3D Dose Distributions Research Articles

Evaluating dosimetric accuracy of the 6 MV calibration on EBT3 film in the use of Ir-192 high dose rate brachytherapy.

PurposeTo evaluate the dosimetric accuracy of EBT3 film calibrated with a 6 MV beam for high dose rate brachytherapy and propose a novel method for direct film calibration with an Ir‐192 source.MethodsThe 6 MV calibration was performed in water on a linear accelerator (linac). The Ir‐192 calibration was accomplished by irradiating the film wrapped around a cylinder applicator with an Ir‐192 source. All films were scanned 1‐day post‐irradiation to acquire calibration curves for all three (red, blue, and green) channels. The Ir‐192 calibration films were also used for single‐dose comparison. Moreover, an independent test film under a H.A.M. applicator was irradiated and the 2D dose distribution was obtained separately for each calibration using the red channel data. Gamma analysis and point‐by‐point profile comparison were performed to evaluate the performance of both calibrations. The uncertainty budget for each calibration system was analyzed.ResultsThe red channel had the best performance for both calibration systems in the single‐dose comparison. We found a significant 4.89% difference from the reference for doses <250 cGy using the 6 MV calibration, while the difference was only 0.87% for doses >600 cGy. Gamma analysis of the 2D dose distribution showed the Ir‐192 calibration had a higher passing rate of 91.9% for the 1 mm/2% criterion, compared to 83.5% for the 6 MV calibration. Most failing points were in the low‐dose region (<200 cGy). The point‐by‐point profile comparison reported a discrepancy of 2%–3.6% between the Ir‐192 and 6 MV calibrations in this low‐dose region. The linac‐ and Ir‐192‐based dosimetry systems had an uncertainty of 4.1% (k = 2) and 5.66% (k = 2), respectively.ConclusionsDirect calibration of EBT3 films with an Ir‐192 source is feasible and reliable, while the dosimetric accuracy of 6 MV calibration depends on the dose range. The Ir‐192 calibration should be used when the measurement dose range is below 250 cGy.

A pilot study of a novel method to visualize three-dimensional dose distribution on skin surface images to evaluate radiation dermatitis

Predicting the radiation dose‒toxicity relationship is important for local tumor control and patients’ quality of life. We developed a first intuitive evaluation system that directly matches the three-dimensional (3D) dose distribution with the skin surface image of patients with radiation dermatitis (RD) to predict RD in patients undergoing radiotherapy. Using an RGB-D camera, 82 3D skin surface images (3DSSIs) were acquired from 19 patients who underwent radiotherapy. 3DSSI data acquired included 3D skin surface shape and optical imaging of the area where RD occurs. Surface registration between 3D skin dose (3DSD) and 3DSSI is performed using the iterative closest point algorithm, then reconstructed as a two-dimensional color image. The developed system successfully matched 3DSSI and 3DSD, and visualized the planned dose distribution onto the patient's RD image. The dose distribution pattern was consistent with the occurrence pattern of RD. This new approach facilitated the evaluation of the direct correlation between skin-dose distribution and RD and, therefore, provides a potential to predict the probability of RD and thereby decrease RD severity by enabling informed treatment decision making by physicians. However, the results need to be interpreted with caution due to the small sample size.

Radiomic and Dosiomic Features for the Prediction of Radiation Pneumonitis Across Esophageal Cancer and Lung Cancer.

