z-logo
open-access-imgOpen Access
Development and validation of a nomogram to predict the prognosis of patients with squamous cell carcinoma of the bladder
Author(s) -
Meidi Hu,
Sihai Chen,
Yuan Liu,
Linghua Jia
Publication year - 2019
Publication title -
bioscience reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.938
H-Index - 77
eISSN - 1573-4935
pISSN - 0144-8463
DOI - 10.1042/bsr20193459
Subject(s) - nomogram , medicine , stage (stratigraphy) , confidence interval , oncology , epidemiology , surveillance, epidemiology, and end results , t stage , urology , overall survival , cancer registry , paleontology , biology
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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom