Room: Exhibit Hall | Forum 3
Purpose: To evaluate the behavior and dosimetric performance of an in-house developed atlas-based automated planning approach applied to prostate treatment planning.
Methods: The automated planning pipeline was trained on 94 sets consisting of CT scans, structure sets, and dose distributions. Image features were extracted and used to train two atlas-selection models: one to determine atlases for spatial dose prediction using an equal weighted combination of the Gamma metric and a dose volume histogram (DVH) difference, and one to predict atlases with the closest dose distribution (â€˜dose priorâ€™) using mean DVH difference. For each independent testing set (n=20) the 4 closest spatial atlases were used to create a probabilistic dose distribution and the 4 closest dose prior atlases were subsequently used to determine the most probable dose distribution. Finally, dose-mimicking was used to create clinically-deliverable plans in a commercial treatment planning system. Automated plans were evaluated using our institutional clinical criteria. To investigate the underlying performance of the atlas-based technique, we determined the frequency of atlas selection and investigated atlas distance metrics for predicting automated plan quality.
Results: Automated plans were successfully generated for all 20 testing sets, 3 were discarded due to hip implants. 16/17 automated plans met all clinical DVH goals. One plan failed to meet dosimetric criteria (D30%) for rectum wall. The distance metrics were not predictive of this failure and may not be suitable for inter-patient plan quality prediction. In this cohort, most patient plans were well-predicted by a small number of representative atlases: of 94 training atlases, 21 were used for spatial dose prediction and 13 were used for dose prior construction.
Conclusion: Atlas-based automated planning is capable of producing clinically-deliverable prostate VMAT plans that exceed clinical goals for the majority of patients. Future work will further investigate metrics to predict the quality of automated plans.