PurposeThe aim was to investigate the advantages of dosiomic and radiomic features over traditional dose-volume histogram (DVH) features for predicting the development of radiation pneumonitis (RP), to validate the generalizability of dosiomic and radiomic features by using features selected from an esophageal cancer dataset and to use these features with a lung cancer dataset.Materials and MethodsA dataset containing 101 patients with esophageal cancer and 93 patients with lung cancer was included in this study. DVH and dosiomic features were extracted from 3D dose distributions. Radiomic features were extracted from pretreatment CT images. Feature selection was performed using only the esophageal cancer dataset. Four predictive models for RP (DVH, dosiomic, radiomic and dosiomic + radiomic models) were compared on the esophageal cancer dataset. We further used a lung cancer dataset for the external validation of the selected dosiomic and radiomic features from the esophageal cancer dataset. The performance of the predictive models was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve (ROCAUC) and the AUC of the precision recall curve (PRAUC) metrics.ResultThe ROCAUCs and PRAUCs of the DVH, dosiomic, radiomic and dosiomic + radiomic models on esophageal cancer dataset were 0.67 ± 0.11 and 0.75 ± 0.10, 0.71 ± 0.10 and 0.77 ± 0.09, 0.71 ± 0.11 and 0.79 ± 0.09, and 0.75 ± 0.10 and 0.81 ± 0.09, respectively. The predictive performance of the dosiomic- and radiomic-based models was significantly higher than that of the DVH-based model with respect to esophageal cancer. The ROCAUCs and PRAUCs of the DVH, dosiomic, radiomic and dosiomic + radiomic models on the lung cancer dataset were 0.64 ± 0.18 and 0.37 ± 0.20, 0.67 ± 0.17 and 0.37 ± 0.20, 0.67 ± 0.16 and 0.45 ± 0.23, and 0.68 ± 0.16 and 0.44 ± 0.22, respectively. On the lung cancer dataset, the predictive performance of the radiomic and dosiomic + radiomic models was significantly higher than that of the DVH-based model. However, the PRAUC of the dosiomic-based model showed no significant difference relative to the corresponding RP prediction performance on the lung cancer dataset.ConclusionThe results suggested that dosiomic and CT radiomic features could improve RP prediction in thoracic radiotherapy. Dosiomic and radiomic feature knowledge might be transferrable from esophageal cancer to lung cancer.

Comparison between dose distribution from 103Pd, 131Cs, and 125I plaques in a real human eye model with different tumor size

Knowledge of the energy deposition in different eye components is a critical decision-making to the overall effectivity of ocular melanoma treatment with plaques loaded with low-energy sources. The aim of this study is using the GATE 8.2 Monte Carlo code to calculate the 3D dose distribution in a realistic eye model. At first, we validated the GATE simulation for 125I, 103Pd, and 131Cs seeds by calculating the dose rate constant, radial dose function, and anisotropy function of the three radioactive sources. Then, a 12-mm Collaborative Ocular Melanoma Study (COMS) eye plaque was simulated in the eye phantoms to evaluate dose distribution due to low-energy gamma emitters on the three simulated medium-sized tumors. The findings of this study indicate that the estimated doses received by different eye substructures strongly depend on the source type. The results show that the type of seeds used in the plaque, as well as the size of the eye tumor, have significant effects on the dose deposition in the different structures of the eye and dose deposition uniformity. Moreover, comparing different radionuclides showed that the COMS plaque fully loaded with 103Pd presents a higher dose delivery to the tumor and a lower one to the critical structures for medium-sized tumors, while the plaque fully loaded with 131Cs produces the most uniform dose distribution in the tumor.

Site-agnostic 3D dose distribution prediction with deep learning neural networks.

Typically, the current dose prediction models are limited to small amounts of data and require retraining for a specific site, often leading to suboptimal performance. We propose a site-agnostic, three-dimensional dose distribution prediction model using deep learning that can leverage data from any treatment site, thus increasing the total data available to train the model. Applying our proposed model to a new target treatment site requires only a brief fine-tuning of the model to the new data and involves no modifications to the model input channels or its parameters. Thus, it can be efficiently adapted to a different treatment site, even with a small training dataset. This study uses two separate datasets/treatment sites: data from patients with prostate cancer treated with intensity-modulated radiation therapy (source data), and data from patients with head-and-neck cancer treated with volumetric-modulated arc therapy (target data). We first developed a source model with 3D UNet architecture, trained from random initial weights on the source data. We evaluated the performance of this model on the source data. We then studied the generalizability of the model to the new target dataset via transfer learning. To do this, we built three more models, all with the same 3D UNet architecture: target model, adapted model, and combined model. The source and target models were trained on the source and target data from random initial weights, respectively. The adapted model fine-tuned the source model to the target domain by using the target data. Finally, the combined model was trained from random initial weights on a combined data pool consisting of both target and source datasets. We tested all four models on the target dataset and evaluated quantitative dose-volume histogram metrics for the planning target volume (PTV) and organs at risk (OARs). When tested on the source treatment site, the source model accurately predicted the dose distributions with average (mean, max) absolute dose errors of (0.32%±0.14, 2.37%±0.93) (PTV) relative to the prescription dose, and highest mean dose error of 1.68%±0.76, and highest max dose error of 5.47%± 3.31 for femoral head right. The error in PTV dose coverage prediction is 3.21%±1.51 for D98 , 3.04%±1.69 for D95 , and 1.83%±1.01 for D02 . Averaging across all OARs, the source model predicted the OAR mean dose within 1.38% and the OAR max dose within 3.64%. For the target treatment site, the target model average (mean, max) absolute dose errors relative to the prescription dose for the PTV were (1.08%±0.95, 2.90%±1.35). Left cochlea had the highest mean and max dose errors of 5.37%±5.82 and 8.33%±8.88, respectively. The errors in PTV dose coverage prediction for D98 and D95 were 2.88%±1.59 and 2.55%±1.28, respectively. The target model can predict the OAR mean dose within 2.43% and the OAR max dose within 4.33% on average across all OARs. We developed a site-agnostic model for three-dimensional dose prediction and tested its adaptability to a new target treatment site via transfer learning. Our proposed model can make accurate predictions with limited training data.

