A Model-Based Approach to Predict Short-Term Toxicity Benefits With Proton Therapy for Oropharyngeal Cancer.

PURPOSE: The aim of this study was to generate normal tissue complication probability (NTCP) models in patients treated with either proton beam therapy (PBT) or intensity-modulated radiation therapy (IMRT) for oropharynx cancer and to use a model-based approach to investigate the added value of PBT in preventing treatment complications.

METHODS AND MATERIALS: For patients with advanced-stage oropharynx cancer treated with curative intent (PBT, n = 30; IMRT, n = 175), NTCP models were developed using multivariable logistic regression analysis with backward selection. For PBT-treated patients, an equivalent IMRT plan was generated to serve as a reference to determine the benefit of PBT in terms of NTCP. The models were then applied to the PBT-treated patients to compare predicted and observed clinical outcomes (calibration-in-the-large). Five binary endpoints were analyzed at 6 months after treatment: dysphagia ≥ grade 2, dysphagia ≥ grade 3, xerostomia ≥ grade 2, salivary duct inflammation ≥ grade 2, and feeding tube dependence. Corresponding toxicity grading was based on National Cancer Institute Common Terminology Criteria for Adverse Events version 4. Paired t tests and Wilcoxon rank tests were used to compare mean NTCP results for endpoints between PBT and IMRT.

RESULTS: NTCP models developed based on outcomes from all patients were applied to those receiving PBT. NTCP values were calculated for the equivalent IMRT plans for all PBT-treated patients, revealing significantly higher NTCP values with IMRT. PBT was associated with statistically significant reductions in the mean NTCP values for each endpoint at 6 months after treatment, with the largest absolute differences in rates of ≥grade 2 dysphagia and ≥grade 2 xerostomia.

CONCLUSIONS: NTCP models predict significant improvements in the probability of short-term, treatment-related toxicity with PBT compared with IMRT for oropharyngeal cancer. This study demonstrates an NTCP model-based approach to compare predicted patient outcomes when randomized data are not available.