列线图
医学
单变量
肿瘤科
队列
比例危险模型
T级
阶段(地层学)
内科学
泌尿科
膀胱癌
多元分析
曲线下面积
逐步回归
单变量分析
多元统计
总体生存率
癌症
统计
数学
生物
古生物学
作者
Xingxing Zhang,Bin Zhang,Yang He,Wei Xiong,Yuelin Du,Panfeng Shang
出处
期刊:Cancer Biomarkers
[IOS Press]
日期:2024-12-01
卷期号:41 (3-4)
标识
DOI:10.1177/18758592241296279
摘要
Background Distant metastasis (DM) remains the most commonly reported cause of death in patients with urothelial carcinoma of the bladder (UCB). Objective We aimed to develop a robust prognostic model to assess the risk of DM in patients with UCB. Methods We collected clinical data of 206 UCB patients treated with RC. Patients treated with RC between 2011–2015 that were enrolled as the training cohort (n = 105), while the patients between 2016–2019 were enrolled as the validation cohort (n = 101). Univariate and multivariate Cox regression models were used to identify independent risk factors associated with DM. We identified the variables by stepwise regression and established nomogram. We evaluated the nomograms using C-index, calibration and ROC curves. Decision curve analysis was performed to compare the net benefits between the nomogram and TNM staging. We divided the patients into high and low risk groups according to the nomogram and compared the DM between the groups. Results The neutrophil-lymphocyte ratio (NLR) was an independent predictor of DM. We established nomogram by T-stage, N-stage and NLR. The C-index of the nomogram was 0.766 and 0.739 respectively in the two cohorts. In the training cohort, AUC for the nomogram at 1, 2 and 3 years was 0.816, 0.812 and 0.812, respectively. In the validation cohort, the AUC for the nomogram at 1, 2 and 3 years was 0.751, 0.757 and 0.716, respectively. The calibration curve was satisfactory. The nomogram has a higher clinical benefit compared to the TNM staging system. Kaplan-Meier curves showed that patients from the high-risk group had a higher probability of DM than patients from the low-risk group. Conclusions Nomograms established by NLR, T-stage and N-stage can accurately predict distant metastases in patients with UCB.
科研通智能强力驱动
Strongly Powered by AbleSci AI