Automatic IMRT treatment planning through fluence prediction and plan fine-tuning for nasopharyngeal carcinoma.

BACKGROUND: At present, the implementation of intensity-modulated radiation therapy (IMRT) treatment planning for geometrically complex nasopharyngeal carcinoma (NPC) through manual trial-and-error fashion presents challenges to the improvement of planning efficiency and the obtaining of high-consistency plan quality. This paper aims to propose an automatic IMRT plan generation method through fluence prediction and further plan fine-tuning for patients with NPC and evaluates the planning efficiency and plan quality. METHODS: A total of 38 patients with NPC treated with nine-beam IMRT were enrolled in this study and automatically re-planned with the proposed method. A trained deep learning model was employed to generate static field fluence maps for each patient with 3D computed tomography images and structure contours as input. Automatic IMRT treatment planning was achieved by using its generated dose with slight tightening for further plan fine-tuning. Lastly, the plan quality was compared between automatic plans and clinical plans. RESULTS: The average time for automatic plan generation was less than 4 min, including fluence maps prediction with a python script and automated plan tuning with a C# script. Compared with clinical plans, automatic plans showed better conformity and homogeneity for planning target volumes (PTVs) except for the conformity of PTV-1. Meanwhile, the dosimetric metrics for most organs at risk (OARs) were ameliorated in the automatic plan, especially Dmax of the brainstem and spinal cord, and Dmean of the left and right parotid glands significantly decreased (P < 0.05). CONCLUSION: We have successfully implemented an automatic IMRT plan generation method for patients with NPC. This method shows high planning efficiency and comparable or superior plan quality than clinical plans. The qualitative results before and after the plan fine-tuning indicates that further optimization using dose objectives generated by predicted fluence maps is crucial to obtain high-quality automatic plans.

The performance of a new type accelerator uRT-linac 506c evaluated by a quality assurance automation system.

PURPOSE: The purpose of this study was to evaluate the performance of our quality assurance (QA) automation system and to evaluate the machine performance of a new type linear accelerator uRT-linac 506c within 6 months using this system. METHODS: This QA automation system consists of a hollow cylindrical phantom with 18 steel balls in the phantom surface and an analysis software to process electronic portal imaging device (EPID) measurement image data and report the results. The performance of the QA automation system was evaluated by the tests of repeatability, archivable precision, detectability of introduced errors, and the impact of set-up errors on QA results. The performance of this linac was evaluated by 31 items using this QA system over 6 months. RESULTS: This QA system was able to automatically deliver QA plan, EPID image acquisition, and automatic analysis. All images acquiring and analysis took approximately 4.6 min per energy. The preset error of 0.1 mm in multi-leaf collimator (MLC) leaf were detected as 0.12 ± 0.01 mm for Bank A and 0.10 ± 0.01 mm in Bank B. The 2 mm setup error was detected as -1.95 ± 0.01 mm, -2.02 ± 0.01 mm, 2.01 ± 0.01 mm for X, Y, Z directions, respectively. And data from the tests of repeatability and detectability of introduced errors showed the standard deviation were all within 0.1 mm and 0.1°. and data of the machine performance were all within the tolerance specified by AAPM TG-142. CONCLUSIONS: The QA automation system has high precision and good performance, and it can improve the QA efficiency. The performance of the new accelerator has also performed very well during the testing period.

Machine learning-based ensemble prediction model for the gamma passing rate of VMAT-SBRT plan.

PURPOSE: The purpose of this study was to accurately predict or classify the beam GPR with an ensemble model by using machine learning for SBRT(VMAT) plans. METHODS: A total of 128 SBRT VMAT plans with 330 arc beams were retrospectively selected, and 216 radiomics and 34 plan complexity features were calculated for each arc beam. Three models for GPR prediction and classification using support vector machine algorithm were as follows: (1) plan complexity feature-based model (plan model); (2) radiomics feature-based model (radiomics model); and (3) an ensemble model combining the two models (ensemble model). The prediction performance was evaluated by calculating the mean absolute error (MAE), root mean square error (RMSE), and Spearman's correlation coefficient (SC), and the classification performance was measured by calculating the area under the receiver operating characteristic curve (AUC), accuracy, specificity, and sensitivity. RESULTS: The MAE, RMSE and SC at the 2 %/2 mm gamma criterion in the test dataset were 1.4 %, 2.57 %, and 0.563, respectively, for the plan model; 1.42 %, and 2.51 %, and 0.508, respectively, for the radiomics model; and 1.33 %, 2.49 %, and 0.611, respectively, for the ensemble model. The accuracy, specificity, sensitivity, and AUC at the 2 %/2 mm gamma criterion in the test dataset were 0.807, 0.824, 0.681, and 0.854, respectively, for the plan model; 0.860, 0.893, 0.624, and 0.877, respectively, for the radiomics model; and 0.852, 0.871, 0.710, and 0.896, respectively, for the ensemble model. CONCLUSIONS: The ensemble model can improve the prediction and classification performance for the GPR of SBRT (VMAT).

