ArticleDiscover oncology2025
Machine learning predicts prognosis in patients with gastroenteropancreatic neuroendocrine tumors with liver metastases.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Multimodal Deep Learning for Prediction of Progression-Free Survival in Patients with Neuroendocrine Tumors UndergoingCancers · 2026Article
- Interpretable machine learning prognostication of gastroenteropancreatic neuroendocrine tumors across Chinese and United States cohorts.Frontiers in oncology · 2026Article
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Authors and funding
3 authors.
Funding
Abstract
backgroundPatients with gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) and liver metastases typically exhibit poor prognoses. However, accurate survival prediction models remain insufficient. This study aimed to develop machine learning-based models to predict the 1-year, 3-year, and 5-year overall survival in these patients.
methodsWe retrospectively analyzed patients diagnosed with GEP-NENs and liver metastases from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into training and testing sets in a 7:3 ratio. Seven machine learning models were constructed: cox regression, lasso regression, random survival forest (RSF), extreme gradient boosting (XGBoost), decision tree, gradient boosting machine (GBM), and neural network. Model performance was evaluated using C-index, AUC, Calibration curve, Brier score, and decision curve analysis (DCA). The optimal model was further interpreted through variable importance analysis, partial dependence plots, and individual prediction plots.
resultsA total of 4,528 patients were included, with 3,165 in the training set and 1,363 in the testing set. Among the seven models, the RSF model demonstrated the best overall performance. In the training set, it achieved a C-index of 0.815, with 1-year, 3-year, and 5-year AUC values of 0.895, 0.907, and 0.905, respectively, and Brier scores of 0.121, 0.128, and 0.128. The calibration curve shows good predictive performance, while the DCA highlights its strong net benefit in clinical decision-making. In the testing set, it maintained robust performance (C-index: 0.785; AUC: 0.855/0.859/0.841). The five most influential variables in the RSF model were tumor grade, surgical intervention, tumor site, age, and histology.
conclusionThe RSF model provides a reliable tool for predicting overall survival in GEP-NEN patients with liver metastases.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.