A novel deep-learning approach for monitoring gastrointestinal air variation during radiotherapy in young patients using radiographs
DL-powered radiographs quantified internal GI gas changes with promising performance and generalisability to diverse ages and demographics. This was the first step towards a novel personalised image-guided radiotherapy traffic-light workflow for abdominal PBT with the goal of reducing the need for CBCT to required fractions.
Authors
- A. Nisbet (ORCID: https://orcid.org/0000-0002-0652-808X)
- Catarina Veiga (ORCID: https://orcid.org/0000-0002-4132-2554)
- Mikaël Simard (ORCID: https://orcid.org/0000-0003-0610-6319)
- Yibao Zhang (ORCID: https://orcid.org/0000-0003-3658-0687)
- Ana-Cristina Ghica
- Shutong Yu
- Pei Lim
- Jennifer Gains (ORCID: https://orcid.org/0000-0002-1045-9216)
Institutions
- University College London Hospitals NHS Foundation Trust (GB)
- Peking University (CN)
- Peking University Cancer Hospital (CN)
- University College London (GB)
Publication Details
- Journal
- Physics in Medicine and Biology
- Published
- 2026-05-12
- DOI
- https://doi.org/10.1088/1361-6560/ae6222
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Cancer Research UK
- Royal Academy of Engineering
- Royal Society
- National Natural Science Foundation of China
- H2020 Marie Skłodowska-Curie Actions