Blood Test Detects Ovarian Cancer with High Accuracy

Ovarian cancer is the fifth leading cause of cancer-related deaths among women, largely due to late-stage diagnosis. More than 90% of women experience symptoms in Stage I, yet only 20% are diagnosed at an early stage because signs such as bloating, abdominal pain, and digestive issues often resemble benign conditions. Existing diagnostic tools, including invasive procedures and single biomarkers, frequently fail to identify early disease. Now, a new blood test with high accuracy offers a promising solution.

Diagnostics company AOA Dx (Denver, CO, USA), in collaboration with researchers from the University of Manchester (Manchester, UK) and the University of Colorado (Boulder, CO, USA), has developed a platform that combines protein and lipid biomarkers with machine learning to identify ovarian cancer. The technology analyzes multiple groups of markers from a single blood sample, offering a noninvasive, cost-effective diagnostic tool. By integrating diverse molecular data, the test captures the complexity of ovarian cancer subtypes and stages, outperforming traditional approaches.

The test was evaluated in a clinical study involving over 950 patients across the two institutions. In samples from the University of Colorado, the test achieved 93% accuracy across all cancer stages and 91% for early-stage disease. In samples from the University of Manchester, the test’s accuracy reached 92% overall and 88% for early-stage cases. Published in Cancer Research Communications, the results showed that the test outperformed biomarker methods that have been used for decades, yet achieved less than 90% accuracy.

These findings mark a major milestone in ovarian cancer diagnostics. The platform could become a streamlined, globally relevant test, designed to provide faster, more reliable results for symptomatic women. The success of this study will inform the final design of the test and support regulatory approval efforts in the US and Europe, with plans to eventually introduce it to the NHS and other international markets.

If validated in further trials, the test has the potential to significantly improve patient care by enabling earlier detection, guiding treatment decisions, and reducing unnecessary delays. The researchers emphasize its promise as both a clinical tool and a practical solution for healthcare systems seeking cost-effective cancer diagnostics.

“Our platform detects ovarian cancer at early stages and with greater accuracy than current tools,” said Alex Fisher COO and Co-Founder of AOA Dx. “These findings show its potential to aid clinicians in making faster, more informed decisions for women who need urgent clarity during a challenging diagnostic process.”

“By using machine learning to combine multiple biomarker types, we’ve developed a diagnostic tool that detects ovarian cancer across the molecular complexity of the disease in sub-types and stages,” said Dr. Abigail McElhinny, Chief Science Officer of AOA Dx. “This platform offers a great opportunity to improve the early diagnosis of ovarian cancer, potentially resulting in better patient outcomes and lower costs to the healthcare system.”

Related Links:
AOA Dx
University of Manchester 
University of Colorado