Comparative Validation of Breast Cancer Risk Prediction Models and Projections for Future Risk Stratification
Comparative Validation of Breast Cancer Risk Prediction Models and Projections for Future Risk Stratification
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DOI：
10.1093/jnci/djz113
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发表时间：
2020-03-01
影响因子：
10.3
通讯作者：
Garcia-Closas, Montserrat
中科院分区：
文献类型：
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作者：
Choudhury, Parichoy Pal;Wilcox, Amber N.;Garcia-Closas, Montserrat
背景：风险模型的外部验证对于风险分层的乳腺癌预防至关重要。我们使用个体化一致绝对风险估计（iCARE）作为风险模型开发和比较模型验证的灵活工具，并对人群风险分层进行预测。方法：两个最近开发的模型的性能，一个基于乳腺癌和前列腺癌队列联盟分析（iCARE-BPC 3）和另一项基于文献综述的研究（iCARE-Lit），与两个已建立的模型进行了比较（乳腺癌风险评估工具和国际乳腺癌干预研究模型），基于64874名白色非西班牙裔女性的英国队列中的经典风险因素（863例患者）年龄35- 74岁。通过增加乳房X线摄影乳腺密度（MD）和多基因风险评分（PRS），对年龄在50- 70岁的美国白色非西班牙裔女性目标人群进行风险预测，评估风险分层的潜在改善。最佳校准模型为iCARE-Lit（预期观察到的病例数[E/O] = 0.98，95%置信区间[CI] = 0.87 - 1.11），年龄小于50岁的女性和iCARE-BPC 3（E/O=1.00，95%CI = 0.93 - 1.09），50岁或以上女性。使用iCARE-BPC 3进行的风险预测表明，经典风险因素可以在目标人群中识别出约50万名中度至高度风险（>3%的5年风险）的女性。MD和313变异PRS的增加，预计这一数字将增加到约350万妇女，其中，约153 000人预计将发展为浸润性乳腺癌在5 years.Conclusions：iCARE模型的基础上，经典的风险因素进行类似或优于BCRAT或IBIS在白色非西班牙裔妇女。增加MD和PRS可显著改善风险分层。然而，这些集成模型在广泛的临床应用之前需要独立的前瞻性验证。
Background: External validation of risk models is critical for risk-stratified breast cancer prevention. We used the Individualized Coherent Absolute Risk Estimation (iCARE) as a flexible tool for risk model development and comparative model validation and to make projections for population risk stratification.Methods: Performance of two recently developed models, one based on the Breast and Prostate Cancer Cohort Consortium analysis (iCARE-BPC3) and another based on a literature review (iCARE-Lit), were compared with two established models (Breast Cancer Risk Assessment Tool and International Breast Cancer Intervention Study Model) based on classical risk factors in a UK-based cohort of 64 874 white non-Hispanic women (863 patients) age 35-74years. Risk projections in a target population of US white non-Hispanic women age 50-70years assessed potential improvements in risk stratification by adding mammographic breast density (MD) and polygenic risk score (PRS).Results: The best calibrated models were iCARE-Lit (expected to observed number of cases [E/O] = 0.98, 95% confidence interval [CI] = 0.87 to 1.11) for women younger than 50years, and iCARE-BPC3 (E/O=1.00, 95% CI = 0.93 to 1.09) for women 50years or older. Risk projections using iCARE-BPC3 indicated classical risk factors can identify approximately 500 000 women at moderate to high risk (>3% 5-year risk) in the target population. Addition of MD and a 313-variant PRS is expected to increase this number to approximately 3.5 million women, and among them, approximately 153 000 are expected to develop invasive breast cancer within 5 years.Conclusions: iCARE models based on classical risk factors perform similarly to or better than BCRAT or IBIS in white non-Hispanic women. Addition of MD and PRS can lead to substantial improvements in risk stratification. However, these integrated models require independent prospective validation before broad clinical applications.