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Int J Gynaecol Obstet. 2008 Jun;101(3):253-8. doi: 10.1016/j.ijgo.2008.01.018. Epub 2008 Mar 6.

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

Author information

1
The 2nd Affiliated Hospital, Department of Gynecology, Zhejiang University School of Medicine, Hangzhou, China.

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.

PMID:
18325521
DOI:
10.1016/j.ijgo.2008.01.018
[Indexed for MEDLINE]

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