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||A Simulation Modeling Study to Support Personalized Breast Cancer Prevention and Early Detection in High-Risk Women
Personalized care is complex in this unprecedented era of discovery and ‘big data’. The proposed study focuses
on the real-world choices facing over 100,000 US women each year who are at higher than average risk of
developing breast cancer due to risk factors such as breast density and genetic predisposition. Women at high
risk of developing breast cancer are eligible for various breast cancer prevention and early detection options.
Current clinical guidelines recommend that these women are offered risk reducing medication, and
supplemental imaging with magnetic resonance imaging (MRI) in addition to annual mammography. Each of
these choices has a different profile of benefits and harms that will depend on individual risk factors. Annual
mammography and MRI can detect tumors early, leading to early diagnosis and improved survival, but have
harms related to false positives linked to breast density. Risk-reducing medications reduce the likelihood of
developing breast cancer by nearly half, but these medications can induce menopausal symptoms based on age,
and in a small percent of women, increase the risk of endometrial cancer or other conditions. Ultimately, a
woman’s choice of intervention may depend on how she will weigh harms against benefits for these different
options and outcomes given individual risk. To address these complexities, past studies have focused on either
on single risk factors, risk prediction tools with selected factors, or screening strategies alone.
We propose to use an extant Cancer Intervention and Surveillance Modeling Network (CISNET) simulation
model to synthesize data on clinical risk factors and the impact of early detection with screening and primary
prevention with risk-reducing medication to provide personalized data that will help identify women who are
more likely to benefit from various interventions or combinations of interventions with the least harms. The
aims are to: Aim 1: a) Provide data on the benefits (e.g. avoiding breast cancer; early detection and improved
survival) and harms (side effects of risk-reducing drugs; false positives with screening) of various combinations
of risk reducing medication and screening strategies personalized by individual 5-year risk of developing breast
cancer, breast density, and preferences (utility weights) for different experiences; and b) Conduct sensitivity
analysis to estimate the effects of uncertainty in model inputs or assumptions on results. Aim 2: Explore the
impact of adding PRS to 5-year risk estimates to further personalize information on the balance of benefits and
harms of various risk-reducing medication and screening strategies. Aim 3: Conduct key informant interviews
with health care providers to guide the future use of model results to support shared decision making.
The results of this study will provide novel data to guide personalized care for high-risk women. In future
research, these data could be integrated into a conversation aid to facilitate shared decision making during
clinical encounters. This study contributes to the National Cancer Institute’s mission to support advances in
cancer prevention and control, and use ‘big data’ to enable the translation of research into clinical practice.
Reflecting on 20 years of breast cancer modeling in CISNET: Recommendations for future cancer systems modeling efforts.
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PLoS computational biology, 2021 06; 17(6), p. e1009020.