Rapid dose prediction for lung CyberKnife radiotherapy plans utilizing a deep learning approach by incorporating dosimetric features delivered by noncoplanar beams

Purpose. The dose distribution of lung cancer patients treated with the CyberKnife (CK) system is influenced by various factors, including tumor location and the direction of CK beams. The objective of this study is to present a deep learning approach that integrates CK beam dose characteristics into CK planning dose calculations.Methods. The inputs utilized for the geometry and dosimetry method (GDM) include the patient's CT, the PTV structure, and multiple CK noncoplanar beam dose deposition features. The dose distributions were calculated using the Monte Carlo (MC) algorithm provided with the CK system and served as the ground truth dose label. Additionally, dose prediction was conducted through the geometry method (GM) for comparative analysis. The gamma pass rateγ(1 mm,1%),γ(2 mm,2%) andγ(3 mm,3%) were calculated between the predicted model and the MC method.Results. Compared to the GDM, the GM shows a significant dose difference from the MC approach in the low-dose region (<5 Gy) outside the target created by the various CK noncoplanar beams. The GDM increased theγ(1 mm, 1%) from 49.55% to 81.69%,γ(2 mm, 2%) from 73.24% to 98.11% and theγ(3 mm, 3%) from 81.69% to 99.37% when compared with the GM's results.Conclusions. This work proposed a deep learning dose calculation method by using patient geometry and dosimetry features in CK plans. The proposed method extends the geometric and dosimetric feature-driven deep learning dose calculation method to CK application scenarios, which has a great potential to accelerate the CK planning dose calculation and improve the planning efficiency.