Commissioning of GPU-based multi-criteria optimizer combined with plan navigation tools for high-dose-rate brachytherapy.

PurposeRecently, our GPU-based multi-criteria optimization (gMCO) algorithm has been integrated in a graphical user interface (gMCO-GUI) that allows real-time plan navigation through a gMCO-generated set of Pareto-optimal plans for high-dose-rate (HDR) brachytherapy. This work reports on the commissioning of the gMCO algorithm into clinical workflow.Material and methodsOur MCO workflow was validated against Oncentra Prostate v. 4.2.2 (OcP) and Oncentra Brachy v. 4.6.0 (OcB). 40 HDR prostate brachytherapy patients (20 with OcP and 20 with OcB) were retrospectively re-planned with gMCO algorithm by generating 2,000 Pareto-optimal plans. A single gMCO treatment plan was exported using gMCO-GUI plan navigation tools. The optimized dwell positions and dwell times of gMCO plans were exported via DICOM RTPLAN files to OcP/OcB, where final dosimetry was calculated. TG43 implementation in gMCO was validated against the consensus data of flexisource. Five analytical shapes were used as the ground truth for volume calculations. Dose-volume histogram (DVH) curves generated by gMCO were compared with the ones generated by OcP/OcB. 3D dose distributions (and isodose lines) were validated against OcP/OcB using dice similarity coefficient (DSC), 95% undirected Hausdorff distance (95% HD), and γ analysis.ResultsDifferences between –0.4% and 0.3% were observed between gMCO calculated dose rates and the flexisource consensus data. gMCO volumes were within ±2% agreement in 3/5 volumes (deviations within –2.9% and 0.1%). For 9 key DVH indices, the differences between gMCO and OcP/OcB were within ±1.2%. Regarding the accuracy of key isodose lines, the mean DSC was greater than 0.98, and the mean 95% HD was below 0.4 mm. The fraction of voxels with γ ≤ 1 was greater than 99% for all cases with 1%/1 mm threshold.ConclusionsThe GPU-based MCO workflow was successfully integrated into the clinical workflow and validated against OcP and OcB.

Monte Carlo-based software for 3D personalized dose calculations in image-guided radiotherapy.

Image-guided radiotherapy (IGRT) involves frequent in-room imaging sessions contributing to additional patient irradiation. The present work provided patient-specific dosimetric data related to different imaging protocols and anatomical sites. We developed a Monte Carlo based software able to calculate 3D personalized dose distributions for five imaging devices delivering kV-CBCT (Elekta and Varian linacs), MV-CT (Tomotherapy machines) and 2D-kV stereoscopic images from BrainLab and Accuray. Our study reported the dose distributions calculated for pelvis, head and neck and breast cases based on dose volume histograms for several organs at risk. 2D-kV imaging provided the minimum dose with less than 1mGy per image pair. For a single kV-CBCT and MV-CT, median dose to organs were respectively around 30mGy and 15mGy for the pelvis, around 7mGy and 10mGy for the head and neck and around 5mGy and 15mGy for the breast. While MV-CT dose varied sparsely with tissues, dose from kV imaging was around 1.7 times higher in bones than in soft tissue. Daily kV-CBCT along 40 sessions of prostate radiotherapy delivered up to 3.5Gy to the femoral heads. The dose level for head and neck and breast appeared to be lower than 0.4Gy for every organ in case of a daily imaging session. This study showed the dosimetric impact of IGRT procedures. Acquisition parameters should therefore be chosen wisely depending on the clinical purposes and tailored to morphology. Indeed, imaging dose could be reduced up to a factor 10 with optimized protocols.

