Department of Urology, West China Hospital, Sichuan University, Chengdu, People's Republic of China.
West China School of Medicine/West China Hospital, Sichuan University, Chengdu, People's Republic of China.
Cancer Med. 2020 Dec;9(24):9303-9314. doi: 10.1002/cam4.3535. Epub 2020 Oct 16.
To establish a prognostic model to estimate the cancer-specific survival (CSS) for urothelial carcinoma of bladder (UCB) patients after radical cystectomy (RC).
A total of 8650 candidates (2004-2011) obtained from the Surveillance, Epidemiology, and End Results (SEER) database were randomly split into development cohort (n = 4323) and validation cohort (n = 4327). We performed Cox regression analysis to identify prognostic factors and Kaplan-Meier analysis to assess survival outcome. A nomogram predicting CSS was constructed. Its performance was validated by calibration curves, the receiver operating characteristic (ROC) curves, concordance index (C-index), decision curve analysis (DCA), the net reclassification improvement (NRI), and the integrated discrimination improvement (IDI).
The nomogram incorporated marital status, T stage, N stage, tumor size, and chemotherapy. In validation cohort, C-index of the nomogram was 0.707. AUC of the nomogram and AJCC stage were 0.767 versus 0.674. Calibration plots for 3- and 5-year CSS displayed good concordance. DCA curves of the nomogram exhibited larger benefits than the AJCC stage. The NRI and IDI indicated the nomogram outperformed AJCC stage.
We have established a prognostic nomogram with improved discriminative ability and clinical benefits for UCB patients after RC. The nomogram alongside an easy access web tool may assist clinicians in optimizing the postoperative management.
建立一个预后模型,以估计根治性膀胱切除术 (RC) 后膀胱癌 (UCB) 患者的癌症特异性生存 (CSS)。
从监测、流行病学和最终结果 (SEER) 数据库中随机抽取 8650 名候选人(2004-2011 年),分为开发队列 (n=4323) 和验证队列 (n=4327)。我们进行 Cox 回归分析以确定预后因素,并进行 Kaplan-Meier 分析以评估生存结果。构建了一个预测 CSS 的列线图。通过校准曲线、接收者操作特征 (ROC) 曲线、一致性指数 (C-index)、决策曲线分析 (DCA)、净重新分类改善 (NRI) 和综合判别改善 (IDI) 验证了其性能。
该列线图纳入了婚姻状况、T 分期、N 分期、肿瘤大小和化疗。在验证队列中,列线图的 C-index 为 0.707。列线图和 AJCC 分期的 AUC 分别为 0.767 与 0.674。3 年和 5 年 CSS 的校准图显示出良好的一致性。DCA 曲线表明列线图的获益大于 AJCC 分期。NRI 和 IDI 表明列线图优于 AJCC 分期。
我们建立了一个具有更好鉴别能力和临床获益的 RC 后 UCB 患者预后列线图。该列线图和一个易于访问的网络工具可以帮助临床医生优化术后管理。