Projecting absolute invasive breast cancer risk in white women with a model that includes mammographic density
Projecting absolute invasive breast cancer risk in white women with a model that includes mammographic density
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DOI：
10.1093/jnci/djj332
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发表时间：
2006-09-06
期刊：
影响因子：
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通讯作者：
Gail, Mitchell H.
中科院分区：
文献类型：
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作者：
Chen, Jinbo;Pee, David;Gail, Mitchell H.
背景。为了提高Gail模型预测浸润性乳腺癌绝对风险的歧视性，我们之前开发了一个相对风险模型，该模型结合了乳腺癌检测示范项目（BCDDP）中白人女性的乳房x线摄影密度（density）数据。该模型还包括第一个活产儿出生时的年龄（AGEFLB）、受影响的母亲或姐妹数量（NUMREL）、既往良性乳腺活检检查次数（NBIOPS）和体重（weight）。在本研究中，我们建立了相应的绝对风险模型。方法：我们将相对风险模型与来自2000年全国健康访谈调查的变量AGEFLB、NUMREL、NBIOPS和WEIGHT的分布数据、BCDDP中其他危险因素的密度条件分布数据、来自美国国家癌症研究所监测、流行病学和最终结果项目的乳腺癌发病率数据以及国家死亡率相结合。置信区间（ci）解释了相对风险估计值和风险因素分布的可变性。我们将新模型的5年绝对风险预测与盖尔模型对1744名白人女性的预测进行了比较。结果：50岁以下和50岁以上女性的乳腺癌归因风险分别与密度、AGEFLB、NUMREL、NBIOPS和体重相关，分别为0.779 （95% CI = 0.733 ~ 0.819）和0.747 （95% CI = 0.702 ~ 0.788）。对于乳房密度较高的女性，该模型比盖尔模型预测的风险更高。然而，新模型在不同年龄组中的平均风险预测与Gail模型相似，这表明新模型校准得很好。结论：与Gail模型相比，白人女性浸润性乳腺癌绝对风险的新模型有望在歧视权力方面有所改善，但需要用独立数据进行验证。
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. 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. Conclusions: 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.