Evaluating breast cancer risk projections for Hispanic women

Understanding Benign Breast Disease and Subsequent Breast Cancer in Hispanic White Females: A Step Closer to Evidence-Based Management

INTRODUCTION

Although Hispanic White (HW) females have a lower incidence of breast cancer than non-Hispanic White (NHW) females, breast cancer risk is unclear for HW females after benign breast disease (BBD).

METHODS

We compared BBD characteristics and subsequent breast cancer risk among HW and NHW females in New Mexico using a population-based collection of benign breast biopsies (1996-2007). BBD was categorized as nonproliferative disease (NPD), proliferative disease without atypia (PDWA), or atypical hyperplasia (AH). Breast cancer risk was assessed as absolute risk (AR) using cumulative incidence and RR by comparing the number of breast cancer events in BBDs to non-BBD.

RESULTS

This study included 3,684 HW and 6,587 NHW females with BBD. HW females had similar proportions of NPD (58.6% vs. 54.3%), PDWA (21.4% vs. 23.5%), and AH (3.6% vs. 3.3%) as NHW females. Breast cancer risk among all females with BBD was higher than population-based expected rates (RR, 1.87) and was similar for HW and NHW subgroups (RR = 1.99 vs. 1.84). As expected, breast cancer risk increased with increasing BBD severity, both overall [RR, 1.81 (NPD), 1.85 (PDWA), and 3.10 (AH)] and in the HW and NHW subgroups. Adjusted AR of breast cancer at 5 years also increased with the severity of BBD (HW vs. NHW; NPD: 1.4% vs. 2.1%; PDWA: 1.5% vs. 2.7%; AH: 6% vs. 4.8%).

CONCLUSIONS

We found similar breast cancer RRs and ARs in HW and NHW. Risk counseling should ensure that HW females receive breast cancer clinical management warranted by their similar absolute risks.

IMPACT

The present population-based provides evidence for the clinical management of HW females with BBD for the prevention of breast cancer.

Breast cancer risk prediction combining a convolutional neural network-based mammographic evaluation with clinical factors

PURPOSE

Deep learning techniques, including convolutional neural networks (CNN), have the potential to improve breast cancer risk prediction compared to traditional risk models. We assessed whether combining a CNN-based mammographic evaluation with clinical factors in the Breast Cancer Surveillance Consortium (BCSC) model improved risk prediction.

METHODS

We conducted a retrospective cohort study among 23,467 women, age 35-74, undergoing screening mammography (2014-2018). We extracted electronic health record (EHR) data on risk factors. We identified 121 women who subsequently developed invasive breast cancer at least 1 year after the baseline mammogram. Mammograms were analyzed with a pixel-wise mammographic evaluation using CNN architecture. We used logistic regression models with breast cancer incidence as the outcome and predictors including clinical factors only (BCSC model) or combined with CNN risk score (hybrid model). We compared model prediction performance via area under the receiver operating characteristics curves (AUCs).

RESULTS

Mean age was 55.9 years (SD, 9.5) with 9.3% non-Hispanic Black and 36% Hispanic. Our hybrid model did not significantly improve risk prediction compared to the BCSC model (AUC of 0.654 vs 0.624, respectively, p = 0.063). In subgroup analyses, the hybrid model outperformed the BCSC model among non-Hispanic Blacks (AUC 0.845 vs. 0.589; p = 0.026) and Hispanics (AUC 0.650 vs 0.595; p = 0.049).

CONCLUSION

We aimed to develop an efficient breast cancer risk assessment method using CNN risk score and clinical factors from the EHR. With future validation in a larger cohort, our CNN model combined with clinical factors may help predict breast cancer risk in a cohort of racially/ethnically diverse women undergoing screening.

A Review of Breast Cancer Risk Factors in Adolescents and Young Adults

Simple Summary

Cancer diagnosed in patients between the ages of 15 and 39 deserves special consideration. Diagnoses within this cohort of adolescents and young adults include childhood cancers which present at an older age than expected, or an early presentation of cancers that are typically observed in older adults, such as breast cancer. Cancers within this age group are associated with worse disease-free and overall survival rates, and the incidence of these cases are rising. Knowing an individual’s susceptibility to disease can change their clinical management and allow for the risk-testing of relatives. This review discusses the risk factors that contribute to breast cancer in this unique cohort of patients, including inherited genetic risk factors, as well as environmental and lifestyle factors. We also describe risk models that allow clinicians to quantify a patient’s lifetime risk of developing disease.

Abstract

Cancer in adolescents and young adults (AYAs) deserves special consideration for several reasons. AYA cancers encompass paediatric malignancies that present at an older age than expected, or early-onset of cancers that are typically observed in adults. However, disease diagnosed in the AYA population is distinct to those same cancers which are diagnosed in a paediatric or older adult setting. Worse disease-free and overall survival outcomes are observed in the AYA setting, and the incidence of AYA cancers is increasing. Knowledge of an individual’s underlying cancer predisposition can influence their clinical care and may facilitate early tumour surveillance strategies and cascade testing of at-risk relatives. This information can further influence reproductive decision making. In this review we discuss the risk factors contributing to AYA breast cancer, such as heritable predisposition, environmental, and lifestyle factors. We also describe a number of risk models which incorporate genetic factors that aid clinicians in quantifying an individual’s lifetime risk of disease.

Comparative Validation of Breast Cancer Risk Prediction Models and Projections for Future Risk Stratification

BACKGROUND

External validation of risk models is critical for risk-stratified breast cancer prevention. We used the Individualized Coherent Absolute Risk Estimation (iCARE) as a flexible tool for risk model development and comparative model validation and to make projections for population risk stratification.

METHODS

Performance of two recently developed models, one based on the Breast and Prostate Cancer Cohort Consortium analysis (iCARE-BPC3) and another based on a literature review (iCARE-Lit), were compared with two established models (Breast Cancer Risk Assessment Tool and International Breast Cancer Intervention Study Model) based on classical risk factors in a UK-based cohort of 64 874 white non-Hispanic women (863 patients) age 35-74 years. Risk projections in a target population of US white non-Hispanic women age 50-70 years assessed potential improvements in risk stratification by adding mammographic breast density (MD) and polygenic risk score (PRS).

RESULTS

The best calibrated models were iCARE-Lit (expected to observed number of cases [E/O] = 0.98, 95% confidence interval [CI] = 0.87 to 1.11) for women younger than 50 years, and iCARE-BPC3 (E/O = 1.00, 95% CI = 0.93 to 1.09) for women 50 years or older. Risk projections using iCARE-BPC3 indicated classical risk factors can identify approximately 500 000 women at moderate to high risk (>3% 5-year risk) in the target population. Addition of MD and a 313-variant PRS is expected to increase this number to approximately 3.5 million women, and among them, approximately 153 000 are expected to develop invasive breast cancer within 5 years.

CONCLUSIONS

iCARE models based on classical risk factors perform similarly to or better than BCRAT or IBIS in white non-Hispanic women. Addition of MD and PRS can lead to substantial improvements in risk stratification. However, these integrated models require independent prospective validation before broad clinical applications.

Risk Models for Breast Cancer and Their Validation

Strategies to prevent cancer and diagnose it early when it is most treatable are needed to reduce the public health burden from rising disease incidence. Risk assessment is playing an increasingly important role in targeting individuals in need of such interventions. For breast cancer many individual risk factors have been well understood for a long time, but the development of a fully comprehensive risk model has not been straightforward, in part because there have been limited data where joint effects of an extensive set of risk factors may be estimated with precision. In this article we first review the approach taken to develop the IBIS (Tyrer-Cuzick) model, and describe recent updates. We then review and develop methods to assess calibration of models such as this one, where the risk of disease allowing for competing mortality over a long follow-up time or lifetime is estimated. The breast cancer risk model model and calibration assessment methods are demonstrated using a cohort of 132,139 women attending mammography screening in the State of Washington, USA.

