Abstract 393P
Background
Selecting the better of the two institutionally available techniques to harbor optimal motion management, either a high dose rate stereotactic linear accelerator delivery using TrueBeam (TBX) or Magnetic Resonance (MR)-guided gated delivery using MRIdian (MRG), can be time-consuming and costly as independent treatment plan generation is a prerequisite. To address this challenge, we aimed to develop a decision-supporting algorithm that predicts the optimal treatment modality based on a combination of deep learning-generated dose distributions and relevant clinical data.
Methods
Clinical data and images were collected for 65 patients diagnosed with primary liver cancer (N=40) and pancreatic cancer (N=25) to facilitate the development of a predictive model for RT dose distribution. Treatment plans were created for both TBX and MRG. A three-dimensional U-Net deep learning approach was employed to predict RT dose distribution and generate dose volume histograms (DVHs). Generated DVH profiles were included in the Bayesian network (BN) decision-supporting algorithm by incorporating clinical data to nominate superior treatment plan.
Results
The root-mean-square error (RMSE) of voxels between the ground truth and the predictive dose from developed model was 3.04 (with a range of 2.29 to 3.79). The RMSE for the MRG prediction model was lower than that of the TBX. In general, the predictive normalized DVH metrics demonstrated superior performance in the MRG prediction models. Then, a BN model was established for determining the optimal treatment plan. The BN structure incorporated factors such as age, tumor volume, tumor to hollow viscus relationship as abutting vs apart, and the normalized value of the maximum dose to the neighboring dose limiting organ for MRG. The overall area under the receiver operating characteristic curve was 0.84.
Conclusions
We demonstrated a decision-supporting algorithm that could provide guidance to clinicians to select the superior plan from two competing RT systems for upper GI malignancy. This is anticipated to save resources in terms of manpower, time, and cost for both care-provider and the patient.
Legal entity responsible for the study
The authors.
Funding
Ministry of Science and ICT, Republic of Korea.
Disclosure
All authors have declared no conflicts of interest.