AI-enhanced cancer radiotherapy quality assessment: utilizing daily linac performance, radiomics, dosimetrics, and planning complexity
The developed Informer-CNN model significantly enhances the prediction accuracy and classification of gamma passing rates in VMAT treatment plans. It facilitates early integration of daily accelerator performance data, improving the assessment and verification of treatment plans for better patient-specific quality assurance.
Authors
- Jia Deng (ORCID: https://orcid.org/0000-0002-9251-6818)
- Dorothy Jane Huang
- Xiangyang Wu (ORCID: https://orcid.org/0000-0003-2599-506X)
- Yaolin Zhao (ORCID: https://orcid.org/0000-0001-7522-0807)
- Qi Zhang (ORCID: https://orcid.org/0000-0001-9916-8919)
- Hong Ye
Institutions
- Guizhou Electromechanical Research and Design Institute (CN)
- Xi'an Jiaotong University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-03-13
- DOI
- https://doi.org/10.3389/fonc.2025.1503188
- Citations
- 1
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.88
Funders
- Xi'an Municipal Bureau of Science and Technology
- Key Industry Innovation Chain of Shaanxi