A predicted three-dimensional dose sequence based treatment planning optimization method for gynecologic IMRT.

In knowledge-based treatment planning (KBTP) for intensity-modulated radiation therapy (IMRT), the quality of the plan is dependent on the sophistication of the predicted dosimetric information and its application. In this paper, we propose a KBTP method that based on the effective and reasonable utilization of a three-dimensional (3D) dose prediction on planning optimization. We used an organs-at-risk (OARs) dose distribution prediction model to create a voxel-based dose sequence based optimization objective for OARs doses. This objective was used to reformulate a traditional fluence map optimization model, which involves a tolerable spatial re-assignment of the predicted dose distribution to the OAR voxels based on their current doses' positions at a sorted dose sequencing. The feasibility of this method was evaluated with ten gynecology (GYN) cancer IMRT cases by comparing its generated plan quality with the original clinical plan. Results showed feasible plan by proposed method, with comparable planning target volume (PTV) dose coverage and greater dose sparing of the OARs. Among ten GYN cases, the average V30 and V45 of rectum were decreased by 4%±4% (p = 0.02) and 4%±3% (p<0.01), respectively. V30 and V45 of bladder were decreased by 8%±2% (p<0.01) and 3%±2% (p<0.01), respectively. Our predicted dose sequence-based planning optimization method for GYN IMRT offered a flexible use of predicted 3D doses while ensuring the output plan consistency.

Simultaneous dose distribution and fluence prediction for nasopharyngeal carcinoma IMRT.

BACKGROUND: Current intensity-modulated radiation therapy (IMRT) treatment planning is still a manual and time/resource consuming task, knowledge-based planning methods with appropriate predictions have been shown to enhance the plan quality consistency and improve planning efficiency. This study aims to develop a novel prediction framework to simultaneously predict dose distribution and fluence for nasopharyngeal carcinoma treated with IMRT, the predicted dose information and fluence can be used as the dose objectives and initial solution for an automatic IMRT plan optimization scheme, respectively. METHODS: We proposed a shared encoder network to simultaneously generate dose distribution and fluence maps. The same inputs (three-dimensional contours and CT images) were used for both dose distribution and fluence prediction. The model was trained with datasets of 340 nasopharyngeal carcinoma patients (260 cases for training, 40 cases for validation, 40 cases for testing) treated with nine-beam IMRT. The predicted fluence was then imported back to treatment planning system to generate the final deliverable plan. Predicted fluence accuracy was quantitatively evaluated within projected planning target volumes in beams-eye-view with 5 mm margin. The comparison between predicted doses, predicted fluence generated doses and ground truth doses were also conducted inside patient body. RESULTS: The proposed network successfully predicted similar dose distribution and fluence maps compared with ground truth. The quantitative evaluation showed that the pixel-based mean absolute error between predicted fluence and ground truth fluence was 0.53% ± 0.13%. The structural similarity index also showed high fluence similarity with values of 0.96 ± 0.02. Meanwhile, the difference in the clinical dose indices for most structures between predicted dose, predicted fluence generated dose and ground truth dose were less than 1 Gy. As a comparison, the predicted dose achieved better target dose coverage and dose hot spot than predicted fluence generated dose compared with ground truth dose. CONCLUSION: We proposed an approach to predict 3D dose distribution and fluence maps simultaneously for nasopharyngeal carcinoma patients. Hence, the proposed method can be potentially integrated in a fast automatic plan generation scheme by using predicted dose as dose objectives and predicted fluence as a warm start.

Multimodality MRI synchronous construction based deep learning framework for MRI-guided radiotherapy synthetic CT generation.

