Identifying biomarkers of endometriosis using serum protein fingerprinting and artificial neural networks

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Abstract
Objectives To use surface‐enhanced laser desorption/ionization time‐of‐flight mass spectrometry (SELDI‐TOF‐MS) protein chip array technology to detect proteomic patterns in the serum of women with endometriosis; build diagnostic models; and evaluate their clinical significance. Methods Serum samples from women with endometriosis and healthy women were studied using SELDI‐TOF‐MS protein chip technology. For every matched pair, two‐thirds of the samples were used to look for different patterns and one‐third was used for cross‐validation. Results Five potential biomarkers were found and the diagnostic system distinguished endometriosis from validation samples with a sensitivity of 91.7% and a specificity of 90.0%. Conclusion This method shows great potential in identifying biomarkers to be used for endometriosis screening.