Bayesian and deep‐learning models applied to the early detection of ovarian cancer using multiple longitudinal biomarkers - Abrego, Luis; Zaikin, Alexey; Marino, Ines P.; Krivonosov, Mikhail I.; Jacobs, Ian; Menon, Usha; Gentry‐Maharaj, Aleksandra; Blyuss, Oleg (Cancer medicine (Malden, MA), 2024)
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. Using the data from largest prospective ovarian cancer clinical trial, we assessed, for the first time, the performance of deep and statistical learning approaches in evaluating of the panel longitudinal biomarkers. Our results demonstrate that these models outperform CA125, the single best ovarian cancer biomarker. These findings underscore the potential of multimarker models in improving the detection rate of ovarian cancer and have significant implications for the field of cancer screening and early detection.