A new blood test using machine learning accurately detects early ovarian cancer, offering a breakthrough in women’s cancer diagnostics.
DENVER: A pioneering blood test developed by U.S.-based AOA Dx has shown promising results in detecting early-stage ovarian cancer, offering new hope for faster diagnosis and treatment. The experimental test demonstrated a 92% accuracy rate in identifying ovarian cancer in its various stages—and 88% accuracy specifically in the early Stage I and II cases.
Currently, there are no widely reliable non-invasive tests for detecting ovarian cancer at an early stage. Symptoms such as bloating, abdominal pain, and digestive issues are often misdiagnosed as minor ailments. This delay in diagnosis contributes to ovarian cancer being the fifth leading cause of cancer-related deaths among women.
The new blood test uses machine learning to analyse biomarkers across multiple biological processes, allowing it to identify all subtypes of ovarian cancer. Researchers tested it on blood samples from nearly 400 women showing vague but potentially concerning symptoms. According to a report published in Cancer Research Communications, the test successfully identified nearly all confirmed cases.
Oriana Papin-Zoghbi, CEO of AOA Dx, said the blood test for early ovarian cancer is a potential game-changer in helping physicians make faster, more informed decisions. “It provides clarity for women during one of the most uncertain diagnostic processes in healthcare,” she stated.
The test’s ability to detect early ovarian cancer could dramatically improve patient outcomes. When identified at early stages, treatment is far more effective and survival rates are significantly higher.
The breakthrough represents a major step forward in women’s cancer diagnostics and paves the way for broader clinical trials before potential approval and widespread use.
