Adding breast density to risk model could improve cancer prediction
Jeffrey A. Tice, MD, University of California at San Francisco, and colleagues, undertook the study to develop and validate an easy-to-use breast cancer risk prediction model that includes breast density because the current models for assessing breast cancer risk are complex and do not include breast density.
The investigators designed an empirical model based on surveillance, epidemiology, end results incidence and relative hazards from a prospective cohort.
The researchers gathered their data from mammography sites participating in the Breast Cancer Surveillance Consortium, and examined the records of approximately 1.1 million women undergoing mammography who had no previous diagnosis of breast cancer.
Tice and colleagues looked at factors of self-reported age, race or ethnicity, family history of breast cancer, and history of breast biopsy, as well as how community radiologists rated breast density by using four breast imaging reporting and data system categories.
During 5.3 years of follow-up, invasive breast cancer was diagnosed in 14,766 women, according to the authors. They found that the breast density model was well calibrated overall (expected–observed ratio, 1.03; 0.99 to 1.06) and in racial and ethnic subgroups.
The breast density also had modest discriminatory accuracy (concordance index, 0.66; 0.65 to 0.67).
Women with low-density mammograms had five-year risks of less than 1.67 percent unless they had a family history of breast cancer and were older than age 65, the investigators found.
However, the authors acknowledged that the model has only a modest ability to discriminate between women who will develop breast cancer and those who will not.
The researchers said they developed a breast cancer risk prediction model that incorporates a measure of breast density routinely reported with mammography, and its predictions were accurate, but it had only modest ability to distinguish women who did not develop cancer from those who did, and it misclassified risk in some subgroups.
Tice and colleagues concluded that a breast cancer prediction model that incorporates breast density does well in some but not all domains of predicting risk, and its accuracy should be better characterized before it is used clinically.