Deep learning combining imaging, dose and clinical data for predicting bowel toxicity after pelvic radiotherapy

Elhaminia, B., Gilbert, A. orcid.org/0000-0002-9142-1227, Scarsbrook, A. orcid.org/0000-0002-4243-032X et al. (3 more authors) (2025) Deep learning combining imaging, dose and clinical data for predicting bowel toxicity after pelvic radiotherapy. Physics and Imaging in Radiation Oncology, 33. 100710. ISSN 2405-6316

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Item Type: Article
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© 2025 The Authors. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited.

Keywords: Radiotherapy toxicity; Deep learning; Toxicity prediction; Convolutional network; Attention; Pelvic radiotherapy
Dates:
  • Accepted: 23 January 2025
  • Published (online): 30 January 2025
  • Published: January 2025
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Inst of Biomed & Clin Sciences (LIBACS) (Leeds)
The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 19 May 2025 14:13
Last Modified: 19 May 2025 14:13
Status: Published
Publisher: Elsevier
Identification Number: 10.1016/j.phro.2025.100710
Open Archives Initiative ID (OAI ID):

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