Dosimetric comparison and validation of Eclipse Anisotropic Analytical Algorithm (AAA) and AcurosXB (AXB) algorithms in RapidArc-based radiosurgery plans of patients with solitary brain metastasis.

Stereotactic radiosurgery (SRS) is increasingly being used to manage solitary or multiple brain metastasis. This study aims to compare and validate Anisotropic Analytical Algorithm (AAA) and AcurosXB (AXB) algorithms of Eclipse Treatment Planning System (TPS) in RapidArc-based SRS plans of patients with solitary brain metastasis. Twenty patients with solitary brain metastasis who have been already treated with RapidArc SRS plans calculated using AAA plans were selected for this study. These plans were recalculated using AXB algorithm keeping the same arc orientations, multi-leaf collimator apertures, and monitor units. The two algorithms were compared for target coverage parameters, isodose volumes, plan quality metrics, dose to organs at risk and integral dose. The dose calculated by the TPS using AAA and AXB algorithms was validated against measured dose for all patient plans using an in-house developed cylindrical phantom. An Exradin A14SL ionization chamber was positioned at the center of this phantom to measure the in-field dose. NanoDot Optically Stimulated Luminescent Dosimeters (OSLDs) (Landauer Inc.) were placed at distances 3.0 cm, 4.0 cm, 5.0 cm, and 6.0 cm respectively from the center of the phantom to measure the non-target dose. In addition, the planar dose distribution was measured using amorphous silicon aS1000 Electronic Portal Imaging Device. The measured 2D dose distribution was compared against AAA and AXB estimated 2D distribution using gamma analysis. All results were tested for significance using the paired t-test at 5% level of significance. Significant differences between the AAA and AXB plans were found only for a few parameters analyzed in this study. In the experimental verification using cylindrical phantom, the difference between the AAA calculated dose and the measured dose was found to be highly significant (p < 0.001). However, the difference between the AXB calculated dose and the measured dose was not significant (p = 0.197). The difference between AAA/AXB calculated and measured at non-target locations was statistically insignificant at all four non-target locations and the dose calculated by both AAA and AXB algorithms shows a strong positive correlation with the measured dose. The results of the gamma analysis show that the AXB calculated planar dose is in better agreement with measurements compared to the AAA. Even though the results of the dosimetric comparison show that the differences are mostly not significant, the measurements show that there are differences between the two algorithms within the target volume. The AXB algorithm may be therefore more accurate in the dose calculation of VMAT plans for the treatment of small intracranial targets. For non-target locations either algorithm can be used for the estimation of dose accounting for their limitations in non-target dose estimations.

On the interplay between dosiomics and genomics in radiation-induced lymphopenia of lung cancer patients

PurposeTo investigate the interplay between spatial dose patterns and single nucleotide polymorphisms in the development of radiation-induced lymphopenia (RIL) in 186 non-small-cell lung cancer (NSCLC) patients undergoing chemo-radiotherapy (RT). MethodsThis study included NSCLC patients enrolled in a randomized trial of protons vs. photons with available absolute lymphocyte counts at baseline and during RT and XRCC1-rs25487 genotyping data. After masking the GTV, planning CT scans and dose maps were spatially normalized to a common anatomical reference. A Voxel-Based Analysis (VBA) was performed to assess voxel-wise relationships of dosiomic and genomic explanatory variables with RIL. The underlying generalized linear model was designed to include both the explanatory variables (3D dose distributions and the XRCC1-rs25487 genotypes) and possible nuisance variables significantly correlated with RIL. The maps of model coefficients as well as their significance maps were generated. ResultsMeasures for RIL definition during RT were characterized, including kinetic parameters for lymphocyte loss. The VBA generated three-dimensional maps of correlation between RIL and dose in lymphoid organs as well as organs with abundant blood pools. The identified voxel-wise relationships account for XRCC1-rs25487 polymorphism and demonstrate the variant AA genotype being detrimental to lymphocyte depletion (p = 0.03). ConclusionThe performed analyses blindly highlighted relevant anatomical regions that contributed most to lymphocyte depletion during RT and the interplay of the variant XRCC1-rs25487 AA genotype with the dose delivered to the primary lymphoid organs. These findings may help to guide the development of dosimetric RIL mitigation strategies for the application of effective individualized RT.

