Deep Learning-Based Dose Prediction Model for Automated Spot-Scanning Proton Arc Planning

Document Type

Conference Proceeding

Publication Date


Publication Title

International Journal of Radiation Oncology, Biology, Physics


Purpose/Objective(s): Spot-scanning proton arc (SPArc) is a novel technique that employs a planning optimization algorithm to select the energies and positions of spots along a dynamic rotational arc trajectory. The SPArc technique has the potential to achieve superior dose conformality and treatment delivery efficiency over intensity-modulated proton therapy. However, creating such a SPArc plan using existing approaches is time-consuming and computationally extensively. This study investigated the feasibility of using the deep learning (DL) technique to predict the 3D dose distribution of the SPArc treatment plan, leveraging the prior knowledge acquired from conventional intensity-modulated radiation therapy (IMRT) plans. Materials/Methods: A DL model, 3D-Unet with residual connections and attention gates, was trained using an open-source database of CT images, critical structures, and IMRT plans from 340 head and neck cancer patients (HNC) as the base model. Transfer learning technique was applied to finetune the model parameters using the SPArc treatment plans created on the HNC patients from an in-house dataset, where the SPArc treatment plans (including control point sampling, energy layer distribution, arc trajectory, etc.,) were optimized using a previously developed iterative approach. The performance of the DL model was evaluated by comparing predicted and planned doses over 17 SPArc treatment plans by using 4-fold crossvalidation. Results: The SPArc planning time per patient was 8»12 hours, while the dose prediction time was reduced to 2»3 minutes using the proposed DL model. The deviation of D95 in the target was (-1.8§1.6) %. The deviation of the mean dose in the parotids, cord, mandible, and brainstem were (2.5§6.5) %, (-0.5§4.3) %, (1.4§3.9) %, and (3.4§8) % of the prescription, respectively. The dice similarity coefficients of the 80%, 70%, and 60% isodose lines were (0.9§0.09), (0.93§0.01), and (0.94§0.01), respectively. Conclusion: Our results demonstrate that a DL-based dose prediction model can be created with a limited number of SPArc treatment plans through transfer learning. The DL model can directly predict the 3D dose distribution in minutes for automated planning. This study paves the roadmap to develop a quick clinical decision platform for the optimal selection among the multi-treatment modalities.




2 Suppl.

First Page



American Society for Radiation Oncology 65th Annual Meeting ASTRO 2023, October 1-4, 2023, San Diego, CA

American Association of Physicists in Medicine 65th Annual Meeting & Exhibition, July 23-27, 2023, Houston, TX



e213.pdf (543 kB)