Feasibility study of automatic radiotherapy treatment planning for cervical cancer using a large language model
Through an assessment of LLM-generated plans and clinical plans in terms of target volume conformity and OARs dose sparing, this study provides preliminary evidence supporting the viability of LLMs for optimizing radiotherapy treatment plans. The implementation of LLMs demonstrates the potential for enhancing clinical workflows and reducing the workload associated with treatment planning.
Authors
- Jingru Yang (ORCID: https://orcid.org/0000-0001-7087-0114)
- Shuoyang Wei
- Lang Yu
- Wenbo Li (ORCID: https://orcid.org/0000-0003-2593-6131)
- Bo Yang (ORCID: https://orcid.org/0000-0002-6156-8957)
- Ankang Hu (ORCID: https://orcid.org/0009-0002-8510-8186)
- Yongguang Liang (ORCID: https://orcid.org/0000-0001-8514-7252)
- Jie Qiu
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking Union Medical College Hospital (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- Radiation Oncology
- Published
- 2025-05-15
- DOI
- https://doi.org/10.1186/s13014-025-02660-5
- Citations
- 4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 5.44
Funders
- Peking Union Medical College Hospital
- National Key Research and Development Program of China