ArticleTranslational cancer research2024
Development and validation of a nomogram to predict overall survival of gastroenteropancreatic neuroendocrine carcinoma: a SEER database analysis.
Article in Translational cancer research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Machine learning and deep learning to improve overall survival prediction in cervical cancer patients.Translational cancer research · 2025Article
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7 authors.
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Abstract
Background: Gastroenteropancreatic neuroendocrine carcinoma (GEP-NEC) is a rare group of diseases with poor prognosis and the assessment of its prognosis is a significant challenge. This study aimed to develop and validate a prognostic nomogram to assess overall survival (OS) in patients with GEP-NEC. Methods: Patients diagnosed with poorly differentiated GEP-NEC were collected from the Surveillance, Epidemiology, and End Results (SEER) database between 2011 and 2015 and were randomly assigned to the training or validation cohort in a 7:3 ratio. The data included details of clinicopathological characteristics, therapeutic interventions and survival outcomes. Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors. Nomogram was used to predict OS at 1 and 2 years. The nomogram was internally validated with validation cohort, and its predictive ability was evaluated using concordance index (C-index), receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA), and integrated discrimination improvement (IDI) index. Results: A total of 887 patients were divided into the training group (n=623) and the validation group (n=264). A total of 476 patients (53.66%) were in stage IV. Based on multivariate analysis, a nomogram was constructed with age, gender, N stage, tumor size, primary tumor resection, radiotherapy and chemotherapy (P<0.05). The C-index was 0.701 [95% confidential interval (CI): 0.677-0.725] and 0.731 (95% CI: 0.698-0.764) for the training and validation groups, respectively. The C-index, ROC, IDI and DCA results indicated that this nomogram model has a good predictive value. Conclusions: In this study, a nomogram model based on seven independent prognostic factors provided visualization of the risk and could help clinicians predict the 1-year and 2-year OS for GEP-NEC. This tool can provide personalized survival predictions and improve clinical decision making for the management of GEP-NEC.
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