Diagnostic prediction of early silicosis patients using neural network and MALDI-TOF-MS in serum.
- Author:
Qingbo MA
1
;
Wei LIU
;
Shixin WANG
;
Hua XIANG
Author Information
1. Key Laboratory of Medical Diagnostics of Ministry of Education, Faculty of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, China.
- Publication Type:Journal Article
- MeSH:
Biomarkers;
blood;
Blood Proteins;
analysis;
Humans;
Neural Networks (Computer);
Sensitivity and Specificity;
Silicosis;
blood;
classification;
diagnosis;
Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization;
methods
- From:
Journal of Biomedical Engineering
2011;28(1):142-147
- CountryChina
- Language:Chinese
-
Abstract:
Serum of 79 workers exposed to silica and 25 healthy controls cases were determined by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). 7 protein peaks were selected and used by artificial neural network (ANN) to establish a diagnostic model. A blinded test showed that accuracy, sensitivity and specificity were 91.35%, 93.69%, and 84.52%, respectively. The diagnostic pattern was also established to distinguish each stage of silica-exposed population. The diagnostic pattern worked excellently with 89.23%, 94.20% and 92.37% of accurate rate for classifying phase 0, phase 0+, and phase I of silicosis, respectively.
