Predictive dose accumulation for HN adaptive radiotherapy

Abstract

During radiation therapy (RT) of head and neck (HN) cancer, the shape and volume of the parotid glands (PG) may change significantly, resulting in clinically relevant deviations of delivered dose from the planning dose. Early and accurate longitudinal prediction of PG anatomical changes during the RT can be valuable to inform decisions on plan adaptation. We developed a deep neural network for longitudinal predictions using the displacement fields (DFs) between the planning computed tomography (pCT) and weekly cone beam computed tomography (CBCT). Sixty-three HN patients treated with volumetric modulated arc were retrospectively studied. We calculated DFs between pCT and week 1–3 CBCT by B-spline and Demon deformable image registration (DIR). The resultant DFs were subsequently used as input to our novel network to predict the week 4 to 6 DFs for generating predicted weekly PG contours and weekly dose distributions. For evaluation, we measured dice similarity (DICE), and the uncertainty of accumulated dose. Moreover, we compared the detection accuracies of candidates for adaptive radiotherapy (ART) when the trigger criteria were mean dose difference more than 10%, 7.5%, and 5%, respectively. The DICE of ipsilateral/contralateral PG at week 4 to 6 using the prediction model trained with B-spline were 0.81 0.07/0.81 0.04 (week 4), 0.79 0.06/0.81 0.05 (week 5) and 0.78 0.06/0.82 (week 6). The DICE with the Demons model were 0.78 0.08/0.82 0.03 (week 4), 0.77 0.07/0.82 0.04 (week 5) and 0.75 0.07/0.82 0.02 (week 6). The dose volume histogram (DVH) analysis with the predicted accumulated dose showed the feasibility of predicting dose uncertainty due to the PG anatomical changes. The AUC of ART candidate detection with our predictive model was over 0.90. In conclusion, the proposed network was able to predict future anatomical changes and dose uncertainty of PGs with clinically acceptable accuracy, and hence can be readily integrated into the ART workflow.