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BCSC Breast Cancer Risk Models: Predicting Breast Cancer Diagnosis

The Breast Cancer Surveillance Consortium (BCSC) utilizes population-based screening mammography data and risk factor information to develop breast cancer risk models. These models, such as the Gail model and the Tice model, provide short-term and long-term predictions of breast cancer diagnosis. By incorporating breast density as a risk factor, these models can better estimate individual risk and inform prevention efforts. However, despite improvements, risk models still struggle with precise risk estimation.

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BCSC Breast Cancer Risk Models: Predicting Breast Cancer Diagnosis

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  1. Breast Cancer Surveillance Consortium (BCSC):A Research Infrastructure sponsored by the National Cancer InstituteBreast Cancer Risk Models William Barlow, PhD Senior Biostatistician, Cancer Research and Biostatistics Research Professor, Dept. of Biostatistics

  2. Breast cancer risk models in 1994 • Gail model (Gail et al., JNCI, 1989) • Claus model (several references) • BRCAPRO (many references)

  3. Advantage of BCSC data for estimating risk • Population-based screening mammography population • Prospectively collected risk factor information coded in an uniform manner • Huge longitudinal dataset that is increasing • Can examine the very important role of breast density as a risk factor, available on almost all screening mammograms

  4. BCSC breast cancer risk models • Barlow et al., JNCI, 2006 (77 citations) • Short-term prediction of a diagnosis of breast cancer • Tice, et al., Annals Int Med, 2008 (17 citations) • Long-term prediction of a diagnosis of breast cancer • Simplified model to be used in clinical care • Janes, Pepe, and Gu, Annals Int Med, 2009 (17 citations) • Compares the change in risk prediction from the Gail model to the Tice model • Statistical technique for evaluating how well a model improves on past models for identifying high risk individuals

  5. BCSC risk prediction model • Published in the same JNCI issue as the revised Gail model which also includes breast density • Based on one million women and 2.4 million screening mammograms • Published in the same JNCI issue as the revised Gail model which also includes breast density

  6. BCSC risk factors & outcomes • Prospective collection of self-reported risk factors: • demographic information (age, race, ethnicity) • family history of breast cancer • previous breast procedures • menopausal status and use of hormone therapy • body mass index (BMI) • Breast density (BI-RADS scale) and mammographic assessment reported by the radiologist • Outcome is a diagnosis of breast cancer within one year of the screening mammogram • Invasive breast cancer • DCIS

  7. BCSC risk data • Available online along with the risk program and documentation: • http://breastscreening.cancer.gov/ • Dataset has been downloaded 228 times • What’s not included? • Mammographic outcomes; location; individual-level data • What’s missing? • genetic markers • in-depth family history assessment • environmental factors • dietary factors (other than BMI) • exercise levels

  8. Purpose of the risk model • Prediction of a diagnosis of breast cancer within a year of a screening mammogram • Extrapolated to 5-years for comparison to the Gail model, but not intended for long-term prediction • Most Gail risk factors are included • Adds breast density to the risk model

  9. Observed incidence by age and breast density

  10. Risk model factors and assessment • Risk factors for pre-menopausal and post-menopausal women are different • Required that a factor be significant to the 0.0001 level before including it • Goal was to keep the model as simple as possible • Develop the model on 75% and validate it on the remaining 25% • Good calibration in the validation sample (correct estimation of cancer rates) • Good prediction of individual outcomes (c-statistic)

  11. Risk Model for Premenopausal Women c = 0.63

  12. Postmenopausal Predictors (c=0.62) • Age • Race • Hispanic ethnicity • Body mass index (BMI) • Age at first birth • Family history of breast cancer • Previous breast surgery • Menopausal status • Type of menopause • Hormone therapy use • Breast density • Previous screening result (false positive/true negative)

  13. Five-year risk of invasive breast cancer based on extrapolation of the model: High and low risk women

  14. Risk model summary • This particular model did somewhat better than the Gail model • All risk models have difficult getting beyond a c-statistic of 0.65 • Still cannot identify a group of women who are risk-free • Risk models may identify high-risk groups, but still have difficulty with precise estimation of risk for a particular woman

  15. Clinical implications • Breast density was a known risk factor, but we did not appreciate its importance relative to other risk factors • Can better estimate risk for prevention efforts • Breast density may be an early marker of a reduction in risk

  16. Next steps • Augment the screening mammogram assessment with risk factor information to better estimate outcomes from positive or negative screening mammograms • Use changes in risk factors to develop better models of long-term risk prediction • Incorporate extensive family history and and SNPs into risk models with breast density (P01 application by BCSC)

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