Validation of the breast cancer surveillance consortium model of breast cancer risk

Tice, Jeffrey A.; Bissell, Michael C. S.; Miglioretti, Diana L.; Gard, Charlotte C.; Rauscher, Garth H.; Dabbous, Firas; Kerlikowske, Karla

doi:10.1007/s10549-019-05167-2

Cited by 57 publications

(40 citation statements)

References 15 publications

(21 reference statements)

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“…The BI‐RADS scale is a breast density measurement on a 4‐point scale, which ranges from fatty tissue to extremely dense tissue. We defined clinically elevated breast cancer risk based on the BCSC 5‐year risk calculator as either 1) an intermediate 5‐year invasive breast cancer risk (1.67%‐2.49%) and extremely dense breasts or 2) a high 5‐year cancer risk (≥2.50%) and either heterogeneously dense or extremely dense breasts. We excluded women with a history of lobular carcinoma in situ, any prior cancer diagnosis (including ductal carcinoma in situ and excluding nonmelanoma skin cancer), or a previous referral for cancer genetic counseling and/or prior genetic testing.…”

Section: Methodsmentioning

confidence: 99%

Prior breast density awareness, knowledge, and communication in a health system–embedded behavioral intervention trial

Mahorter

Knerr

Bowles

et al. 2020

Cancer

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Background Breast density is an important breast cancer risk factor and a focus of recent national and state health policy efforts. This article describes breast density awareness, knowledge, and communication among participants in a health system–embedded trial with clinically elevated breast cancer risk 1 year before state‐mandated density disclosure. Methods Trial participants' demographics and prior health history were ascertained from electronic health records. The proportions of women reporting prior breast density awareness, knowledge of density's masking effect, and communication with a provider about their own breast density were calculated using baseline interview data collected from 2017 to 2018. Multiple logistic regression was used to estimate associations between women's characteristics and density awareness, knowledge, and communication. Results Although the overwhelming majority of participants had heard of breast density (91%) and were aware of breast density's masking effect (87%), only 60% had ever discussed their breast density with a provider. Annual mammography screening was associated with prior breast density awareness (odds ratio [OR], 2.97; 95% confidence interval [CI], 1.29‐6.81), knowledge (OR, 2.83; 95% CI, 1.20‐6.66), and communication (OR, 2.87; 95% CI, 1.34‐6.16) compared with an infrequent or unknown screening interval. Receipt of breast biopsy was also associated with prior knowledge (OR, 1.60; 95% CI, 1.04‐2.45) and communication (OR, 1.36; 95% CI, 1.00‐1.85). Conclusions Breast density awareness and knowledge are high among insured women participating in clinical research, even in the absence of mandated density disclosure. Patient‐provider communication about personal density status is less common, particularly among women with fewer interactions with breast health specialists.

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Section: Methodsmentioning

confidence: 99%

Prior breast density awareness, knowledge, and communication in a health system–embedded behavioral intervention trial

Mahorter

Knerr

Bowles

et al. 2020

Cancer

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“…The Breast Cancer Surveillance Consortium (BCSC) risk model was developed in US women undergoing mammography, incorporates BI-RADS breast density, 3 has accurate calibration, predicts 5-and 10-year invasive cancer risk in the most prevalent racial/ethnic groups in the United States, and has been externally validated in 2 different mammography cohorts. 4 The Tyer-Cuzick model was developed based on white women in the United Kingdom to calculate 10-year invasive cancer risk and incorporates qualitative or quantitative density measures, but is less well calibrated for US women undergoing mammography. 5 The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm is a genetic-based risk model that incorporates BI-RADS density measures and predicts 10-year invasive cancer risk.…”

mentioning

confidence: 99%

Discussions of Dense Breasts, Breast Cancer Risk, and Screening Choices in 2019

Kerlikowske

Miglioretti

Vachon

2019

JAMA

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Breast density, a radiologic term that describes the proportion of parenchymal relative to fatty tissue in mammograms, is a strong and prevalent risk factor. With increasing breast density, the risk of having a breast cancer masked or hidden on mammography increases, as does future breast cancer risk. 1 Almost 50% of US women aged 40 to 74 years have dense breasts (an estimated 27.6 million women). The widespread incorporation of breast density information into screening mammography reports in 36 US states and suggestion to consider supplemental imaging has resulted in women raising questions about breast density and supplemental imaging with their clinicians. 2 Thus, clinicians need to be knowledgeable of the clinical significance of breast density and how it may be useful when combined with breast cancer risk to inform screening discussions. In clinical practice, breast density is assigned by radiologists into one of 4 breast imaging reporting and data system (BI-RADS) categories: almost entirely fatty, scattered areas of fibroglandular density, heterogeneously dense, or extremely dense. The first 2 categories are considered nondense and the last 2 are considered dense. Clinical BI-RADS density assessment is subjective, with interrater and intrarater reliability resulting in moderate to substantial agreement. Commercial software is available to provide automated BI-RADS and quantitative volumetric density estimates on digital mammography. Automated measures are more reproducible than clinical BI-RADS density classification on repeated examinations and similarly predict overall invasive and interval cancer risk (ie, risk of being

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“…15 The BCSC was the first model to include breast density, a wellestablished and nonmodifiable independent risk for breast cancer, with higher-density categories increasing a woman's risk by 2to 4-fold in some studies. 16 The BCSC is well calibrated for the diverse US population 17 and has the advantage of being quick and easy to use (available online or as a phone app). However, this model does not include hormonal risk factors and considers a more limited family history: it includes only first-degree relatives with breast cancer but not age of diagnosis or family history of ovarian cancer.…”

Section: Step 3: Choose An Appropriate Risk-prediction Modelmentioning

confidence: 99%