Deep learning-based multiple-CT optimization: An adaptive treatment planning approach to account for anatomical changes in intensity-modulated proton therapy for head and neck cancers.

Backgrounds: Intensity-modulated proton therapy (IMPT) is particularly susceptible to range and setup uncertainties, as well as anatomical changes.
Purpose: We present a framework for IMPT planning that employs a deep learning method for dose prediction based on multiple-CT (MCT). The extra CTs are created from cone-beam CT (CBCT) using deformable registration with the primary planning CT (PCT). Our method also includes a dose mimicking algorithm.
Methods: The MCT IMPT planning pipeline involves prediction of robust dose from input images using a deep learning model with a U-net architecture. Deliverable plans may then be created by solving a dose mimicking problem with the predictions as reference dose. Model training, dose prediction and plan generation are performed using a dataset of 55 patients with head and neck cancer in this retrospective study. Among them, 38 patients were used as training set, 7 patients were used as validation set, and 10 patients were reserved as test set for final evaluation.
Results: We demonstrated that the deliverable plans generated through subsequent MCT dose mimicking exhibited greater robustness than the robust plans produced by the PCT, as well as enhanced dose sparing for organs at risk. MCT plans had lower D _2% (76.1 Gy vs. 82.4 Gy), better homogeneity index (7.7% vs. 16.4%) of CTV1 and better conformity index (70.5% vs. 61.5%) of CTV2 than the robust plans produced by the primary planning CT for all test patients.
Conclusions: We demonstrated the feasibility and advantages of incorporating daily CBCT images into MCT optimization. This approach improves plan robustness against anatomical changes and may reduce the need for plan adaptations in head and neck cancer treatments.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024. Published by Elsevier B.V.)