Synthesizing computed tomography (CT) images from magnetic resonance imaging (MRI) data can provide the necessary electron density information for accurate dose calculation in the treatment planning of MRI-guided radiation therapy (MRIgRT). Inputting multimodality MRI data can provide sufficient information for accurate CT synthesis: however, obtaining the necessary number of MRI modalities is clinically expensive and time-consuming. In this study, we propose a multimodality MRI synchronous construction based deep learning framework from a single T1-weight (T1) image for MRIgRT synthetic CT (sCT) image generation. The network is mainly based on a generative adversarial network with sequential subtasks of intermediately generating synthetic MRIs and jointly generating the sCT image from the single T1 MRI. It contains a multitask generator and a multibranch discriminator, where the generator consists of a shared encoder and a splitted multibranch decoder. Specific attention modules are designed within the generator for feasible high-dimensional feature representation and fusion. Fifty patients with nasopharyngeal carcinoma who had undergone radiotherapy and had CT and sufficient MRI modalities scanned (5550 image slices for each modality) were used in the experiment. Results showed that our proposed network outperforms state-of-the-art sCT generation methods well with the least MAE, NRMSE, and comparable PSNR and SSIM index measure. Our proposed network exhibits comparable or even superior performance than the multimodality MRI-based generation method although it only takes a single T1 MRI image as input, thereby providing a more effective and economic solution for the laborious and high-cost generation of sCT images in clinical applications.

Cross-engine transformation-based fast dose calculation for MRI-Linac online treatment planning.

PURPOSE: To propose a novel magnetic field dose calculation method based on transformation from pencil beam (PB) to Monte Carlo (MC) distribution for MRI-Linac online treatment planning. METHODS: The novel magnetic field dose calculation algorithm was established by a PB dose engine and a magnetic field with tissue inhomogeneity influence correction network. The correction network was constructed with a Res-UNet framework, including residual modules and an encoding-decoding path, by inputting three-dimensional PB dose and patient electron density map, and outputting transformed dose distribution. The influences of magnetic fields and tissue heterogeneity were considered and corrected simultaneously in the correction model. A total of 110 clinically treated static beam IMRT plans were collected, including plans for brain, head-and-neck, lung, and rectum cases. A total of 90 cases were used and enhanced to train and validate the model, and the other 20 cases were for test. By comparing the proposed pipeline-generated dose distribution with original input PB dose and corresponding MC dose, the feasibility and effectiveness of the method was evaluated. RESULTS: Results on both beam dose and plan dose accuracy comparisons on all investigated four tumor sites show great consistency between the cross-dose-engine transformation generations and the MC results, with averaged plan mean absolute error of 0.90% ± 0.13% for the voxel-wise dose difference and 98.33% ± 1.07% gamma passing rate at the 2%/2 mm criteria. The whole PB calculation and transformation process can be completed within second. CONCLUSIONS: We have successfully developed a fast novel magnetic field dose calculation pipeline based on transformation from PB distribution to MC distribution for MRI-Linac online treatment planning.

TransDose: a transformer-based UNet model for fast and accurate dose calculation for MR-LINACs.

Objective. To present a transformer-based UNet model (TransDose) for fast and accurate dose calculation for magnetic resonance-linear accelerators (MR-LINACs).Approach. A 2D fluence map from each beam was first projected into a 3D fluence volume and then fed into the TransDose model together with patient density volume and output predicted beam dose. The proposed TransDose model combined a 3D residual UNet with a transformer encoder, where convolutional layers extracted the volumetric spatial features, and the transformer encoder processed the long-range dependencies in a global space. Ninety-eight cases with four tumor sites (brain, nasopharynx, lung, and rectum) treated with fixed-beam intensity-modulated radiotherapy were included in the dataset; 78 cases were used for model training and validation; and 20 cases were used for testing. The ground-truth beam doses were calculated with Monte Carlo (MC) simulations within 1% statistical uncertainty and magnetic field strengthB = 1.5 T in the superior and inferior direction. Beam angles from the training and validation datasets were rotated 2-5 times, and doses were recalculated to augment the datasets.Results. The dose-volume histograms and indices between the predicted and MC doses showed good consistency. The average 3Dγ-passing rates (3%/2 mm, for dose regions above 10% of maximum dose) were 99.13 ± 0.89% (brain), 98.31 ± 1.92% (nasopharynx), 98.74 ± 0.70% (lung), and 99.28 ± 0.25% (rectum). The average dose calculation time, which included the fluence projection and model prediction, was less than 310 ms for each beam.Significance. We successfully developed a transformer-based UNet dose calculation model-TransDose in magnetic fields. Its accuracy and efficiency indicated its potential for use in online adaptive plan optimization for MR-LINACs.