Genetic and Genomic Advances in Breast Cancer Diagnosis and Treatment

Advances in genetic testing for people at high risk for cancer and in targeted gene therapy for breast cancer are rapidly emerging, including newly developed key hormone receptor-targeted therapies and individualized molecular fusion identification and treatment options. These advances are contributing to a new era in cancer treatment modalities and care delivery. As more innovative and advanced treatment options emerge, women's health outcomes and survival rates may improve. Nursing professionals in primary care and women's health specialties must be aware of the latest options for testing, referrals, and treatment modalities.

Review of non-clinical risk models to aid prevention of breast cancer

A disease risk model is a statistical method which assesses the probability that an individual will develop one or more diseases within a stated period of time. Such models take into account the presence or absence of specific epidemiological risk factors associated with the disease and thereby potentially identify individuals at higher risk. Such models are currently used clinically to identify people at higher risk, including identifying women who are at increased risk of developing breast cancer. Many genetic and non-genetic breast cancer risk models have been developed previously. We have evaluated existing non-genetic/non-clinical models for breast cancer that incorporate modifiable risk factors. This review focuses on risk models that can be used by women themselves in the community in the absence of clinical risk factors characterization. The inclusion of modifiable factors in these models means that they can be used to improve primary prevention and health education pertinent for breast cancer. Literature searches were conducted using PubMed, ScienceDirect and the Cochrane Database of Systematic Reviews. Fourteen studies were eligible for review with sample sizes ranging from 654 to 248,407 participants. All models reviewed had acceptable calibration measures, with expected/observed (E/O) ratios ranging from 0.79 to 1.17. However, discrimination measures were variable across studies with concordance statistics (C-statistics) ranging from 0.56 to 0.89. We conclude that breast cancer risk models that include modifiable risk factors have been well calibrated but have less ability to discriminate. The latter may be a consequence of the omission of some significant risk factors in the models or from applying models to studies with limited sample sizes. More importantly, external validation is missing for most of the models. Generalization across models is also problematic as some variables may not be considered applicable to some populations and each model performance is conditioned by particular population characteristics. In conclusion, it is clear that there is still a need to develop a more reliable model for estimating breast cancer risk which has a good calibration, ability to accurately discriminate high risk and with better generalizability across populations.

Assessment of performance of the Gail model for predicting breast cancer risk: a systematic review and meta-analysis with trial sequential analysis

BACKGROUND

The Gail model has been widely used and validated with conflicting results. The current study aims to evaluate the performance of different versions of the Gail model by means of systematic review and meta-analysis with trial sequential analysis (TSA).

METHODS

Three systematic review and meta-analyses were conducted. Pooled expected-to-observed (E/O) ratio and pooled area under the curve (AUC) were calculated using the DerSimonian and Laird random-effects model. Pooled sensitivity, specificity and diagnostic odds ratio were evaluated by bivariate mixed-effects model. TSA was also conducted to determine whether the evidence was sufficient and conclusive.

