The measurement step in the test used 200 μL of blood serum, which is vaporized by hot helium plasma. As the molecules from the serum become electrically charged, a mass spectrometer is used to measure their relative abundance.

The test looks at metabolites in the serum. Sera were obtained from 44 women with ovarian cancer, and 50 women with either benign conditions or were healthy. The sera were analyzed by high throughput ambient ionization technique for mass spectrometry. This gives direct real time analysis of the profile relative metabolites levels in the sera. The profiles were input to a customized functional support vector machine–based machine-learning algorithm for diagnostic classification. Performance was evaluated through a 64-30 split validation test and with a stringent series of leave-one-out cross-validations.

The scientists at Georgia Institute of Technology, (Atlanta, GA, USA), reported that the assay did extremely well in initial tests involving 94 subjects. In addition to being able to generate results using only a drop of blood serum, the test proved to be 100% accurate in distinguishing sera from women with ovarian cancer from normal controls. In addition, it registered neither a single false positive nor a false negative.

John McDonald, Ph.D. the Chief Research Scientist at Georgia Tech., said, "Because ovarian cancer is a disease of relatively low prevalence, it is essential that tests for it be extremely accurate. We believe we may have developed such a test." The results of the study were published online in July 2010 in the journal Cancer Epidemiology, Biomarkers, & Prevention Research. In addition to having a relatively low prevalence, ovarian cancer is also asymptomatic in the early stages. Therefore, if further testing confirms the ability to accurately detect ovarian cancer by analyzing metabolites in the serum of women, doctors will be able detect the disease early and save many lives.

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Georgia Institute of Technology