, 2.39; 95%CI: 1.95–2.93, and 1.63; 95%CI: 1.32–2.02 for those diagnosed at incident and prevalent screens, respectively).

Conclusion

Our study showed that the risk of breast cancer conferred by a benign breast disease differed according to type of screen (prevalent or incident). To our knowledge, this is the first study to analyse the impact of the screening type on benign breast disease prognosis.

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Breast cancer risk after a benign breast disease varied with the screening type.

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Incident benign breast disease had a higher breast cancer risk than prevalent.

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The risk remained increased regardless of benign breast disease subtype.

Association between breast cancer risk and disease aggressiveness: Characterizing underlying gene expression patterns

The association between breast cancer risk defined by the Tyrer-Cuzick score (TC) and disease prognosis is not well established. Here, we investigated the relationship between 5-year TC and disease aggressiveness and then characterized underlying molecular processes. In a case-only study (n = 2474), we studied the association of TC with molecular subtypes and tumor characteristics. In a subset of patients (n = 672), we correlated gene expression to TC and computed a low-risk TC gene expression (TC-Gx) profile, that is, a profile expected to be negatively associated with risk, which we used to test for association with disease aggressiveness. We performed enrichment analysis to pinpoint molecular processes likely to be altered in low-risk tumors. A higher TC was found to be inversely associated with more aggressive surrogate molecular subtypes and tumor characteristics (P < .05) including Ki-67 proliferation status (P < 5 × 10^-07 ). Our low-risk TC-Gx, based on the weighted sum of 37 expression values of genes strongly correlated with TC, was associated with basal-like (P < 5 × 10^-13 ), HER2-enriched subtype (P < 5 × 10^-07 ) and worse 10-year breast cancer-specific survival (log-rank P < 5 × 10^-04 ). Associations between low-risk TC-Gx and more aggressive molecular subtypes were replicated in an independent cohort from The Cancer Genome Atlas database (n = 975). Gene expression that correlated with low TC was enriched in proliferation and oncogenic signaling pathways (FDR < 0.05). Moreover, higher proliferation was a key factor explaining the association with worse survival. Women who developed breast cancer despite having a low risk were diagnosed with more aggressive tumors and had a worse prognosis, most likely driven by increased proliferation. Our findings imply the need to establish risk factors associated with more aggressive breast cancer subtypes.

Background Parenchymal Uptake on Molecular Breast Imaging and Breast Cancer Risk: A Cohort Study

BACKGROUND. Background parenchymal uptake (BPU) on molecular breast imaging (MBI) was identified in a case-control study as a breast cancer risk factor beyond mammographic density. To our knowledge, this finding has not yet been confirmed in a cohort study. OBJECTIVE. The objectives of this study were to examine the association of BPU with breast cancer and to estimate the absolute risk and discriminatory accuracy of BPU in a cohort study. METHODS. A retrospective cohort was established that included women without a history of breast cancer who underwent MBI from 2004 to 2015. Radiologists who were blinded to future breast cancer diagnoses assessed BPU on baseline MBI examinations as low (photopenic or minimal) or elevated (mild, moderate, or marked). Associations of BPU with breast cancer were estimated using multivariable Cox proportional hazards models of the time to diagnosis. The 5-year absolute risk was calculated for study subgroups. The discriminatory accuracy of BPU was also assessed. RESULTS. Among 2992 women (mean age, 56.3 years; SD, 10.6 years) who underwent MBI, breast cancer events occurred in 144 women (median follow-up, 7.3 years). Median time to diagnosis after MBI was 4.2 years (range, 0.5-11.6 years). Elevated BPU was associated with a greater breast cancer risk (hazard ratio [HR], 2.39; 95% CI, 1.68-3.41; p ≤ .001). This association remained in postmenopausal women (HR, 3.50; 95% CI, 2.31-5.31; p < .001) but was not significant in premenopausal women (HR, 1.29; 95% CI, 0.72-2.32; p = .39). The 5-year absolute risk of breast cancer was 4.3% (95% CI, 2.9-5.7%) for women with elevated BPU versus 2.5% (95% CI, 1.8-3.1%) for those with low BPU. Postmenopausal women with dense breasts and elevated BPU had a 5-year absolute risk of 8.1% (95% CI, 4.3-11.8%) versus 2.8% (1.8-3.8%) for those with low BPU. Among postmenopausal women, discriminatory accuracy for invasive cancer was improved with the addition of BPU versus use of the Gail risk score alone (C statistic, 65.1 vs 59.1; p = .04) or use of the Breast Cancer Surveillance Consortium risk score alone (C statistic, 66.4 vs 60.4; p = .04). CONCLUSION. BPU on MBI is an independent risk factor for breast cancer, with the strongest association observed among postmenopausal women with dense breasts. In postmenopausal women, BPU provides incremental discrimination in predicting breast cancer when combined with either the Gail model or the Breast Cancer Surveillance Consortium model. CLINICAL IMPACT. Observation of elevated BPU on MBI may identify a subset of women with dense breasts who would benefit most from supplemental screening or preventive options.

Breast cancer risk assessment and management programs: A practical guide

Breast cancer risk assessment continues to evolve as emerging knowledge of breast cancer risk drivers and modifiers enables better identification of high-risk women who may benefit from increased screening or targeted risk-reduction protocols. The ongoing development of breast cancer Risk Assessment and Management Programs (RAMPs) presents an opportunity to decrease breast cancer disease incidence with evidence-based interventions. The goal of this review was to provide a practical guide for providers seeking to establish or update a breast cancer risk assessment and management program. We outline genetic/familial, personal, reproductive, and lifestyle-related factors while discussing the incorporation of risk modeling for precise risk estimate personalization. We further describe the process for determining a risk management plan: information gathering, generation of a risk profile, and articulation and implementation of risk reduction. We also include an overview of clinical workflows in breast cancer management programs and underlines the logistics of establishing a program as well as general principles for guiding the formulation of an individualized risk management plan. We discuss practical considerations, such as clinic structure and operation, allocation of resources, and patient education. Other critical aspects of program design, including identification of the target population, delineation of the core components of the clinical experience, definition of provider roles, description of referral mechanisms, and the launching of a marketing plan are also addressed. The process of risk assessment is both anxiety-provoking and empowering for women at increased risk. New knowledge has enabled strategies to both understand the risk and control it through evidence-based risk management. These benefits can now be realized by an increasing number of unaffected, high-risk patients collaborating with risk management practitioners. Continuation of these efforts will lead to further progress in both risk stratification and risk management of women at elevated breast cancer risk in the near future.