A prediction model to simultaneously estimate personal risk of breast cancer and death from other causes in women aged 55 and older
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The incidence of breast cancer increases with age. While mammography screening reduces breast cancer mortality in women 40-74 years its efficacy in women >75 years in not known and on average it takes 10.7 years before 1 in 1,000 women avoids breast cancer death as a result of being screened. There are also risks to being screened, which include overdiagnosis (detection of non-lethal tumors). Thus, guidelines recommend that clinicians consider older women's breast cancer risk and life expectancy when deciding on screening. Yet, clinicians find it difficult to assess how an older woman's breast cancer risk and health interact to determine whether the potential benefits of screening outweigh the risks. Likely because existing models were not developed for use with older women and no model simultaneously predicts breast cancer and non-breast cancer (BC) death to help inform these decisions. To improve breast cancer prediction in older women, we previously developed a novel model to predict 5-year breast cancer risk among postmenopausal women >55 years using competing risk regression (CRR) and data from the Nurses' Health Study (NHS). We then examined our model's performance among Women's Health Initiative (WHI) participants. Our model considers age, family history, health behaviors, reproductive factors, and health in assessing breast cancer risk. We found that our model accurately predicted breast cancer in women 55-74 but underpredicted breast cancer in women >75 in WHI. Our model's discrimination was similar to that of the Breast Cancer Risk Assessment Tool (BCRAT, the most common risk model used in primary care) in WHI but our model accurately risk-stratified more older women than BCRAT. Before implementing our model, we aim to extend it to predict 10-year risk of death from causes other than breast cancer using CRR and NHS data because consideration of older women's 10-year life expectancy is as important as considering their breast cancer risk in making appropriate mammography screening decisions. We also aim to improve our model's generalizability by using Black Women's Health Study data and CRR to determine race-specific risk factor hazard ratios for breast cancer (BC) and non-BC death to use in our model and by calibrating our model to population based breast cancer and non-BC death incidence rates. We will examine our final model's performance in two diverse independent cohorts (WHI and the Multiethnic cohort) and will compare its performance in predicting breast cancer to that of the BCRAT and the Tyrer-Cuzick (TC) models since TC is also increasingly recommended for use in a general population. In sensitivity analyses, we will examine the effect of adding breast density and genomic data to our model. To make our model easily accessible we will add it to our widely used ePrognosis website and to ensure our model's webpage is user-friendly we will test its acceptability with end users. Reducing over- screening for breast cancer in older women is an NCI priority and we anticipate that use of our model will help optimize older women's use of mammography and as a result will improve their care and quality of life.
