Nanodosimetric quantity-weighted dose optimization for carbon-ion treatment planning.

Dose verification of treatment plans is an essential step in radiotherapy workflows. In this work, we propose a novel method of treatment planning based on nanodosimetric quantity-weighted dose (NQWD), which could realize biological representation using pure physical quantities for biological-oriented carbon ion-beam treatment plans and their direct verification. The relationship between nanodosimetric quantities and relative biological effectiveness (RBE) was studied with the linear least-squares method for carbon-ion radiation fields. Next, under the framework of the matRad treatment planning platform, NQWD was optimized using the existing RBE-weighted dose (RWD) optimization algorithm. The schemes of NQWD-based treatment planning were compared with the RWD treatment plans in term of the microdosimetric kinetic model (MKM). The results showed that the nanodosimetric quantity F3 - 10 had a good correlation with the radiobiological effect reflected by the relationship between RBE and F3 - 10. Moreover, the NQWD-based treatment plans reproduced the RWD plans generally. Therefore, F3 - 10 could be adopted as a radiation quality descriptor for carbon-ion treatment planning. The novel method proposed herein not only might be helpful for rapid physical verification of biological-oriented ion-beam treatment plans with the development of experimental nanodosimetry, but also makes the direct comparison of ion-beam treatment plans in different institutions possible. Thus, our proposed method might be potentially developed to be a new strategy for carbon-ion treatment planning and improve patient safety for carbon-ion radiotherapy.

Primary study of the relative and compound biological effectiveness model for boron neutron capture therapy based on nanodosimetry.

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 ß, along with RBE values, exhibit variations in response to varying intracellular 10B concentrations. Notably, the nanodosimetric parameter M 1 C 2 $M_1^{{{\mathrm{C}}}_2}$ 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.

Selection of breathing phase number in 4D scanned proton treatment planning optimization for lung tumors.

The standard four-dimensional (4D) treatment planning includes all breathing states in the optimization process, which is time-consuming. This work was aimed to optimize the number of intermediate phases needed for 4D proton treatment planning optimization to reduce the computational cost. Five 4D optimization strategies adopting different numbers of intermediate states and one three-dimensional (3D) optimization plan were studied for fifteen lung cancer patients treated with scanned protons, optimizing on all ten phases (4D_10), two extreme phases (4D_2), six phases during the exhalation stage (4D_6EX), six phases during the inhalation stage (4D_6IN), two extreme phases plus an intermediate state (4D_3) and average computed tomography image (3D), respectively. The 4D dose evaluation was conducted on all the ten phases, considering the interplay effect. The resulting doses accumulated on the reference phase were computed and compared. Compared to the 4D optimization plans, the 3D optimization plan performed inferiorly in target coverage, but superiorly in organ at risks (OARs) sparing. For the 4D optimization, all the five 4D plans showed similar performance in OARs protection. However, the 4D_6EX and 4D_6IN strategies out-performed the 4D_2 and 4D_3 plans in dose homogeneity. The computing times of the 4D_2, 4D_3, 4D_6EX and 4D_6IN approaches decreased to 32%, 41%, 66% and 67% of the 4D_10 method, respectively. Thus, our study suggested that the use of all phases during inhalation or exhalation stage might be a feasible approach substituting for the full phase strategy to reduce the calculation load while guaranteeing the plan quality for scanned proton therapy.

Deep learning-based fast denoising of Monte Carlo dose calculation in carbon ion radiotherapy.