Development of a GPU-superposition Monte Carlo code for fast dose calculation in magnetic fields.

Objective.To develop and validate a graphics processing unit (GPU) based superposition Monte Carlo (SMC) code for efficient and accurate dose calculation in magnetic fields.Approach.A series of mono-energy photons ranging from 25 keV to 7.7 MeV were simulated with EGSnrc in a water phantom to generate particle tracks database. SMC physics was extended with charged particle transport in magnetic fields and subsequently programmed on GPU as gSMC. Optimized simulation scheme was designed by combining variance reduction techniques to relieve the thread divergence issue in general GPU-MC codes and improve the calculation efficiency. The gSMC code's dose calculation accuracy and efficiency were assessed through both phantoms and patient cases.Main results.gSMC accurately calculated the dose in various phantoms for bothB = 0 T andB = 1.5 T, and it matched EGSnrc well with a root mean square error of less than 1.0% for the entire depth dose region. Patient cases validation also showed a high dose agreement with EGSnrc with 3D gamma passing rate (2%/2 mm) large than 97% for all tested tumor sites. Combined with photon splitting and particle track repeating techniques, gSMC resolved the thread divergence issue and showed an efficiency gain of 186-304 relative to EGSnrc with 10 CPU threads.Significance.A GPU-superposition Monte Carlo code called gSMC was developed and validated for dose calculation in magnetic fields. The developed code's high calculation accuracy and efficiency make it suitable for dose calculation tasks in online adaptive radiotherapy with MR-LINAC.

Applying pytorch toolkit to plan optimization for circular cone based robotic radiotherapy.

BACKGROUND: Robotic linac is ideally suited to deliver hypo-fractionated radiotherapy due to its compact head and flexible positioning. The non-coplanar treatment space improves the delivery versatility but the complexity also leads to prolonged optimization and treatment time. METHODS: In this study, we attempted to use the deep learning (pytorch) framework for the plan optimization of circular cone based robotic radiotherapy. The optimization problem was topologized into a simple feedforward neural network, thus the treatment plan optimization was transformed into network training. With this transformation, the pytorch toolkit with high-efficiency automatic differentiation (AD) for gradient calculation was used as the optimization solver. To improve the treatment efficiency, plans with fewer nodes and beams were sought. The least absolute shrinkage and selection operator (lasso) and the group lasso were employed to address the "sparsity" issue. RESULTS: The AD-S (AD sparse) approach was validated on 6 brain and 6 liver cancer cases and the results were compared with the commercial MultiPlan (MLP) system. It was found that the AD-S plans achieved rapid dose fall-off and satisfactory sparing of organs at risk (OARs). Treatment efficiency was improved by the reduction in the number of nodes (28%) and beams (18%), and monitor unit (MU, 24%), respectively. The computational time was shortened to 47.3 s on average. CONCLUSIONS: In summary, this first attempt of applying deep learning framework to the robotic radiotherapy plan optimization is promising and has the potential to be used clinically.

Dosimetric Accuracy of MR-Guided Online Adaptive Planning for Nasopharyngeal Carcinoma Radiotherapy on 1.5 T MR-Linac.

