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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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DOI (Published version): 10.1038/s41598-024-79153-0

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
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URI: https://cronfa.swan.ac.uk/Record/cronfa68350
first_indexed 2024-11-27T13:46:53Z
last_indexed 2025-01-13T20:34:21Z
id cronfa68350
recordtype SURis
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spelling 2025-01-13T15:23:58.2862430 v2 68350 2024-11-27 Advancing frontline early pancreatic cancer detection using within-class feature extraction in FTIR spectroscopy 557f93dfae240600e5bd4398bf203821 0000-0002-2945-3519 Benjamin Mora Benjamin Mora true false a18d76438369122184e83fb683d8d787 0000-0002-7528-8649 Deb Roy Deb Roy true false 2024-11-27 MACS 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. Journal Article Scientific Reports 14 1 Springer Science and Business Media LLC 2045-2322 22 11 2024 2024-11-22 10.1038/s41598-024-79153-0 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Other 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. 2025-01-13T15:23:58.2862430 2024-11-27T10:49:54.9550683 Faculty of Science and Engineering School of Engineering and Applied Sciences - Chemistry Zheng Tang 1 Edward Duckworth 2 Benjamin Mora 0000-0002-2945-3519 3 Bilal Al - Sarireh 4 Matthew Mortimer 5 Deb Roy 0000-0002-7528-8649 6
title Advancing frontline early pancreatic cancer detection using within-class feature extraction in FTIR spectroscopy
spellingShingle Advancing frontline early pancreatic cancer detection using within-class feature extraction in FTIR spectroscopy
Benjamin Mora
Deb Roy
title_short Advancing frontline early pancreatic cancer detection using within-class feature extraction in FTIR spectroscopy
title_full Advancing frontline early pancreatic cancer detection using within-class feature extraction in FTIR spectroscopy
title_fullStr Advancing frontline early pancreatic cancer detection using within-class feature extraction in FTIR spectroscopy
title_full_unstemmed Advancing frontline early pancreatic cancer detection using within-class feature extraction in FTIR spectroscopy
title_sort Advancing frontline early pancreatic cancer detection using within-class feature extraction in FTIR spectroscopy
author_id_str_mv 557f93dfae240600e5bd4398bf203821
a18d76438369122184e83fb683d8d787
author_id_fullname_str_mv 557f93dfae240600e5bd4398bf203821_***_Benjamin Mora
a18d76438369122184e83fb683d8d787_***_Deb Roy
author Benjamin Mora
Deb Roy
author2 Zheng Tang
Edward Duckworth
Benjamin Mora
Bilal Al - Sarireh
Matthew Mortimer
Deb Roy
format Journal article
container_title Scientific Reports
container_volume 14
container_issue 1
publishDate 2024
institution Swansea University
issn 2045-2322
doi_str_mv 10.1038/s41598-024-79153-0
publisher Springer Science and Business Media LLC
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Chemistry{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Chemistry
document_store_str 0
active_str 0
description 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.
published_date 2024-11-22T08:36:37Z
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score 11.070971

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