Using probe electrospray ionization mass spectrometry and machine learning for detecting pancreatic cancer with high performance

Chung, WY; Correa, ES; Yoshimura, K; Chang, MC; Dennison, A; Takeda, S; Chang, YT

Abstract

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. [Abstract copyright: AJTR Copyright © 2020.]

Citation

Chung, W., Correa, E., Yoshimura, K., Chang, M., Dennison, A., Takeda, S., & Chang, Y. (2020). Using probe electrospray ionization mass spectrometry and machine learning for detecting pancreatic cancer with high performance. American Journal of Translational Research, 12(1), 171-179

• Journal Article Type: Article
• Acceptance Date: Dec 25, 2019
• Online Publication Date: Jan 15, 2020
• Publication Date: Jan 30, 2020
• Deposit Date: Feb 27, 2020
• Publicly Available Date: Feb 27, 2020
• Journal: American journal of translational research
• Publisher: e-Century Publishing
• Volume: 12
• Issue: 1
• Pages: 171-179
• Keywords: Probe electrospray ionization mass spectrometry (PESI-MS), machine learning, pancreatic ductal adenocarcinoma (PDAC)
• Publisher URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7013221/
• Related Public URLs: http://www.ajtr.org/index.html
• Additional Information: Additional Information : ** From PubMed via Jisc Publications Router **Journal IDs: pissn 1943-8141 **Article IDs: pmc: PMC7013221 **History: accepted 25-12-2019; submitted 23-11-2019