ArticleCommunications medicine2023
Serum biomarker-based early detection of pancreatic ductal adenocarcinomas with ensemble learning.
Article in Communications medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- Pooled it
- Machine learning-assisted detection of canine mammary tumors using serum autoantibody signatures.The veterinary quarterly · 2026Article
- Ginsenoside Rb1 From the Functional FoodFood science & nutrition · 2026Article
- Article
- Spotlight on AMIGO2 as a novel prognostic indicator in pancreatic cancer.Journal of gastrointestinal oncology · 2025Article
- Integrating ctDNA testing forAdvances in laboratory medicine · 2025Review
- Article
- Review
- A High Performing Biomarker Signature for Detecting Early-Stage Pancreatic Ductal Adenocarcinoma in High-Risk Individuals.Cancers · 2025Article
- Incorporation of explainable artificial intelligence in ensemble machine learning-driven pancreatic cancer diagnosis.Scientific reports · 2025Article
- Construction of feature selection and efficacy prediction model for transformation therapy of locally advanced pancreatic cancer based on CT,Cancer cell international · 2025Article
- Review
- Multibiomarker panels in liquid biopsy for early detection of pancreatic cancer - a comprehensive review.Journal of experimental & clinical cancer research : CR · 2024Review
- The role of biomarkers in the early detection of pancreatic cancer.Familial cancer · 2024Review
- Identification of a serum proteomic biomarker panel using diagnosis specific ensemble learning and symptoms for early pancreatic cancer detection.PLoS computational biology · 2024Article
- An integrated computational biology approach defines the crucial role of TRIP13 in pancreatic cancer.Computational and structural biotechnology journal · 2023Article
Corrections and comments
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Authors and funding
17 authors at 7 institutions in 2 countries.
Funding
Abstract
backgroundEarlier detection of pancreatic ductal adenocarcinoma (PDAC) is key to improving patient outcomes, as it is mostly detected at advanced stages which are associated with poor survival. Developing non-invasive blood tests for early detection would be an important breakthrough.
methodsThe primary objective of the work presented here is to use a dataset that is prospectively collected, to quantify a set of cancer-associated proteins and construct multi-marker models with the capacity to predict PDAC years before diagnosis. The data used is part of a nested case-control study within the UK Collaborative Trial of Ovarian Cancer Screening and is comprised of 218 samples, collected from a total of 143 post-menopausal women who were diagnosed with pancreatic cancer within 70 months after sample collection, and 249 matched non-cancer controls. We develop a stacked ensemble modelling technique to achieve robustness in predictions and, therefore, improve performance in newly collected datasets.
resultsHere we show that with ensemble learning we can predict PDAC status with an AUC of 0.91 (95% CI 0.75-1.0), sensitivity of 92% (95% CI 0.54-1.0) at 90% specificity, up to 1 year prior to diagnosis, and at an AUC of 0.85 (95% CI 0.74-0.93) up to 2 years prior to diagnosis (sensitivity of 61%, 95% CI 0.17-0.83, at 90% specificity).
conclusionsThe ensemble modelling strategy explored here outperforms considerably biomarker combinations cited in the literature. Further developments in the selection of classifiers balancing performance and heterogeneity should further enhance the predictive capacity of the method.
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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.