Purpose: The aim of this study is to evaluate the dose accuracy of bulk relative electron density (rED) approach for application in 1.5 T MR-Linac and assess the reliability of this approach in the case of online adaptive MR-guided radiotherapy for nasopharyngeal carcinoma (NPC) patients. Methods: Ten NPC patients formerly treated on conventional linac were included in this study, with their original planning CT and MRI collected. For each patient, structures such as the targets, organs at risk, bone, and air regions were delineated on the original CT in the Monaco system (v5.40.02). To simulate the online adaptive workflow, firstly all contours were transferred to MRI from the original CT using rigid registration in the Monaco system. Based on the structures, three different types of synthetic CT (sCT) were generated from MRI using the bulk rED assignment approach: the sCTICRU uses the rED values recommended by ICRU46, the sCTtailor uses the patient-specific mean rED values, and the sCTHomogeneity uses homogeneous water equivalent values. The same treatment plan was calculated on the three sCTs and the original CT. Dose calculation accuracy was investigated in terms of gamma analysis, point dose comparison, and dose volume histogram (DVH) parameters. Results: Good agreement of dose distribution was observed between sCTtailor and the original CT, with a gamma passing rate (3%/3 mm) of 97.81% ± 1.06%, higher than that of sCTICRU (94.27% ± 1.48%, p = 0.005) and sCTHomogeneity (96.50% ± 1.02%, p = 0.005). For stricter criteria 1%/1 mm, gamma passing rates for plans on sCTtailor, sCTICRU, and sCTHomogeneity were 86.79% ± 4.31%, 79.81% ± 3.63%, and 77.56% ± 4.64%, respectively. The mean point dose difference in PTVnx between sCTtailor and planning CT was -0.14% ± 1.44%, much lower than that calculated on sCTICRU (-8.77% ± 2.33%) and sCTHomogeneity (1.65% ± 2.57%), all with p < 0.05. The DVH differences for the plan based on sCTtailor were much smaller than sCTICRU and sCTHomogeneity. Conclusions: The bulk rED-assigned sCT by adopting the patient-specific rED values can achieve a clinically acceptable level of dose calculation accuracy in the presence of a 1.5 T magnetic field, making it suitable for online adaptive MR-guided radiotherapy for NPC patients.

Multisequence MR-generated sCT is promising for HNC MR-only RT: A comprehensive evaluation of previously developed sCT generation networks.

PURPOSE: To verify the feasibility of our in-house developed multisequence magnetic resonance (MR)-generated synthetic computed tomography (sCT) for accurate dose calculation and fractional positioning for head and neck MR-only radiation therapy (RT). METHODS: Forty-five patients with nasopharyngeal carcinoma were retrospectively studied. By applying our previously in-house developed network, a patient's sCT can rapidly be generated with respect to feeding the sole T1 image, T1C image, T1DixonC image, T2 image, and their combination (five pipelines in total). A k(5)-fold strategy was implemented during model establishment. Dose recalculation was performed for each pipeline generation to attain a dosimetric feasibility evaluation. Fractional positioning evaluation was performed by calculating the digitally reconstructed radiograph (DRR) of the sCT and planning CT and their offset to the portal image. RESULTS: The dose mean absolute error values were (0.47±0.16)%, (0.48±0.15)% (p < 0.05), (0.50±0.16)% (p < 0.05), (0.50±0.15)% (p < 0.05), and (0.45±0.16)% (p < 0.05) for the T1, T1C, T1Dixon C, T2, and 4-channel generated sCT to the prescription dose, respectively. The 4-channel-generated sCT outperforms any other single-sequence pipeline. Among the single-sequence MR imaging-generated sCTs, the T1-generated sCT shows the most accurate HU image quality and provides a reliable dose result. Quantified positioning errors with calculation of the difference to the planning CT offsets are (-0.26±0.50) mm, (-0.58±0.52) mm (p < 0.05), (-0.27±0.57) mm (p > 0.05), (-0.31±0.44) mm (p > 0.05), and (-0.19±0.37) mm (p > 0.05) at LNG and (0.34±0.53) mm, (0.48±0.56) mm (p > 0.05), (0.55±0.56) mm (p > 0.05), (0.37±0.61) mm (p > 0.05), and (0.24±0.43) mm (p > 0.05) at LAT of the anterior-posterior direction for the five pipelines. CONCLUSION: Multisequence MR-generated sCT allows for accurate dose calculation and fractional positioning for head and neck MR-only RT.

Deep learning-based 3Din vivodose reconstruction with an electronic portal imaging device for magnetic resonance-linear accelerators: a proof of concept study.

