Nene, Nuno RR, Ney, Alexander, Nazarenko, Tatiana, Blyuss, Oleg, Johnston, Harvey EE, Whitwell, Harry JJ, Sedlak, Eva, Gentry-Maharaj, Aleksandra, Apostolidou, Sophia, Costello, Eithne et al (show 7 more authors)
(2023)
Serum biomarker-based early detection of pancreatic ductal adenocarcinomas with ensemble learning.
COMMUNICATIONS MEDICINE, 3 (1).
10-.
Abstract
<h4>Background</h4>Earlier 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.<h4>Methods</h4>The 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.<h4>Results</h4>Here 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).<h4>Conclusions</h4>The 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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Clinical Research, Pancreatic Cancer, Digestive Diseases, Rare Diseases, Prevention, Cancer, 4 Detection, screening and diagnosis, 4.2 Evaluation of markers and technologies, Cancer |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 19 Apr 2023 10:14 |
| Last Modified: | 12 Apr 2024 18:12 |
| DOI: | 10.1038/s43856-023-00237-5 |
| Open Access URL: | https://doi.org/10.1038/s43856-023-00237-5 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3169735 |
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