A probability model for anatomical robust optimisation in head and neck cancer proton therapy. (1st July 2023) - Zhang, Ying; Alshaikhi, Jailan; Tan, Wenyong; Royle, Gary; Bär, Esther
Abstract: Objective. Develop an anatomical model based on the statistics of the population data and evaluate the model for anatomical robust optimisation in head and neck cancer proton therapy. Approach. Deformable image registration was used to build the probability model (PM) that captured the major deformation from patient population data and quantified the probability of each deformation. A cohort of 20 nasopharynx patients was included in this retrospective study. Each patient had a planning CT and 6 weekly CTs during radiotherapy. We applied the model to 5 test patients. Each test patient used the remaining 19 training patients to build the PM and estimate the likelihood of a certain anatomical deformation to happen. For each test patient, a spot scanning proton plan was created. The PM was evaluated using proton spot location deviation and dose distribution. Main results . Using the proton spot range, the PM can simulate small non-rigid variations in the first treatment week within 0.21 ± 0.13 mm. For overall anatomical uncertainty prediction, the PM can reduce anatomical uncertainty from 4.47 ± 1.23 mm (no model) to 1.49 ± 1.08 mm at week 6. The 95% confidence interval (CI) of dose metric variations caused by actual anatomical deformations in the first week is −0.59% ∼ −0.31% for low-risk CT D 95, and 0.84–3.04 Gy for parotid D mean . On the other hand, the 95% CI of dose metric variations simulated by the PM at the first week is −0.52 ∼ −0.34% for low-risk CTV D 95, and 0.58 ∼ 2.22 Gy for parotid D mean . Significance. The PM improves the estimation accuracy of anatomical uncertainty compared to the previous models and does not depend on the acquisition of the weekly CTs during the treatment. We also provided a solution to quantify the probability of an anatomical deformation. The potential of the model for anatomical robust optimisation is discussed.