"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."
"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."
More than 300,000 women, mostly over 50, are diagnosed with ovarian cancer worldwide each year. The disease is often detected late because symptoms such as bloating, feeling full quickly after eating, and frequent urination are not always recognised as cancer signs. Current diagnosis uses scans and biopsies including ultrasound, CT, needle biopsy, laparoscopy, or surgery. A blood test detects fragments shed by cancer cells that carry lipids and proteins, forming a biological fingerprint. A machine-learning algorithm trained on thousands of patient samples recognises subtle biomarker patterns, enabling earlier and more accurate detection across subtypes and stages.
Read at www.theguardian.com
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