SynthesisJournal of medical Internet research2025
AI-Derived Blood Biomarkers for Ovarian Cancer Diagnosis: Systematic Review and Meta-Analysis.
Synthesis in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Performance of Computed Tomography-Based Artificial Intelligence for Early Recurrence of Cholangiocarcinoma: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Nano-biosensors for circulating tumor markers: advancing liquid biopsy toward precision cancer diagnostics.Journal of cancer research and clinical oncology · 2026Review
- Temporal deep learning for 28-day mortality prediction in critically ill ovarian cancer patients: a multicenter development and validation study using hourly vital signs.Journal of ovarian research · 2026Article
- Artificial intelligence in ovarian cancer: advancing in precision diagnosis and clinical management.Frontiers in immunology · 2026Review
- Artificial intelligence in ovarian cancer prevention and control: a brief review of detection, treatment, and equity (2021-2026).Frontiers in oncology · 2026Review
- Ovarian Tumor Biomarkers: Correlation Between Tumor Type and Marker Expression, and Their Role in Guiding Therapeutic Strategies.International journal of molecular sciences · 2025Review
- Machine Learning Models for Predicting Gynecological Cancers: Advances, Challenges, and Future Directions.Cancers · 2025Review
- Integrative multi-stage deep learning framework for ovarian tumor ultrasound classification with explainability and confidence estimation.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
21 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEmerging evidence underscores the potential application of artificial intelligence (AI) in discovering noninvasive blood biomarkers. However, the diagnostic value of AI-derived blood biomarkers for ovarian cancer (OC) remains inconsistent.
objectiveWe aimed to evaluate the research quality and the validity of AI-based blood biomarkers in OC diagnosis.
methodsA systematic search was performed in the MEDLINE, Embase, IEEE Xplore, PubMed, Web of Science, and the Cochrane Library databases. Studies examining the diagnostic accuracy of AI in discovering OC blood biomarkers were identified. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-AI tool. Pooled sensitivity, specificity, and area under the curve (AUC) were estimated using a bivariate model for the diagnostic meta-analysis.
resultsA total of 40 studies were ultimately included. Most (n=31, 78%) included studies were evaluated as low risk of bias. Overall, the pooled sensitivity, specificity, and AUC were 85% (95% CI 83%-87%), 91% (95% CI 90%-92%), and 0.95 (95% CI 0.92-0.96), respectively. For contingency tables with the highest accuracy, the pooled sensitivity, specificity, and AUC were 95% (95% CI 90%-97%), 97% (95% CI 95%-98%), and 0.99 (95% CI 0.98-1.00), respectively. Stratification by AI algorithms revealed higher sensitivity and specificity in studies using machine learning (sensitivity=85% and specificity=92%) compared to those using deep learning (sensitivity=77% and specificity=85%). In addition, studies using serum reported substantially higher sensitivity (94%) and specificity (96%) than those using plasma (sensitivity=83% and specificity=91%). Stratification by external validation demonstrated significantly higher specificity in studies with external validation (specificity=94%) compared to those without external validation (specificity=89%), while the reverse was observed for sensitivity (74% vs 90%). No publication bias was detected in this meta-analysis.
conclusionsAI algorithms demonstrate satisfactory performance in the diagnosis of OC using blood biomarkers and are anticipated to become an effective diagnostic modality in the future, potentially avoiding unnecessary surgeries. Future research is warranted to incorporate external validation into AI diagnostic models, as well as to prioritize the adoption of deep learning methodologies.
trial registrationPROSPERO CRD42023481232; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023481232.
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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.