Predicting Breast Cancer by Paper Spray Ion Mobility Spectrometry Mass Spectrometry and Machine Learning
Autor: | Ewelina P. Dutkiewicz, Chih-Lin Chen, Hua-Yi Hsieh, Cheng-Chih Hsu, Ying-Chen Huang, Ming-Yang Wang, Hsin-Hsiang Chung, Bo-Rong Chen |
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Rok vydání: | 2019 |
Předmět: |
Paper
Core needle Spectrometry Mass Electrospray Ionization Ion-mobility spectrometry Electrospray ionization Breast Neoplasms 010402 general chemistry Machine learning computer.software_genre Mass spectrometry 01 natural sciences Analytical Chemistry Machine Learning Breast cancer Ion Mobility Spectrometry medicine Humans business.industry Chemistry 010401 analytical chemistry medicine.disease Mass spectrometric 0104 chemical sciences Ion-mobility spectrometry–mass spectrometry Female Artificial intelligence Asymmetric waveform business computer Algorithms |
Zdroj: | Analytical Chemistry. 92:1653-1657 |
ISSN: | 1520-6882 0003-2700 |
Popis: | Paper spray ionization has been used as a fast sampling/ionization method for the direct mass spectrometric analysis of biological samples at ambient conditions. Here, we demonstrated that by utilizing paper spray ionization-mass spectrometry (PSI-MS) coupled with field asymmetric waveform ion mobility spectrometry (FAIMS), predictive metabolic and lipidomic profiles of routine breast core needle biopsies could be obtained effectively. By the combination of machine learning algorithms and pathological examination reports, we developed a classification model, which has an overall accuracy of 87.5% for an instantaneous differentiation between cancerous and noncancerous breast tissues utilizing metabolic and lipidomic profiles. Our results suggested that paper spray ionization-ion mobility spectrometry-mass spectrometry (PSI-IMS-MS) is a powerful approach for rapid breast cancer diagnosis based on altered metabolic and lipidomic profiles. |
Databáze: | OpenAIRE |
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