BACKGROUND: Plan verification is one of the important steps of quality assurance (QA) in carbon ion radiotherapy. Conventional methods of plan verification are based on phantom measurement, which is labor-intensive and time-consuming. Although the plan verification method based on Monte Carlo (MC) simulation provides a more accurate modeling of the physics, it is also time-consuming when simulating with a large number of particles. Therefore, how to ensure the accuracy of simulation results while reducing simulation time is the current difficulty and focus. PURPOSE: The purpose of this work was to evaluate the feasibility of using deep learning-based MC denoising method to accelerate carbon-ion radiotherapy plan verification. METHODS: Three models, including CycleGAN, 3DUNet and GhostUNet with Ghost module, were used to denoise the 1 × 106 carbon ions-based MC dose distribution to the accuracy of 1 × 108 carbon ions-based dose distribution. The CycleGAN's generator, 3DUNet and GhostUNet were all derived from the 3DUNet network. A total of 59 cases including 29 patients with head-and-neck cancers and 30 patients with lung cancers were collected, and 48 cases were randomly selected as the training set of the CycleGAN network and six cases as the test set. For the 3DUNet and GhostUNet models, the numbers of training set, validation set, and test set were 47, 6, and 6, respectively. Finally, the three models were evaluated qualitatively and quantitatively using RMSE and three-dimensional gamma analysis (3 mm, 3%). RESULTS: The three end-to-end trained models could be used for denoising the 1 × 106 carbon ions-based dose distribution, and their generalization was proved. The GhostUNet obtained the lowest RMSE value of 0.075, indicating the smallest difference between its denoised and 1 × 108 carbon ions-based dose distributions. The average gamma passing rate (GPR) between the GhostUNet denoising-based versus 1 × 108 carbon ions-based dose distributions was 99.1%, higher than that of the CycleGAN at 94.3% and the 3DUNet at 96.2%. Among the three models, the GhostUNet model had the fewest parameters (4.27 million) and the shortest training time (99 s per epoch) but achieved the best denoising results. CONCLUSION: The end-to-end deep network GhostUNet outperforms the CycleGAN, 3DUNet models in denoising MC dose distributions for carbon ion radiotherapy. The network requires less than 5 s to denoise a sample of MC simulation with few particles to obtain a qualitative and quantitative result comparable to the dose distribution simulated by MC with relatively large number particles, offering a significant reduction in computation time.

Deep learning-based internal gross target volume definition in 4D CT images of lung cancer patients.

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.

DR-only Carbon-ion radiotherapy treatment planning via deep learning.

PURPOSE: To evaluate the feasibility of patient-specific digital radiography (DR)-only treatment planning for carbon ion radiotherapy in anthropomorphic thorax-and-abdomen phantom and head-and-neck patients. METHODS: The study was conducted on the anthropomorphic phantom and head-and-neck patients. We collected computed tomography (CT) and DR images of the phantom and cone beam CT (CBCT) and DR images of the patients, respectively. Two different deep neural networks were established to correlate the relationships between DR and digitally reconstructed radiograph (DRR) images, as well as DRR and CT images. The similarity between CT and predicted CT images was evaluated by computing the mean absolute error (MAE), root mean square error (RMSE), peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), respectively. Dose calculations on the predicted CT images were compared against the true CT-based dose distributions for carbon-ion radiotherapy treatment planning with intensity-modulated pencil-beam spot scanning. Relative dose differences in the target volumes and organ-at-risks were computed and three-dimensional gamma analyses (3 mm, 3%) were performed. RESULTS: The average MAE, RMSE, PSNR and SSIM of the framework were 0.007, 0.144, 37.496 and 0.973, respectively. The average relative dose differences between the predicted CT- and CT-based dose distributions at the same carbon-ion irradiation settings for the phantom and the patients were <2% and ≤4%, respectively. The average gamma pass-rates were >98% for the predicted CT-based versus CT-based carbon ion plans of the phantom and the patients. CONCLUSION: We have demonstrated the feasibility of a patient-specific DR-only treatment planning workflow for heavy ion radiotherapy by using deep learning approach.

Validation and testing of a novel pencil-beam model derived from Monte Carlo simulations in carbon-ion treatment planning for different scenarios.

