Use of a neural network‐based prediction method to calculate the therapeutic dose in boron neutron capture therapy of patients with glioblastoma

Abstract

Background: Boron neutron capture therapy (BNCT) is a binary radiotherapy based on the 10 B(n, α) 7 Li capture reaction. Nonradioactive isotope 10 B 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. Purpose: A 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. Methods: The 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. Results: The 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 (D 95 ), 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. Conclusions: This 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.

From nuclear track characterization to machine learning based image classification in neutron autoradiography for boron neutron capture therapy

“…Because of the interdisciplinary nature of BNCT, there are numerous applications to explore. In particular, a neural network-based dose prediction method has been recently reported [ 36 ]. As for our laboratory, further work will be devoted to the use of Convolutional Neural Networks, widely used for medical applications [ 37 ], for tasks such as the segmentation of nuclear tracks and cell compartments.…”

Neutron flux evaluation algorithm with a combination of Monte Carlo and removal‐diffusion calculation methods for boron neutron capture therapy

“…[17][18][19] Recent studies have focused on machine learning for fast dose calculation. 27 However, the calculation accuracy depends on the learning data. On the other hand, the MC-RD calculation was conducted based on physical principles, that is, the particle transport equation, as with the conventionally used dose calculation method and can be useful for rapid calculations.…”

“…Recent studies have focused on machine learning for fast dose calculation 27 . However, the calculation accuracy depends on the learning data.…”

A physically constrained Monte Carlo–Neural Network coupling algorithm for BNCT dose calculation

“…Furthermore, a study on NN-based patient dose prediction was performed for BNCT. 19 Using data from 122 patients and leveraging a 3D-Unet model, this method reduced the dose calculation time from 6 h (using conventional MC methods) to less than 1 s. This demonstrates that the method is capable of fast BNCT dose calculations. However, from a clinical perspective, there are two critical limitations to this study.…”