883eP AI-based multimodal prediction of MSI/MMR status in colorectal cancer from H&E-stained slides and clinicopathological data

Bennett, N., Bass, C., Ntelemis, F. et al. (15 more authors) (2025) 883eP AI-based multimodal prediction of MSI/MMR status in colorectal cancer from H&E-stained slides and clinicopathological data. In: ESMO Congress 2025, 17-21 Oct 2025, Berlin, Germany.

Metadata

Item Type: Conference or Workshop Item
Authors/Creators:
  • Bennett, N.
  • Bass, C.
  • Ntelemis, F.
  • Geraldes, A.
  • Schmidt, J.
  • Mehrotra, D.
  • Singhal, S.
  • Kumar, N.
  • Blackwood, J.
  • Hyde, M.
  • Mistry, B.
  • Rogerson, G.
  • Freer, C.
  • Walsh, E.
  • Pandya, P.
  • Kather, J.N.
  • Orsi, N.
  • Arslan, S.
Dates:
  • Published (online): 5 November 2025
  • Published: 5 November 2025
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds)
Date Deposited: 04 Feb 2026 11:06
Last Modified: 04 Feb 2026 11:06
Published Version: https://www.sciencedirect.com/science/article/pii/...
Status: Published
Publisher: Elsevier
Identification Number: 10.1016/j.annonc.2025.08.1454
Related URLs:
Open Archives Initiative ID (OAI ID):

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