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 ## Paper
 
-Data and notebooks used to create the figures appearing in our [preprint](https://arxiv.org/abs/2105.07024) "A feasibility study of a hyperparameter tuning approach to automated inverse planning in radiotherapy" can be found in [results](/results).
+Data and notebooks used to create the figures appearing in our [preprint](https://arxiv.org/abs/2105.07024) "A hyperparameter-tuning approach to automated inverse planning" can be found in [results](/results).
 
 #### Abstract
-Radiotherapy inverse planning requires treatment planners to modify multiple parameters in the objective function to produce clinically acceptable plans. Due to manual steps in this process, plan quality can vary widely depending on planning time available and planner's skills. The purpose of this study is to automate the inverse planning process to reduce active planning time while maintaining plan quality. We propose a hyperparameter tuning approach for automated inverse planning, where a treatment plan utility is maximized with respect to the limit dose parameters and weights of each organ-at-risk (OAR) objective. Using 6 patient cases, we investigated the impact of the choice of dose parameters, random and Bayesian search methods, and utility function form on planning time and plan quality. For given parameters, the plan was optimized in RayStation, using the scripting interface to obtain the dose distributions deliverable. We normalized all plans to have the same target coverage and compared the OAR dose metrics in the automatically generated plans with those in the manually generated clinical plans. Using 100 samples was found to produce satisfactory plan quality, and the average planning time was 2.3 hours. The OAR doses in the automatically generated plans were lower than the clinical plans by up to 76.8%. When the OAR doses were larger than the clinical plans, they were still between 0.57% above and 98.9% below the limit doses, indicating they are clinically acceptable. For a challenging case, a dimensionality reduction strategy produced a 92.9% higher utility using only 38.5% of the time needed to optimize over the original problem. This study demonstrates our hyperparameter tuning framework for automated inverse planning can significantly reduce the treatment planner's planning time with plan quality that is similar to or better than manually generated plans.
+**Purpose:** Due to the manual step of adjusting objective function parameters in radiotherapy inverse planning, plan quality can vary depending on the planning time available and the planner’s skills. This study investigates the feasibility of hyperparameter-tuning approaches to 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.
+
+**Method:** We retrospectively selected 10 patients who received lung SBRT using manually-generated clinical plans. We implemented random sampling (RS) and Bayesian optimization (BO) to automatically tune objective function parameters using linear-quadratic utility functions based on 11 clinical goals. Normalizing all plans to have PTV D95 equal to 48 Gy, 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 RS and BO plans with and without stopping criteria.
+
+**Results:** Without stopping criteria, the median planning time was 1.9 and 2.3 hours for RS and BO plans, respectively. The organ-at-risk (OAR) doses in the RS and BO 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 RS and BO plans, but the median planning time was reduced to 0.5 and 0.7 hours, 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.
+
+**Conclusions:**
+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.