Domain-Adaptive and Per-Fraction Guided Deep Learning Framework for Magnetic Resonance Imaging-Based Segmentation of Organs at Risk in Gynecologic Cancers
This novel framework holds promise to bridge the semantic gap between computed tomography and magnetic resonance imaging databases, potentially facilitating adaptive RT treatments and reducing treatment times as well as clinician burden. The utility of this framework can extend beyond gynecologic and pelvic cancers.
Authors
- Jessica Winfield (ORCID: https://orcid.org/0000-0001-6069-5252)
- M. Ingle (ORCID: https://orcid.org/0000-0002-6226-2474)
- Matthew Blackledge (ORCID: https://orcid.org/0000-0001-8117-9003)
- Dow‐Mu Koh (ORCID: https://orcid.org/0000-0001-7654-8011)
- Reza Kalantar (ORCID: https://orcid.org/0000-0002-6211-9475)
- Romélie Rieu (ORCID: https://orcid.org/0000-0001-9750-7200)
- Christina Messiou (ORCID: https://orcid.org/0000-0002-0557-9379)
- Gigin Lin (ORCID: https://orcid.org/0000-0001-7246-1058)
- Susan Lalondrelle (ORCID: https://orcid.org/0000-0002-1147-3618)
- Sebastian Curcean (ORCID: https://orcid.org/0000-0002-7256-461X)
Institutions
- Institute of Cancer Research (GB)
- Chang Gung University (TW)
- Iuliu Hațieganu University of Medicine and Pharmacy (RO)
- Linkou Chang Gung Memorial Hospital (TW)
- Royal Marsden Hospital (GB)
Publication Details
- Journal
- Advances in Radiation Oncology
- Published
- 2025-02-22
- DOI
- https://doi.org/10.1016/j.adro.2025.101745
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Royal Marsden NHS Foundation Trust
- NIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research
- National Institute for Health and Care Research
- Institute of Cancer Research
- Ministry of Science and Technology, Taiwan