PURPOSE: The calculation ability of the newly-proposed accurate beam model, the double Gaussian-logistic (DG-L) model, was validated in both homogeneous and heterogeneous phantoms to provide helpful information for its future application in clinical carbon-ion treatment planning system (TPS). METHODS: MatRad was used as the new algorithm test platform. Based on Monte Carlo (MC) method, the basic database in matRad was generated, then comparative dosimetric analyses between the single Gaussian (SG), double Gaussian (DG) and DG-L models against the MC recalculations were performed on the treatment plans of a cubic water phantom, a TG119 phantom and a liver patient scenario. Absolute dose differences, dose-volume histograms (DVHs) and global γ-index analyses derived from the treatment plans were evaluated. RESULTS: Calculated with the DG-L model, the deviations of the target dose coverage (D95) for the cubic water phantom, the TG119 phantom and the liver patient case against the MC recalculations could be reduced from -2.5%, -4.6% and -6.4% to -0.3%, -2.0% and -4.5% respectively compared to the SG model, while the γ pass rates (3%/3mm) could be enhanced from 98.0%, 90.6% and 90.1% to 99.8%, 95.7% and 91.6%, respectively. The novel beam model also shows improved performance compared with the DG model, without substantially increasing the computation time. CONCLUSIONS: The DG-L model could effectively improve the dose calculation accuracy and mitigate the delivered dose deficiency in target volumes compared to the SG and DG models. The lateral heterogeneities should be considered for its future implementation in a clinical TPS.

Carbon Ion Radiotherapy Acts as the Optimal Treatment Strategy for Unresectable Liver Cancer During the Coronavirus Disease 2019 Crisis.

The coronavirus disease 2019 (COVID-19) pandemic has greatly disrupted the normal treatment of patients with liver cancer and increased their risk of death. The weight of therapeutic safety was significantly amplified for decision-making to minimize the risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Herein, the safety and effectiveness of carbon ion radiotherapy (CIRT) for unresectable liver cancer (ULC) were evaluated, and Chinese experiences were shared to solve the predicament of ULC treatment caused by SARS-CoV-2. Worldwide studies were collected to evaluate CIRT for ULC as the world has become a community due to the COVID-19 pandemic. We not only searched five international databases including the Cochrane Library, Web of Science, PubMed, Embase, and Scopus but also performed supplementary retrieval with other sources. Chinese experiences of fighting against COVID-19 were introduced based on the advancements of CIRT in China and a prospective clinical trial of CIRT for treating ULC. A total of 19 studies involving 813 patients with ULC were included in the systematic review. The qualitative synthetic evaluation showed that compared with transarterial chemoembolization (TACE), CIRT could achieve superior overall survival, local control, and relative hepatic protection. The systematic results indicated that non-invasive CIRT could significantly minimize harms to patients with ULC and concurrently obtain superior anti-cancer effectiveness. According to the Chinese experience, CIRT allows telemedicine within the hospital (TMIH) to keep a sufficient person-to-person physical distance in the whole process of treatment for ULC, which is significant for cutting off the transmission route of SARS-CoV-2. Additionally, CIRT could maximize the utilization rate of hospitalization and outpatient care (UHO). Collectively, CIRT for ULC patients not only allows TMIH and the maximized UHO but also has the compatible advantages of safety and effectiveness. Therefore, CIRT should be identified as the optimal strategy for treating appropriate ULC when we need to minimize the risk of SARS-CoV-2 infection and to improve the capacity of medical service in the context of the unprecedented COVID-19 crisis.

Hyperthermia ablation combined with transarterial chemoembolization versus monotherapy for hepatocellular carcinoma: A systematic review and meta-analysis.

BACKGROUND AND AIMS: The existing evidence has indicated that hyperthermia ablation (HA) and HA combined with transarterial chemoembolization (HATACE) are the optimal alternative to surgical resection for patients with hepatocellular carcinoma (HCC) in the COVID-19 crisis. However, the evidence for decision-making is lacking in terms of comparison between HA and HATACE. Herein, a comprehensive evaluation was performed to compare the efficacy and safety of HATACE with monotherapy. MATERIALS AND METHODS: Worldwide studies were collected to evaluate the HATACE regimen for HCC due to the practical need for global extrapolation of applicative population. Meta-analyses were performed using the RevMan 5.3 software (The Nordic Cochrane Centre, The Cochrane Collaboration, Copenhagen, Denmark). RESULTS: Thirty-six studies involving a large sample of 5036 patients were included finally. Compared with HA alone, HATACE produced the advantage of 5-year overall survival (OS) rate (OR:1.90; 95%CI:1.46,2.46; p < 0.05) without increasing toxicity (p ≥ 0.05). Compared with TACE alone, HATACE was associated with superior 5-year OS rate (OR:3.54; 95%CI:1.96,6.37; p < 0.05) and significantly reduced the incidences of severe liver damage (OR:0.32; 95%CI:0.11,0.96; p < 0.05) and ascites (OR:0.42; 95%CI:0.20,0.88; p < 0.05). Subgroup analysis results of small (≤3 cm) HCC revealed that there were no significant differences between the HATACE group and HA monotherapy group in regard to the OS rates (p ≥ 0.05). CONCLUSIONS: Compared with TACE alone, HATACE was more effective and safe for HCC. Compared with HA alone, HATACE was more effective for non-small-sized (>3 cm) HCC with comparable safety. However, the survival benefit of adjuvant TACE in HATACE regimen was not found for the patients with small (≤3 cm) HCC.

