Foresight planning: Radiotherapy plan optimization via self-supervised model predictive control

Treatment planning for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) relies on meticulous operation of the inverse optimization, a complex and iterative process of adjusting dose-volume objectives to achieve an optimal dose distribution. This is a trial-and-error process due to the black box nature of the optimization engine.

To propose a novel foresight planning strategy that models the inverse optimization process, providing direct guidance to streamline optimization toward the desired dose distribution with efficiency and consistency.

The proposed strategy involves two stages. First, a Deep-Dose-Predictive (DDP) model was trained to predict the dose response based on historical plan states and dose-volume objective (DVO) adjustments. The training of the DDP model mirrored that of a clinical planner, during which it gained intelligence in understanding dose-response principles by witnessing extensive plan state transition history. The training dataset was generated using Monte Carlo sampling without any intervention. In the second stage, the trained DDP model was employed for automatic DVO adjustments via model predictive control. By forecasting future plan states based on potential DVO modifications, the model assessed plan quality using a score function, which evaluated predicted dose responses across all possible adjustments. The adjustment maximizing the score function was selected, enabling the strategy to adapt to specific clinical priorities through score function weighting, without requiring model retraining. The feasibility of the method was validated in head-and-neck cancer IMRT. A total of 40 cases were used to collect plan state transitions for model training, while an additional 40 patients were utilized for model evaluation. The proposed framework was tasked to generate plans with different parotid-sparing priorities (i.e., bilateral and unilateral sparing) based on patient-specific conditions.

The automatically generated plans demonstrate clinically comparable quality for both bilateral and unilateral sparing cases. For bilateral sparing cases, the automated plans achieve non-inferior organ-at-risk (OAR) sparing while exhibiting superior conformity indices-1.62 and 1.15 for the primary PTV and boost PTV, respectively, compared to 1.79 and 1.47 for clinical plans. Similarly, for unilateral cases, the automated plans maintain non-inferior OAR sparing with improved conformity indices of 1.78 and 1.14, compared to 2.07 and 1.42 for clinical plans.

The proposed strategy effectively automates radiation therapy planning, achieving clinically comparable plan quality with improved efficiency and adaptability. This approach introduces a transformative perspective to automating treatment planning, paving the way for more intelligent and flexible AI solutions for radiation therapy treatment planning.