Projecting Absolute Invasive Breast Cancer Risk in White Women With a Model That Includes Mammographic Density

Chen, Jinbo; Pee, David; Ayyagari, Rajeev; Graubard, Barry I.; Schairer, Catherine; Byrne, Celia; Bénichou, Jacques; Gail, Mitchell H.

doi:10.1093/jnci/djj332

Cited by 318 publications

(245 citation statements)

References 11 publications

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“…The difference in risk between women with extremely dense, as opposed to predominantly fatty breasts is approximately 4-to 6-fold (15). Incorporation of density into the standard risk prediction models is associated with some improvement in risk prediction (16,17). Mutations in breast cancer genes such as BRCA1 and BRCA2 are too infrequent to affect risk prediction appreciably in the models for the general population.…”

Section: Introductionmentioning

confidence: 99%

Assessing Individual Breast Cancer Risk within the U.K. National Health Service Breast Screening Program: A New Paradigm for Cancer Prevention

Evans

Warwick

Astley

et al. 2012

Cancer Prevention Research

109

121

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The aim of this study is to determine breast cancer risk at mammographic screening episodes and integrate standard risk factors with mammographic density and genetic data to assess changing the screening interval based on risk and offer women at high risk preventive strategies. We report our experience of assessing breast cancer risk within the U.K. National Health Service Breast Screening Program using results from the first 10,000 women entered into the "Predicting Risk Of breast Cancer At Screening" study. Of the first 28,849 women attending for screening at fifteen sites in Manchester 10,000 (35%) consented to study entry and completed the questionnaire. The median 10-year Tyrer-Cuzick breast cancer risk was 2.65% (interquartile range, 2.10-3.45). A total of 107 women (1.07%) had 10-year risks 8% or higher (high breast cancer risk), with a further 8.20% having moderately increased risk (5%-8%). Mammographic density (percent dense area) was 60% or more in 8.3% of women. We collected saliva samples from 478 women for genetic analysis and will extend this to 18% of participants. At time of consent to the study, 95.0% of women indicated they wished to know their risk. Women with a 10-year risk of 8% or more or 5% to 8% and mammographic density of 60% or higher were invited to attend or be telephoned to receive risk counseling; 81.9% of those wishing to know their risk have received risk counseling and 85.7% of these were found to be eligible for a risk-reducing intervention. These results confirm the feasibility of determining breast cancer risk and acting on the information in the context of population-based mammographic screening. Cancer Prev Res; 5(7);

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Section: Introductionmentioning

confidence: 99%

Assessing Individual Breast Cancer Risk within the U.K. National Health Service Breast Screening Program: A New Paradigm for Cancer Prevention

Evans

Warwick

Astley

et al. 2012

Cancer Prevention Research

109

121

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“…The major factors missing are measurement of mammographic density, plasma androgens and estrogens, bone density and BMI, and a history of weight gain, age of menopause, and fracture. Recent studies have included breast density and BMI to enhance the power of risk prediction but these factors have not yet been generally applied (Chen et al 2006a).…”

Section: Introductionmentioning

confidence: 99%

Critical assessment of new risk factors for breast cancer: considerations for development of an improved risk prediction model

Santen

Boyd

Chlebowski

et al. 2007

Endocr Relat Cancer

176

129

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The majority of candidates for breast cancer prevention have not accepted tamoxifen because of the perception of an unfavorable risk/benefit ratio and the acceptance of raloxifene remains to be determined. One means of improving this ratio is to identify women at very high risk of breast cancer. Family history, age, atypia in a benign biopsy, and reproductive factors are the main parameters currently used to determine risk. The most powerful risk factor, mammographic density, is not presently employed routinely. Other potentially important factors are plasma estrogen and androgen levels, bone density, weight gain, age of menopause, and fracture history, which are also not currently used in a comprehensive risk prediction model because of lack of prospective validation. The Breast Cancer Prevention Collaborative Group (BCPCG) met to critically examine and prioritize risk factors that might be selected for further testing by multivariate analysis using existing clinical material. The BCPCG reached a consensus that quantitative breast density, state of the art plasma estrogen and androgen measurements, history of fracture and height loss, BMI, and waist-hip ratio had sufficient priority for further testing. As a practical approach, these parameters could be added to the existing Tyrer-Cuzick model which encompasses factors included in both the Claus and Gail models. The BCPCG analyzed potentially available clinical material from previous prospective studies and determined that a large case/control study to evaluate these new factors might be feasible at this time.

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“…First, the HRM had modest discriminatory accuracy, which highlights the need for considering additional risk factors, such as mammographic density (26)(27)(28)(29) or genetic variants (30,31). Nevertheless, an advantage of the HRM, like BCRAT, is that the information required is available from self-report.…”

Section: Discussionmentioning

confidence: 99%

Projecting Individualized Absolute Invasive Breast Cancer Risk in US Hispanic Women

Banegas

John

Slattery

et al. 2016

JNCI J Natl Cancer Inst

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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 ¼ 1.07, 95% confidence interval [CI] ¼ 0.81 to 1.40), but seemed to overestimate risk in foreign-born women (O/E ratio ¼ 0.66, 95% CI ¼ 0.41 to 1.07). The AUC was 0.564 (95% CI ¼ 0.485 to 0.644) for US-born and 0.625 (95% CI ¼ 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.

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Projecting Absolute Invasive Breast Cancer Risk in White Women With a Model That Includes Mammographic Density

Cited by 318 publications

References 11 publications

Assessing Individual Breast Cancer Risk within the U.K. National Health Service Breast Screening Program: A New Paradigm for Cancer Prevention

Assessing Individual Breast Cancer Risk within the U.K. National Health Service Breast Screening Program: A New Paradigm for Cancer Prevention

Critical assessment of new risk factors for breast cancer: considerations for development of an improved risk prediction model

Projecting Individualized Absolute Invasive Breast Cancer Risk in US Hispanic Women

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