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.

中文翻译：

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预测欧洲子宫内膜癌风险的流行病学模型。


子宫内膜癌（EC）是欧洲女性第四大常见癌症，随着其发病率的增加，预防策略变得更加针对性。风险预测模型可以成为识别可能受益于有针对性的预防措施的妇女的有用工具。根据欧洲癌症和营养前瞻性调查队列中 201,811 名女性（大多数年龄在 30-65 岁）的数据，包括来自 8 个国家的 855 例 EC 病例，开发了一个预测 EC 的模型。使用逐步模型选择过程来选择已确认的预测流行病学危险因素。在特定原因的竞争风险模型中估计了 5 年年龄间隔的分段恒定危险率，并应用五倍交叉验证进行内部验证。风险预测模型中包括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、整体和不同BMI类别以及总体使用时间、产次、首次完全避孕的年龄。足月妊娠、绝经期激素治疗持续时间和吸烟状况（特别针对绝经前、围绝经期和绝经后妇女）。这些变量将预测 5 年以上风险的辨别能力从仅基于年龄的模型的 71% 提高到 77%（总体 C 统计量），并且该模型经过良好校准（预期病例与观察病例的比率 = 0.99）。我们的模型可用于识别西欧 EC 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合方法来评估和调整人群差异。