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

Chung, WY, Correa, ES ORCID: https://orcid.org/0000-0002-5122-4384, Yoshimura, K, Chang, MC, Dennison, A, Takeda, S and Chang, YT 2020, 'Using probe electrospray ionization mass spectrometry and machine learning for detecting pancreatic cancer with high performance' , American journal of translational research, 12 (1) , pp. 171-179.

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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.]

Item Type: Article
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
Uncontrolled Keywords: Probe electrospray ionization mass spectrometry (PESI-MS), machine learning, pancreatic ductal adenocarcinoma (PDAC)
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: American journal of translational research
Publisher: e-Century Publishing
ISSN: 1943-8141
Related URLs:
SWORD Depositor: Publications Router
Depositing User: Publications Router
Date Deposited: 27 Feb 2020 09:35
Last Modified: 27 Feb 2020 09:45
URI: http://usir.salford.ac.uk/id/eprint/56513

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