Scientists have developed a simple blood test to spot ovarian cancer early that could “significantly improve” outcomes for women with the disease.
More than 300,000 women, mostly over the age of 50, are diagnosed worldwide each year, according to the World Cancer Research Fund. Ovarian cancer is often diagnosed late, which makes treating the condition more difficult.
The test trialled by UK and US researchers looks for two different types of blood markers in those showing symptoms of the disease, which include pelvic pain and a bloated tummy. It then uses machine learning to recognise patterns that would be difficult for humans to detect.
Currently, the disease is usually diagnosed using a mix of scans and biopsies, such as an ultrasound scan, a CT scan, a needle biopsy, a laparoscopy or surgery to remove tissue or possibly the ovaries.
It is often detected late because symptoms such as bloating, feeling full quickly after eating or having to pee frequently are not always obvious potential signs of cancer.
The blood test looks for what ovarian cancer sheds into the bloodstream, even in its early stages.
Cancer cells release fragments into the blood that carry tiny, fat-like molecules known as lipids, along with certain proteins. This combination of lipids and proteins are like a biological fingerprint for ovarian cancer, according to AOA Dx, which developed the test.
It also uses an algorithm that has been trained on thousands of patient samples to recognise subtle patterns across these lipids and proteins that signal ovarian cancer.
The test can detect the disease “at early stages and with greater accuracy than current tools”, according to Alex Fisher, the chief operating officer and co-founder of AOA Dx.
Dr Abigail McElhinny, the chief science officer at AOA Dx, 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.
“This platform offers a great opportunity to improve the early diagnosis of ovarian cancer, potentially resulting in better patient outcomes and lower costs to the healthcare system.”
A study, led by the universities of Manchester and Colorado and published in the American Association of Cancer Research journal Cancer Research Communications, tested 832 samples using the AOA Dx platform.
In samples from the University of Colorado, the test was able to accurately detect ovarian cancer across all stages of the disease 93% of the time, and 91% in the early stages.
In samples from the University of Manchester, the test showed 92% accuracy at all stages and 88% accuracy in early stages.
Emma Crosbie, a professor at the University of Manchester and an honorary consultant in gynaecological oncology at Manchester University NHS Foundation Trust, said: “AOA Dx’s platform has the potential to significantly improve patient care and outcomes for women diagnosed with ovarian cancer.
“We are eager to continue advancing this important research through additional prospective trials to further validate and expand our understanding of how this could be integrated into existing healthcare systems.”
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