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Correa, E (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. ISSN 1943-8141
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Chung et al. - Am J Transl Res 2020.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial. Download (998kB) | Preview |
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.
| Item Type: | Article |
|---|---|
| Subjects: | Q Science > QA Mathematics R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer) |
| Divisions: | Applied Mathematics (merged with Comp Sci 10 Aug 20) |
| Publisher: | e-Century Publishing Corporation |
| Related URLs: | |
| Date Deposited: | 24 Jul 2020 15:41 |
| Last Modified: | 04 Sep 2021 06:53 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/13395 |
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