Predicting the benefit of adaptive radiotherapy in head and neck Cancer using cone-beam computed tomography

Adaptive radiotherapy for head and neck cancer is resource-intensive, and existing geometric triggers lack a quantitative dose basis. We developed a model based on dose differences derived from cone-beam computed tomography to predict adaptive radiotherapy benefit.

Seventy-four patients with head and neck cancer treated with sequential-phase intensity-modulated radiotherapy and offline adaptive radiotherapy were analyzed. Patients were labeled as high or low adaptive radiotherapy benefit by k-means clustering of dose features derived from the resimulation computed tomography. A multivariable logistic regression classifier was trained on percent mean dose differences measured on the week 3 cone-beam computed tomography for five normal tissues (both parotid glands, both submandibular glands, and the oral cavity) and evaluated by leave-one-out cross-validation, alongside a parallel classifier using normal tissue volume changes.

Dose differences correlated with adaptive radiotherapy benefit for all five normal tissues (Spearman ${\rho }_s$ = -0.76 to -0.43; all p < 0.01, Jonckheere-Terpstra trend test), whereas volume changes showed no significant association (all p > 0.10). The dose model achieved an area under the receiver operating characteristic curve of 0.78 versus 0.54 for the volume model. On univariate analysis the ipsilateral parotid gland was the strongest single-organ predictor, whereas the multivariable model assigned the largest standardized coefficient to the ipsilateral submandibular gland, reflecting collinearity between the parotid glands.

Dose differences derived from cone-beam computed tomography provide an objective, quantitative basis for predicting adaptive radiotherapy benefit in head and neck cancer, offering a practical alternative to geometric triggers and supporting selective, resource-efficient adaptation.