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  5. Prediction of pathological response to neo‐adjuvant chemoradiotherapy for oesophageal cancer using vibrational spectroscopy
 
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Prediction of pathological response to neo‐adjuvant chemoradiotherapy for oesophageal cancer using vibrational spectroscopy

Author(s)
Nguyen, Thi N. Q.  
Maguire, Adrian  
Mooney, Catherine  
et al.  
Uri
http://hdl.handle.net/10197/12181
Date Issued
2021-03
Date Available
2021-05-19T16:43:17Z
Abstract
In oesophageal cancer (OC) neo‐adjuvant chemoradiotherapy (neoCRT) is used to debulk tumour size prior to surgery, with a complete pathological response (pCR) observed in approximately ∼30% of patients. Presently no predictive quantitative methodology exists which can predict response, in particular a pCR or major response (MR), in patients prior to therapy. Raman and Fourier transform infrared imaging were performed on OC tissue specimens acquired from 50 patients prior to therapy, to develop a computational model linking spectral data to treatment outcome. Modelling sensitivities and specificities above 85% were achieved using this approach. Parallel in‐vitro studies using an isogenic model of radioresistant OC supplied further insight into OC cell spectral response to ionising radiation where a potential spectral biomarker of radioresistance was observed at 977 cm−1. This work demonstrates that chemical imaging may provide an option for triage of patients prior to neoCRT treatment allowing more precise prescription of treatment.
Sponsorship
Health Research Board
Science Foundation Ireland
Type of Material
Journal Article
Publisher
Wiley
Journal
Translational Biophotonics
Volume
3
Issue
1
Copyright (Published Version)
2020 the Authors
Subjects

Complete pathological...

FTIR spectroscopy

Machine learning

Neo-adjuvant chemorad...

Raman spectroscopy

DOI
10.1002/tbio.202000014
Language
English
Status of Item
Peer reviewed
ISSN
2627-1850
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by/3.0/ie/
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Meade_2020.pdf

Size

1.96 MB

Format

Adobe PDF

Checksum (MD5)

c6b78c61471ad65510cc45ac25bf3784

Owning collection
Computer Science Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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