Du Kunpeng, Li Lixian, Wang Qi, Zou Jingwen, Yu Zhongjian, Li Jiqiang, Zheng Yanfang
Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China.
Oncology Center, Zhujiang Hospital of Southern Medical University, Guangzhou, China.
Ann Transl Med. 2021 Oct;9(20):1546. doi: 10.21037/atm-21-4964.
Current prediction models of esophageal cancer (EC) are limited to predicting at a specific time point, and ignore changes in hazard ratios of predictive variables, known as time-varying effects. Our study aimed to investigate variables with time-varying effects in EC and to develop a prediction model that can update the 5-year predicted dynamic overall survival (DOS) probability during the follow-up period.
Firstly, the clinicopathological information and survival data of 4,541 patients with EC was obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2007 and 2011 for modeling. Secondly, the time-varying effect of variables was assessed and the dynamic prediction model was developed based on the proportional baselines landmark supermodel.
Here, we found that age at diagnosis, sex, location of primary tumor, histological type, chemotherapy, surgery, and T stage showed significant time-varying effects on overall survival. Thirdly, the prediction model was validated by an internal SEER validation cohort and a Chinese patient cohort, respectively, and achieved promising results as follows: area under the curve (AUC) =0.733 (internal validation) and 0.864 (external validation). The heuristic shrinkage factor was 0.995. Finally, several clear cases were selected as examples for model application to map the patient's 5-year DOS curves and to respectively demonstrate the impact of different variables' time-varying effect on survival.
Overall, our results suggest that the existence of time-varying effect highlights the importance of updating the predicted survival probability during the follow-up period. Moreover, this prediction model can be used to assist doctors in making more-individualized treatment decisions based on a dynamic assessment of patient prognosis.
目前食管癌（EC）的预测模型仅限于在特定时间点进行预测，而忽略了预测变量风险比的变化，即所谓的时变效应。我们的研究旨在调查EC中具有时变效应的变量，并开发一种预测模型，该模型可以在随访期间更新5年预测动态总生存（DOS）概率。
首先，从监测、流行病学和最终结果（SEER）数据库中获取2007年至2011年间4541例EC患者的临床病理信息和生存数据用于建模。其次，评估变量的时变效应，并基于比例基线地标超模型开发动态预测模型。
在此，我们发现诊断时年龄、性别、原发肿瘤位置、组织学类型、化疗、手术和T分期对总生存有显著时变效应。第三，该预测模型分别通过SEER内部验证队列和中国患者队列进行验证，并取得了如下良好结果：曲线下面积（AUC）=0.733（内部验证）和0.864（外部验证）。启发式收缩因子为0.995。最后，选择几个典型病例作为模型应用示例，绘制患者的5年DOS曲线，并分别展示不同变量时变效应对生存的影响。
总体而言，我们的结果表明时变效应的存在凸显了在随访期间更新预测生存概率的重要性。此外，该预测模型可用于协助医生基于对患者预后的动态评估做出更个体化的治疗决策。