RESULTS

Gail model 1 accurately predicted breast cancer risk in American women (pooled E/O = 1.03; 95% CI 0.76-1.40). The pooled E/O ratios of Caucasian-American Gail model 2 in American, European and Asian women were 0.98 (95% CI 0.91-1.06), 1.07 (95% CI 0.66-1.74) and 2.29 (95% CI 1.95-2.68), respectively. Additionally, Asian-American Gail model 2 overestimated the risk for Asian women about two times (pooled E/O = 1.82; 95% CI 1.31-2.51). TSA showed that evidence in Asian women was sufficient; nonetheless, the results in American and European women need further verification. The pooled AUCs for Gail model 1 in American and European women and Asian females were 0.55 (95% CI 0.53-0.56) and 0.75 (95% CI 0.63-0.88), respectively, and the pooled AUCs of Caucasian-American Gail model 2 for American, Asian and European females were 0.61 (95% CI 0.59-0.63), 0.55 (95% CI 0.52-0.58) and 0.58 (95% CI 0.55-0.62), respectively. The pooled sensitivity, specificity and diagnostic odds ratio of Gail model 1 were 0.63 (95% CI 0.27-0.89), 0.91 (95% CI 0.87-0.94) and 17.38 (95% CI 2.66-113.70), respectively, and the corresponding indexes of Gail model 2 were 0.35 (95% CI 0.17-0.59), 0.86 (95% CI 0.76-0.92) and 3.38 (95% CI 1.40-8.17), respectively.

CONCLUSIONS

The Gail model was more accurate in predicting the incidence of breast cancer in American and European females, while far less useful for individual-level risk prediction. Moreover, the Gail model may overestimate the risk in Asian women and the results were further validated by TSA, which is an addition to the three previous systematic review and meta-analyses.

TRIAL REGISTRATION

PROSPERO CRD42016047215 .

Development of a Cancer Risk Prediction Tool for Use in the UK Primary Care and Community Settings

Several multivariable risk prediction models have been developed to asses an individual's risk of developing specific cancers. Such models can be used in a variety of settings for prevention, screening, and guiding investigations and treatments. Models aimed at predicting future disease risk that contains lifestyle factors may be of particular use for targeting health promotion activities at an individual level. This type of cancer risk prediction is not yet available in the UK. We have adopted the approach used by the well-established U.S.-derived "YourCancerRisk" model for use in the UK population, which allow users to quantify their individual risk of developing individual cancers relative to the population average risk. The UK version of "YourCancerRisk" computes 10-year cancer risk estimates for 11 cancers utilizing UK figures for prevalence of risk factors and cancer incidence. Because the prevalence of risk factors and the incidence rates for cancer are different between the U.S. and the UK population, this UK model provides more accurate estimates of risks for a UK population. Using an example of breast cancer and data from UK Biobank cohort, we demonstrate that the individual risk factor estimates are similar for the U.S. and UK populations. Assessment of the performance and validation of the multivariate model predictions based on a binary score confirm the model's applicability. The model can be used to estimate absolute and relative cancer risk for use in Primary Care and community settings and is being used in the community to guide lifestyle change. Cancer Prev Res; 10(7); 421-30. ©2017 AACR.

Projecting Individualized Absolute Invasive Breast Cancer Risk in US Hispanic Women

Background

There is no model to estimate absolute invasive breast cancer risk for Hispanic women.

Methods

The San Francisco Bay Area Breast Cancer Study (SFBCS) provided data on Hispanic breast cancer case patients (533 US-born, 553 foreign-born) and control participants (464 US-born, 947 foreign-born). These data yielded estimates of relative risk (RR) and attributable risk (AR) separately for US-born and foreign-born women. Nativity-specific absolute risks were estimated by combining RR and AR information with nativity-specific invasive breast cancer incidence and competing mortality rates from the California Cancer Registry and Surveillance, Epidemiology, and End Results program to develop the Hispanic risk model (HRM). In independent data, we assessed model calibration through observed/expected (O/E) ratios, and we estimated discriminatory accuracy with the area under the receiver operating characteristic curve (AUC) statistic.

Results

The US-born HRM included age at first full-term pregnancy, biopsy for benign breast disease, and family history of breast cancer; the foreign-born HRM also included age at menarche. The HRM estimated lower risks than the National Cancer Institute's Breast Cancer Risk Assessment Tool (BCRAT) for US-born Hispanic women, but higher risks in foreign-born women. In independent data from the Women's Health Initiative, the HRM was well calibrated for US-born women (observed/expected [O/E] ratio = 1.07, 95% confidence interval [CI] = 0.81 to 1.40), but seemed to overestimate risk in foreign-born women (O/E ratio = 0.66, 95% CI = 0.41 to 1.07). The AUC was 0.564 (95% CI = 0.485 to 0.644) for US-born and 0.625 (95% CI = 0.487 to 0.764) for foreign-born women.