China's Particle Therapy Equipment Market: Opportunities Outweigh Challenges.

Since 2019, China has been the second largest medical device market in the world. At present, high-end radiotherapy equipment such as particle therapy system has a huge market potential due to the grim situation of cancer prevention and control and the growth of people's wealth in China. However, China's MedTech industry, especially the particle therapy equipment field, still faces the influence of policy, fluctuation of market demand, strengthening of industry supervision, and even geopolitical realities. This paper reviews the market prospect of particle therapy medical devices from the perspective of China's medical device policy and demand information analysis, which is conducive to the research on the industrial layout of particle therapy medical physics, and also helps high-performance medical device manufacturers to expand their business visions. MedTech manufacturers should actively adjust their business strategy and implement scientific and technological innovation on the basis of compliance with industry regulatory requirements in order to seize opportunities from challenges and gain profits growth.

Nanodosimetric understanding to the dependence of the relationship between dose-averaged lineal energy on nanoscale and LET on ion species.

With the extension of ion species in ion-beam radiotherapy, the sole dependence of relative biological effectiveness (RBE) on linear energy transfer (LET) is insufficient when comparing RBE for ion beams with the same LET value. The aim of the present study was to provide a systematic study of the nanodosimetry for ion beams with the same LET value. Based on the calculated LET profiles of ion beams with range about 130 mm, lineal energy spectra and dose-averaged lineal energy [Formula: see text] on 4 nm site for various clinical ion beams were obtained. Then, the lineal energy spectra and [Formula: see text] values were compared for ion beams with the same LET values. The results showed that the relationships between [Formula: see text] and LET for various ion beams present an dependence on ion species. For ion beams with the same LET value, the ion beams with smaller nucleon number yielded greater [Formula: see text] values. The probability of the small-nucleon-number ion beams to generate large energy deposition events on nanoscale was higher than that of the large-nucleon-number ion beams. The dependence of the relationship between RBE and LET on ion species might be attributed to the fluctuation of energy depositions on nanometer scale.

Nanodosimetric quantities and RBE of a clinically relevant carbon-ion beam.

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 ( M 1 ), the first moment of conditional cluster size ( M 1 C 2 ), cumulative probability ( F 2 ), and conditional cumulative probability ( F 3 C 2 ) 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 M 1 , M 1 C 2 , F 2 , and F 3 C 2 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, M 1 and F 2 decreased laterally with increasing the distance to the beam central axis while M 1 C 2 and F 3 C 2 remained nearly unchanged at first and then decreased except for M 1 C 2 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.

Technical Note: Effect of magnetic fields on the microdosimetry of carbon-ion beams.

