https://scholars.lib.ntu.edu.tw/handle/123456789/548426
標題: | Using probe electrospray ionization mass spectrometry and machine learning for detecting pancreatic cancer with high performance | 作者: | Chung, Wen Y Correa, Elon Yoshimura, Kentaro MING-CHU CHANG Dennison, Ashley Takeda, Sen YU-TING CHANG |
關鍵字: | Probe electrospray ionization mass spectrometry (PESI-MS); machine learning; pancreatic ductal adenocarcinoma (PDAC) | 公開日期: | 2020 | 卷: | 12 | 期: | 1 | 來源出版物: | American journal of translational research | 摘要: | A rapid blood-based diagnostic modality to detect pancreatic ductal adenocarcinoma (PDAC) with high accuracy is an unmet medical need. The study aimed to validate a unique diagnosis system using Probe Electrospray Ionization Mass Spectrometry (PESI-MS) and Machine Learning to the diagnosis of PDAC. Peripheral blood samples were collected from a total of 322 consecutive PDAC patients and 265 controls with a family history of PDAC. Five µl of serum samples were analyzed using PESI-MS system. The mass spectra from each specimen were then fed into machine learning algorithms to discriminate between control and cancer cases. A total of 587 serum samples were analyzed. The sensitivity of the machine learning algorithm using PESI-MS profiles to identify PDAC is 90.8% with specificity of 91.7% (95% CI 83.9%-97.4% and 82.8%-97.7% respectively). Combined PESI-MS profiles with age and CA19-9 as predictors, the accuracy for stage 1 or 2 of PDAC is 92.9% and for stage 3 or 4 is 93% (95% CI 86.3-98.2; 87.9-97.4 respectively). The accuracy and simplicity of the PESI-MS profiles combined with machine learning provide an opportunity to detect PDAC at an early stage and must be applicable to the examination of at-risk populations. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/548426 | ISSN: | 1943-8141 |
顯示於: | 醫學系 |
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