Objective.To develop a novel deep learning-based 3Din vivodose reconstruction framework with an electronic portal imaging device (EPID) for magnetic resonance-linear accelerators (MR-LINACs).Approach.The proposed method directly back-projected 2D portal dose into 3D patient coarse dose, which bypassed the complicated patient-to-EPID scatter estimation step used in conventional methods. A pre-trained convolutional neural network (CNN) was then employed to map the coarse dose to the final accurate dose. The electron return effect caused by the magnetic field was captured with the CNN model. Patient dose and portal dose datasets were synchronously generated with Monte Carlo simulation for 96 patients (78 cases for training and validation and 18 cases for testing) treated with fixed-beam intensity-modulated radiotherapy in four different tumor sites, including the brain, nasopharynx, lung, and rectum. Beam angles from the training dataset were further rotated 2-3 times, and doses were recalculated to augment the datasets.Results.The comparison between reconstructed doses and MC ground truth doses showed mean absolute errors <0.88% for all tumor sites. The averaged 3Dγ-passing rates (3%, 2 mm) were 97.42%±2.66% (brain), 98.53%±0.95% (nasopharynx), 99.41%±0.46% (lung), and 98.63%±1.01% (rectum). The dose volume histograms and indices also showed good consistency. The average dose reconstruction time, including back projection and CNN dose mapping, was less than 3 s for each individual beam.Significance.The proposed method can be potentially used for accurate and fast 3D dosimetric verification for online adaptive radiotherapy using MR-LINACs.

A prior image constraint robust principal component analysis reconstruction method for sparse segmental multi-energy computed tomography.

BACKGROUND: Multi-energy computed tomography (MECT) is a promising technique in medical imaging, especially for quantitative imaging. However, high technical requirements and system costs barrier its step into clinical practice. METHODS: We propose a novel sparse segmental MECT (SSMECT) scheme and corresponding reconstruction method, which is a cost-efficient way to realize MECT on a conventional single-source CT. For the data acquisition, the X-ray source is controlled to maintain an energy within a segmental arc, and then switch alternately to another energy level. This scan only needs to switch tube voltage a few times to acquire multi-energy data, but leads to sparse-view and limited-angle issues in image reconstruction. To solve this problem, we propose a prior image constraint robust principal component analysis (PIC-RPCA) reconstruction method, which introduces structural similarity and spectral correlation into the reconstruction. RESULTS: A numerical simulation and a real phantom experiment were conducted to demonstrate the efficacy and robustness of the scan scheme and reconstruction method. The results showed that our proposed reconstruction method could have achieved better multi-energy images than other competing methods both quantitatively and qualitatively. CONCLUSIONS: Our proposed SSMECT scan with PIC-RPCA reconstruction method could lower kVp switching frequency while achieving satisfactory reconstruction accuracy and image quality.

Extension and validation of a GPU-Monte Carlo dose engine gDPM for 1.5 T MR-LINAC online independent dose verification.

PURPOSE: To extend and validate the accuracy and efficiency of a graphics processing unit (GPU)-Monte Carlo dose engine for Elekta Unity 1.5 T Magnetic Resonance-Linear Accelerator (MR-LINAC) online independent dose verification. METHODS: Electron/positron propagation physics in a uniform magnetic field was implemented in a previously developed GPU-Monte Carlo dose engine-gDPM. The dose calculation accuracy in the magnetic field was first evaluated in heterogeneous phantom with EGSnrc. The dose engine was then commissioned to a Unity machine with a virtual two photon-source model and compared with the Monaco treatment planning system. Fifteen patient plans from five tumor sites were included for the quantification of online dose verification accuracy and efficiency. RESULTS: The extended gDPM accurately calculated the dose in a 1.5 T external magnetic field and was well matched with EGSnrc. The relative dose difference along central beam axis was less than 0.5% for the homogeneous region in water-lung phantom. The maximum difference was found at the build-up regions and heterogeneous interfaces, reaching 1.9% and 2.4% for 2 and 6 MeV mono-energy photon beams, respectively. The root mean square errors for depth-dose fall-off region were less than 0.2% for all field sizes and presented a good match between gDPM and Monaco GPUMCD. For in-field profiles, the dose differences were within 1% for cross-plane and in-plane directions for all calculated depths except dmax. For penumbra regions, the distance-to-agreements between two dose profiles were less than 0.1 cm. For patient plan verification, the maximum relative average dose difference was 1.3%. The gamma passing rates with criteria 3% (2 mm) for dose regions above 20% were between 93% and 98%. gDPM can complete the dose calculation for less than 40 s with 5 × 108 photons on a single NVIDIA GTX-1080Ti GPU and achieve a statistical uncertainty of 0.5%-1.1% for all evaluated cases. CONCLUSIONS: A GPU-Monte Carlo package-gDPM was extended and validated for Elekta Unity online plan verification. Its calculation accuracy and efficiency make it suitable for online independent dose verification for MR-LINAC.

Comparison of Intensity Modulated Radiotherapy Treatment Plans Between 1.5T MR-Linac and Conventional Linac.