PURPOSE: To investigate the influence of magnetic fields on the microdosimetry of carbon-ion beams and the scaling effect of tissue equivalent proportional counter (TEPC) defined as the change of energy deposition in a TEPC from that in a microscopic scale region of interest due to the presence of a magnetic field in combination with the TEPC larger physical dimensions. METHODS: Geant4-based Monte Carlo simulations were conducted to calculate the microdosimetric quantities for carbon-ion beams with different initial energies (10-290 MeV/u) under magnetic fields of various strengths (0.5-3 T). The calculations were performed for a 1 µm spherical volume made of tissue, and for spherical TEPCs of 1 and 10 mm in diameter. Then, values of dose-averaged lineal energy (yD ) were acquired for the different scenarios to analyze the effect of magnetic fields on the microdosimetry of carbon-ion beams and the scaling effect of TEPC. RESULTS: The yD values and lineal energy spectra in the 1 µm spherical tissue volume for the scenarios without magnetic field and with magnetic fields of different strengths and directions remained nearly the same for the various energy carbon-ion beams. However, compared with those of the 1 µm spherical tissue volume, an increase of of yD values and an obvious shift of the lineal energy spectra for the TEPCs of 1 and 10 mm in diameter under magnetic fields were found. CONCLUSIONS: The application of magnetic fields under 3 T has no significant influence on the microdosimetric results of carbon-ion beams. However, there is definitely a scaling effect when using TEPC for microdosimetric study, which alters the reading of TEPC in the presence of magnetic fields. Novel methods to correct the reading of TEPC or scaling effect-resistant microdosimetric measurement detectors are urgently needed to perform experimental microdosimetric studies under magnetic fields.

A novel pencil beam model for carbon-ion dose calculation derived from Monte Carlo simulations.

An accurate kernel model is of vital importance for pencil-beam dose algorithm in charged particle therapy using precise spot-scanning beam delivery, in which an accurate depiction of the low dose envelope is especially crucial. Based on the Monte Carlo method, we investigated the dose contribution of secondary particles to the total dose and proposed a novel beam model to depict the lateral dose distribution of carbon-ion pencil beam in water. We demonstrated that the low dose envelope in single-spot profiles in water could be adequately modelled with the addition of a logistic distribution to a double Gaussian one, which was verified in both single carbon-ion pencil beam and superposed fields of different sizes with multiple pencil beams. Its superiority was mainly manifested at medium depths especially for high-energy beams with small fields compared with single, double and triple Gaussian models, where the secondary particles influenced the total dose considerably. The double Gaussian-logistic model could reduce the deviations from 4.1%, 1.7% to 0.3% in the plateau and peak regions, and from 19.2%, 4.9% to 1.2% in the tail region compared for the field size factor (FSF) calculations of 344 MeV/u carbon-ion pencil beam with the single and double Gaussian models. Compared with the triple Gaussian one, our newly-proposed model was on a par with it, even better than it in the plateau and peak regions. Thus our work will be helpful for improving the dose calculation accuracy for carbon-ion therapy.

Evaluation of mesh- and binary-based contour propagation methods in 4D thoracic radiotherapy treatments using patient 4D CT images.

Based on four dimensional (4D) computed tomography (CT) images, mesh- and binary-based contour propagation algorithms for 4D thoracic radiotherapy treatments were evaluated. Gross tumor volumes (GTVs), lungs, hearts and spinal cords on the CT images at the end-exhale and end-inhale phases for six patients were delineated by the physician. All volumes of interest (VOIs) were automatically propagated from the end-exhale phase to the end-inhale phase using two propagation methods. The propagated VOIs were quantitatively compared with the VOIs contoured at the end-inhale phase by the physician using Dice Similarity Coefficient (DSC), Mean Slicewise Hausdorff Distance (MSHD), Center Of Mass (COM) displacement and volume difference. A two-sided Student's t test was implemented to examine the significance of the differences between the results obtained from the two algorithms. For GTVs, statistically significant differences between the two algorithms were not observed. For all the other VOIs, the mesh-based method showed higher mean DSCs for the heart, left lung, right lung and spinal cord, lower mean MSHD for the spinal cord, lower mean COM displacement for the heart, and lower mean volume differences for the left lung, right lung and spinal cord with statistically significant differences than the binary-based method. The running time for propagation was approximately 3s and 3min for the mesh- and binary-based methods, respectively. Collectively, the mesh-based algorithm provides superiorities in running time and reliability for contour propagation in 4D radiotherapy.

Effectiveness of respiratory-gated radiotherapy with audio-visual biofeedback for synchrotron-based scanned heavy-ion beam delivery.

