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

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Title: Prediction of pathological response to neo‐adjuvant chemoradiotherapy for oesophageal cancer using vibrational spectroscopy
Authors: Nguyen, Thi N. Q.Maguire, AdrianMooney, Catherineet al.
Permanent link: http://hdl.handle.net/10197/12181
Date: Mar-2021
Online since: 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.
Funding Details: 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
Keywords: Complete pathological responseFTIR spectroscopyMachine learningNeo-adjuvant chemoradiotherapyRaman spectroscopy
DOI: 10.1002/tbio.202000014
Language: en
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/
Appears in Collections:Computer Science Research Collection

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