Back to Search
Start Over
Predicting Breast Cancer by Paper Spray Ion Mobility Spectrometry Mass Spectrometry and Machine Learning.
- Source :
-
Analytical chemistry [Anal Chem] 2020 Jan 21; Vol. 92 (2), pp. 1653-1657. Date of Electronic Publication: 2019 Dec 10. - Publication Year :
- 2020
-
Abstract
- 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.
Details
- Language :
- English
- ISSN :
- 1520-6882
- Volume :
- 92
- Issue :
- 2
- Database :
- MEDLINE
- Journal :
- Analytical chemistry
- Publication Type :
- Academic Journal
- Accession number :
- 31809016
- Full Text :
- https://doi.org/10.1021/acs.analchem.9b03966