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

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

 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 Advantage of BCSC data for estimating risk

 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 BCSC breast cancer risk models

 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 BCSC risk prediction model

 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 BCSC risk factors & outcomes

 Available online along with the risk program and documentation:   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 BCSC risk data

 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 Purpose of the risk model

Observed incidence by age and breast density

 Risk factors for pre-menopausal and post-menopausal women are different  Required that a factor be significant to the 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) Risk model factors and assessment

Risk Model for Premenopausal Women Risk Factor LevelsOR 95% CI Age in years (versus Age 35-39) (versus Age 35-39) Age – 1.56 Age – 2.41 Age – 3.02 Prior breast procedure (versus No procedure) (versus No procedure) Yes – 1.65 Unknown – 1.10 First degree family hx (versus No family hx) (versus No family hx) Yes, 1 relative – 1.74 Yes, 2 or more relatives – 3.36 Unknown – 1.07 Breast density (BI-RADS) 1: Almost entirely fat : Scattered fibroglandular densities – : Heterogeneously dense – : Extremely dense – 6.28 Unknown/different system – 5.24 c = 0.63

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)

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

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

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

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)