ArticleAsia-Pacific journal of oncology nursing2022
Machine learning models for predicting survival in patients with ampullary adenocarcinoma.
Article in Asia-Pacific journal of oncology nursing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- A Systematic Review on Machine Learning Techniques for Survival Analysis in Cancer.Cancer medicine · 2025Pooled it
- Histopathological subtypes independently predict survival in ampullary carcinoma: a contemporary SEER nomogram study.Journal of gastrointestinal oncology · 2026Article
- Integrating Blood-Based Immune-Inflammation Biomarkers into Artificial Intelligence-Driven Prognostic Models in Oncology.International journal of molecular sciences · 2026Review
- Survival Modeling Using Deep Learning, Machine Learning, and Statistical Methods: A Comparative Analysis for Predicting Mortality After Hospital Admission.Health data science · 2026Article
- Predicting the prognosis of epithelial ovarian cancer patients based on deep learning models.Frontiers in oncology · 2025Article
- Using machine learning to predict five-year transplant-free survival among infants with hypoplastic left heart syndrome.Scientific reports · 2024Article
- A comparison of random survival forest and Cox regression for prediction of mortality in patients with hemorrhagic stroke.BMC medical informatics and decision making · 2023Article
- Using a Hybrid Neural Network and a Regularized Extreme Learning Machine for Human Activity Recognition with Smartphone and Smartwatch.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The aim of this study was to predict the long-term survival probability of patients with ampullary adenocarcinoma (AAC), which would provide a theoretical basis for the long-term care of these patients. Methods: Data on patients with AAC during 2004-2015 were obtained from the Surveillance, Epidemiology, and End Results database, which were split at a 7:3 ratio into two independent cohorts: training and testing cohorts. Differences in survival between the two groups were tested using the Kaplan-Meier estimator and log-rank test methods. We constructed six survival analysis methods: the American Joint Committee on Cancer TNM stage, Cox Proportional Hazards regression, CoxTime, DeepSurv, XGBoost Survival Embeddings, and Random Survival Forest. The performances of these models were evaluated using the C-index, receiver operating characteristic (ROC), and calibration curves. Results: This study included 2,935 patients with AAC. Univariate Cox regression analyses of the training cohort indicated that race, marital status at diagnosis, scope of regional lymph node surgery, tumor grade, summary stage, American Joint Committee on Cancer stage, TNM stage T, and TNM stage N were important factors affecting survival ( Conclusions: Machine learning models such as DeepSurv have a stronger performance in the survival analysis of patients with AAC.
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Registered trials
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