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Automated 4π Radiotherapy Treatment Planning with Evolving Knowledge-Base.

PURPOSE
Non-coplanar 4π radiotherapy generalizes IMRT to automate beam geometry selection but requires complicated hyperparameter tuning to attain superior plan quality, which can be tedious and inconsistent. In this study, a fully automated 4π treatment planning was developed using evolving-knowledge-base (EKB) planning guided by dose prediction.


METHODS
20 4π cases each of lung and head and neck (HN) were included. A statistical voxel dose learning model was initially trained on low-quality plans created using generic hyperparameter templates without manual tuning. To improve the automated plan quality without being limited by the training data quality, a new 4π optimization problem was formulated to include a one-sided penalty on the OAR dose deviation from the predicted dose. This directional OAR penalty encourages superior OAR sparing. The Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) was used to solve the large-scale beam orientation optimization problem. With the improved plans, new predictions were created to guide the next loop of EKB planning for a total of 10 loops. Plan quality was evaluated using a plan quality metric (PQM) points system based on clinical dose constraints and compared with automated planning approaches guided by manual high quality plans using all non-coplanar beams, automated plans using individually evolved targeted dose, and manually-created 4π plans.


RESULTS
For the lung cases, the final EKB plans had significantly higher PQM than manually-created 4π (+2.60%). The improvements plateaued after the 3rd loop. The final HN EKB plans and manually-created 4π plans had comparable PQMs, but had lower PQM compared to automated plans using a high quality training set (-3.00% and -4.44%, respectively). The PQM consistently increased up to the 6th loop. Individually evolved plans were able to improve the plan quality from initial condition due to the one-sided cost function but the 60% of them were trapped in undesired local minima that were substantially worse than their corresponding EKB plans.


CONCLUSION
EKB planning is a novel automated planning technique guided by the predicted 3D dose distribution, which can evolve from low-quality plans. EKB allows new beams to be used in the automated planning workflow for superior plan quality. This article is protected by copyright. All rights reserved.