A groundbreaking experimental blood test has shown promise in detecting early-stage ovarian cancer in patients presenting with vague symptoms that often go misdiagnosed, according to a recent report in Cancer Research Communications.
Currently, no reliable blood tests exist for patients with subtle early symptoms, and invasive methods frequently fail to identify tumors at the initial stages. Using advanced machine learning tools, researchers have identified multiple biomarkers from various molecular and biological processes. These markers can be combined into a single test capable of detecting all subtypes of ovarian cancer at any stage.
In trials conducted at a major medical center with blood samples from nearly 400 women showing potential symptoms of ovarian cancer, the test achieved 92% accuracy in identifying patients with any stage of the disease. Notably, it demonstrated 88% accuracy for detecting Stage I and Stage II ovarian cancer, which are typically difficult to diagnose.
Oriana Papin-Zoghbi, CEO of Denver-based AOA Dx, the company developing the test, said the findings highlight the test’s potential to help clinicians make faster and more informed decisions for women facing a challenging diagnostic process.
Ovarian cancer is the fifth leading cause of cancer-related deaths among women, largely due to late diagnosis after the disease has spread, making treatment more difficult. More than 90% of patients with early-stage ovarian cancer experience symptoms that can be mistaken for benign conditions, including bloating, abdominal discomfort, and digestive issues.
The development of this blood test could mark a significant step forward in early detection, potentially improving survival rates and providing women with quicker access to appropriate treatment.
–Input WAM
