A simple blood test could accurately detect ovarian cancer in its early stages, potentially transforming care for thousands of women diagnosed with the disease each year. The test uses machine learning to analyse blood markers and recognise patterns that signal cancer presence.
The innovative diagnostic tool, developed by AOA Dx, looks for lipids and proteins that cancer cells release into the bloodstream. This combination creates a biological fingerprint for ovarian cancer that the test can identify even in early stages.
Researchers tested 832 samples across universities in Manchester and Colorado, with results published in Cancer Research Communications. The test achieved 93% accuracy across all disease stages and 91% accuracy in early stages using Colorado samples, while Manchester samples showed 92% accuracy overall and 88% accuracy in early stages.
Technology breakthrough
Current ovarian cancer diagnosis typically relies on scans, blood tests and sometimes biopsies, but the disease is often detected too late when treatment becomes more difficult. Around 7,500 new cases occur in the UK annually, usually affecting women over 50.
The machine learning algorithm has been trained on thousands of patient samples to detect patterns that would be imperceptible to human analysis. Cancer cells shed fragments carrying tiny fat-like molecules and certain proteins, creating the distinctive signature the test identifies.
Alex Fisher, chief operating officer and co-founder of AOA Dx, said the test can detect the disease "at early stages and with greater accuracy than current tools". Dr Abigail McElhinny, chief science officer, added: "By using machine learning to combine multiple biomarker types, we've developed a diagnostic tool that detects ovarian cancer across the molecular complexity of the disease in sub-types and stages."
Expert validation
Emma Crosbie, professor at the University of Manchester and honorary consultant in gynaecological oncology, said: "AOA Dx's platform has the potential to significantly improve patient care and outcomes for women diagnosed with ovarian cancer." She emphasised the need for additional prospective trials to validate integration into existing healthcare systems.
Symptoms of ovarian cancer include persistent pelvic pain, bloating, feeling full quickly after eating, and frequent urination. These signs may not always be obvious, contributing to late diagnosis when treatment options become limited.
Global impact potential
The Guardian reports that 300,000 women are diagnosed with ovarian cancer worldwide annually. Current diagnostic methods can include ultrasound, CT scans, needle biopsy, laparoscopy, and surgical tissue removal.
Experts hope the test could eventually be used on the NHS, subject to regulatory approval. The potential for early detection could significantly improve treatment outcomes and reduce healthcare system costs.
Sources used: "PA Media", "The Independent", "The Guardian" Note: This article has been edited with the help of Artificial Intelligence.
