Clinical target volume (CTV) automatic delineation using deep learning network for cervical cancer radiotherapy: A study with external validation
We demonstrated the problem of model generalizability for DL-based automatic delineation. The proposed network can improve the performance of automatic delineation for cervical cancer and shorten manual delineation time at no expense to quality. The network showed excellent clinical viability, which can also be even generalized for endometrial cancer with excellent performance.
Authors
- Shengxian Peng
- Mujun Liu (ORCID: https://orcid.org/0000-0001-8948-2429)
- Zhe Wu (ORCID: https://orcid.org/0000-0001-5191-0248)
- Dong Wang (ORCID: https://orcid.org/0000-0003-0634-953X)
- Xu Cheng (ORCID: https://orcid.org/0000-0001-5611-4689)
- Yi Wu (ORCID: https://orcid.org/0000-0003-3838-7418)
- Lihua Deng
Institutions
- Army Medical University (CN)
- Beijing Luhe Hospital Affiliated to Capital Medical University (CN)
- Zigong First People's Hospital (CN)
- Yueyang Changling Equipment Research Institute (China) (CN)
Publication Details
- Journal
- Journal of Applied Clinical Medical Physics
- Published
- 2024-10-14
- DOI
- https://doi.org/10.1002/acm2.14553
- Citations
- 1
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.61
Funders
- National Natural Science Foundation of China
- Centre Scientifique et Technique du Bâtiment