Criteria-calibration approaches to deep learning-based cervical cancer radiation treatment auto-planning
This study demonstrated an automatic KBP workflow and compared three criteria-calibration approaches to address the data imbalance problem in dose prediction. These approaches can partially calibrate pre-existing models to accommodate newly added criteria and could be implemented according to specific requirements in different scenarios. Although there are trade-offs in various aspects, they all can generate feasible radiation treatment plans.
Authors
- Jingru Yang (ORCID: https://orcid.org/0000-0001-7087-0114)
- Shuoyang Wei
- Shumeng He (ORCID: https://orcid.org/0000-0003-1267-0098)
- Kang Zhang
- Yanfei Liu (ORCID: https://orcid.org/0000-0003-2231-840X)
- Bo Yang (ORCID: https://orcid.org/0000-0002-6156-8957)
- Yongguang Liang
- Jie Qiu
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking Union Medical College Hospital (CN)
- United Imaging Healthcare (China) (CN)
Publication Details
- Journal
- Radiation Oncology
- Published
- 2025-08-20
- DOI
- https://doi.org/10.1186/s13014-025-02684-x
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ministry of Science and Technology of the People's Republic of China
- National Key Research and Development Program of China