Integrating dose–volume histogram parameters and radiomics-based machine learning to identify carbon ion radiotherapy-induced acute oral mucositis in patients with head and neck cancer
Dose-volume radiomics effectively identified low-risk AOM patients in the high-dose group. The integrated model demonstrated high accuracy in identifying HNC patients with grade ≥ 2 AOM during CIRT.
Authors
- Makoto Sakai (ORCID: https://orcid.org/0000-0001-9928-2375)
- Tatsuya Ohno (ORCID: https://orcid.org/0000-0002-6703-3144)
- Atsushi Musha (ORCID: https://orcid.org/0000-0002-5210-8764)
- Hidemasa Kawamura (ORCID: https://orcid.org/0000-0003-4899-1715)
- Xiangdi Meng (ORCID: https://orcid.org/0000-0002-2019-504X)
- Yang Li (ORCID: https://orcid.org/0000-0002-5020-3362)
- Zhuojun Ju (ORCID: https://orcid.org/0009-0000-0270-1044)
- Nobuteru Kubo
Institutions
- Harbin Medical University (CN)
- Gunma University (JP)
- Third Affiliated Hospital of Harbin Medical University (CN)
- Gunma University Hospital (JP)
Publication Details
- Journal
- Physics and Imaging in Radiation Oncology
- Published
- 2025-09-25
- DOI
- https://doi.org/10.1016/j.phro.2025.100842
- Primary Topic
- Oral health in cancer treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00