ArticleJournal of cancer research and clinical oncology2023
Machine learning for optimized individual survival prediction in resectable upper gastrointestinal cancer.
Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines 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
- Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases.Frontiers in artificial intelligence · 2024Pooled it
- AI Trustworthiness in the Perioperative Period for Patients with Serious Illness: Scoping Narrative Review.JMIR perioperative medicine · 2026Article
- The Cutting Edge: A Systematic Review of Artificial Intelligence and Machine Learning in Predicting Esophagectomy Outcomes.Annals of thoracic surgery short reports · 2026Article
- Diagnostic accuracy of image-based deep learning for glaucomatous optic neuropathy detection: a systematic review and meta-analysis.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- Cross-cancer survival prediction using machine learning models.Scientific reports · 2026Article
- Advancements in Cancer Survival Prediction: A Systematic Review of Classical and Modern Approaches.Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine · 2025Review
- AI-driven preoperative risk assessment in kidney cancer surgery: A comparative feasibility study of machine learning models.BJUI compass · 2025Article
- Recent advances in machine learning for precision diagnosis and treatment of esophageal disorders.World journal of gastroenterology · 2025Review
- The application of artificial intelligence in upper gastrointestinal cancers.Journal of the National Cancer Center · 2025Review
- Developing practical machine learning survival models to identify high-risk patients for in-hospital mortality following traumatic brain injury.Scientific reports · 2025Article
- Interpretable machine learning models to predict survival in esophageal cancer: a study based on the SEER database and external validation in China.Frontiers in physiology · 2025Article
- Prognostic Relevance of the Proximal Resection Margin Distance in Distal Gastrectomy for Gastric Adenocarcinoma.Annals of surgical oncology · 2024Article
- Factors affecting the survival of prediabetic patients: comparison of Cox proportional hazards model and random survival forest method.BMC medical informatics and decision making · 2024Article
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- Sex Differences in Conversion Risk from Mild Cognitive Impairment to Alzheimer's Disease: An Explainable Machine Learning Study with Random Survival Forests and SHAP.Brain sciences · 2024Article
- Artificial intelligence: clinical applications and future advancement in gastrointestinal cancers.Frontiers in artificial intelligence · 2024Review
- Explainability of random survival forests in predicting conversion risk from mild cognitive impairment to Alzheimer's disease.Brain informatics · 2023Article
- Machine learning for predicting the survival in osteosarcoma patients: Analysis based on American and Hebei Province cohort.Biomolecules & biomedicine · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeSurgical oncologists are frequently confronted with the question of expected long-term prognosis. The aim of this study was to apply machine learning algorithms to optimize survival prediction after oncological resection of gastroesophageal cancers.
methodsEligible patients underwent oncological resection of gastric or distal esophageal cancer between 2001 and 2020 at Heidelberg University Hospital, Department of General Surgery. Machine learning methods such as multi-task logistic regression and survival forests were compared with usual algorithms to establish an individual estimation.
resultsThe study included 117 variables with a total of 1360 patients. The overall missingness was 1.3%. Out of eight machine learning algorithms, the random survival forest (RSF) performed best with a concordance index of 0.736 and an integrated Brier score of 0.166. The RSF demonstrated a mean area under the curve (AUC) of 0.814 over a time period of 10 years after diagnosis. The most important long-term outcome predictor was lymph node ratio with a mean AUC of 0.730. A numeric risk score was calculated by the RSF for each patient and three risk groups were defined accordingly. Median survival time was 18.8 months in the high-risk group, 44.6 months in the medium-risk group and above 10 years in the low-risk group.
conclusionThe results of this study suggest that RSF is most appropriate to accurately answer the question of long-term prognosis. Furthermore, we could establish a compact risk score model with 20 input parameters and thus provide a clinical tool to improve prediction of oncological outcome after upper gastrointestinal surgery.
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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.