Breast cancer screening, outside the population-screening program, of women from breast cancer families without proven BRCA1/BRCA2 mutations: a simulation study

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, for 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 chose 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 cost-effective 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 cost-effective 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 cost-effective in women with strong family histories of breast cancer, even when no BRCA1/BRCA2 mutation was found in affected family members

Differences and similarities in breast cancer risk assessment models in clinical practice: which model to choose?

To show differences and similarities between risk estimation models for breast cancer in healthy women from BRCA1/2-negative or untested families. After a systematic literature search seven models were selected: Gail-2, Claus Model, Claus Tables, BOADICEA, Jonker Model, Claus-Extended Formula, and Tyrer-Cuzick. Life-time risks (LTRs) for developing breast cancer were estimated for two healthy counsellees, aged 40, with a variety in family histories and personal risk factors. Comparisons were made with guideline thresholds for individual screening. Without a clinically significant family history LTRs varied from 6.7% (Gail-2 Model) to 12.8% (Tyrer-Cuzick Model). Adding more information on personal risk factors increased the LTRs and yearly mammography will be advised in most situations. Older models (i.e. Gail-2 and Claus) are likely to underestimate the LTR for developing breast cancer as their baseline risk for women is too low. When models include personal risk factors, surveillance thresholds have to be reformulated. For current clinical practice, the Tyrer-Cuzick Model and the BOADICEA Model seem good choices

Modeling familial clustered breast cancer using published data

The purpose of this research was to model the familial clustering of breast cancer and to provide an accurate risk estimate for individuals from the general population, based on their family history of breast and ovarian cancer. We constructed a genetic model as an extension of a model by Claus et al. (E. B. Claus et aL, Am. J. Hum. Genet., 48: 232-242, 1991), with three breast cancer genes, BRCA1, BRCA2, and a hypothetical BRCAu, in two variants, one in which BRCAu was dominant and one in which BRCAu was recessive. The model parameters were estimated using published estimates of population incidence and relative risks. Risk estimation was performed for a set of 196 counselees and for a set of simulated counselees with both the dominant BRCAu and the recessive BRCAu model, and compared relating to medical management. Estimates of the model parameters were found. Relative risks among family members were comparable between the model of Claus et aL (E. U. Claus et aL, Am. J. Hum. Genet., 48: 232-242, 1991) and our model. The dominant and the recessive model provided approximately similar lifetime risks for breast cancer. Our model is suitable for breast cancer risk estimation in a health care setting