Denver: An experimental blood test has shown significant promise in detecting early-stage ovarian cancer in patients who present with vague symptoms that are often misdiagnosed using current methods. Researchers highlighted these findings in a new report, emphasizing the potential of this test to fill a critical gap in ovarian cancer diagnostics.
According to Emirates News Agency, the report published in Cancer Research Communications states that there are currently no reliable blood tests available for these patients, and existing invasive procedures frequently miss early-stage ovarian tumors. The researchers utilized machine learning tools to identify multiple biomarkers across a wide range of molecules and bodily processes, which they combined into a single test capable of detecting all sub-types of the disease at any stage.
The blood test was trialed at a large medical center, analyzing samples from nearly 400 women exhibiting potential symptoms of ovarian cancer. The test demonstrated a 92 percent accu
racy rate in identifying any stage of ovarian cancer and an 88 percent accuracy rate for Stage I or Stage II diseases, as reported.
Oriana Papin-Zoghbi, CEO of AOA Dx, the Denver, Colorado-based company developing the test, stated that these findings reveal the test’s potential to aid in making faster, more informed decisions for women requiring urgent diagnostic clarity.
Ovarian cancer ranks as the fifth leading cause of cancer-related deaths among women, primarily due to delays in diagnosis until after the disease has spread, at which point treatment becomes more challenging. Over 90 percent of patients with early-stage ovarian cancer experience symptoms that are often mistaken for benign conditions, such as bloating, abdominal pain, and digestive issues.