In this study, we assess the dosimetric qualities and usability of planning for 1.5 T MR-Linac based intensity modulated radiotherapy (MRL-IMRT) for various clinical sites in comparison with IMRT plans using a conventional linac. In total of 30 patients with disease sites in the brain, esophagus, lung, rectum and vertebra were re-planned retrospectively for simulated MRL-IMRT using the Elekta Unity dedicated treatment planning system (TPS) Monaco (v5.40.01). Currently, the step-and-shoot (ss) is the only delivery technique for IMRT available on Unity. All patients were treated on an Elekta Versa HDTM with IMRT using the dynamic multileaf collimator (dMLC) technique, and the plans were designed using Monaco v5.11. For comparison, the same dMLC-IMRT plan was recalculated with the same machine and TPS but only changing the technique to step-and-shoot. The dosimetric qualities of the MRL-IMRT plans, to be evaluated by the Dose Volume Histograms (DVH) metrics, Homogeneity Index and Conformality Index, were compared with the clinical plans. The planning usability was measured by the optimization time and the number of Monitor Units (MUs). Comparing MRL-IMRT with conventional linac based plans, all created plans were clinically equivalent to current clinical practice. However, MRL-IMRT plans had higher dose to skin and larger low dose region of normal tissues. Furthermore, MRL-IMRT plans had significantly reduced optimization time by comparing conventional linac based plans. The number of MUs of MRL-IMRT was increased by 23% compared with ss-IMRT, and no difference from dMLC-IMRT. In conclusion, clinically acceptable plans can be achieved with 1.5 T MR-Linac system for multiple tumor sites. Given the differences in machine characteristics, some minor differences in plan quality were found between MR-Linac plans and current clinical practice and this should be considered in clinical practice.

In-Air Electron Streaming Effect for Esophageal Cancer Radiotherapy With a 1.5 T Perpendicular Magnetic Field: A Treatment Planning Study.

PURPOSE: To investigate the in-air out-of-field electron streaming effect (ESE) for esophageal cancer radiotherapy in the presence of 1.5 T perpendicular magnetic field. METHODS: Ten esophageal cancer patients treated with conventional Linac were retrospectively enrolled into a cohort of this study, with the prescription of 4,400 cGy/20 fx. All cases received IMRT replanning using Elekta Unity MR-Linac specified Monaco system, denoted as primary plan. To visualize the in-air dose outside the body in Monaco system, an auxiliary structure was created by extending the external structure. For each case, another comparable plan with no magnetic field was created using the same planning parameters. The plan was also recalculated by placing a bolus upon the neck and chin area to investigate its shielding effect for ESE. Dosimetric evaluations of the out-of-field neck and chin skin area and statistical analysis for these plans were then performed. RESULTS: Out-of-field ESE was also observed in esophageal cancer treatment planning under 1.5 T magnetic field, while totally absent for plans with no magnetic field. On average, the maximum dose to the neck and chin skin area of the primary plan (657.92 ± 69.07 cGy) was higher than that of plan with no magnetic field (281.78 ± 36.59 cGy, p = 0.005) and plan with bolus (398.43 ± 69.19 cGy, p = 0.007). DVH metrics D1cc (the minimum dose to 1 cc volume) of the neck and chin skin for primary plan was 382.06 ± 44.14 cGy, which can be reduced to 212.42 ± 23.65 cGy by using the 1 cm bolus (with p = 0.005), even lower than the plan without magnetic field (214.45 ± 23.82, p = 0.005). No statistically significant difference of the neck and chin skin dose between the plan with bolus and plan with no magnetic field was observed (all with p > 0.05). CONCLUSION: For MRI guided esophageal cancer radiotherapy, a relatively high out-of-field neck and chin skin doses will be introduced by ESE in the presence of magnetic field. It is therefore recommended to take this into account during the planning phase. Adding bolus could effectively reduce the ESE dose contributions, achieve the shielding effect almost equivalent to the scenario with no magnetic field. Further explorations of measurement verifications for the ESE dose distributions are required.

Feasibility of using a commercial collapsed cone dose engine for 1.5T MR-LINAC online independent dose verification.

