Zhan Xiangpeng, Jiang Ming, Deng Wen, Liu Xiaoqiang, Chen Luyao, Fu Bin
Department of Urology, The First Affiliated Hospital of Nanchang University, Nanchang, China.
Jiangxi Institute of Urology, Nanchang, China.
Front Oncol. 2022 Feb 3;12:789028. doi: 10.3389/fonc.2022.789028. eCollection 2022.
To construct a prognostic model to predict the cancer-specific survival (CSS) for bladder cancer patients with lymph node-positive.
We enrolled 2,050 patients diagnosed with lymph node-positive bladder cancer from the Surveillance Epidemiology and End Results (SEER) database (2004-2015). All patients were randomly split into development cohort (n = 1,438) and validation cohort (n = 612) at a ratio of 7:3. The univariate and multivariate Cox regression analysis were performed to identify prognostic factors. A nomogram predicting CSS was established based on the results of multivariate Cox analysis. Its performance was evaluated by calibration curves, the receiver operating characteristic (ROC) curves, and the concordance index (C-index). Internal verification was performed in the validation cohort. The Kaplan-Meier method with the log-rank test was applied in the different risk groups.
The nomogram incorporated summary stage, tumor size, chemotherapy, regional nodes examined and positive lymph nodes. The C-index of the nomogram in the development cohort was 0.716 (0.707-0.725), while the value of the C-index was 0.691 (0.689-0.693) in the validation cohort. The AUC of the nomogram was 0.803 for 3-year and 0.854 for 5-year in the development cohort, while was 0.773 for 3-year and 0.809 for 5-year in the validation cohort. Calibration plots for 3-year and 5-year CSS showed good concordance. Significant differences were observed between high, medium, and low risk groups (0.001).
We have established a prognostic nomogram providing an accurate individualized probability of cancer-specific survival in bladder cancer patients with lymph node-positive. The nomogram could contribute to patient counseling, follow-up scheduling, and selection of treatment.
构建一个预测淋巴结阳性膀胱癌患者癌症特异性生存(CSS)的预后模型。
我们从监测、流行病学与最终结果(SEER)数据库(2004 - 2015年)中纳入了2050例诊断为淋巴结阳性膀胱癌的患者。所有患者以7:3的比例随机分为开发队列(n = 1438)和验证队列(n = 612)。进行单因素和多因素Cox回归分析以确定预后因素。基于多因素Cox分析结果建立了预测CSS的列线图。通过校准曲线、受试者操作特征(ROC)曲线和一致性指数(C指数)评估其性能。在验证队列中进行内部验证。对不同风险组应用Kaplan-Meier法和对数秩检验。
该列线图纳入了总结分期、肿瘤大小、化疗、检查的区域淋巴结和阳性淋巴结。开发队列中列线图的C指数为0.716(0.707 - 0.725),而验证队列中C指数的值为0.691(0.689 - 0.693)。开发队列中列线图的3年AUC为0.803,5年AUC为0.854,而验证队列中3年AUC为0.773,5年AUC为0.809。3年和5年CSS的校准图显示出良好的一致性。高、中、低风险组之间观察到显著差异(P = 0.001)。
我们建立了一个预后列线图,可为淋巴结阳性膀胱癌患者提供准确的癌症特异性生存个体化概率。该列线图有助于患者咨询、随访安排和治疗选择。