Identification and validation of a machine learning model of complete response to radiation in rectal cancer reveals immune infiltrate and TGFβ as key predictors

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Domingo, E., Rathee, S., Blake, A. et al. (22 more authors) (2024) Identification and validation of a machine learning model of complete response to radiation in rectal cancer reveals immune infiltrate and TGFβ as key predictors. EBioMedicine, 106. 105228. ISSN 2352-3964

Abstract

Metadata

Item Type: Article
Authors/Creators:
  • Domingo, E.
  • Rathee, S.
  • Blake, A.
  • Samuel, L.
  • Murray, G.
  • Sebag-Montefiore, D.
  • Gollins, S.
  • West, N.
  • Begum, R.
  • Richman, S. ORCID logo https://orcid.org/0000-0003-3993-5041
  • Quirke, P.
  • Redmond, K.
  • Chatzipli, A.
  • Barberis, A.
  • Hassanieh, S.
  • Mahmood, U.
  • Youdell, M.
  • McDermott, U.
  • Koelzer, V.
  • Leedham, S.
  • Tomlinson, I.
  • Dunne, P.
  • S:CORT Consortium
  • Buffa, F.M.
  • Maughan, T.S.
Copyright, Publisher and Additional Information:

© 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Keywords: Rectal neoplasms; Radiotherapy; Precision medicine; Prediction; TGFβ; Immune response; Genes
Dates:
  • Published: August 2024
  • Published (online): 15 July 2024
  • Accepted: 20 June 2024
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Medical Research (LIMR) > Division of Pathology and Data Analytics
Funding Information:
Funder
Grant number
MRC (Medical Research Council)
MR/M016587/1
Depositing User: Symplectic Publications
Date Deposited: 17 Jul 2024 12:30
Last Modified: 30 Jul 2024 10:45
Published Version: https://www.thelancet.com/journals/ebiom/article/P...
Status: Published
Publisher: Elsevier
Identification Number: 10.1016/j.ebiom.2024.105228
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