An experimental blood test detected early-stage ovarian cancer in patients with vague symptoms that would likely be misdiagnosed using currently available methods, researchers said in a new report.
There are no reliable blood tests for these patients, and existing invasive tests often miss early-stage ovarian tumours, the researchers wrote in Cancer Research Communications.
Using machine learning tools, the researchers identified multiple biomarkers - from across a wide range of molecules and processes in the body - that could be combined into a single test that detects all sub-types of the disease, at all stages.
Tested at a large medical centre on blood samples from nearly 400 women with possible symptoms of ovarian cancer, the test was 92 percent accurate at identifying those with any stage of ovarian cancer and 88 percent accurate at identifying those with Stage I or Stage II disease, according to the report.
Oriana Papin-Zoghbi, chief executive officer of the company developing the new test -Denver, Colorado-based AOA Dx - said the findings show its potential to aid “in making faster, more informed decisions for women who need urgent clarity during a challenging diagnostic process.”
Ovarian cancer is the fifth leading cause of cancer-related deaths among women, largely due to delays in diagnosis until after the disease has spread in the body, at which point it’s harder to treat.
More than 90 percent of patients with early-stage ovarian cancer experience symptoms that can be mistaken for benign conditions, such as bloating, abdominal pain, and digestive issues.
WAM