Conclusions

The HRM is the first absolute risk model that is based entirely on data specific to Hispanic women by nativity. Further studies in Hispanic women are warranted to evaluate its validity.

Predictors of Breast Cancer Worry in a Hispanic and Predominantly Immigrant Mammography Screening Population

OBJECTIVE

Worry about developing breast cancer (BC) has been associated with participation in screening and genetic testing and with follow-up of abnormal screening results. Little is known about the scope and predictors of BC worry in Hispanic and immigrant populations.

METHODS

We collected in-person interview data from 250 self-identified Hispanic women recruited from an urban mammography facility (average age 50.4 years; 82% foreign-born). Women reported whether they worried about developing breast cancer rarely/never (low worry), sometimes (moderate worry), or often/all the time (high worry). We examined whether sociocultural and psychological factors (e.g., acculturation, education, perceived risk), and risk factors and objective risk for BC (e.g., family history, Gail model 5-year risk estimates, parity) predicted BC worry using multinomial and logistic regression.

RESULTS

In multivariable models, women who perceived higher absolute BC risk (odds ratio, 1.66 [95% confidence interval, 1.28-2.14] for a one-unit increase in perceived lifetime risk) and comparative BC risk (e.g., odds ratio, 2.73, 95% confidence interval, 1.23-6.06) were more likely to report high BC worry than moderate or low BC worry. There were no associations between BC worry and indicators of objective risk or acculturation.

CONCLUSIONS

In Hispanic women undergoing screening mammography, higher perceptions of BC risk, in both absolute and comparative terms, were associated independently with high BC worry, and were stronger predictors of BC worry than indicators of objective BC risk, including family history, mammographic density, and personal BC risk estimates.

SNPs and breast cancer risk prediction for African American and Hispanic women

For African American or Hispanic women, the extent to which clinical breast cancer risk prediction models are improved by including information on susceptibility single nucleotide polymorphisms (SNPs) is unknown, even though these women comprise increasing proportions of the US population and represent a large proportion of the world’s population. We studied 7539 African American and 3363 Hispanic women from the Women’s Health Initiative. The age-adjusted 5-year risks from the BCRAT and IBIS risk prediction models were measured and combined with a risk score based on >70 independent susceptibility SNPs. Logistic regression, adjusting for age group, was used to estimate risk associations with log-transformed age-adjusted 5-year risks. Discrimination was measured by the odds ratio (OR) per standard deviation (SD) and the area under the receiver operator curve (AUC). When considered alone, the ORs for African American women were 1.28 for BCRAT, and 1.04 for IBIS. When combined with the SNP risk score (OR 1.23), the corresponding ORs were 1.39 and 1.22. For Hispanic women the corresponding ORs were 1.25 for BCRAT, and 1.15 for IBIS. When combined with the SNP risk score (OR 1.39), the corresponding ORs were 1.48 and 1.42. There was no evidence that any of the combined models were not well calibrated. Including information on known breast cancer susceptibility loci provides approximately 10 and 19 % improvement in risk prediction using BCRAT for African Americans and Hispanics, respectively. The corresponding figures for IBIS are approximately 18 and 26 %, respectively.

