Projecting Absolute Invasive Breast Cancer Risk in White Women With a Model That Includes Mammographic Density

作者：Jinbo Chen, David Pee, Rajeev Ayyagari, Barry I. Graubard, Catherine Schairer, Celia Byrne, Jacques Bénichou, Mitchell H. Gail · 发表于：JNCI Journal of the National Cancer Institute · 年份：2006 · DOI：10.1093/jnci/djj332 · 被引用次数：350 · 研究领域：Digital Radiography and Breast Imaging、Breast Lesions and Carcinomas、AI in cancer detection

BACKGROUND: To improve the discriminatory power of the Gail model for predicting absolute risk of invasive breast cancer, we previously developed a relative risk model that incorporated mammographic density (DENSITY) from data on white women in the Breast Cancer Detection Demonstration Project (BCDDP). That model also included the variables age at birth of first live child (AGEFLB), number of affected mother or sisters (NUMREL), number of previous benign breast biopsy examinations (NBIOPS), and weight (WEIGHT). In this study, we developed the corresponding model for absolute risk. METHODS: We combined the relative risk model with data on the distribution of the variables AGEFLB, NUMREL, NBIOPS, and WEIGHT from the 2000 National Health Interview Survey, with data on the conditional distribution of DENSITY given other risk factors in BCDDP, with breast cancer incidence rates from the Surveillance, Epidemiology, and End Results program of the National Cancer Institute, and with national mortality rates. Confidence intervals (CIs) accounted for variability of estimates of relative risks and of risk factor distributions. We compared the absolute 5-year risk projections from the new model with those from the Gail model on 1744 white women. RESULTS: Attributable risks of breast cancer associated with DENSITY, AGEFLB, NUMREL, NBIOPS, and WEIGHT were 0.779 (95% CI = 0.733 to 0.819) and 0.747 (95% CI = 0.702 to 0.788) for women younger than 50 years and 50 years or older, respectively....