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Background: The current radiobiological model employed for boron neutron capture therapy (BNCT) treatment planning, which relies on microdosimetry, fails to provide an accurate representation the biological effects of BNCT. The precision in calculating the relative biological effectiveness (RBE) and compound biological effectiveness (CBE) plays a pivotal role in determining the therapeutic efficacy of BNCT. Therefore, this study focuses on how to improve the accuracy of the biological effects of BNCT. Purpose: The purpose of this study is to propose new radiation biology models based on nanodosimetry to accurately assess RBE and CBE for BNCT. Methods: Nanodosimetry, rooted in ionization cluster size distributions (ICSD), introduces a novel approach to characterize radiation quality by effectively delineating RBE through the ion track structure at the nanoscale. In the context of prior research, this study presents a computational model for the nanoscale assessment of RBE and CBE. We establish a simplified model of DNA chromatin fiber using the Monte Carlo code TOPAS‐nBio to evaluate the applicability of ICSD to BNCT and compute nanodosimetric parameters. Results: Our investigation reveals that both homogeneous and heterogeneous nanodosimetric parameters, as well as the corresponding biological model coefficients α and β, along with RBE values, exhibit variations in response to varying intracellular 10B concentrations. Notably, the nanodosimetric parameter M1C2$M_1^{{{\mathrm{C}}}_2}$ effectively captures the fluctuations in model coefficients α and RBE. Conclusion: Our model facilitates a nanoscale analysis of BNCT, enabling predictions of nanodosimetric quantities for secondary ions as well as RBE, CBE, and other essential biological metrics related to the distribution of boron. This contribution significantly enhances the precision of RBE calculations and holds substantial promise for future applications in treatment planning. [ABSTRACT FROM AUTHOR]

In medical physics it is desirable to have a Monte Carlo code that is less complex, reliable yet flexible for dose verification, optimization, and component design. TOPAS is a newly developed Monte Carlo simulation tool which combines extensive radiation physics libraries available in Geant4 code, easyto-use geometry and support for visualization. Although TOPAS has been widely tested and verified in simulations of proton therapy, there has been no reported application for carbon ion therapy. To evaluate the feasibility and accuracy of TOPAS simulations for carbon ion therapy, a licensed TOPAS code (version 3_0_p1) was used to carry out a dosimetric study of therapeutic carbon ions. Results of depth dose profile based on different physics models have been obtained and compared with the measurements. It is found that the G4QMD model is at least as accurate as the TOPAS default BIC physics model for carbon ions, but when the energy is increased to relatively high levels such as 400 MeV/u, the G4QMD model shows preferable performance. Also, simulations of special components used in the treatment head at the Institute of Modern Physics facility was conducted to investigate the Spread-Out dose distribution in water. The physical dose in water of SOBP was found to be consistent with the aim of the 6 cm ridge filter.

Purpose: Although carbon‐ion therapy is becoming increasingly attractive to the treatment of tumors, details about the ionization pattern formed by therapeutic carbon‐ion beam in tissue have not been fully investigated. In this work, systematic calculations for the nanodosimetric quantities and relative biological effectiveness (RBE) of a clinically relevant carbon‐ion beam were studied for the first time. Methods: The method combining both track structure and condensed history Monte Carlo (MC) simulations was adopted to calculate the nanodosimetric quantities. Fragments and energy spectra at different positions of the radiation field of a clinically relevant carbon‐ion pencil beam were generated by means of MC simulations in water. Nanodosimetric quantities such as mean ionization cluster size (M1), the first moment of conditional cluster size (M1C2), cumulative probability (F2), and conditional cumulative probability (F3C2) at these positions were then acquired based on the spectra and the pre‐calculated nanodosimetric database created by track structure MC simulations. What's more, a novel approach to calculate RBE based on the said nanodosimetric quantities was introduced. The RBE calculations were then conducted for the carbon‐ion beam at different water‐equivalent depths. Results: Lateral distributions at various water‐equivalent depths of both the nanodosimetric quantities and RBE values were obtained. The values of M1, M1C2, F2, and F3C2 were 1.49, 2.67, 0.30, and 0.38 at the plateau at the beam central axis and maximized at 2.79, 5.69, 0.47, and 0.68 at the depths around the Bragg peak, respectively. At a given depth, M1 and F2 decreased laterally with increasing the distance to the beam central axis while M1C2 and F3C2 remained nearly unchanged at first and then decreased except for M1C2 at the rising edge of the Bragg peak. The calculated RBE values were 1.07 at the plateau and 3.13 around the Bragg peak. Good agreement between the calculated RBE values and experimental data was obtained. Conclusions: Different nanodosimetric quantities feature the track structure of therapeutic carbon‐ion beam in different manners. Detailed ionization patterns generated by carbon‐ion beam could be characterized by nanodosimetric quantities. Moreover the combined method adopted in this work to calculate nanodosimetric quantities is not only valid but also convenient. Nanodosimetric quantities are significantly helpful for the RBE calculations in carbon‐ion therapy. [ABSTRACT FROM AUTHOR]

