Related Experiment Video
Updated: Jul 6, 2026
Platform for Quantitative Detection of Endometrial Immune Cells Based on Immunohistochemistry and Digital Image Analysis
Published on: October 13, 2023
Identifying biomarkers of endometriosis using serum protein fingerprinting and artificial neural networks
Liang Wang1, Wei Zheng, Lin Mu
1The 2nd Affiliated Hospital, Department of Gynecology, Zhejiang University School of Medicine, Hangzhou, China.
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.
