Normal tissue complication probability model for acute oral mucositis in patients with head and neck cancer undergoing carbon ion radiation therapy based on dosimetry, radiomics, and dosiomics.

BACKGROUND AND PURPOSE

To develop a normal tissue complication probability (NTCP) model for predicting grade ≥ 2 acute oral mucositis (AOM) in head and neck cancer patients undergoing carbon-ion radiation therapy (CIRT).

METHODS AND MATERIALS

We retrospectively included 178 patients, collecting clinical, dose-volume histogram (DVH), radiomics, and dosiomics data. Patients were randomly divided into training (70%) and test sets (30%). Feature selection involved univariable logistic regression, least absolute shrinkage and selection operator regression, stepwise backward regression, and Spearman's correlation test, with the bootstrap method ensuring reliability. Multivariable models were built on the training set and evaluated using the test set.

RESULTS

The optimal NTCP model incorporated a DVH parameter (V), radiomics, and dosiomics features, achieving an area under the curve (AUC) of 0.932 in the training set and 0.959 in the test set. This hybrid model outperformed those based on single DVH, radiomics, dosiomics, or clinical data (Bonferroni-adjusted p < 0.001 and ΔAUC > 0 for all comparisons in 1,000 bootstrap validations). Calibration curves showed strong agreement between predictions and outcomes. A 44.0 % AOM risk threshold was proposed, yielding accuracies of 87.1 % in the training set and 90.7 % in the test set.

CONCLUSIONS

We developed the first NTCP model for estimating AOM risk in head and neck cancer patients undergoing CIRT and proposed a risk stratification. This model may assist in clinical decision-making and improve treatment planning for AOM prevention and management by identifying high-risk patients.