A synchrotron-based heavy-ion accelerator operates in pulse mode at a low repetition rate that is comparable to a patient's breathing rate. To overcome inefficiencies and interplay effects between the residual motion of the target and the scanned heavy-ion beam delivery process for conventional free breathing (FB)-based gating therapy, a novel respiratory guidance method was developed to help patients synchronize their breathing patterns with the synchrotron excitation patterns by performing short breath holds with the aid of personalized audio-visual biofeedback (BFB) system. The purpose of this study was to evaluate the treatment precision, efficiency and reproducibility of the respiratory guidance method in scanned heavy-ion beam delivery mode. Using 96 breathing traces from eight healthy volunteers who were asked to breathe freely and guided to perform short breath holds with the aid of BFB, a series of dedicated four-dimensional dose calculations (4DDC) were performed on a geometric model which was developed assuming a linear relationship between external surrogate and internal tumor motions. The outcome of the 4DDCs was quantified in terms of the treatment time, dose-volume histograms (DVH) and dose homogeneity index. Our results show that with the respiratory guidance method the treatment efficiency increased by a factor of 2.23-3.94 compared with FB gating, depending on the duty cycle settings. The magnitude of dose inhomogeneity for the respiratory guidance methods was 7.5 times less than that of the non-gated irradiation, and good reproducibility of breathing guidance among different fractions was achieved. Thus, our study indicates that the respiratory guidance method not only improved the overall treatment efficiency of respiratory-gated scanned heavy-ion beam delivery, but also had the advantages of lower dose uncertainty and better reproducibility among fractions.

Theoretical substantiation of biological efficacy enhancement for ß-delayed particle decay (9)C beam: A Monte Carlo study in combination with analysis with the local effect model approach.

PURPOSE: To improve the efficacy of heavy ion therapy, ß-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.

Carbon ions induce autophagy effectively through stimulating the unfolded protein response and subsequent inhibiting Akt phosphorylation in tumor cells.

Heavy ion beams have advantages over conventional radiation in radiotherapy due to their superb biological effectiveness and dose conformity. However, little information is currently available concerning the cellular and molecular basis for heavy ion radiation-induced autophagy. In this study, human glioblastoma SHG44 and cervical cancer HeLa cells were irradiated with carbon ions of different linear energy transfers (LETs) and X-rays. Our results revealed increased LC3-II and decreased p62 levels in SHG44 and HeLa cells post-irradiation, indicating marked induction of autophagy. The autophagic level of tumor cells after irradiation increased in a LET-dependent manner and was inversely correlated with the sensitivity to radiations of various qualities. Furthermore, we demonstrated that high-LET carbon ions stimulated the unfolded protein response (UPR) and mediated autophagy via the UPR-eIF2α-CHOP-Akt signaling axis. High-LET carbon ions more severely inhibited Akt-mTOR through UPR to effectively induce autophagy. Thus, the present data could serve as an important radiobiological basis to further understand the molecular mechanisms by which high-LET radiation induces cell death.

The dependence of radiation enhancement effect on the concentration of gold nanoparticles exposed to low- and high-LET radiations.

We examined the dependence of hydroxyl radical production on the concentration of 15 nm citrate-capped AuNPs and dose using coumarin-3-carboxylic acid in phosphate buffered saline (PBS), and investigated the radiosensitisation of different concentration AuNPs on human cervix carcinoma HeLa cells through clonogenic survival assay for X-rays and carbon ions. The enhancement factor of AuNPs for hydroxyl radical production reached a maximum 3.66 for X-rays at the concentration of 0.1 µg/mL while the maximum was 5.52 for carbon ions in presence of 1.0 µg/mL AuNPs in PBS. At 50% survival level, the sensitizer enhancement ratios of X-rays and carbon ions varied from 1.14 to 2.88 and from 1.27 to 1.44, respectively, when cells were co-cultured with 1.5-15.0 µg/mL AuNPs. Our data indicate AuNPs showed radiosensitisation in terms of hydroxyl radical production and cell killing for low- and high-LET radiations. The concentration of AuNPs in PBS and cells played an important role in radiosensitizing effect. Based on the fact-the AuNPs in PBS could improve the production of hydroxyl radical and no accumulation of cells in the G2/M phase was observed, we deduce that the increment of hydroxyl radical production with AuNPs provided a mechanism for radiosensitisation.