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 AU

SUBMITTER: Abrego L 

PROVIDER: S-EPMC11004913 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature