Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
J Natl Cancer Inst. 2023 May 8;115(5):552-559. doi: 10.1093/jnci/djad014.
Endometrial cancer risk stratification may help target interventions, screening, or prophylactic hysterectomy to mitigate the rising burden of this cancer. However, existing prediction models have been developed in select cohorts and have not considered genetic factors.
We developed endometrial cancer risk prediction models using data on postmenopausal White women aged 45-85 years from 19 case-control studies in the Epidemiology of Endometrial Cancer Consortium (E2C2). Relative risk estimates for predictors were combined with age-specific endometrial cancer incidence rates and estimates for the underlying risk factor distribution. We externally validated the models in 3 cohorts: Nurses' Health Study (NHS), NHS II, and the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial.
Area under the receiver operating characteristic curves for the epidemiologic model ranged from 0.64 (95% confidence interval [CI] = 0.62 to 0.67) to 0.69 (95% CI = 0.66 to 0.72). Improvements in discrimination from the addition of genetic factors were modest (no change in area under the receiver operating characteristic curves in NHS; PLCO = 0.64 to 0.66). The epidemiologic model was well calibrated in NHS II (overall expected-to-observed ratio [E/O] = 1.09, 95% CI = 0.98 to 1.22) and PLCO (overall E/O = 1.04, 95% CI = 0.95 to 1.13) but poorly calibrated in NHS (overall E/O = 0.55, 95% CI = 0.51 to 0.59).
Using data from the largest, most heterogeneous study population to date (to our knowledge), prediction models based on epidemiologic factors alone successfully identified women at high risk of endometrial cancer. Genetic factors offered limited improvements in discrimination. Further work is needed to refine this tool for clinical or public health practice and expand these models to multiethnic populations.
子宫内膜癌风险分层有助于针对干预措施、筛查或预防性子宫切除术，以减轻这种癌症不断增加的负担。然而，现有的预测模型是在特定队列中开发的，并未考虑遗传因素。
我们使用来自流行病学子宫内膜癌联盟（E2C2）的 19 项病例对照研究中 45-85 岁绝经后白人女性的数据，建立了子宫内膜癌风险预测模型。将预测因子的相对风险估计值与特定年龄的子宫内膜癌发病率和潜在风险因素分布的估计值相结合。我们在 3 个队列中对模型进行了外部验证：护士健康研究（NHS）、NHS II 和前列腺癌、肺癌、结直肠癌和卵巢癌（PLCO）筛查试验。
流行病学模型的受试者工作特征曲线下面积范围为 0.64（95%置信区间[CI]：0.62 至 0.67）至 0.69（95% CI：0.66 至 0.72）。遗传因素的加入对区分度的改善较小（NHS 中曲线下面积无变化；PLCO = 0.64 至 0.66）。在 NHS II（总体预期观察比[E/O] = 1.09，95%CI：0.98 至 1.22）和 PLCO（总体 E/O = 1.04，95%CI：0.95 至 1.13）中，流行病学模型校准良好，但在 NHS 中校准效果较差（总体 E/O = 0.55，95%CI：0.51 至 0.59）。
使用迄今为止最大、最具异质性的研究人群的数据（据我们所知），仅基于流行病学因素的预测模型成功地识别了子宫内膜癌风险较高的女性。遗传因素对区分度的改善有限。需要进一步努力改进该工具，以用于临床或公共卫生实践，并将这些模型扩展到多民族人群。