Development of a risk assessment tool for projecting individualized probabilities of developing breast cancer for Chinese women

The optimal approach regarding breast cancer screening for Chinese women is unclear due to the relative low incidence rate. A risk assessment tool may be useful for selection of high-risk subsets of population for mammography screening in low-incidence and resource-limited developing country. The odd ratios for six main risk factors of breast cancer were pooled by review manager after a systematic research of literature. Health risk appraisal (HRA) model was developed to predict an individual's risk of developing breast cancer in the next 5 years from current age. The performance of this HRA model was assessed based on a first-round screening database. Estimated risk of breast cancer increased with age. Increases in the 5-year risk of developing breast cancer were found with the existence of any of included risk factors. When individuals who had risk above median risk (3.3‰) were selected from the validation database, the sensitivity is 60.0% and the specificity is 47.8%. The unweighted area under the curve (AUC) was 0.64 (95% CI = 0.50-0.78). The risk-prediction model reported in this article is based on a combination of risk factors and shows good overall predictive power, but it is still weak at predicting which particular women will develop the disease. It would be very helpful for the improvement of a current model if more population-based prospective follow-up studies were used for the validation.

Perceived versus objective breast cancer risk in diverse women

BACKGROUND

Prior research suggests that women do not accurately estimate their risk for breast cancer. Estimating and informing women of their risk is essential for tailoring appropriate screening and risk reduction strategies.

METHODS

Data were collected for BreastCARE, a randomized controlled trial designed to evaluate a PC-tablet based intervention providing multiethnic women and their primary care physicians with tailored information about breast cancer risk. We included women ages 40-74 visiting general internal medicine primary care clinics at one academic practice and one safety net practice who spoke English, Spanish, or Cantonese, and had no personal history of breast cancer. We collected baseline information regarding risk perception and concern. Women were categorized as high risk (vs. average risk) if their family history met criteria for referral to genetic counseling or if they were in the top 5% of risk for their age based on the Gail or Breast Cancer Surveillance Consortium Model (BCSC) breast cancer risk model.

RESULTS

Of 1,261 participants, 25% (N=314) were classified as high risk. More average risk than high risk women had correct risk perception (72% vs. 18%); 25% of both average and high risk women reported being very concerned about breast cancer. Average risk women with correct risk perception were less likely to be concerned about breast cancer (odds ratio [OR]=0.3; 95% confidence interval [CI]=0.2-0.4) while high risk women with correct risk perception were more likely to be concerned about breast cancer (OR=5.1; 95%CI=2.7-9.6).

CONCLUSIONS

Many women did not accurately perceive their risk for breast cancer. Women with accurate risk perception had an appropriate level of concern about breast cancer. Improved methods of assessing and informing women of their breast cancer risk could motivate high risk women to apply appropriate prevention strategies and allay unnecessary concern among average risk women.

An assessment of existing models for individualized breast cancer risk estimation in a screening program in Spain

Background

The aim of this study was to evaluate the calibration and discriminatory power of three predictive models of breast cancer risk.

Methods

We included 13,760 women who were first-time participants in the Sabadell-Cerdanyola Breast Cancer Screening Program, in Catalonia, Spain. Projections of risk were obtained at three and five years for invasive cancer using the Gail, Chen and Barlow models. Incidence and mortality data were obtained from the Catalan registries. The calibration and discrimination of the models were assessed using the Hosmer-Lemeshow C statistic, the area under the receiver operating characteristic curve (AUC) and the Harrell’s C statistic.

Results

The Gail and Chen models showed good calibration while the Barlow model overestimated the number of cases: the ratio between estimated and observed values at 5 years ranged from 0.86 to 1.55 for the first two models and from 1.82 to 3.44 for the Barlow model. The 5-year projection for the Chen and Barlow models had the highest discrimination, with an AUC around 0.58. The Harrell’s C statistic showed very similar values in the 5-year projection for each of the models. Although they passed the calibration test, the Gail and Chen models overestimated the number of cases in some breast density categories.

Conclusions

These models cannot be used as a measure of individual risk in early detection programs to customize screening strategies. The inclusion of longitudinal measures of breast density or other risk factors in joint models of survival and longitudinal data may be a step towards personalized early detection of BC.

