Su Hao, Xue Xiaoqiang, Wang Yutao, Lu Yi, Ma Chengquan, Ji Zhigang, Su Xiaozhe
Department of Urology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China.
Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China.
J Oncol. 2022 Aug 25;2022:9577904. doi: 10.1155/2022/9577904. eCollection 2022.
Noncancer death accounts for a high proportion of all patients with bladder cancer, while these patients are often excluded from the survival analysis, which increases the selection bias of the study subjects in the prediction model.
Clinicopathological information of bladder cancer patients was retrieved from the Surveillance, Epidemiology, and End Results (SEER) database, and the patients were categorized at random into the training and validation cohorts. The random forest method was used to calculate the importance of clinical variables in the training cohort. Multivariate and univariate analyses were undertaken to assess the risk indicators, and the prediction nomogram based on the competitive risk model was constructed. The model's performance was evaluated utilizing the calibration curve, consistency index (C index), and the area under the receiver operator characteristic curve (AUC).
In total, we enrolled 39285 bladder cancer patients in the study (27500 patients were allotted to the training cohort, whereas 11785 were allotted to the validation cohort). A competitive risk model was constructed to predict bladder cancer-specific mortality. The overall C index of patients in the training cohort was 0.876, and the AUC values were 0.891, 0.871, and 0.853, correspondingly, for 1-, 3-, and 5-year cancer-specific mortality. On the other hand, the overall C index of patients in the validation cohort was 0.877, and the AUC values were 0.894, 0.870, and 0.847 for 1-, 3-, and 5-year correspondingly, suggesting a remarkable predictive performance of the model.
The competitive risk model proved to be of great accuracy and reliability and could help clinical decision-makers improve their management and approaches for managing bladder cancer patients.
非癌症死亡在所有膀胱癌患者中占比很高，而这些患者在生存分析中常被排除，这增加了预测模型中研究对象的选择偏倚。
从监测、流行病学和最终结果（SEER）数据库中检索膀胱癌患者的临床病理信息，并将患者随机分为训练队列和验证队列。采用随机森林方法计算训练队列中临床变量的重要性。进行多变量和单变量分析以评估风险指标，并构建基于竞争风险模型的预测列线图。利用校准曲线、一致性指数（C指数）和受试者操作特征曲线下面积（AUC）评估模型性能。
本研究共纳入39285例膀胱癌患者（27500例分配至训练队列，11785例分配至验证队列）。构建了一个竞争风险模型来预测膀胱癌特异性死亡率。训练队列患者的总体C指数为0.876，1年、3年和5年癌症特异性死亡率的AUC值分别为0.891、0.871和0.853。另一方面，验证队列患者的总体C指数为0.877，1年、3年和5年的AUC值分别为0.894、0.870和0.847，表明该模型具有显著的预测性能。
竞争风险模型被证明具有很高的准确性和可靠性，可帮助临床决策者改进对膀胱癌患者的管理和治疗方法。
