Breast Cancer Prediction Tool for Black Women Developed


The relatively small number of Black women enrolled in epidemiologic studies of breast cancer has hampered efforts to derive and test models for use in Black women.

“Because U.S. Black women have a disproportionately high rate of breast cancer deaths, improvement in early detection of breast cancer in this population is critical, especially in young Black women who have not yet reached the ages at which mammographic screening is typically begun,” explained corresponding author Julie Palmer, ScD, director of BU’s Slone Epidemiology Center and the Karin Grunebaum Professor in Cancer Research at Boston University School of Medicine.

Palmer and colleagues used epidemiologic data from three case-control studies of Black women from various regions of the U.S. to build a new risk prediction model. They then tested the model using 15 years of follow-up data from 51,798 participants in the Boston University Black Women’s Health Study. The model was found to be well-calibrated. Discriminatory accuracy, which reflects how well a model predicts risk for an individual woman, was similar to that of the most frequently used questionnaire-based breast cancer risk prediction models in White women, and was best for women under age 40.

According to the researchers, the model is simple to use and all the information required can be obtained from the women themselves with a few simple questions. “This new tool for personalized prediction of breast cancer risk in Black women can be easily used by primary care providers to guide screening recommendations and/or referral for genetic testing, particularly for young Black women, thus leading to earlier diagnosis and reduced mortality,” said Palmer.

These findings appear online in the Journal of Clinical Oncology.

This research was supported in part by National Institutes of Health grants R01CA228357 to J.R.P., U01CA164974 to L.R. and J.R.P., R01CA058420 to L.R., R01CA100598 to C.B.A., P50CA58223 to M.A.T., the Susan G Komen Foundation SAC180086 to J.R.P. and the Karin Grunebaum Foundation.

Source: Eurekalert



Source link