PURPOSE: To validate the feasibility and accuracy of commonly used collapsed cone (CC) dose engine for Elekta Unity 1.5T MR-LINAC online independent dose verification. MATERIALS AND METHODS: The Unity beam model was built and commissioned in RayStation treatment planning system with CC dose engine. Four AAPM TG-119 test plans were created and measured with ArcCHECK phantom for comparison, another thirty patient plans from six tumor sites were also included. The dosimetric criteria for various ROIs and 3D gamma passing rates were quantitatively evaluated, and the effects of magnetic field and dose deposition type on the dose difference between two systems were further analyzed. RESULTS: ArcCHECK based measurement showed a clear magnetic field induced profile shift between CC with both measurement and GPUMCD. For clinical plans, gamma passing rates with criteria (3%, 3 mm) between GPUMCD and CC large than 90% can be achieved for most tumor sites except esophagus and lung cases, the mean dose difference of 3% can be satisfied for most ROIs from all tumor sites. The magnetic field caused a large dose impact on low density areas, the average gamma passing rates were improved from 85.54% to 96.43% and 87.40% to 99.54% for esophagus and lung cases when the magnetic field effect was excluded. CONCLUSIONS: It is feasible to use CC dose engine as a secondary dose calculation tool for Elekta Unity system for most tumor sites, while the accuracy is limited and should be used carefully for low density areas, such as esophagus and lung cases.

Two ferritin genes (MdFerH and MdFerL) are involved in iron homeostasis, antioxidation and immune defense in housefly Musca domestica.

Ferritin is a ubiquitous multi-subunit iron storage protein, made up of heavy chain and light chain subunits. In recent years, invertebrate ferritins have emerged as an important, yet largely underappreciated, component of host defense and antioxidant system. Here, two alternatively spliced transcripts encoding for a unique ferritin heavy chain homolog (MdFerH), and a transcript encoding for a light chain homolog (MdFerL) are cloned and characterized from Musca domestica. Comparing with MdFerH1, a fragment is absent at the 5' untranslated region of MdFerH2, where a putative iron response element is present. Amino acid sequence analysis shows that MdFerH possesses a strictly conserved ferroxidase site, while MdFerL has a putative atypical active center. Tissue distribution analysis indicates that MdFers are enriched expressed in gut. When the larvae receive diverse stimulations, including challenge by bacteria, exposure to excess Fe2+, doxorubicin or ultraviolet, the expression of MdFers is positively up-regulated in different degrees and different temporal patterns, indicating their potential roles in oxidative stress. The two mRNA isoforms of MdFerH appear to be differentially expressed in different tissues, but seem to show the similar expression patterns under diverse stress conditions. Further investigation reveals that silencing MdFers can alter the redox homeostasis, leading elevated mortalities of larvae following bacterial infection. Inspiringly, recombinant MdFerL produced in Pichia pastoris shows significant iron-chelating activity in vitro. These results suggest a pivotal role of ferritins from housefly in iron homeostasis, antibacterial immunity and redox balance.

Dosiomics improves prediction of locoregional recurrence for intensity modulated radiotherapy treated head and neck cancer cases.

OBJECTIVES: To investigate whether dosiomics can benefit to IMRT treated patient's locoregional recurrences (LR) prediction through a comparative study on prediction performance inspection between radiomics methods and that integrating dosiomics in head and neck cancer cases. MATERIALS AND METHODS: A cohort of 237 patients with head and neck cancer from four different institutions was obtained from The Cancer Imaging Archive and utilized to train and validate the radiomics-only prognostic model and integrate the dosiomics prognostic model. For radiomics, the radiomics features were initially extracted from images, including CTs and PETs, and selected on the basis of their concordance index (CI) values, then condensed via principle component analysis. Lastly, multivariate Cox proportional hazards regression models were constructed with class-imbalance adjustment as the LR prediction models by inputting those condensed features. For dosiomics integration model establishment, the initial features were similar, but with additional 3-dimensional dose distribution from radiation treatment plans. The CI and the Kaplan-Meier curves with log-rank analysis were used to assess and compare these models. RESULTS: Observed from the independent validation dataset, the CI of the model for dosiomics integration (0.66) was significantly different from that for radiomics (0.59) (Wilcoxon test, p=5.9×10-31). The integrated model successfully classified the patients into high- and low-risk groups (log-rank test, p=2.5×10-02), whereas the radiomics model was not able to provide such classification (log-rank test, p=0.37). CONCLUSION: Dosiomics can benefit in predicting the LR in IMRT-treated patients and should not be neglected for related investigations.