Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
Division of Breast Surgery, Department of Surgery, Yonsei University College of Medicine, Seoul, Korea.
Cancer Epidemiol Biomarkers Prev. 2020 Jun;29(6):1271-1277. doi: 10.1158/1055-9965.EPI-19-1478. Epub 2020 Apr 3.
Risk prediction models may be useful for precision breast cancer screening. We aimed to evaluate the performance of breast cancer risk models developed in European-ancestry studies in a Korean population.
We compared discrimination and calibration of three multivariable risk models in a cohort of 77,457 women from the Korean Cancer Prevention Study (KCPS)-II. The first incorporated U.S. breast cancer incidence and mortality rates, U.S. risk factor distributions, and RR estimates from European-ancestry studies. The second recalibrated the first by using Korean incidence and mortality rates and Korean risk factor distributions, while retaining the European-ancestry RR estimates. Finally, we derived a Korea-specific model incorporating the RR estimates from KCPS.
The U.S. European-ancestry breast cancer risk model was well calibrated among Korean women <50 years [expected/observed = 1.124 (0.989, 1.278)] but markedly overestimated the risk for those ≥50 years [E/O = 2.472 (2.005, 3.049)]. Recalibrating absolute risk estimates using Korean breast cancer rates and risk distributions markedly improved the calibration in women ≥50 [E/O = 1.018 (0.825, 1.255)]. The model incorporating Korean-based RRs had similar but not clearly improved performance relative to the recalibrated model.
The poor performance of the U.S. European-ancestry breast cancer risk model among older Korean women highlights the importance of tailoring absolute risk models to specific populations. Recalibrating the model using Korean incidence and mortality rates and risk factor distributions greatly improved performance.
The data will provide valuable information to plan and evaluate actions against breast cancer focused on primary prevention and early detection in Korean women.
风险预测模型可能有助于精确的乳腺癌筛查。我们旨在评估在韩国人群中开发的欧洲裔研究乳腺癌风险模型的性能。
我们比较了三种多变量风险模型在韩国癌症预防研究（KCPS）-II 队列中 77457 名女性中的区分度和校准度。第一个模型纳入了美国乳腺癌发病率和死亡率、美国风险因素分布以及欧洲裔研究的 RR 估计值。第二个模型通过使用韩国的发病率和死亡率以及韩国的风险因素分布重新校准了第一个模型，同时保留了欧洲裔的 RR 估计值。最后，我们得出了一个包含 KCPS 中 RR 估计值的韩国特定模型。
美国欧洲裔乳腺癌风险模型在韩国<50 岁的女性中校准良好[预期/观察值=1.124（0.989，1.278）]，但对≥50 岁的女性风险估计过高[E/O=2.472（2.005，3.049）]。使用韩国乳腺癌发病率和风险分布重新校准绝对风险估计值，显著改善了≥50 岁女性的校准[E/O=1.018（0.825，1.255）]。纳入基于韩国的 RR 的模型与重新校准的模型相比，表现相似但不明显改善。
在美国年龄较大的韩国女性中，美国欧洲裔乳腺癌风险模型表现不佳，这突出了针对特定人群定制绝对风险模型的重要性。使用韩国的发病率和死亡率以及风险因素分布重新校准模型极大地提高了性能。
这些数据将为韩国女性的乳腺癌一级预防和早期检测的规划和评估行动提供有价值的信息。
