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Bladder cancer (BC) is a common malignancy associated with high morbidity and mortality, however, accurate and convenient risk assessment tools applicable to BC patients are currently lacking. Previous studies using nomograms to evaluate bladder cancer (BC) survival have been based on small samples. Using a large dataset, this study aimed to construct more precise clinical nomograms to effectively predict bladder cancer survival.Data on patients with pathologically-confirmed bladder cancer were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Additional BC patient data for an external validation cohort were extracted from the Cancer Genome Atlas (TCGA) database. Clinical parameters that constituted potential risk factors were reviewed and analyzed using univariate and multivariate Cox proportional hazards regression. A nomogram was constructed with parameters that significantly correlated with the overall survival (OS). Prognostic performance of a nomogram was assessed using the concordance index (c-index), area under the receiver operating characteristic curve (AUC), and a calibration curve. The model was then tested with data from an internal and external validation cohort. Patients' survival was analyzed and compared with the Kaplan-Meier (KM) method.Multivariate Cox regression showed that age, sex, race, stage_T1, stage_T2a, stage_T2b, stage_T3a, stage_Ta, stage_Tis, stage_N, stage_M were independent predictors of BC survival. A nomogram was constructed based on these factors. The c-index of the nomogram was 0.7916 (95% confidence interval CI, 0.79-0.80). The calibration curve showed excellent agreement between the predicted and observed values. The c-index for the internal validation cohort was 0.7917 (95% CI 0.79-0.80), which was higher than for the training cohort, suggesting robustness of the model. For the training cohort, the AUC for the 3- and the 5-year survival was 0.82 and 0.813, respectively. The c-index for the TNM-based model was superior to that for the AJCC-TNM classification.The models presented in this study might be suitable for clinical use, supporting clinicians in their individualized assessment of expected survival in BC patients. They might also be used as a layered tool for clinical research.
Pubmed ID: 31689813
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View all literature mentionsSEER collects cancer incidence data from population-based cancer registries covering approximately 47.9 percent of the U.S. population. The SEER registries collect data on patient demographics, primary tumor site, tumor morphology, stage at diagnosis, and first course of treatment, and they follow up with patients for vital status.There are two data products available: SEER Research and SEER Research Plus. This was motivated because of concerns about the increasing risk of re-identifiability of individuals. The Research Plus databases require more rigorous process for access that includes user authentication through Institutional Account or multiple-step request process for Non-Institutional users.
View all literature mentionsA unified data repository of the National Cancer Institute (NCI)'s Genomic Data Commons (GDC) that enables data sharing across cancer genomic studies in support of precision medicine. The GDC supports several cancer genome programs at the NCI Center for Cancer Genomics (CCG), including The Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and the Cancer Genome Characterization Initiative (CGCI). The GDC Data Portal provides a platform for efficiently querying and downloading high quality and complete data. The GDC also provides a GDC Data Transfer Tool and a GDC API for programmatic access.
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