Prediction of pathological response to neo‐adjuvant chemoradiotherapy for oesophageal cancer using vibrational spectroscopy. Issue 1 (29th September 2020)
- Record Type:
- Journal Article
- Title:
- Prediction of pathological response to neo‐adjuvant chemoradiotherapy for oesophageal cancer using vibrational spectroscopy. Issue 1 (29th September 2020)
- Main Title:
- Prediction of pathological response to neo‐adjuvant chemoradiotherapy for oesophageal cancer using vibrational spectroscopy
- Authors:
- Nguyen, Thi N. Q.
Maguire, Adrian
Mooney, Catherine
Jackson, Naomi
Lynam‐Lennon, Niamh
Weldon, Vicki
Muldoon, Cian
Maguire, Aoife A.
O'Toole, D.
Ravi, Narayanasamy
Reynolds, John V.
O'Sullivan, Jacintha
Meade, Aidan D. - Abstract:
- 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. 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. In this study Raman+FTIR imaging were employed to develop a machine learning model predicting pCR within OC patients with classification rates above 85%.
- Is Part Of:
- Translational biophotonics. Volume 3:Issue 1(2021)
- Journal:
- Translational biophotonics
- Issue:
- Volume 3:Issue 1(2021)
- Issue Display:
- Volume 3, Issue 1 (2021)
- Year:
- 2021
- Volume:
- 3
- Issue:
- 1
- Issue Sort Value:
- 2021-0003-0001-0000
- Page Start:
- n/a
- Page End:
- n/a
- Publication Date:
- 2020-09-29
- Subjects:
- complete pathological response -- FTIR spectroscopy -- machine learning -- neo‐adjuvant chemoradiotherapy -- Raman spectroscopy
Imaging systems in medicine -- Periodicals
Biosensors -- Optical properties -- Periodicals
Photonics -- Periodicals
Imaging systems in medicine
Photonics
Optics and Photonics
Translational Medical Research
Periodicals
Periodical
621.365 - Journal URLs:
- https://onlinelibrary.wiley.com/loi/26271850 ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1002/tbio.202000014 ↗
- Languages:
- English
- ISSNs:
- 2627-1850
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
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- 15865.xml