Establishment and validation of a nomogram to predict overall survival for patients with primary renal neuroendocrine tumor
Year: 2025
Type: article
Study design: Observational Study
Abstract: Our study aimed to develop a nomogram to predict overall survival (OS) at 1, 3, and 5 years for patients with primary renal neuroendocrine tumor (PRNET). The Surveillance, Epidemiology, and End Results database (2000-2021) was utilized to gather cases and extract data. We performed a multivariate analysis using a Cox proportional-hazards model to identify prognostic factors independently affecting OS. Based on these predictors, a nomogram was constructed and validated internally via a bootstrap resampling method. Finally, we included 266 PRNET patients. The multivariate analysis demonstrated that age, Fuhrman grade, surgery, summary stage, N stage, and histology were prognostic factors independently affecting OS (all P < 0.05). A nomogram was then constructed using the abovementioned predictors, except for the N stage. The bootstrap-corrected concordance index (C-index) of the nomogram was 0.820 (95% CI 0.805-0.835), surpassing the C-index of the TNM stage (0.571, 95% CI 0.550-0.592, P < 0.001). Based on time-dependent C-index results, the nomogram demonstrated a better discriminative ability compared to the TNM staging system. There was a good consistency between the observed values and predicted probabilities indicated by the calibration curves. The nomogram's clinical utility was supported by the decision curve analysis. Additionally, the nomogram can classify PRNET patients into low-risk and high-risk subgroups, with high-risk patients having poorer OS (P < 0.0001). The prognostic nomogram, based on individualized clinicopathological information, may be helpful in predicting survival outcomes for PRNET patients more accurately. Further external validation is required in future studies to confirm our developed nomogram's prognostic accuracy and clinical applicability.
Source: Scientific Reports
Authors: Yang Wang, Hua Yang, Yanlin Zhu, Wenhui Luo, Qicheng Long, Yajun Fu, Xiaoke Chen
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W4409684113
