ArticleCancer medicine2024
Bayesian and deep-learning models applied to the early detection of ovarian cancer using multiple longitudinal biomarkers.
Article in Cancer medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- Artificial intelligence in ovarian cancer: advancing in precision diagnosis and clinical management.Frontiers in immunology · 2026Review
- Liquid biopsy, multi-cancer early detection, and artificial intelligence: new frontiers in cancer screening from a technological and immunological perspective.Frontiers in immunology · 2026Review
- Deep Learning Applications in Clinical Cancer Detection: A Review of Implementation Challenges and Solutions.Mayo Clinic proceedings. Digital health · 2025Review
- A Review on Biomarker-Enhanced Machine Learning for Early Diagnosis and Outcome Prediction in Ovarian Cancer Management.Cancer medicine · 2025Review
- Review
- Recent therapeutic advances in gynecologic oncology: evolving roles of immunotherapy, antibody-drug conjugates, and clinical trial innovations.Frontiers in oncology · 2025Review
- Bayesian and deep-learning models applied to the early detection of ovarian cancer using multiple longitudinal biomarkers.Cancer medicine · 2024Article
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Authors and funding
8 authors at 5 institutions in 3 countries.
Funding
Abstract
backgroundOvarian 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.
methodsOur 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.
resultsWe 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.
conclusionsOur 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.