Chen Jinbo, Pee David, Ayyagari Rajeev, Graubard Barry, Schairer Catherine, Byrne Celia, Benichou Jacques, Gail Mitchell H
Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA, USA.
J Natl Cancer Inst. 2006 Sep 6;98(17):1215-26. doi: 10.1093/jnci/djj332.
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.
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.
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. The model predicted higher risks than the Gail model for women with a high percentage of dense breast area. However, the average risk projections from the new model in various age groups were similar to those from the Gail model, suggesting that the new model is well calibrated.
This new model for absolute invasive breast cancer risk in white women promises modest improvements in discriminatory power compared with the Gail model but needs to be validated with independent data.
为提高盖尔模型预测浸润性乳腺癌绝对风险的鉴别能力，我们之前开发了一种相对风险模型，该模型纳入了乳腺癌检测示范项目（BCDDP）中白人女性数据的乳腺X线密度（DENSITY）。该模型还包括首次生育时的年龄（AGEFLB）、患癌母亲或姐妹的数量（NUMREL）、既往良性乳腺活检检查的次数（NBIOPS）以及体重（WEIGHT）等变量。在本研究中，我们开发了相应的绝对风险模型。
我们将相对风险模型与2000年全国健康访谈调查中AGEFLB、NUMREL、NBIOPS和WEIGHT变量的分布数据、BCDDP中其他风险因素条件下DENSITY的分布数据、美国国立癌症研究所监测、流行病学和最终结果计划的乳腺癌发病率数据以及全国死亡率数据相结合。置信区间（CIs）考虑了相对风险估计值和风险因素分布的变异性。我们将新模型对1744名白人女性的5年绝对风险预测与盖尔模型的预测进行了比较。
对于年龄小于50岁和50岁及以上的女性，与DENSITY、AGEFLB、NUMREL、NBIOPS和WEIGHT相关的乳腺癌归因风险分别为0.779（95%CI = 0.733至0.819）和0.747（95%CI = 0.702至0.788）。对于乳腺致密面积百分比高的女性，该模型预测的风险高于盖尔模型。然而，新模型在各年龄组的平均风险预测与盖尔模型相似，表明新模型校准良好。
与盖尔模型相比，这种用于白人女性浸润性乳腺癌绝对风险的新模型有望在鉴别能力上有适度提高，但需要用独立数据进行验证。
