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.

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_x) for early identification of grade ≥ 2 AOM.

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_x, 10-50 Gy(RBE) in 10-Gy increments]. Feature selection employed logistic regression, random forest, and XGBoost, followed by Spearman's correlation test. S

SUBMITTER: Meng X 

PROVIDER: S-EPMC12513067 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature