ArticleScientific reports2024
Automated early ovarian cancer detection system based on bioinformatics.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Comparative Analysis of Serum and Tissue miRNA Expression Profiles and Regulatory Pathways in Early-Stage Ovarian Cancer Using Public Databases.International journal of molecular sciences · 2026Article
- Study of Estrogen Receptor Alpha Gene Polymorphisms (International journal of molecular sciences · 2026Article
- CircRNA14781 promotes olaparib resistance of ovarian cancer cells by regulating miR-330-5p/NGFR pathway.Journal of ovarian research · 2026Article
- Inflammation and Immune Escape in Ovarian Cancer: Pathways and Therapeutic Opportunities.Journal of inflammation research · 2025Review
- Low Serum Cholinesterase Levels Predict Poor Prognosis in Patients with Ovarian Cancer.International journal of general medicine · 2025Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ovarian cancer is a common gynecological tumor, with a high mortality rate and difficult clinical treatment. Early detection of ovarian cancer has significant diagnostic value. In response to the problem of poor diagnostic performance of traditional early diagnosis methods, this article designed an automated early ovarian cancer detection system to improve the detection of early ovarian cancer. The conventional early diagnosis methods include serum CA125 (carbohydrate antigen 125) detection and positron emission tomography/computed tomography (PET/CT) imaging. This article combined serum CA125 detection and PET/CT imaging to detect the CA125 level and maximum standardized uptake value (SUV) in patient's serum. When the CA125 level exceeded 35U/ml and the maximum SUV value exceeded 2.5, the test was considered positive. This article selected 200 patients from Jingzhou Hospital for the experiment and compared the three detection methods. The average specificity of single serum CA125 detection, single PET/CT imaging, and automated detection in patients under 50 were 61.24%, 79.57%, and 97.79%, respectively. The automated early ovarian cancer detection system designed in this article can significantly improve the specificity of early ovarian cancer detection and has excellent application value for early ovarian cancer detection.
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Registered trials
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