Room: Room 207
Purpose: Treatment planning in radiotherapy is time-consuming process and its quality relies on expertise of planner. Prediction of achievable dose volume histogram (DVH) of organ at risks (OARs) is expected to improve efficiency and quality of treatment plan. Many studies have proposed knowledge-based DVH prediction models for intensity-modulated radiotherapy (IMRT). It is unsuitable, however, to apply these prediction models to proton beam therapy (PBT), because dose distribution in PBT is strongly dependent on beam angle and weight unlike in IMRT. The aim of this study is to develop a DVH prediction model for OARs in PBT plan.
Methods: The OAR close to the target was divided into sub-volumes depending on the distance from the target boundary. The shapes of the normalized differential DVH (dDVH) curve of each sub-volume was assumed to be represented as skew-normal distribution. Considering the dependencies of beam angle and weight on dose distribution, skew-normal parameters describing dDVH profiles brought from each field are determined field by filed by fitting the training data. The field-specific dDVH model for each sub-volume is obtained from the averaged skew-normal parameters of the training data. The final DVH of the OAR is therefore calculated as the summation of the field-specific dDVH models for all fields through all sub-volumes. The model was trained using cohort of 11 prostate cancer patients treated with PBT at Hokkaido University Hospital and validated using validation cohort of 3 prostate cancer patients which were not contained training dataset.
Results: The predicted DVH was in good agreement with actual one. Average root mean square error (RMSE) between actual and predicted V60 and V37.5 for rectum were 3.0% and 1.5%, respectively, and average RMSE of V37.5 for bladder was 1.8%.
Conclusion: The DVH prediction method proposed here shows its potential efficacy for patient specific DVH prediction in PBT.
Dose Volume Histograms, Treatment Planning
TH- External Beam- Particle therapy: Proton therapy - treatment planning/virtual clinical studies