• Corpus ID: 248986370

High-dimensional Automated Radiation Therapy Treatment Planning via Bayesian Optimization

@inproceedings{Wang2022HighdimensionalAR,
  title={High-dimensional Automated Radiation Therapy Treatment Planning via Bayesian Optimization},
  author={Qingying Wang and Ruoxi Wang and Jiacheng Liu and Fan Jiang and Haizhen Yue and Yi Du and Hao-Nan Wu},
  year={2022}
}
Purpose: Radiation therapy treatment planning can be viewed as an iterative hyperparameter tuning process to balance conflicting clinical goals. In this work, we investigated the performance of modern Bayesian Optimization (BO) methods on automated treatment planning problems in high-dimensional settings. Methods: 20 locally advanced rectal cancer patients treated with intensity-modulated radiation therapy (IMRT) were retrospectively selected as test cases. The adjustable planning parameters… 

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References

SHOWING 1-10 OF 54 REFERENCES

A hyperparameter-tuning approach to automated inverse planning.

TLDR
It is demonstrated 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.

Meta-optimization for fully automated radiation therapy treatment planning

TLDR
The proposed MP method promises to substantially reduce the workload of treatment planners while maintaining or improving plan quality and provides a general framework for fully automated treatment planning that produces high quality treatment plans.

A methodology for automatic intensity-modulated radiation treatment planning for lung cancer.

TLDR
It is concluded that the mdaccAutoPlan system can potentially improve the quality and consistency of treatment planning for lung cancer.

Fully Automated Radiation Therapy Treatment Planning Through Knowledge-Based Dose Predictions

TLDR
This study developed a technique for fully automated radiotherapy treatment planning with the guidance of dose predictions using high quality or evolving knowledge bases and established a robust and accurate KBP dose prediction technique, which was utilized in the automated planning protocol.

Automating proton treatment planning with beam angle selection using Bayesian optimization.

TLDR
A fully automated and efficient treatment planning process for proton therapy, including beam angle optimization was developed using a novel Bayesian optimization approach and previously-developed constrained hierarchical fluence optimization method.

Reduced-order parameter optimization for simplifying prostate IMRT planning

TLDR
Two main advances that simplify the parameter adjustment process for five-field prostate IMRT planning are described and it is shown that a recursive random search over the six most sensitive parameters as an outer loop in IM RT planning can quickly and automatically determine parameters for the cost function that lead to a plan meeting the clinical requirements.

Tree-based exploration of the optimization objectives for automatic cervical cancer IMRT treatment planning.

TLDR
The plans created by OOTSA have been shown marginally better than the manual plans, especially in preserving OARs and the time of automatic treatment planning has shown a reduction compared to a manual planning process, and the variation of plan quality was greatly reduced.

Operating a Treatment Planning System using a Deep-Reinforcement-Learning based Virtual Treatment Planner for Prostate Cancer Intensity-Modulated Radiation Therapy Treatment Planning.

TLDR
This was the first time that intelligent treatment planning behaviors of human planner in external beam IMRT are autonomously encoded in an artificial intelligence system.

Automatic treatment planning based on three‐dimensional dose distribution predicted from deep learning technique

TLDR
A new automated radiotherapy treatment planning system based on a deep learning-based three-dimensional dose prediction and 3D dose distribution-based optimization is developed, which is a promising approach for realizing automated treatment planning in the future.
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