https://scholars.lib.ntu.edu.tw/handle/123456789/543230
標題: | Predicting Breast Cancer by Paper Spray Ion Mobility Spectrometry Mass Spectrometry and Machine Learning | 作者: | Huang, Y.-C. Chung, H.-H. Dutkiewicz, E.P. Chen, C.-L. Hsieh, H.-Y. Chen, B.-R. Wang, M.-Y. CHENG-CHIH HSU |
公開日期: | 2020 | 卷: | 92 | 期: | 2 | 起(迄)頁: | 1653-1657 | 來源出版物: | Analytical Chemistry | 摘要: | 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. ? 2019 American Chemical Society. |
URI: | https://www.scopus.com/inward/record.url?eid=2-s2.0-85076594284&partnerID=40&md5=c51b40c9363f771dd56ea17db53e9988 https://scholars.lib.ntu.edu.tw/handle/123456789/543230 |
DOI: | 10.1021/acs.analchem.9b03966 | SDG/關鍵字: | Biopsy; Diseases; Ion mobility spectrometers; Ionization; Ions; Learning algorithms; Mass spectrometry; Metabolism; Paper; Biological samples; Breast cancer diagnosis; Classification models; Core-needle biopsies; Field asymmetric waveform ion mobility spectrometry; Ion mobility spectrometry; Mass spectrometric analysis; Overall accuracies; Machine learning |
顯示於: | 化學系 |
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