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

DC FieldValueLanguage
dc.contributor.authorNguyen, Thi N. Q.-
dc.contributor.authorMaguire, Adrian-
dc.contributor.authorMooney, Catherine-
dc.contributor.authoret al.-
dc.date.accessioned2021-05-19T16:43:17Z-
dc.date.available2021-05-19T16:43:17Z-
dc.date.copyright2020 the Authorsen_US
dc.date.issued2021-03-
dc.identifier.citationTranslational Biophotonicsen_US
dc.identifier.issn2627-1850-
dc.identifier.urihttp://hdl.handle.net/10197/12181-
dc.description.abstractIn 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.en_US
dc.description.sponsorshipHealth Research Boarden_US
dc.description.sponsorshipScience Foundation Irelanden_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.rightsThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.en_US
dc.subjectComplete pathological responseen_US
dc.subjectFTIR spectroscopyen_US
dc.subjectMachine learningen_US
dc.subjectNeo-adjuvant chemoradiotherapyen_US
dc.subjectRaman spectroscopyen_US
dc.titlePrediction of pathological response to neo‐adjuvant chemoradiotherapy for oesophageal cancer using vibrational spectroscopyen_US
dc.typeJournal Articleen_US
dc.internal.authorcontactothercatherine.mooney@ucd.ieen_US
dc.statusPeer revieweden_US
dc.identifier.volume3en_US
dc.identifier.issue1en_US
dc.citation.otherArticle Number: e202000014en_US
dc.identifier.doi10.1002/tbio.202000014-
dc.neeo.contributorNguyen|Thi N. Q.|aut|-
dc.neeo.contributorMaguire|Adrian|aut|-
dc.neeo.contributorMooney|Catherine|aut|-
dc.neeo.contributoret al.||aut|-
dc.date.updated2021-01-16T10:54:48Z-
dc.identifier.grantid13/RC/2106-
dc.identifier.grantid12/TIDA/B2406-
dc.identifier.grantidHRA-POR-2015-1314-
dc.rights.licensehttps://creativecommons.org/licenses/by/3.0/ie/en_US
item.grantfulltextopen-
item.fulltextWith Fulltext-
Appears in Collections:Computer Science Research Collection
Files in This Item:
 File SizeFormat
DownloadMeade_2020.pdf2 MBAdobe PDF
Show simple item record

Page view(s)

75
Last Week
2
Last month
14
checked on Jul 24, 2021

Download(s)

8
checked on Jul 24, 2021

Google ScholarTM

Check

Altmetric


If you are a publisher or author and have copyright concerns for any item, please email research.repository@ucd.ie and the item will be withdrawn immediately. The author or person responsible for depositing the article will be contacted within one business day.