ArticleCancer medicine2023
Predicting the survival of patients with pancreatic neuroendocrine neoplasms using deep learning: A study based on Surveillance, Epidemiology, and End Results database.
Article in Cancer medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Artificial Intelligence Models for Time-to-Event Outcome Applied in Cardiovascular Disease Risk Prediction.Journal of medical systems · 2024Pooled it
- Multimodal Deep Learning for Prediction of Progression-Free Survival in Patients with Neuroendocrine Tumors UndergoingCancers · 2026Article
- Predicting the Unpredictable: AI-Driven Prognosis in Pancreatic Neuroendocrine Neoplasms.Cancers · 2026Review
- Interpretable deep learning model and nomogram for predicting pathological grading of PNETs based on endoscopic ultrasound.BMC medical informatics and decision making · 2025Article
- Artificial Intelligence for Prognosis of Gastro-Entero-Pancreatic Neuroendocrine Neoplasms.Cancers · 2025Review
- Machine learning predicts prognosis in patients with gastroenteropancreatic neuroendocrine tumors with liver metastases.Discover oncology · 2025Article
- Estimating prognosis of gastric neuroendocrine neoplasms using machine learning: A step towards precision medicine.World journal of gastrointestinal oncology · 2024Article
- Deep Multiple Instance Learning Model to Predict Outcome of Pancreatic Cancer Following Surgery.Biomedicines · 2024Article
- Deep learning models for predicting the survival of patients with medulloblastoma based on a surveillance, epidemiology, and end results analysis.Scientific reports · 2024Article
- Predicting the survival of patients with pancreatic neuroendocrine neoplasms using deep learning: A study based on Surveillance, Epidemiology, and End Results database.Cancer medicine · 2023Article
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6 authors.
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Abstract
backgroundThe study aims to evaluate the performance of three advanced machine learning algorithms and a traditional Cox proportional hazard (CoxPH) model in predicting the overall survival (OS) of patients with pancreatic neuroendocrine neoplasms (PNENs).
methodThe clinicopathological dataset obtained from the Surveillance, Epidemiology, and End Results database was randomly assigned to the training set and testing set at a ratio of 7:3. The concordance index (C-index) and integrated Brier score (IBS) were used to compare the predictive performance of the models. The accuracy of the model in predicting the 5-year and 10-year survival rates was compared using the receiver operating characteristic curve, decision curve analysis (DCA) and calibration curve.
resultsThis study included 3239 patients with PNENs in total. The DeepSurv model had the highest C-index of 0.7882 in the testing set and training set and the lowest IBS of 0.1278 in the testing set compared with the CoxPH, neural multitask logistic and random survival forest models (C-index = 0.7501, 0.7616, and 0.7612, respectively; IBS = 0.1397, 0.1418, and 0.1432, respectively). Moreover, the DeepSurv model had the highest accuracy in predicting 5- and 10-year OS rates (area under the curve: 0.87 and 0.90). DCA showed that the DeepSurv model had high potential for clinical decisions in 5- and 10-year OS models. Finally, we developed an online application based on the DeepSurv model for clinical use (https://whuh-ml-neuroendocrinetumor-app-predict-oyw5km.streamlit.app/).
conclusionsAll four models analyzed above can predict the prognosis of PNENs well, among which the DeepSurv model has the best prediction performance.
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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.