Establishing a program for individuals at high risk for breast cancer

Our need to create a program for individuals at high risk for breast cancer development led us to research the available data on such programs. In this paper, we summarize our findings and our thinking process as we developed our own program. Breast cancer incidence is increasing worldwide. Even though there are known risk factors for breast cancer development, approximately 60% of patients with breast cancer have no known risk factor, although this situation will probably change with further research, especially in genetics. For patients with risk factors based on personal or family history, different models are available for assessing and quantifying risk. Assignment of risk levels permits tailored screening and risk reduction strategies. Potential benefits of specialized programs for women with high breast cancer risk include more cost -effective interventions as a result of patient stratification on the basis of risk; generation of valuable data to advance science; and differentiation of breast programs from other breast cancer units, which can result in increased revenue that can be directed to further improvements in patient care. Guidelines for care of patients at high risk for breast cancer are available from various groups. However, running a high-risk breast program involves much more than applying a guideline. Each high-risk program needs to be designed by its institution with consideration of local resources and country legislation, especially related to genetic issues. Development of a successful high-risk program includes identifying strengths, weaknesses, opportunities, and threats; developing a promotion plan; choosing a risk assessment tool; defining "high risk"; and planning screening and risk reduction strategies for the specific population served by the program. The information in this article may be useful for other institutions considering creation of programs for patients with high breast cancer risk.

The risk of developing invasive breast cancer in Hispanic women : a look across Hispanic subgroups

BACKGROUND

Current evidence on breast cancer among US Hispanic women indicates a significant public health threat, although few studies have assessed the heterogeneity in breast cancer risk among Hispanics of different origin.

METHODS

The 2000 and 2005 National Health Interview Survey Cancer Control Modules were used to examine the Breast Cancer Risk Assessment Tool (BCRAT) 5-year risk and lifetime risk of invasive breast cancer among Mexican/Mexican American, Puerto Rican, Cuban/Cuban American, Dominican (Republic), Central/South American, Other Hispanic, and non-Hispanic white (NHW) women ages 35 to 84 years. Multiple linear regression models were used to compare the BCRAT 5-year and lifetime breast cancer risk between 1) Hispanics and NHWs and 2) Hispanic subgroups.

RESULTS

Hispanic women had significantly lower mean BCRAT 5-year and lifetime breast cancer risk compared with NHW women (P < .001). Among Hispanic subgroups, Cuban/Cuban Americans had a higher BCRAT 5-year risk (P < .05), whereas Dominicans had a higher lifetime risk (P < .001) compared with Mexican/Mexican Americans. Approximately 2.6% of Hispanic women were at high risk for breast cancer (BCRAT 5-year risk ≥1.67%), ranging from 1% of Central/South Americans to 3.7% of Puerto Ricans; few Hispanics (0.2%) had a lifetime risk ≥20%.

CONCLUSIONS

The current findings indicate that Hispanic women have a significantly lower risk of breast cancer compared with NHW women, although the risk according to BCRAT differed significantly between specific Hispanic subgroups. We provide estimates of the number of US Hispanic women from six subgroups who may be eligible for prophylactic breast cancer chemoprevention. The authors concluded that future studies should further investigate the heterogeneity in breast cancer risk and risk factors between Hispanic women of different origins.

Recalibration of the Gail model for predicting invasive breast cancer risk in Spanish women: a population-based cohort study

