A hyperparameter-tuning approach to automated inverse planning

In current practice, radiotherapy inverse planning often requires treatment planners to modify multiple parameters in the treatment planning system's objective function to produce clinically acceptable plans. Due to the manual steps in this process, plan quality can vary depending on the planning time available and the planner's skills.

This study investigates the feasibility of two hyperparameter-tuning methods for automated inverse planning. Because this framework does not train a model on previously optimized plans, it can be readily adapted to practice pattern changes, and the resulting plan quality is not limited by that of a training cohort.

We retrospectively selected 10 patients who received lung stereotactic body radiation therapy using manually generated clinical plans. We implemented random sampling and Bayesian optimization to automatically tune objective function parameters using linear-quadratic utility functions based on 11 clinical goals. Normalizing all plans to deliver a minimum dose of 48 Gy to 95% of the planning target volume, we compared plan quality for the automatically generated plans to the manually generated plans. We also investigated the impact of iteration count on the automatically generated plans, comparing planning time and plan utility for randomized and Bayesian plans with and without stopping criteria.

Without stopping criteria, the median planning time was 1.9 and 2.3 h for randomized and Bayesian plans, respectively. The organ-at-risk (OAR) doses in the randomized and Bayesian plans had a median percent difference (MPD) of 48.7% and 60.4% below clinical dose limits and an MPD of 2.8% and 3.3% below clinical plan doses. With stopping criteria, the utility decreased by an MPD of 5.3% and 3.9% for randomized and Bayesian plans, but the median planning time was reduced to 0.5 and 0.7 h, and the OAR doses still had an MPD of 42.9% and 49.7% below clinical dose limits and an MPD of 0.3% and 1.8% below clinical plan doses.

This study demonstrates that hyperparameter-tuning approaches to automated inverse planning can reduce the treatment planner's active planning time with plan quality that is similar to or better than manually generated plans.