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.

Xiangdi Meng, Zhuojun Ju, Makoto Sakai, Yang Li, Atsushi Musha, Nobuteru Kubo, Hidemasa Kawamura, Tatsuya Ohno

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Article in Physics and imaging in radiation oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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8 authors.

Xiangdi MengDepartment of Radiation Oncology, Gunma University Graduate School of Medicine, Maebashi, Gunma 3718511, Japan.

Zhuojun JuDepartment of Radiation Oncology, Gunma University Graduate School of Medicine, Maebashi, Gunma 3718511, Japan.

Makoto SakaiGunma University Heavy Ion Medical Center, Maebashi, Gunma 3718511, Japan.

Yang LiDepartment of Radiation Oncology, Harbin Medical University Cancer Hospital, Harbin 150040, China.

Atsushi MushaGunma University Heavy Ion Medical Center, Maebashi, Gunma 3718511, Japan.

Nobuteru KuboGunma University Heavy Ion Medical Center, Maebashi, Gunma 3718511, Japan.

Hidemasa KawamuraGunma University Heavy Ion Medical Center, Maebashi, Gunma 3718511, Japan.

Tatsuya OhnoDepartment of Radiation Oncology, Gunma University Graduate School of Medicine, Maebashi, Gunma 3718511, Japan.

Funding

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6 · The paper itself

Abstract

Background and purpose: Acute oral mucositis (AOM) is a critical adverse event in head and neck cancer (HNC) requiring early intervention during carbon ion radiotherapy (CIRT). Considering external irradiation and internal mucosal heterogeneity, this study developed a classification model integrating maximum dose (Dmax) and dose-volume-based radiomics (RaV Methods and materials: We retrospectively analyzed 190 HNC patients treated with CIRT (training: test = 7:3). Radiomic features were extracted from the entire oral mucosa and dose-specific subregions [RaV Results: Grade ≥ 2 AOM occurred in 61.6 % of patients. The Dmax model with a 50 Gy(RBE) threshold achieved 87.2 % accuracy but had a high false-positive rate (FPR = 31.4 %). The RaV Conclusions: 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.

Indexed as

Carbon ion radiation therapyClassification modelDose–volume histogram parametersHead and neck cancerRadiomics

Identifiers

PMID41078962

DOI10.1016/j.phro.2025.100842

PMCPMC12513067

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