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Advancing frontline early pancreatic cancer detection using within-class feature extraction in FTIR spectroscopy

Zheng Tang, Edward Duckworth, Benjamin Mora Orcid Logo, Bilal Al - Sarireh, Matthew Mortimer, Deb Roy Orcid Logo

Scientific Reports, Volume: 14, Issue: 1

Swansea University Authors: Benjamin Mora Orcid Logo, Deb Roy Orcid Logo

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Abstract

This study introduces a novel approach for the early detection of pancreatic cancer through biofluid spectroscopy, leveraging a unique machine learning pipeline comprising class-specific principal component analysis (PCA), linear discriminant analysis (LDA), and support vector machine (SVM) in both...

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Published in: Scientific Reports
ISSN: 2045-2322
Published: Springer Science and Business Media LLC 2024
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa68350
Abstract: This study introduces a novel approach for the early detection of pancreatic cancer through biofluid spectroscopy, leveraging a unique machine learning pipeline comprising class-specific principal component analysis (PCA), linear discriminant analysis (LDA), and support vector machine (SVM) in both real patient and synthetic data. By conducting separate PCA on cancerous and non-cancerous samples and integrating the projections prior to LDA and SVM classification, we demonstrate significantly improved diagnostic accuracy compared to traditional methods. This methodology not only enhances predictive performance but also offers deeper insights into the influence of molecular spectra on model efficacy. Our findings, validated on real patient data, suggest a promising avenue for developing non-invasive, accurate diagnostic tools for early-stage pancreatic cancer detection.
College: Faculty of Science and Engineering
Funders: ZT acknowledges financial support from EPSRC and Swansea Bay Health Board. ED and DR acknowledge financial support from Cherish-DE, EPSRC and Swansea University and POLight project. The project POLight has received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme. Ethical approval and consent: Ethical approval was obtained from The Wales Research Ethics Committee (REC) 7 and the SBU Joint Scientific Review Committee. Written informed consent was obtained from the participants.
Issue: 1