Improving workflow for adaptive proton therapy with predictive anatomical modelling: A proof of concept. (August 2022) - Zhang, Ying; Alshaikhi, Jailan; Amos, Richard A.; Lowe, Matthew; Tan, Wenyong; Bär, Esther; Royle, Gary
Highlights: We propose a strategy to use our previously proposed predictive models in head and neck patients in offline adaptive proton therapy. The model provides a prediction of the patient's anatomical progression throughout the course of treatment. The predicted anatomy provides the opportunity to create adaptive radiotherapy plans in advance, increasing workflow efficiency. The adaptive plans based on the predicted anatomy provide clinically acceptable plans with good target coverage, comparable to a standard offline adaption. The predicted plans can be applied as soon as plan adaption is triggered by the clinical criteria. This can help to reduce the mean dose to the parotid glands as compared to a standard offline adaptive strategy. Abstract: Purpose: To demonstrate predictive anatomical modelling for improving the clinical workflow of adaptive intensity-modulated proton therapy (IMPT) for head and neck cancer. Methods: 10 radiotherapy patients with nasopharyngeal cancer were included in this retrospective study. Each patient had a planning CT, weekly verification CTs during radiotherapy and predicted weekly CTs from our anatomical model. Predicted CTs were used to create predicted adaptive plans in advance with the aim of maintaining clinically acceptable dosimetry. Adaption was triggered when the increase in mean dose (Dmean ) to the parotid glands exceeded 3 Gy(RBE). We compared the accumulated dose of two adaptive IMPT strategies: 1) Predicted plan adaption: One adaptive plan per patient was optimised on a predicted CT triggered by replan criteria. 2) Standard replan: One adaptive plan was created reactively in response to the triggering weekly CT. Results: Statistical analysis demonstrates that the accumulated dose differences between two adaptive strategies are not significant ( p >  0.05) for CTVs and OARs. We observed no meaningful differences in D95 between the accumulated dose and the planned dose for the CTVs, with mean differences to the high-risk CTV of −1.20 %, −1.23 % and −1.25 % for no adaption, standard and predicted plan adaption, respectively. The accumulated parotid Dmean using predicted plan adaption is within 3 Gy(RBE) of the planned dose and 0.31 Gy(RBE) lower than the standard replan approach on average. Conclusion: Prediction-based replanning could potentially enable adaptive therapy to be delivered without treatment gaps or sub-optimal fractions, as can occur during a standard replanning strategy, though the benefit of using predicted plan adaption over the standard replan was not shown to be statistically significant with respect to accumulated dose in this study. Nonetheless, a predictive replan approach can offer advantages in improving clinical workflow efficiency.