Division of Cancer Epidemiology (C020), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, 69120, Heidelberg, Germany.
Inserm, Centre for Research in Epidemiology and Population Health (CESP), U1018, Nutrition, Hormones and Women's Health Team, 94805, Villejuif, France.
Eur J Epidemiol. 2016 Jan;31(1):51-60. doi: 10.1007/s10654-015-0030-9. Epub 2015 May 13.
Endometrial cancer (EC) is the fourth most frequent cancer in women in Europe, and as its incidence is increasing, prevention strategies gain further pertinence. Risk prediction models can be a useful tool for identifying women likely to benefit from targeted prevention measures. On the basis of data from 201,811 women (mostly aged 30-65 years) including 855 incident EC cases from eight countries in the European Prospective Investigation into Cancer and Nutrition cohort, a model to predict EC was developed. A step-wise model selection process was used to select confirmed predictive epidemiologic risk factors. Piece-wise constant hazard rates in 5-year age-intervals were estimated in a cause-specific competing risks model, five-fold-cross-validation was applied for internal validation. Risk factors included in the risk prediction model were body-mass index (BMI), menopausal status, age at menarche and at menopause, oral contraceptive use, overall and by different BMI categories and overall duration of use, parity, age at first full-term pregnancy, duration of menopausal hormone therapy and smoking status (specific for pre, peri- and post-menopausal women). These variables improved the discriminating capacity to predict risk over 5 years from 71% for a model based on age alone to 77% (overall C statistic), and the model was well-calibrated (ratio of expected to observed cases = 0.99). Our model could be used for the identification of women at increased risk of EC in Western Europe. To achieve an EC-risk model with general validity, a large-scale cohort-consortium approach would be needed to assess and adjust for population variation.
子宫内膜癌(EC)是欧洲女性中第四常见的癌症,随着其发病率的增加,预防策略变得更加相关。风险预测模型可以成为识别可能受益于靶向预防措施的女性的有用工具。基于来自欧洲前瞻性癌症与营养研究队列的 201,811 名女性(年龄大多在 30-65 岁之间)的数据,包括来自 8 个国家的 855 例子宫内膜癌病例,建立了一种预测子宫内膜癌的模型。使用逐步模型选择过程选择经过验证的预测性流行病学风险因素。在特定于原因的竞争风险模型中,以 5 年年龄间隔估计分段常数危险率,并应用五分法交叉验证进行内部验证。纳入风险预测模型的风险因素包括体重指数(BMI)、绝经状态、初潮年龄和绝经年龄、口服避孕药的使用情况、总体使用情况和按不同 BMI 类别和总使用时间、生育次数、首次足月妊娠年龄、绝经激素治疗的持续时间和吸烟状况(针对绝经前、围绝经期和绝经后妇女)。这些变量提高了预测风险的区分能力,将仅基于年龄的模型的 5 年预测风险从 71%提高到 77%(总体 C 统计量),并且该模型具有良好的校准度(预期病例与观察病例的比值=0.99)。我们的模型可用于识别西欧子宫内膜癌风险增加的女性。为了实现具有普遍有效性的 EC 风险模型,需要采用大规模队列联盟方法来评估和调整人群差异。