The Gail model for predicting the absolute risk of invasive breast cancer has been validated extensively in US populations, but its performance in the international setting remains uncertain. We evaluated the predictive accuracy of the Gail model in 54,649 Spanish women aged 45-68 years who were free of breast cancer at the 1996-1998 baseline mammographic examination in the population-based Navarre Breast Cancer Screening Program. Incident cases of invasive breast cancer and competing deaths were ascertained until the end of 2005 (average follow-up of 7.7 years) through linkage with population-based cancer and mortality registries. The Gail model was tested for calibration and discrimination in its original form and after recalibration to the lower breast cancer incidence and risk factor prevalence in the study cohort, and compared through cross-validation with a Navarre model fully developed from this cohort. The original Gail model overpredicted significantly the 835 cases of invasive breast cancer observed in the cohort (ratio of expected to observed cases 1.46, 95 % CI 1.36-1.56). The recalibrated Gail model was well calibrated overall (expected-to-observed ratio 1.00, 95 % CI 0.94-1.07), but it tended to underestimate risk for women in low-risk quintiles and to overestimate risk in high-risk quintiles (P = 0.01). The Navarre model showed good cross-validated calibration overall (expected-to-observed ratio 0.98, 95 % CI 0.92-1.05) and in different cohort subsets. The Navarre and Gail models had modest cross-validated discrimination indexes of 0.542 (95 % CI 0.521-0.564) and 0.544 (95 % CI 0.523-0.565), respectively. Although the original Gail model cannot be applied directly to populations with different underlying rates of invasive breast cancer, it can readily be recalibrated to provide unbiased estimates of absolute risk in such populations. Nevertheless, its limited discrimination ability at the individual level highlights the need to develop extended models with additional strong risk factors.

Personalized medicine and access to health care: potential for inequitable access?

Personalized medicine promises that an individual's genetic information will be increasingly used to prioritize access to health care. Use of genetic information to inform medical decision making, however, raises questions as to whether such use could be inequitable. Using breast cancer genetic risk prediction models as an example, on the surface clinical use of genetic information is consistent with the tools provided by evidence-based medicine, representing a means to equitably distribute limited health-care resources. However, at present, given limitations inherent to the tools themselves, and the mechanisms surrounding their implementation, it becomes clear that reliance on an individual's genetic information as part of medical decision making could serve as a vehicle through which disparities are perpetuated under public and private health-care delivery models. The potential for inequities arising from using genetic information to determine access to health care has been rarely discussed. Yet, it raises legal and ethical questions distinct from those raised surrounding genetic discrimination in employment or access to private insurance. Given the increasing role personalized medicine is forecast to play in the provision of health care, addressing a broader view of what constitutes genetic discrimination, one that occurs along a continuum and includes inequitable access, will be needed during the implementation of new applications based on individual genetic profiles. Only by anticipating and addressing the potential for inequitable access to health care occurring from using genetic information will we move closer to realizing the goal of personalized medicine: to improve the health of individuals.

Perceived and objective breast cancer risk assessment in Chilean women living in an underserved area

BACKGROUND

Breast cancer is the most frequently diagnosed malignancy among Chilean women and an increasingly significant public health threat. This study assessed the accuracy of breast cancer risk perception among underserved, Chilean women.

METHODS

Women aged 50 to 70 years, with no mammogram during the last 2 years, were randomly selected from a community clinic registry in Santiago, Chile (n = 500). Perceived risk was measured using three methods: absolute risk, comparative risk, and numerical risk. Risk comprehension was measured by comparing women's perceived and objective risk estimates. Multivariate logistic regression was used to assess overestimation of perceived risk.

RESULTS

Women at high risk of breast cancer were more likely than average risk women to perceive themselves at high or higher risk, using absolute and comparative risk approaches (P < 0.001). The majority of participants (67%) overestimated their breast cancer risk, on the basis of risk comprehension; although, participants achieved higher accuracy with comparative risk (40%) and absolute risk (31.6%) methods. [Age, breast cancer knowledge and Breast Cancer Risk Assessment Tool (BCRAT) 5-year risk were significantly associated (P < 0.01) with accuracy of perceived risk].

CONCLUSION

Chilean women residing in an underserved community may not accurately assess their breast cancer risk, although risk perception and level of accuracy differed between perceived risk measures. Comparative and absolute risk methods may better reflect women's interpretation and accuracy of risk perception.

IMPACT

Improving our understanding of Chilean women's perceptions of developing breast cancer may lead to the development of culturally relevant efforts to reduce the breast cancer burden in this population.