UCSF researchers validate new model for breast cancer risk assessment in multiple eth...(r said Tice. Breast density classificat...),Health

The research team used data from more than one million women who visited screening mammography sites across the United States between 1996 and 2003. Model calibration was assessed by calculating the ratio of expected cases of breast cancer to observed cases of breast cancer. Calibration, according to the study, assesses how closely the number of women in whom the model predicts that breast cancer will develop matches with the actual number of women in whom breast cancer is diagnosed. An observed ratio of 1.0 would indicate perfect calibration.

Study results showed the model they developed was well calibrated and reasonably accurate across risk factor subgroups. After five years of follow-up, the observed rate of invasive breast cancer was 1.40 percent (8,784 cases of cancer among 629,229 women) compared to the expected rate created by the model of 1.41 percent. However, the model slightly underestimated breast cancer rates in younger women (age 40-44) and underestimated cancer rates among Asian and Hispanic women.

We found that a model that incorporates mammographic breast density can estimate a womans risk for invasive breast cancer and is convenient enough that it could be incorporated into routine breast cancer screening, said Tice. Primary care physicians could use it to calculate a womans five year risk of developing breast cancer.

Tice warns, however, this is not the definitive model for breast cancer risk assessment and that it is unlikely a single model would be able to address all needs in breast cancer risk assessment. Some women will benefit from genetic counseling and screening, other women will require more d
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