Affiliation:
1. Department of Nuclear Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing People's Republic of China
2. Joint International Research Laboratory on Advanced Particle Therapy Nanjing University of Aeronautics and Astronautics Nanjing People's Republic of China
3. Department of Radiation Oncology Jiangsu Cancer Hospital Nanjing People's Republic of China
Abstract
AbstractBackgroundBoron neutron capture therapy (BNCT) is a binary radiotherapy based on the 10B(n, α)7Li capture reaction. Nonradioactive isotope 10B atoms which selectively concentrated in tumor cells will react with low energy neutrons (mainly thermal neutrons) to produce secondary particles with high linear energy transfer, thus depositing dose in tumor cells. In clinical practice, an appropriate treatment plan needs to be set on the basis of the treatment planning system (TPS). Existing BNCT TPSs usually use the Monte Carlo method to determine the three‐dimensional (3D) therapeutic dose distribution, which often requires a lot of calculation time due to the complexity of simulating neutron transportation.PurposeA neural network‐based BNCT dose prediction method is proposed to achieve the rapid and accurate acquisition of BNCT 3D therapeutic dose distribution for patients with glioblastoma to solve the time‐consuming problem of BNCT dose calculation in clinic.MethodsThe clinical data of 122 patients with glioblastoma are collected. Eighteen patients are used as a test set, and the rest are used as a training set. The 3D‐UNET is constructed through the design optimization of input and output data sets based on radiation field information and patient CT information to enable the prediction of 3D dose distribution of BNCT.ResultsThe average mean absolute error of the predicted and simulated equivalent doses of each organ are all less than 1 Gy. For the dose to 95% of the GTV volume (D95), the relative deviation between predicted and simulated results are all less than 2%. The average 2 mm/2% gamma index is 89.67%, and the average 3 mm/3% gamma index is 96.78%. The calculation takes about 6 h to simulate the 3D therapeutic dose distribution of a patient with glioblastoma by Monte Carlo method using Intel Xeon E5‐2699 v4, whereas the time required by the method proposed in this study is almost less than 1 s using a Titan‐V graphics card.ConclusionsThis study proposes a 3D dose prediction method based on 3D‐UNET architecture in BNCT, and the feasibility of this method is demonstrated. Results indicate that the method can remarkably reduce the time required for calculation and ensure the accuracy of the predicted 3D therapeutic dose‐effect. This work is expected to promote the clinical development of BNCT in the future.
Funder
National Basic Research Program of China
National Natural Science Foundation of China
Natural Science Foundation of Jiangsu Province
Fundamental Research Funds for the Central Universities
Cited by
6 articles.
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