Prior‐guided automatic delineation of post‐radiotherapy gross tumor volume for esophageal cancer
By embedding medical physics priors into a self-configuring nnU-Net v2, our method achieves accurate and robust automated delineation of post- RT GTV in EC across multiple centers. This approach has potential to facilitate the construction of treatment response prediction models.
Authors
- Meng Fan (ORCID: https://orcid.org/0000-0002-3761-4431)
- Zhigao An (ORCID: https://orcid.org/0000-0001-7951-1994)
- Qifeng Wang (ORCID: https://orcid.org/0000-0002-3036-6084)
- Jie Gong (ORCID: https://orcid.org/0000-0002-3923-2488)
- Zihan Shi
- Jie Li (ORCID: https://orcid.org/0000-0002-1558-8623)
- Hongfei Sun (ORCID: https://orcid.org/0000-0002-3995-7251)
- Wei Huang (ORCID: https://orcid.org/0000-0002-7606-345X)
- Lina Zhao (ORCID: https://orcid.org/0000-0002-0011-6471)
- Yufen Liu (ORCID: https://orcid.org/0000-0002-1693-0339)
Institutions
- University of Electronic Science and Technology of China (CN)
- Shandong Tumor Hospital (CN)
- Sichuan Cancer Hospital (CN)
- Xijing Hospital (CN)
- Shandong First Medical University (CN)
- Air Force Medical University (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-10-01
- DOI
- https://doi.org/10.1002/mp.70005
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00