US EditionBlood test could detect ovarian cancer in early stages, researchers say
The AOA Dx blood test uses machine learning to detect ovarian cancer with 91% accuracy in early stages, outperforming traditional methods, potentially improving patient outcomes and care.
- AOA Dx developed a blood test that can detect ovarian cancer with high accuracy in symptomatic women by recognizing complex blood markers.
- This test emerged from a study led by the Universities of Manchester and Colorado, which analyzed 832 samples and used machine learning algorithms to identify cancer patterns.
- The test showed 93% accuracy across all stages in Colorado samples and 92% accuracy in Manchester samples, outperforming traditional biomarkers used for 30 years.
- Alex Fisher, COO of AOA Dx, stated that their platform can identify ovarian cancer in its initial phases with higher precision than existing methods, which may lead to improved early diagnosis and better patient outcomes.
- Regulatory approval and further trials are underway, and experts hope the test could integrate into healthcare systems like the NHS and increase access to early cancer detection.
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22 Articles
New blood test shows 'significant promise' in detecting ovarian cancer early
Scientists have created a revolutionary blood test capable of identifying ovarian cancer with more than 90 per cent precision, including cases in their earliest phases. The diagnostic tool, developed by AOA Dx and validated through research at Manchester and Colorado universities, represents a potential game-changer for a disease that claims the lives of more than 4,000 British women each year.The breakthrough examination involved 950 female par…
·London, United Kingdom
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A simple blood analysis could accurately detect ovarian cancer at the inception stage and has the potential to improve the "significant" care and results for women diagnosed with this disease, reports DPA/PA Media, according to Agerpres.
·Romania
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Leaning Left2Leaning Right2Center8Last UpdatedBias Distribution67% Center
Bias Distribution
- 67% of the sources are Center
67% Center
L 17%
C 67%
R 17%
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