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Fast VMAT planning for prostate radiotherapy: dosimetric validation of a deep learning-based initial segment
Yimin Ni1, Shufei Chen1, Lyndon Hibbard2
1Elekta (Shanghai) Technology Co. Ltd, Shanghai, People's Republic of China.
Physics in Medicine and Biology
|July 13, 2022
Summary
A new deep learning method significantly speeds up prostate radiotherapy planning using volumetric modulated arc therapy (VMAT). This automated approach maintains clinical quality while reducing planning time by nearly 30%.
Area of Science:
- Medical Physics
- Radiotherapy Technology
- Artificial Intelligence in Medicine
Background:
- Volumetric modulated arc therapy (VMAT) is a complex radiotherapy technique requiring precise treatment planning.
- Automated planning methods aim to improve efficiency and consistency in VMAT delivery.
- Deep learning offers potential for accelerating and optimizing radiotherapy planning processes.
Purpose of the Study:
- To develop and evaluate a deep learning-based method for rapid VMAT plan generation in prostate cancer radiotherapy.
- To compare the dosimetric quality and efficiency of the deep learning VMAT plans against a validated automated method.
Main Methods:
- A customized 3D U-Net model was trained to predict initial VMAT segments.
- The predictions were integrated with a treatment planning system for segment shape and weight optimization.
- VMAT plans generated by the deep learning method (VMAT_DL) were compared with reference automated plans (VMAT_ref) for 27 prostate cancer patients.
Main Results:
- All VMAT_DL plans were clinically acceptable, meeting target coverage (V95% > 99%) and organs at risk (OARs) dose constraints.
- No statistically significant difference in target coverage was found between VMAT_DL and VMAT_ref plans (P=0.3243).
- VMAT_DL reduced average optimization time by 29.3% compared to VMAT_ref, with similar OAR dose sparing.
Conclusions:
- The developed deep learning method provides a fully automated and efficient approach for prostate VMAT plan generation.
- The method achieves clinically acceptable dosimetric quality and significantly improves planning efficiency.
- This automated VMAT planning holds potential for clinical application and real-time adaptive radiotherapy after further validation.
