Breast Cancer Screening, Outside the Population-Screening Program, of Women from Breast Cancer Families without Proven BRCA1/BRCA2 Mutations: a Simulation Study

Abstract

Purpose: We assessed the cost-effectiveness of mammography screening for women under the age of 50, from breast cancer families without proven BRCA1/BRCA2 mutations, because current criteria for screening healthy women from breast cancer families are not evidence-based. Methods: We did simulation studies with mathematical models on the cost-effectiveness of mammography screening of women under the age of 50 with breast cancer family histories. Breast cancer screening was simulated with varying screening intervals (6, 12, 18, and 24 months) and screening cohorts (starting at ages 30, 35, 40, and 45, and continuing to age 50). Incremental costs of screening were compared with those of women ages 50 to 52 years, the youngest age group currently routinely screened in the nationwide screening program of the Netherlands, to determine cost-effectiveness. Sensitivity analyses were done to explore the effects of model assumptions. The cost-effectiveness of breast cancer screening for women over the age of 50 was not debated. Results: The most effective screening interval was found to be 12 months, which, however, seems only to be costeffective in a small group of women under the age of 50 with at least two affected relatives, including at least one affected in the first degree diagnosed under the age of 50. Significantly, early breast cancer screening never seemed to be costeffective in women with only one affected first-degree or second-degree relative. Conclusion: Annual breast cancer screening with mammography for women under the age of 50 seems to be costeffective in women with strong family histories of breast cancer, even when no BRCA1/BRCA2 mutation was found in affected family members. (Cancer Epidemiol Biomarkers Prev 2006;15(3):429 -36)

Gene–environment-wide association studies: emerging approaches

“…For example, the optimal mammographic screening interval for women with a strong family history of breast cancer may differ depending on whether they carry a BRCA1 or BRCA2 mutation171. The potentially protective or deleterious effects of folate supplementation on colorectal cancer risk could depend upon genes involved in its metabolism, such as methylenetetrahydrofolate reductase ( MTHFR )172.…”

Theoretical and X‐ray Crystallographic Evidence of a Fluorine‐Imine Gauche Effect: An Addendum to Dunathan’s Stereoelectronic Hypothesis

“…The clinical parameters of the simulation model have been described in previous publications14, 15. For the purpose of follow‐up of women after primary breast cancer, the input parameters of the model were adapted to the current Dutch screening scenario ( Tables 1 and 2 ), and in addition the model was extended and subsequently validated.…”

“…For the purpose of follow‐up of women after primary breast cancer, the input parameters of the model were adapted to the current Dutch screening scenario ( Tables 1 and 2 ), and in addition the model was extended and subsequently validated. Compared with previous publications14, 15, the model was extended to include population death rates, the risk of developing a second primary tumour, the sensitivity of physical examination, the specificity of mammography and physical examination, and patients' compliance. In addition, the input parameters of the model were changed in accordance with the literature ( Table S1 , supporting information)14–16.…”

“…The aim of the present study was to evaluate the effectiveness and costs of different follow‐up strategies for women with a previous breast cancer. For this simulation, a previously validated simulation‐based decision model was extended14, 15. The main outcome of the study was the detection rate of early breast cancer and the associated additional costs.…”

Which screening strategy should be offered to women with BRCA1 or BRCA2 mutations? A simulation of comparative cost-effectiveness

“…We used the SiMRiSC model (Greuter et al , 2010; Lu et al , 2012) based on Jacobi et al (2006) and extended the model by incorporating data on the specificity of screening, on population death rates and on associated costs. In this micro-simulation model, women who had a chance of developing breast cancer and of death were followed up every year, based on probability functions.…”