Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3165
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 317
Function: require_once
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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.
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Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12015505 | PMC |
http://dx.doi.org/10.1038/s41598-025-98228-0 | DOI Listing |