Development and validation of a nomogram to predict the prognosis of patients with squamous cell carcinoma of the bladder

被引:6
|
作者
Hu, Mei-Di [1 ]
Chen, Si-Hai [2 ]
Liu, Yuan [3 ]
Jia, Ling-Hua [4 ]
机构
[1] Nanchang Univ, Affiliated Hosp 1, Dept Gen Practice, Nanchang 330006, Jiangxi, Peoples R China
[2] Nanchang Univ, Affiliated Hosp 1, Dept Gastroenterol, Nanchang 330006, Jiangxi, Peoples R China
[3] Fudan Univ, Peoples Hosp Shanghai 5, Div Nephrol, Shanghai 200240, Peoples R China
[4] Nanchang Univ, Jiangxi Prov Peoples Hosp, Dept Urol, Nanchang 330006, Jiangxi, Peoples R China
关键词
CANCER; SURVIVAL; RISK; MODELS;
D O I
10.1042/BSR20193459
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Background: The present study aimed to develop and validate a nomogram based on expanded TNM staging to predict the prognosis for patients with squamous cell carcinoma of the bladder (SCCB). Methods: A total of 595 eligible patients with SCCB identified in the Surveillance, Epidemiology, and End Results (SEER) dataset were randomly divided into training set (n = 416) and validation set (n = 179). The likelihood ratio test was used to select potentially relevant factors for developing the nomogram. The performance of the nomogram was validated on the training and validation sets using a C-index with 95% confidence interval (95% CI) and calibration curve, and was further compared with TNM staging system. Results: The nomogram included six factors: age, T stage, N stage, M stage, the method of surgery and tumor size. The C-indexes of the nomogram were 0.768 (0.741-0.795) and 0.717 (0.671-0.763) in the training and validation sets, respectively, which were higher than the TNM staging system with C-indexes of 0.580 (0.543-0.617) and 0.540 (0.484-0.596) in the training and validation sets, respectively. Furthermore, the decision curve analysis (DCA) proved that the nomogram provided superior clinical effectiveness. Conclusions: We developed a nomogram that help predict individualized prognosis for patients with SCCB.
引用
收藏
页数:10
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