BACKGROUND: The Breast Cancer Risk Assessment Tool (BCRAT) of the National Cancer Institute is widely used for estimating absolute risk of invasive breast cancer. However, the absolute risk estimates for Asian and Pacific Islander American (APA) women are based on data from white women. We developed a model for projecting absolute invasive breast cancer risk in APA women and compared its projections to those from BCRAT. METHODS: Data from 589 women with breast cancer (case patients) and 952 women without breast cancer (control subjects) in the Asian American Breast Cancer Study were used to compute relative and attributable risks based on the age at menarche, number of affected mothers, sisters, and daughters, and number of previous benign biopsies. Absolute risks were obtained by combining this information with ethnicity-specific data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) program and with US ethnicity-specific mortality data to create the Asian American Breast Cancer Study model (AABCS model). Independent data from APA women in the Women's Health Initiative (WHI) were used to check the calibration and discriminatory accuracy of the AABCS model. RESULTS: The AABCS model estimated absolute risk separately for Chinese, Japanese, Filipino, Hawaiian, Other Pacific Islander, and Other Asian women. Relative and attributable risks for APA women were comparable to those in BCRAT, but the AABCS model usually estimated lower-risk projections than BCRAT in Chinese and Filipino, but not in Hawaiian women, and not in every age and ethnic subgroup. The AABCS model underestimated absolute risk by 17% (95% confidence interval = 1% to 38%) in independent data from WHI, but APA women in the WHI had incidence rates approximately 18% higher than those estimated from the SEER program. CONCLUSIONS: The AABCS model was calibrated to ethnicity-specific incidence rates from the SEER program for projecting absolute invasive breast cancer risk and is preferable to BCRAT for counseling APA women.
BACKGROUND: The Breast Cancer Risk Assessment Tool (BCRAT) of the National Cancer Institute is widely used for estimating absolute risk of invasive breast cancer. However, the absolute risk estimates for Asian and Pacific Islander American (APA) women are based on data from white women. We developed a model for projecting absolute invasive breast cancer risk in APA women and compared its projections to those from BCRAT. METHODS: Data from 589 women with breast cancer (case patients) and 952 women without breast cancer (control subjects) in the Asian American Breast Cancer Study were used to compute relative and attributable risks based on the age at menarche, number of affected mothers, sisters, and daughters, and number of previous benign biopsies. Absolute risks were obtained by combining this information with ethnicity-specific data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) program and with US ethnicity-specific mortality data to create the Asian American Breast Cancer Study model (AABCS model). Independent data from APA women in the Women's Health Initiative (WHI) were used to check the calibration and discriminatory accuracy of the AABCS model. RESULTS: The AABCS model estimated absolute risk separately for Chinese, Japanese, Filipino, Hawaiian, Other Pacific Islander, and Other Asian women. Relative and attributable risks for APA women were comparable to those in BCRAT, but the AABCS model usually estimated lower-risk projections than BCRAT in Chinese and Filipino, but not in Hawaiian women, and not in every age and ethnic subgroup. The AABCS model underestimated absolute risk by 17% (95% confidence interval = 1% to 38%) in independent data from WHI, but APA women in the WHI had incidence rates approximately 18% higher than those estimated from the SEER program. CONCLUSIONS: The AABCS model was calibrated to ethnicity-specific incidence rates from the SEER program for projecting absolute invasive breast cancer risk and is preferable to BCRAT for counseling APA women.
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Authors: Xiangqing Sun; Robert C Elston; Jill S Barnholtz-Sloan; Gary W Falk; William M Grady; Ashley Faulx; Sumeet K Mittal; Marcia Canto; Nicholas J Shaheen; Jean S Wang; Prasad G Iyer; Julian A Abrams; Ye D Tian; Joseph E Willis; Kishore Guda; Sanford D Markowitz; Apoorva Chandar; James M Warfe; Wendy Brock; Amitabh Chak Journal: Cancer Epidemiol Biomarkers Prev Date: 2016-02-29 Impact factor: 4.254
Authors: Matthew P Banegas; Esther M John; Martha L Slattery; Scarlett Lin Gomez; Mandi Yu; Andrea Z LaCroix; David Pee; Rowan T Chlebowski; Lisa M Hines; Cynthia A Thompson; Mitchell H Gail Journal: J Natl Cancer Inst Date: 2016-12-20 Impact factor: 13.506
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