1. Predicting Breast Cancer by Paper Spray Ion Mobility Spectrometry Mass Spectrometry and Machine Learning
- Author
-
Ewelina P. Dutkiewicz, Chih-Lin Chen, Hua-Yi Hsieh, Cheng-Chih Hsu, Ying-Chen Huang, Ming-Yang Wang, Hsin-Hsiang Chung, and Bo-Rong Chen
- Subjects
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 - 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.
- Published
- 2019