ArticleCell reports. Medicine2024
Transformer-based AI technology improves early ovarian cancer diagnosis using cfDNA methylation markers.
Article in Cell reports. Medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-Derived Blood Biomarkers for Ovarian Cancer Diagnosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- DNA methylation biomarkers for early detection of ovarian cancer.Molecular biology reports · 2026Review
- Advancing the applications of liquid biopsies in oncology.Nature reviews. Genetics · 2026Review
- Evaluation of NID2 and CDO1 methylation for ovarian cancer screening.Oncology letters · 2026Article
- Translational Perspectives on Cell-Free Mitochondrial DNA as a Biomarker in Gynecological Cancers: Current Limitations and Future Research Directions.Biomolecules · 2026Review
- Circulating Tumor DNA in Ovarian Cancer: Emerging Roles in Early Detection, Risk Stratification, and Disease Monitoring.Cancers · 2026Review
- Review
- Review
- Development and Evaluation of Multiple Droplet Digital PCR Method for Specific Detection and Differentiation ofAnimals : an open access journal from MDPI · 2026Article
- High-Grade Serous Ovarian Carcinoma in the Genomics Era: Current Applications, Challenges and Future Directions.International journal of molecular sciences · 2026Review
- Artificial intelligence (AI) and machine learning (ML) in ovarian cancer: transforming detection, treatment, and prevention.Journal of ovarian research · 2026Review
- Artificial intelligence in ovarian cancer: advancing in precision diagnosis and clinical management.Frontiers in immunology · 2026Review
- Machine learning and multi-omics technologies for precision cardiovascular medicine: advancing diagnosis, risk prediction, and therapeutic guidance.Frontiers in cardiovascular medicine · 2026Review
- Interpretable multimodal deep learning improves postoperative risk stratification in intrahepatic cholangiocarcinoma in multicentre cohorts.NPJ digital medicine · 2025Article
- Minimal Residual Disease Detection: Bridging Molecular and Clinical Strategies for Recurrence Prevention in Gynecologic Cancers.International journal of molecular sciences · 2025Review
- From multi-omics to deep learning: advances in cfDNA-based liquid biopsy for multi-cancer screening.Biomarker research · 2025Review
- Methylated ARHGAP40 DNA as a potential biomarker for early diagnosis in high-grade ovarian serous cancer.Journal of ovarian research · 2025Article
- Liquid Biopsy: The Challenges of a Revolutionary Approach in Oncology.International journal of molecular sciences · 2025Review
- Detection of Genomic Copy Number Variations in Ovarian Cancer in the Peripheral Blood System.Cancers · 2025Article
- Clinical utility of various liquid biopsy samples for the early detection of ovarian cancer: a comprehensive review.Frontiers in oncology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
24 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epithelial ovarian cancer (EOC) is the deadliest women's cancer and has a poor prognosis. Early detection is the key for improving survival (a 5-year survival rate in stage I/II is over 70% compared to that of 25% in stage III/IV) and can be achieved through methylation markers from circulating cell-free DNA (cfDNA) using a liquid biopsy. In this study, we first identify top 500 EOC markers differentiating EOC from healthy female controls from 3.3 million methylome-wide CpG sites and validated them in 1,800 independent cfDNA samples. We then utilize a pretrained AI transformer system called MethylBERT to develop an EOC diagnostic model which achieves 80% sensitivity and 95% specificity in early-stage EOC diagnosis. We next develop a simple digital droplet PCR (ddPCR) assay which archives good performance, facilitating early EOC detection.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.