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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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A hyperparameter-tuning approach to automated inverse planning.

K Maass¹, A Aravkin¹, M Kim¹

¹University of Washington, Seattle, Washington, USA.

Medical Physics

|February 26, 2022

Summary

This summary is machine-generated.

Automated inverse planning using hyperparameter tuning significantly reduces radiotherapy planning time while maintaining or improving plan quality compared to manual methods. This approach offers a more efficient and consistent treatment planning process.

Keywords:

SBRT automated inverse planning hyperparameter optimization

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Area of Science:

Radiation Oncology
Medical Physics
Computational Biology

Background:

Radiotherapy inverse planning currently involves manual adjustments to objective function parameters.
Manual planning is time-consuming and can lead to variability in plan quality based on planner skill and available time.

Purpose of the Study:

To evaluate the feasibility of two hyperparameter-tuning methods for automated inverse planning.
To develop an automated framework adaptable to practice changes without relying on prior optimized plans.

Main Methods:

Retrospective analysis of 10 lung stereotactic body radiation therapy patients.
Implementation of random sampling and Bayesian optimization for objective function parameter tuning.
Comparison of automated plans against manually generated plans using clinical goals and dose constraints.

Main Results:

Automated planning reduced median planning time to 0.5-0.7 hours with stopping criteria, compared to 1.9-2.3 hours without.
Organ-at-risk doses in automated plans were significantly below clinical limits and manual plan doses.
Plan utility showed a minor decrease (MPD 3.9-5.3%) with stopping criteria.

Conclusions:

Hyperparameter-tuning methods enable automated inverse planning.
Automated planning reduces active planning time for radiation oncologists.
The quality of automatically generated plans is comparable or superior to manually generated plans.