ArticleNature medicine2023
A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories.
Article in Nature medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 165 papers, 7 of them syntheses that pooled it.
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Who cites it
165 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- Deep representation learning for temporal inference in cancer omics: a systematic literature review.Briefings in bioinformatics · 2026Pooled it
- Research on machine learning-based clinical prediction models: a bibliometric analysis.Frontiers in oncology · 2026Pooled 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
- Diagnosis methods for pancreatic cancer with the technique of deep learning: a review and a meta-analysis.Frontiers in oncology · 2025Pooled it
- Machine Learning Models for Pancreatic Cancer Risk Prediction Using Electronic Health Record Data-A Systematic Review and Assessment.The American journal of gastroenterology · 2024Pooled it
- Pooled it
- Critical Analysis of the Current Medical Image-Based Processing Techniques for Automatic Disease Evaluation: Systematic Literature Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Deep learning-based CT model for non-invasive prediction of tertiary lymphoid structures in pancreatic cancer: a multicenter study with prospective validation in an immunochemotherapy cohort.Journal for immunotherapy of cancer · 2026Trial
- Pancreatic Cancer Early Detection Biomarkers for High-Risk Individuals: Insights From the PRECEDE Consortium.International journal of cancer · 2026Review
- Mapping the global landscape of artificial intelligence in pancreatic cancer research: A bibliometric and visualization analysis.Medicine · 2026Article
- Liquid biopsy for early detection of pancreatic ductal adenocarcinoma.Nature medicine · 2026Article
- Perspectives on the Limits and Clinical Alignment of Medical AI from Population Statistics to Individual Care.Bioengineering (Basel, Switzerland) · 2026Article
- Heuriskance: a novel paradigm for systematic earlier detection of sporadic pancreatic cancer.Journal of the National Cancer Institute · 2026Article
- CLEAR: an auditable foundation model for radiology grounded in clinical concepts.Nature biomedical engineering · 2026Article
- Toward a Better Paradigm for Head and Neck Cancer Treatment Applying AI (HNC-TACTIC): Protocol for an International Cohort Study of Electronic Health Records.JMIR research protocols · 2026Observational
- Article
- Predicting health and disease: a conceptual framework for AI in preventive and precision medicine.BMJ health & care informatics · 2026Review
- Prediagnostic prescription patterns in pancreatic cancer: a retrospective primary care cohort study.The British journal of general practice : the journal of the Royal College of General Practitioners · 2026Article
- Machine Learning Predicts Hepatocellular Carcinoma Risk from Routine Clinical Data: A Large Population-Based Multicentric Study.Cancer discovery · 2026Article
- LLPS-based classification and a novel prognostic signature reveal NRF1 as a therapeutic target in pancreatic cancer.Cancer cell international · 2026Article
105 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
25 authors.
Funding
Abstract
Pancreatic cancer is an aggressive disease that typically presents late with poor outcomes, indicating a pronounced need for early detection. In this study, we applied artificial intelligence methods to clinical data from 6 million patients (24,000 pancreatic cancer cases) in Denmark (Danish National Patient Registry (DNPR)) and from 3 million patients (3,900 cases) in the United States (US Veterans Affairs (US-VA)). We trained machine learning models on the sequence of disease codes in clinical histories and tested prediction of cancer occurrence within incremental time windows (CancerRiskNet). For cancer occurrence within 36 months, the performance of the best DNPR model has area under the receiver operating characteristic (AUROC) curve = 0.88 and decreases to AUROC (3m) = 0.83 when disease events within 3 months before cancer diagnosis are excluded from training, with an estimated relative risk of 59 for 1,000 highest-risk patients older than age 50 years. Cross-application of the Danish model to US-VA data had lower performance (AUROC = 0.71), and retraining was needed to improve performance (AUROC = 0.78, AUROC (3m) = 0.76). These results improve the ability to design realistic surveillance programs for patients at elevated risk, potentially benefiting lifespan and quality of life by early detection of this aggressive cancer.
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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 OpenQuestion graph.