ArticleCancers2022
Machine Learning for Risk Prediction of Oesophago-Gastric Cancer in Primary Care: Comparison with Existing Risk-Assessment Tools.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 4 of them syntheses that pooled it.
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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.
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Who cites it
13 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Cancer Risk Prediction Using Machine Learning for Supporting Early Cancer Diagnosis in Symptomatic Patients: A Systematic Review of Model Types.Cancer medicine · 2025Pooled it
- Diagnostic Prediction Models for Primary Care, Based on AI and Electronic Health Records: Systematic Review.JMIR medical informatics · 2025Pooled it
- Diagnostic Risk Prediction Models for Upper Gastrointestinal Cancers: A Systematic Review.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025Pooled it
- Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review.BMC primary care · 2025Pooled it
- Fivefold Cross-Validation Approach in Evaluating the Robustness of Machine Learning Models for Prediction of Esophageal Cancer.Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine · 2026Article
- Artificial intelligence in preventive care in primary health care settings: a scoping review.Archives of medical sciences. Atherosclerotic diseases · 2026Article
- A machine learning tool for identifying metastatic colorectal cancer in primary care.Scandinavian journal of primary health care · 2025Article
- Constructing multicancer risk cohorts using national data from medical helplines and secondary care.NPJ digital medicine · 2025Article
- A Machine Learning Risk Prediction Model for Gastric Cancer with SHapley Additive exPlanations.Cancer research and treatment · 2025Article
- Risk of Gastric Adenocarcinoma in a Multiethnic Population Undergoing Routine Care: An Electronic Health Records Cohort Study.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2024Article
- Challenges involved in the application of artificial intelligence in gastroenterology: The race is on!World journal of gastroenterology · 2023Review
- Transforming early cancer detection in primary care: harnessing the power of machine learning.Oncoscience · 2023Article
- Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Oesophago-gastric cancer is difficult to diagnose in the early stages given its typical non-specific initial manifestation. We hypothesise that machine learning can improve upon the diagnostic performance of current primary care risk-assessment tools by using advanced analytical techniques to exploit the wealth of evidence available in the electronic health record. We used a primary care electronic health record dataset derived from the UK General Practice Research Database (7471 cases; 32,877 controls) and developed five probabilistic machine learning classifiers: Support Vector Machine, Random Forest, Logistic Regression, Naïve Bayes, and Extreme Gradient Boosted Decision Trees. Features included basic demographics, symptoms, and lab test results. The Logistic Regression, Support Vector Machine, and Extreme Gradient Boosted Decision Tree models achieved the highest performance in terms of accuracy and AUROC (0.89 accuracy, 0.87 AUROC), outperforming a current UK oesophago-gastric cancer risk-assessment tool (ogRAT). Machine learning also identified more cancer patients than the ogRAT: 11.0% more with little to no effect on false positives, or up to 25.0% more with a slight increase in false positives (for Logistic Regression, results threshold-dependent). Feature contribution estimates and individual prediction explanations indicated clinical relevance. We conclude that machine learning could improve primary care cancer risk-assessment tools, potentially helping clinicians to identify additional cancer cases earlier. This could, in turn, improve survival outcomes.
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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 OpenQuestion graph.