Department of Public Health, Erasmus MC, Rotterdam, The Netherlands.
Department of Public Health, Erasmus MC, Rotterdam, The Netherlands.
Crit Rev Oncol Hematol. 2018 Jun;126:92-99. doi: 10.1016/j.critrevonc.2018.03.023. Epub 2018 Mar 31.
To provide an overview of prediction models for the risk of developing endometrial cancer in women of the general population or for the presence of endometrial cancer in symptomatic women.
We systematically searched the Embase and Pubmed database until September 2017 for relevant publications. We included studies describing the development, the external validation, or the updating of a multivariable model for predicting endometrial cancer in the general population or symptomatic women.
Out of 2756 references screened, 14 studies were included. We found two prediction models for developing endometrial cancer in the general population (risk models) and one extension. Eight studies described the development of models for symptomatic women (diagnostic models), one comparison of the performance of two diagnostic models and two external validation. Sample size varied from 60 (10 with cancer) to 201,811 (855 with cancer) women. The age of the women was included as a predictor in almost all models. The risk models included epidemiological variables related to the reproductive history of women, hormone use, BMI, and smoking history. The diagnostic models also included clinical predictors, such as endometrial thickness and recurrent bleeding. The concordance statistic (c), assessing the discriminative ability, varied from 0.68 to 0.77 in the risk models and from 0.73 to 0.957 in the diagnostic models. Methodological information was often limited, especially on the handling of missing data, and the selection of predictors. One risk model and four diagnostic models were externally validated.
Only a few models have been developed to predict endometrial cancer in asymptomatic or symptomatic women. The usefulness of most models is unclear considering methodological shortcomings and lack of external validation. Future research should focus on external validation and extension with new predictors or biomarkers, such as genetic and epigenetic markers.
综述预测一般人群子宫内膜癌风险或有症状妇女子宫内膜癌存在的预测模型。
我们系统地检索了 Embase 和 Pubmed 数据库，直到 2017 年 9 月，以寻找相关文献。我们纳入了描述一般人群或有症状妇女的多变量预测模型的开发、外部验证或更新的研究。
在筛选出的 2756 篇参考文献中，有 14 项研究被纳入。我们发现了两个用于预测一般人群子宫内膜癌发病风险的预测模型（风险模型）和一个扩展。八项研究描述了用于有症状妇女的模型的开发（诊断模型），一项比较了两种诊断模型的性能，两项外部验证。样本量从 60 名（10 名患有癌症）到 201811 名（855 名患有癌症）妇女不等。几乎所有模型都将女性的年龄作为预测因子。风险模型包括与女性生殖史、激素使用、BMI 和吸烟史相关的流行病学变量。诊断模型还包括临床预测因子，如子宫内膜厚度和反复出血。评估区分能力的一致性统计量（c）在风险模型中从 0.68 到 0.77 不等，在诊断模型中从 0.73 到 0.957 不等。方法学信息通常有限，尤其是在处理缺失数据和选择预测因子方面。一个风险模型和四个诊断模型进行了外部验证。
只有少数模型被开发用于预测无症状或有症状妇女的子宫内膜癌。考虑到方法学上的缺陷和缺乏外部验证，大多数模型的实用性尚不清楚。未来的研究应侧重于外部验证和扩展，纳入新的预测因子或生物标志物，如遗传和表观遗传标志物。
