Center for Clinical Cancer Genetics and Global Health, Department of Medicine, University of Chicago, Chicago, Illinois.
Department of Surgery, College of Medicine, University of Ibadan, Ibadan, Nigeria.
Cancer Epidemiol Biomarkers Prev. 2018 Jun;27(6):636-643. doi: 10.1158/1055-9965.EPI-17-1128. Epub 2018 Apr 20.
Risk prediction models have been widely used to identify women at higher risk of breast cancer. We aimed to develop a model for absolute breast cancer risk prediction for Nigerian women. A total of 1,811 breast cancer cases and 2,225 controls from the Nigerian Breast Cancer Study (NBCS, 1998-2015) were included. Subjects were randomly divided into the training and validation sets. Incorporating local incidence rates, multivariable logistic regressions were used to develop the model. The NBCS model included age, age at menarche, parity, duration of breastfeeding, family history of breast cancer, height, body mass index, benign breast diseases, and alcohol consumption. The model developed in the training set performed well in the validation set. The discriminating accuracy of the NBCS model [area under ROC curve (AUC) = 0.703, 95% confidence interval (CI), 0.687-0.719] was better than the Black Women's Health Study (BWHS) model (AUC = 0.605; 95% CI, 0.586-0.624), Gail model for white population (AUC = 0.551; 95% CI, 0.531-0.571), and Gail model for black population (AUC = 0.545; 95% CI, 0.525-0.565). Compared with the BWHS and two Gail models, the net reclassification improvement of the NBCS model were 8.26%, 13.45%, and 14.19%, respectively. We have developed a breast cancer risk prediction model specific to women in Nigeria, which provides a promising and indispensable tool to identify women in need of breast cancer early detection in Sub-Saharan Africa populations. Our model is the first breast cancer risk prediction model in Africa. It can be used to identify women at high risk for breast cancer screening. .
风险预测模型已被广泛用于识别乳腺癌风险较高的女性。我们旨在为尼日利亚女性建立一种绝对乳腺癌风险预测模型。该研究共纳入了来自尼日利亚乳腺癌研究（NBCS，1998-2015 年）的 1811 例乳腺癌病例和 2225 例对照。研究对象被随机分为训练集和验证集。通过多变量逻辑回归，结合当地发病率数据，建立模型。NBCS 模型包含年龄、初潮年龄、产次、母乳喂养时间、乳腺癌家族史、身高、体重指数、良性乳腺疾病和饮酒情况。该模型在训练集中表现良好，在验证集中的判别准确率为 0.703（95%置信区间：0.687-0.719），优于黑种人妇女健康研究（BWHS）模型（AUC=0.605；95%CI：0.586-0.624）、白人 Gail 模型（AUC=0.551；95%CI：0.531-0.571）和黑人 Gail 模型（AUC=0.545；95%CI：0.525-0.565）。与 BWHS 和两个 Gail 模型相比，NBCS 模型的净重新分类改善率分别为 8.26%、13.45%和 14.19%。我们建立了一个专门针对尼日利亚女性的乳腺癌风险预测模型，为撒哈拉以南非洲地区需要进行乳腺癌早期检测的女性提供了一种有前途和不可或缺的工具。我们的模型是非洲首个乳腺癌风险预测模型，可用于识别乳腺癌筛查高危女性。
