First Application of Predictive Model to Assist Adaptive Proton Therapy

Abstract

<h3>Purpose/Objective(s)</h3> To assess a model to predict anatomical changes in head and neck cancer patients and explore the applicability of the model as a tool for adaptive replanning in intensity-modulated proton therapy (IMPT). <h3>Materials/Methods</h3> 20 radiotherapy patients with nasopharyngeal cancer were included in this retrospective study. Each patient had a planning CT and weekly CTs during radiotherapy. To build the anatomical model, we deform the weekly CTs to planning CTs of our training population (n = 19) and obtain the average anatomical change per week. To predict a deformation for the remaining patient, the average deformation of the training population is applied to the patient's planning CT. The model is updated based on the patient's progression during treatment. K-fold cross validation (n = 5) was used to obtain a sample of 5 patients. For those 5 patients, we compare the accumulated dose of two adaptive IMPT strategies. 1) Predictive replan (PR): Replans were optimized on predicted images of weeks 3 and 5 and applied to week 3/4 and week 5/6 respectively. 2) Best-case replan (BcR): Adaptive replan on weekly CT is triggered when target coverage D95% criteria are not met and parotid gland mean dose deviation > 3 Gy (RBE). All plans (original plan, PR, BcR) were robustly optimized (+/-3 mm setup and +/-3.5% range uncertainty). Dosimetric goals: CTV D95(%) > 95% prescription dose in robust evaluation; maximum dose to spinal cord and brainstem < 45 Gy (RBE) and < 55 Gy (RBE), respectively. To ensure fairness of comparison, we kept dosimetric parameters between BcR and PR consistent. <h3>Results</h3> Accumulated dose differences from two adaptive strategies (PR-BcR) of our 5 testing patients are shown (tab 1). We observe a maximum dose difference of 2.2% in the CTV D₉₅, -1.35 Gy (RBE) in the brainstem D_max, and -1.24 Gy (RBE) in the parotid D_mean. The parentheses indicate the dose difference of the replans only (no accumulation). <h3>Conclusion</h3> A predictive model was applied to the replanning process for adaptive proton therapy and compared to a best-case replan strategy. Our suggested PR produces clinically acceptable plans, comparable to the best-case replan strategy, suggesting that the predictive models can be used for adaptive replanning. Application of predictive replanning provides the possibility to prepare adaptive plans in advance, streamlining the clinical workflow.