Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard School of Public Health, Boston, MA, USA.
J Natl Cancer Inst. 2024 Jan 10;116(1):81-96. doi: 10.1093/jnci/djad188.
To support mammography screening decision making, we developed a competing-risk model to estimate 5-year breast cancer risk and 10-year nonbreast cancer death for women aged 55 years and older using Nurses' Health Study data and examined model performance in the Black Women's Health Study (BWHS). Here, we examine model performance in predicting 10-year outcomes in the BWHS, Women's Health Initiative-Extension Study (WHI-ES), and Multiethnic Cohort (MEC) and compare model performance to existing breast cancer prediction models.
We used competing-risk regression and Royston and Altman methods for validating survival models to calculate our model's calibration and discrimination (C index) in BWHS (n = 17 380), WHI-ES (n = 106 894), and MEC (n = 49 668). The Nurses' Health Study development cohort (n = 48 102) regression coefficients were applied to the validation cohorts. We compared our model's performance with breast cancer risk assessment tool (Gail) and International Breast Cancer Intervention Study (IBIS) models by computing breast cancer risk estimates and C statistics.
When predicting 10-year breast cancer risk, our model's C index was 0.569 in BWHS, 0.572 in WHI-ES, and 0.576 in MEC. The Gail model's C statistic was 0.554 in BWHS, 0.564 in WHI-ES, and 0.551 in MEC; IBIS's C statistic was 0.547 in BWHS, 0.552 in WHI-ES, and 0.562 in MEC. The Gail model underpredicted breast cancer risk in WHI-ES; IBIS underpredicted breast cancer risk in WHI-ES and in MEC but overpredicted breast cancer risk in BWHS. Our model calibrated well. Our model's C index for predicting 10-year nonbreast cancer death was 0.760 in WHI-ES and 0.763 in MEC.
Our competing-risk model performs as well as existing breast cancer prediction models in diverse cohorts and predicts nonbreast cancer death. We are developing a website to disseminate our model.
为了支持乳腺癌筛查决策，我们使用护士健康研究的数据开发了一种竞争风险模型，用于估计年龄在 55 岁及以上的女性的 5 年乳腺癌风险和 10 年非乳腺癌死亡风险，并在黑人女性健康研究（BWHS）中检验了模型性能。在这里，我们研究了该模型在预测 BWHS、妇女健康倡议-扩展研究（WHI-ES）和多种族队列（MEC）中 10 年结局方面的性能，并将模型性能与现有的乳腺癌预测模型进行了比较。
我们使用竞争风险回归和 Royston 和 Altman 方法验证生存模型，以计算我们模型在 BWHS（n=17380）、WHI-ES（n=106894）和 MEC（n=49668）中的校准和区分（C 指数）。将护士健康研究开发队列（n=48102）的回归系数应用于验证队列。我们通过计算乳腺癌风险估计和 C 统计量来比较我们的模型与乳腺癌风险评估工具（Gail）和国际乳腺癌干预研究（IBIS）模型的性能。
在预测 10 年乳腺癌风险时，我们的模型在 BWHS 中的 C 指数为 0.569，在 WHI-ES 中的 C 指数为 0.572，在 MEC 中的 C 指数为 0.576。Gail 模型在 BWHS 中的 C 统计量为 0.554，在 WHI-ES 中的 C 统计量为 0.564，在 MEC 中的 C 统计量为 0.551；IBIS 的 C 统计量在 BWHS 中为 0.547，在 WHI-ES 中为 0.552，在 MEC 中为 0.562。Gail 模型低估了 WHI-ES 中的乳腺癌风险；IBIS 低估了 WHI-ES 和 MEC 中的乳腺癌风险，但高估了 BWHS 中的乳腺癌风险。我们的模型校准良好。我们的模型在 WHI-ES 和 MEC 中预测 10 年非乳腺癌死亡的 C 指数分别为 0.760 和 0.763。
我们的竞争风险模型在不同队列中的表现与现有的乳腺癌预测模型相当，并可预测非乳腺癌死亡。我们正在开发一个网站来传播我们的模型。
