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Frontline clinical diagnosis—FTIR on pancreatic cancer

Edward Duckworth, Matthew Mortimer, Bilal Al‐Sarireh, Venkat Kanamarlapudi Orcid Logo, Deb Roy Orcid Logo

Cancer Medicine, Volume: 12, Issue: 16, Pages: 17340 - 17345

Swansea University Authors: Edward Duckworth, Venkat Kanamarlapudi Orcid Logo, Deb Roy Orcid Logo

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DOI (Published version): 10.1002/cam4.6346

Abstract

Objective: Accurate, easily accessible and economically viable cancer diagnostic tools are pivotal in improving the abysmal 5% survival rate of pancreatic cancer. Methods: A novel, affordable, non-invasive diagnostic method has been developed by combining measurement precision of infrared spectrosco...

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Published in: Cancer Medicine
ISSN: 2045-7634 2045-7634
Published: Wiley 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa65016
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first_indexed 2023-11-24T11:56:21Z
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spelling v2 65016 2023-11-20 Frontline clinical diagnosis—FTIR on pancreatic cancer 39d5a0d074f0a9dfcc832fbd8965d392 Edward Duckworth Edward Duckworth true false 63741801137148abfa4c00cd547dcdfa 0000-0002-8739-1483 Venkat Kanamarlapudi Venkat Kanamarlapudi true false a18d76438369122184e83fb683d8d787 0000-0002-7528-8649 Deb Roy Deb Roy true false 2023-11-20 CHEM Objective: Accurate, easily accessible and economically viable cancer diagnostic tools are pivotal in improving the abysmal 5% survival rate of pancreatic cancer. Methods: A novel, affordable, non-invasive diagnostic method has been developed by combining measurement precision of infrared spectroscopy with classification using machine learning tools. Results: Diagnosis accuracy as high as 90% has been achieved. The study investigated urine and blood from pancreas cancer patients and healthy volunteers, and significantly improved accuracy by focusing on sweet spots within blood plasma fractions containing molecules within a narrow range of molecular weights. Journal Article Cancer Medicine 12 16 17340 17345 Wiley 2045-7634 2045-7634 Biomarker, cancer, diagnosis, FTIR, pancreatic, PCA, spectroscopy, SVM 31 8 2023 2023-08-31 10.1002/cam4.6346 http://dx.doi.org/10.1002/cam4.6346 COLLEGE NANME Chemistry COLLEGE CODE CHEM Swansea University SU Library paid the OA fee (TA Institutional Deal) ED and DR acknowledge financial support from Cherish-DE, EPSRC and Swansea University. This 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. 2023-12-13T12:23:02.4761015 2023-11-20T14:46:58.0160319 Faculty of Science and Engineering School of Engineering and Applied Sciences - Chemistry Edward Duckworth 1 Matthew Mortimer 2 Bilal Al‐Sarireh 3 Venkat Kanamarlapudi 0000-0002-8739-1483 4 Deb Roy 0000-0002-7528-8649 5 65016__29257__13fdf981e33d49f8a0bf4014dacab214.pdf 65016.VOR.pdf 2023-12-13T12:21:29.6243951 Output 687547 application/pdf Version of Record true © 2023 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. Distributed under the terms of a Creative Commons Attribution 4.0 International License (CC BY 4.0). true eng https://creativecommons.org/licenses/by/4.0/
title Frontline clinical diagnosis—FTIR on pancreatic cancer
spellingShingle Frontline clinical diagnosis—FTIR on pancreatic cancer
Edward Duckworth
Venkat Kanamarlapudi
Deb Roy
title_short Frontline clinical diagnosis—FTIR on pancreatic cancer
title_full Frontline clinical diagnosis—FTIR on pancreatic cancer
title_fullStr Frontline clinical diagnosis—FTIR on pancreatic cancer
title_full_unstemmed Frontline clinical diagnosis—FTIR on pancreatic cancer
title_sort Frontline clinical diagnosis—FTIR on pancreatic cancer
author_id_str_mv 39d5a0d074f0a9dfcc832fbd8965d392
63741801137148abfa4c00cd547dcdfa
a18d76438369122184e83fb683d8d787
author_id_fullname_str_mv 39d5a0d074f0a9dfcc832fbd8965d392_***_Edward Duckworth
63741801137148abfa4c00cd547dcdfa_***_Venkat Kanamarlapudi
a18d76438369122184e83fb683d8d787_***_Deb Roy
author Edward Duckworth
Venkat Kanamarlapudi
Deb Roy
author2 Edward Duckworth
Matthew Mortimer
Bilal Al‐Sarireh
Venkat Kanamarlapudi
Deb Roy
format Journal article
container_title Cancer Medicine
container_volume 12
container_issue 16
container_start_page 17340
publishDate 2023
institution Swansea University
issn 2045-7634
2045-7634
doi_str_mv 10.1002/cam4.6346
publisher Wiley
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
url http://dx.doi.org/10.1002/cam4.6346
document_store_str 1
active_str 0
description Objective: Accurate, easily accessible and economically viable cancer diagnostic tools are pivotal in improving the abysmal 5% survival rate of pancreatic cancer. Methods: A novel, affordable, non-invasive diagnostic method has been developed by combining measurement precision of infrared spectroscopy with classification using machine learning tools. Results: Diagnosis accuracy as high as 90% has been achieved. The study investigated urine and blood from pancreas cancer patients and healthy volunteers, and significantly improved accuracy by focusing on sweet spots within blood plasma fractions containing molecules within a narrow range of molecular weights.
published_date 2023-08-31T12:23:03Z
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score 11.013619

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