Abstract
PURPOSE: Osteoradionecrosis (ORN) of the mandible represents a severe, debilitating complication of radiation therapy (RT) for head and neck cancer (HNC). At present, no normal tissue complication probability (NTCP) models for risk of ORN exist. The aim of this study was to develop a multivariable clinical/dose-based NTCP model for the prediction of ORN any grade (ORN(I-IV)) and grade IV (ORN(IV)) after RT (±chemotherapy) in patients with HNC. METHODS AND MATERIALS: Included patients with HNC were treated with (chemo-)RT between 2005 and 2015. Mandible bone radiation dose-volume parameters and clinical variables (ie, age, sex, tumor site, pre-RT dental extractions, chemotherapy history, postoperative RT, and smoking status) were considered as potential predictors. The patient cohort was randomly divided into a training (70%) and independent test (30%) cohort. Bootstrapped forward variable selection was performed in the training cohort to select the predictors for the NTCP models. Final NTCP model(s) were validated on the holdback test subset. RESULTS: Of 1259 included patients with HNC, 13.7% (n = 173 patients) developed any grade ORN (ORN(I-IV) primary endpoint) and 5% (n = 65) ORN(IV) (secondary endpoint). All dose and volume parameters of the mandible bone were significantly associated with the development of ORN in univariable models. Multivariable analyses identified D(30%) and pre-RT dental extraction as independent predictors for both ORN(I-IV) and ORN(IV) best-performing NTCP models with an area under the curve (AUC) of 0.78 (AUC(validation) = 0.75 [0.69-0.82]) and 0.81 (AUC(validation) = 0.82 [0.74-0.89]), respectively. CONCLUSIONS: This study presented NTCP models based on mandible bone D(30%) and pre-RT dental extraction that predict ORN(I-IV) and ORN(IV) (ie, needing invasive surgical intervention) after HNC RT. Our results suggest that less than 30% of the mandible should receive a dose of 35 Gy or more for an ORN(I-IV) risk lower than 5%. These NTCP models can improve ORN prevention and management by identifying patients at risk of ORN.