Bayesian and deep-learning models applied to the early detection of ovarian cancer using multiple longitudinal biomarkers
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, sensitiv- ity 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 multi- marker model outperformed CA125. Conclusions: Our study identified and tested different combination of biomark- ers using longitudinal multivariable models that outperformed CA125 alone. We showed the potential of multivariable models and candidate biomarkers to in- crease the detection rate of ovarian cancer.
Description
This study was funded by Cancer Research UK and EPSRC joint award EDDCPJT/100022. We thank all trial participants and all staff involved in the UKCTOCS trial. IPM acknowledges the support of grant PID2021-125159NB-I00 (TYCHE) funded by MCIN/AEI/10.13039/501100011033 and by 'ERDF A way of making Europe'. MIK acknowledges support by a grant for research centers in the field of artificial intelligence, provided by the Analytical Center for the Government of the Russian Federation in accord- ance with the subsidy agreement (agreement identi- fier 000000D730321P5Q0002) and the agreement with the Ivannikov Institute for System Programming of the Russian Academy of Sciences dated November 2, 2021, No. 70-2021-00142. OB acknowledges support from Barts Charity (G-001522).
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