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

Background and purpose: 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. Materials and methods: 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. Results: Dose differences correlated with adaptive radiotherapy benefit for all five normal tissues (Spearman Conclusions: 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.