A study of criteria‐based online adaptive radiotherapy with radiomics and dosimetry for postoperative prostate cancer
This study is the first to combine radiomics and dosimetric features using machine learning to establish a triggered OART prediction model, demonstrating the feasibility of using CT radiomics and dosimetric features for OART trigger prediction. By employing model integration techniques, we addressed the issue of one-dimensional feature bias, significantly enhancing model performance. This research provides new data analysis methods for adaptive radiotherapy triggering in postoperative prostate cancer, offering substantial clinical value.
Authors
- Guanghui Gan (ORCID: https://orcid.org/0000-0001-6430-2743)
- Lecheng Jia (ORCID: https://orcid.org/0000-0001-8157-4556)
- Yu Hao Wang (ORCID: https://orcid.org/0000-0003-3914-9358)
- Xiaoting Xu (ORCID: https://orcid.org/0000-0001-7357-7431)
- Yuan Xu
- Songbing Qin (ORCID: https://orcid.org/0000-0003-0179-5426)
- Wei Gong
- Zijie Mo
Institutions
- Soochow University (CN)
- Innovative Imaging and Research (United States) (US)
- United Imaging Healthcare (China) (CN)
- First Affiliated Hospital of Soochow University (CN)
- Shenzhen Genoimmune Medical Institute (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-08-01
- DOI
- https://doi.org/10.1002/mp.18058
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- National Natural Science Foundation of China
- Ministry of Science and Technology of the People's Republic of China