Abrego, Luis;
Zaikin, Alexey;
Marino, Ines P;
Krivonosov, Mikhail I;
Jacobs, Ian;
Menon, Usha;
Gentry-Maharaj, Aleksandra;
(2024)
Bayesian and deep-learning models applied to the early detection of ovarian cancer using multiple longitudinal biomarkers.
Cancer Medicine
, 13
(7)
, Article e7163. 10.1002/cam4.7163.
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Abstract
BACKGROUND: Ovarian cancer is the most lethal of all gynecological cancers. Cancer Antigen 125 (CA125) is the best-performing ovarian cancer biomarker which however is still not effective as a screening test in the general population. Recent literature reports additional biomarkers with the potential to improve on CA125 for early detection when using longitudinal multimarker models. METHODS: Our data comprised 180 controls and 44 cases with serum samples sourced from the multimodal arm of UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). Our models were based on Bayesian change-point detection and recurrent neural networks. RESULTS: We obtained a significantly higher performance for CA125-HE4 model using both methodologies (AUC 0.971, sensitivity 96.7% and AUC 0.987, sensitivity 96.7%) with respect to CA125 (AUC 0.949, sensitivity 90.8% and AUC 0.953, sensitivity 92.1%) for Bayesian change-point model (BCP) and recurrent neural networks (RNN) approaches, respectively. One year before diagnosis, the CA125-HE4 model also ranked as the best, whereas at 2 years before diagnosis no multimarker model outperformed CA125. CONCLUSIONS: Our study identified and tested different combination of biomarkers using longitudinal multivariable models that outperformed CA125 alone. We showed the potential of multivariable models and candidate biomarkers to increase the detection rate of ovarian cancer.
| Type: | Article |
|---|---|
| Title: | Bayesian and deep-learning models applied to the early detection of ovarian cancer using multiple longitudinal biomarkers |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/cam4.7163 |
| Publisher version: | http://dx.doi.org/10.1002/cam4.7163 |
| Language: | English |
| Additional information: | This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2024 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. |
| Keywords: | CA125, change‐point detection, longitudinal biomarkers, ovarian cancer, recurrent neural networks, Humans, Female, Bayes Theorem, Deep Learning, Case-Control Studies, Ovarian Neoplasms, Biomarkers, Tumor, Early Detection of Cancer, ROC Curve |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology > MRC Clinical Trials Unit at UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10190800 |
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