A generalizable dose prediction model for automatic radiotherapy planning based on physics-informed priors and large-kernel convolutions
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View abstract on PubMed
Summary
This summary is machine-generated.This study developed a universal deep learning model for accurate radiotherapy dose prediction across various cancer sites and techniques. This generalizable model shows promise for improving automatic treatment planning efficiency and quality.
Area Of Science
- Medical Physics
- Artificial Intelligence in Medicine
- Radiotherapy
Background
- Deep learning for radiotherapy planning offers improved efficiency and consistency.
- Current models are often scenario-specific, limiting clinical adoption.
- A need exists for generalizable dose prediction models.
Purpose Of The Study
- Develop a generalizable dose prediction model for diverse tumor sites, techniques, and doses.
- Achieve high accuracy in dose distribution prediction.
- Demonstrate the feasibility of universal automatic radiotherapy planning.
Main Methods
- Utilized the GDP-HMM dataset (3234 plans) for training and evaluation.
- Employed a 3D MedNeXt architecture with physics-informed priors (density maps, beam plates).
- Incorporated large-kernel convolutions and UpKern initialization for enhanced accuracy.
Main Results
- Achieved median prediction errors within 2 percentage points for DVH metrics.
- Demonstrated significant error reduction with physics-informed inputs and larger kernels.
- Generated deliverable plans comparable to reference plans in test cases.
Conclusions
- Developed a generalizable, accurate dose prediction model for radiotherapy.
- Physics-informed priors and large-kernel convolutions enhance prediction.
- The model enables universal automatic planning, improving efficiency and quality.
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