Deterministic Parsing Model of the  Compound Biological Effectiveness (&amp;lt;i&amp;gt;CBE&amp;lt;/i&amp;gt;) Factor for Intracellular &amp;lt;sup&amp;gt;10&amp;lt;/sup&amp;gt;Boron Distribution in Boron Neutron Capture Therapy

Ishiyama, Shintaro

doi:10.4236/jct.2014.514140

Cited by 8 publications

(1 citation statement)

References 14 publications

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“…The parameter W B is the corresponding CBE factor of D B . When using the amino acid p ‐boronophenylalanine (BPA), W B is 3.8 for tumor; 1.4 for normal organs and 2.5 for skin 29 . The parameter W p is the corresponding the RBE factors of D p , and for all tissues, W p is 3.2.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Methodsmentioning

confidence: 99%

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

et al. 2023

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Dosimetric impact of respiratory motion during boron neutron capture therapy for lung cancer

Geng

Tang

et al. 2020

Radiation Physics and Chemistry

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Monte Carlo study of dose distribution improvement by skin-shielding layer design in boron neutron capture therapy for non-small-cell lung cancer

Tang

Shu

et al. 2018

Radioprotection

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The skin dose exceeds its dose limitation easily in using an accelerator-based neutron source for non-small-cell lung cancer (NSCLC) with boron neutron capture therapy (BNCT). To solve this problem, design analyses in materials and thicknesses of skin-shielding schemes were investigated through Monte Carlo method. Two skin-shielding schemes were better and could reduce the healthy organ dose: scheme A (0.1 cm-thick thermoplastic with 96%-enriched 6LiF), and scheme B (0.6 cm-thick lithium carbonate). Scheme B with shorter irradiation time was the optimized schemes to improve the dose distribution of BNCT for NSCLC.

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Deterministic Parsing Model of the Compound Biological Effectiveness (<i>CBE</i>) Factor for Intracellular <sup>10</sup>Boron Distribution in Boron Neutron Capture Therapy

Cited by 8 publications

References 14 publications

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

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

Dosimetric impact of respiratory motion during boron neutron capture therapy for lung cancer

Monte Carlo study of dose distribution improvement by skin-shielding layer design in boron neutron capture therapy for non-small-cell lung cancer

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