Background: Contouring of internal gross target volume (iGTV) is an essential part of treatment planning in radiotherapy to mitigate the impact of intra-fractional target motion. However, it is usually time-consuming and easily subjected to intra-observer and inter-observer variability. So far, few studies have been explored to directly predict iGTV by deep learning technique, because the iGTV contains not only the gross target volume (GTV) but also the motion information of the GTV., Purpose: This work was an exploratory study to present a deep learning-based framework to segment iGTV rapidly and accurately in 4D CT images for lung cancers., Methods: Five models, including 3D UNet, mmUNet with point-wise add merging approach (mmUNet-add), mmUNet with concatenate fusion strategy (mmUNet-cat), gruUNet with point-wise add fusion approach (gruUNet-add), and gruUNet with concatenate method (gruUNet-cat), were adopted for iGTV segmentation. All the models originated from the 3D UNet network, with multi-channel multi-path and convolutional gated recurrent unit (GRU) added in the mmUNet and gruUNet networks, respectively. Seventy patients with lung cancers were collected and 55 cases were randomly selected as the training set, and 15 cases as the testing set. In addition, the segmentation results of the five models were compared with the ground truths qualitatively and quantitatively., Results: In terms of Dice Similarity Coefficient (DSC), the proposed four networks (mmUNet-add, mmUNet-cat, gruUNet-add, and gruUNet-cat) increased the DSC score of 3D UNet from 0.6945 to 0.7342, 0.7253, 0.7405, and 0.7365, respectively. However, the differences were not statistically significant (p > 0.05). After a simple post-processing to remove the small isolated connected regions, the mean 95th percentile Hausdorff distances (HD_95s) of the 3D UNet, mmUNet-add, mmUNet-cat, gruUNet-add, and gruUNet-cat networks were 19.70, 15.75, 15.84, 15.61, and 15.83 mm, respectively, corresponding to 25.35, 25.96, 25.11, 28.23, and 24.47 mm before the post-processing. With regard to runtime, significant elapsed time growths (about 70s and 230s) were observed both in the mmUNet and gruUNet architectures due to the increasing parameters. But the mmUNet structure showed less growth., Conclusion: Our study demonstrated the ability of the deep learning technique to predict iGTVs directly. With the introduction of multi-channel multi-path and convolutional GRU, the segmentation accuracy was improved under certain conditions with a reduced segmentation efficiency and a further research topic when the 3D UNet network would lead to poor performance is elicited. Less efficiency degradation was observed in the mmUNet structure. Besides, the element-wise add fusing strategy was favorable to increase DSC, whereas HD_95 benefited from the concentrate merging approach. Nevertheless, the segmentation accuracy by deep learning still remains to be improved., (© 2022 American Association of Physicists in Medicine.)

Purpose: To improve the efficacy of heavy ion therapy, β-delayed particle decay (9)C beam as a double irradiation source for cancer therapy has been proposed. The authors' previous experiment showed that relative biological effectiveness (RBE) values at the depths around the Bragg peak of a (9)C beam were enhanced and compared to its stable counterpart (12)C beam. The purpose of this study was to explore the nature of the biological efficacy enhancement theoretically., Methods: A Monte Carlo simulation study was conducted in this study. First a simplified cell model was established so as to form a tumor tissue. Subsequently, the tumor tissue was imported into the Monte Carlo simulation software package gate and then the tumor cells were virtually irradiated with comparable (9)C and (12)C beams, respectively, in the simulations. The transportation and particle deposition data of the (9)C and (12)C beams, derived from the gate simulations, were analyzed with the authors' local effect model implementation so as to deduce cell survival fractions., Results: The particles emitted from the decay process of deposited (9)C particles around a cell nucleus increased the dose delivered to the nucleus and elicited clustered damages around the secondary particles' trajectories. Therefore, compared to the (12)C beam, the RBE value of the (9)C beam increased at the depths around their Bragg peaks., Conclusions: Collectively, the increased local doses and clustered damages due to the decayed particles emitted from deposited (9)C particles led to the RBE enhancement in contrast with the (12)C beam. Thus, the enhanced RBE effect of a (9)C beam for a simplified tumor model was shown theoretically in this study.

Objective: To confirm the decreasing bleeding effect of transamin on the induced abortion for the early pregnancy with more than two months., Methods: A total of 120 women are divided into two groups (60 pairs) based on the pelvic examination and the size of embryo measured by B ultrasound. Those women in the treated group (n = 60) were administed Transamin 1 g/20 ml (iv for 5 minutes) 30 minutes before operation; control group (n = 60) were given 5% glucose and saline 20 ml (iv for 5 minutes) 30 minutes before operation., Results: There wasn't any singnificant difference (P > 0.05) between the two groups in the patient's age, pregnancy age, parities, the cavity volume of uterine, hemoglobin and blood platelet count. The bleeding volume was (51.6 +/- 17.2) ml for the treated group and (63.3 +/- 17.1) ml for the control group (P < 0.05)., Conclusions: Transamin has effective hemostasis during the induced abortion for the early pregnancy with > 2 months. It could decrease the volume of bleeding during the operation and benefit for patients recovering.