Fortner Renée T, Hüsing Anika, Kühn Tilman, Konar Meric, Overvad Kim, Tjønneland Anne, Hansen Louise, Boutron-Ruault Marie-Christine, Severi Gianluca, Fournier Agnès, Boeing Heiner, Trichopoulou Antonia, Benetou Vasiliki, Orfanos Philippos, Masala Giovanna, Agnoli Claudia, Mattiello Amalia, Tumino Rosario, Sacerdote Carlotta, Bueno-de-Mesquita H B As, Peeters Petra H M, Weiderpass Elisabete, Gram Inger T, Gavrilyuk Oxana, Quirós J Ramón, Maria Huerta José, Ardanaz Eva, Larrañaga Nerea, Lujan-Barroso Leila, Sánchez-Cantalejo Emilio, Butt Salma Tunå, Borgquist Signe, Idahl Annika, Lundin Eva, Khaw Kay-Tee, Allen Naomi E, Rinaldi Sabina, Dossus Laure, Gunter Marc, Merritt Melissa A, Tzoulaki Ioanna, Riboli Elio, Kaaks Rudolf
Division of Cancer Epidemiology, German Cancer Research Center (DFKZ), Heidelberg, Germany.
Department of Biostatistics, Hacettepe University, Ankara, Turkey.
Int J Cancer. 2017 Mar 15;140(6):1317-1323. doi: 10.1002/ijc.30560.
Endometrial cancer risk prediction models including lifestyle, anthropometric and reproductive factors have limited discrimination. Adding biomarker data to these models may improve predictive capacity; to our knowledge, this has not been investigated for endometrial cancer. Using a nested case-control study within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we investigated the improvement in discrimination gained by adding serum biomarker concentrations to risk estimates derived from an existing risk prediction model based on epidemiologic factors. Serum concentrations of sex steroid hormones, metabolic markers, growth factors, adipokines and cytokines were evaluated in a step-wise backward selection process; biomarkers were retained at p < 0.157 indicating improvement in the Akaike information criterion (AIC). Improvement in discrimination was assessed using the C-statistic for all biomarkers alone, and change in C-statistic from addition of biomarkers to preexisting absolute risk estimates. We used internal validation with bootstrapping (1000-fold) to adjust for over-fitting. Adiponectin, estrone, interleukin-1 receptor antagonist, tumor necrosis factor-alpha and triglycerides were selected into the model. After accounting for over-fitting, discrimination was improved by 2.0 percentage points when all evaluated biomarkers were included and 1.7 percentage points in the model including the selected biomarkers. Models including etiologic markers on independent pathways and genetic markers may further improve discrimination.
包括生活方式、人体测量学和生殖因素在内的子宫内膜癌风险预测模型的辨别力有限。在这些模型中加入生物标志物数据可能会提高预测能力；据我们所知，尚未针对子宫内膜癌对此进行研究。利用欧洲癌症与营养前瞻性调查（EPIC）队列中的一项巢式病例对照研究，我们调查了将血清生物标志物浓度添加到基于流行病学因素的现有风险预测模型得出的风险估计值中后，辨别力的改善情况。在逐步向后选择过程中评估了性类固醇激素、代谢标志物、生长因子、脂肪因子和细胞因子的血清浓度；当赤池信息准则（AIC）有所改善（p < 0.157）时保留生物标志物。使用C统计量评估所有单独生物标志物的辨别力改善情况，以及从将生物标志物添加到先前存在的绝对风险估计值后C统计量的变化。我们使用自展法（1000倍）进行内部验证以调整过度拟合。脂联素、雌酮、白细胞介素-1受体拮抗剂、肿瘤坏死因子-α和甘油三酯被选入模型。在考虑过度拟合后，当纳入所有评估的生物标志物时，辨别力提高了2.0个百分点，在包括所选生物标志物的模型中提高了1.7个百分点。包括独立途径上的病因标志物和遗传标志物的模型可能会进一步提高辨别力。
