With funding providing by the Laura Crandall Brown Foundation and the Ovarian Cancer Institute, researchers at Georgia Tech have developed a method to detect ovarian cancer that is highly accurate in patients with Stage 1 disease.
The researchers employed high performance mass spectrometry to interrogate the serum metabolome of early-stage ovarian cancer (OC) patients and age-matched control women. The resulting spectral features were used to establish a linear support vector machine (SVM) model of 16 diagnostic metabolites that are able to identify early-stage OC with 100% accuracy in the patient cohort. The results provide evidence for the importance of lipid and fatty acid metabolism in OC and serve as the foundation of a clinically significant diagnostic test.
For now, there is not a timeline of when the test will be available publicly.