Usefulness of Semi-cylindrical Beam Spoiler in Treatment of Early Glottic Cancer Using 6 MV Photon Beam.

To assess the dosimetric influence of a semi-cylindrical beam spoiler (sCBS) for the treatment of early glottic cancer using a 6 MV photon beam. The 2D dose distributions were also calculated and measured with and without the sCBS and with a 0.5 cm thick bolus. A retrospective study of 8 patients treated between 2012 and 2018 was performed. The 2D dose distributions obtained from the treatment planning system (TPS) and film measurements were in good agreement. In the planning study, the V95%, V100%, conformal index (CI), and homogeneity index (HI) of all pPTVs for the sCBS plans were better than those for the open field plans (p<0.01). Especially, sCBS plans had better skin sparing effect than bolus plans (p<0.05). The sCBS of the 6 MV photon beam could be a useful tool for the treatment of early glottic cancers.

A new DOSXYZnrc method for Monte Carlo simulations of 4D dose distributions

The purpose of this study is to present a novel method for generating Monte Carlo 4D dose distributions in a single DOSXYZnrc simulation. During a standard simulation, individual energy deposition events are summed up to generate a 3D dose distribution and their associated temporal information is discarded. This means that in order to determine dose distributions as a function of time, separate simulations would have to be run for each interval of interest. Consequently, it has not been clinically feasible until now to routinely perform Monte Carlo simulations of dose rate, time-resolved dose accumulation, or electronic portal imaging devices (EPID) cine-mode images for volumetric modulated arc therapy (VMAT) plans. To overcome this limitation, we modified DOSXYZnrc and defined new input and output variables that allow a time-like parameter associated with each particle history to be binned in a user-defined manner. Under the new code version, computation times are the same as for a standard simulation, and the time-integrated 4D dose is identical to the standard 3D dose. We present a comparison of scintillator measurements and Monte Carlo simulations for dose rate during a VMAT beam delivery, a study of dose rate in a VMAT total body irradiation plan, and simulations of transit (through-patient) EPID cine-mode images.

Artificial intelligence applications in intensity modulated radiation treatment planning: an overview.

Artificial intelligence (AI) refers to methods that improve and automate challenging human tasks by systematically capturing and applying relevant knowledge in these tasks. Over the past decades, a number of approaches have been developed to address different types and needs of system intelligence ranging from search strategies to knowledge representation and inference to robotic planning. In the context of radiation treatment planning, multiple AI approaches may be adopted to improve the planning quality and efficiency. For example, knowledge representation and inference methods may improve dose prescription by integrating and reasoning about the domain knowledge described in many clinical guidelines and clinical trials reports. In this review, we will focus on the most studied AI approach in intensity modulated radiation therapy (IMRT)/volumetric modulated arc therapy (VMAT)-machine learning (ML) and describe our recent efforts in applying ML to improve the quality, consistency, and efficiency of IMRT/VMAT planning. With the available high-quality data, we can build models to accurately predict critical variables for each step of the planning process and thus automate and improve its outcomes. Specific to the IMRT/VMAT planning process, we can build models for each of the four critical components in the process: dose-volume histogram (DVH), Dose, Fluence, and Human Planner. These models can be divided into two general groups. The first group focuses on encoding prior experience and knowledge through ML and more recently deep learning (DL) from prior clinical plans and using these models to predict the optimal DVH (DVH prediction model), or 3D dose distribution (dose prediction model), or fluence map (fluence map model). The goal of these models is to reduce or remove the trial-and-error process and guarantee consistently high-quality plans. The second group of models focuses on mimicking human planners' decision-making process (planning strategy model) during the iterative adjustments/guidance of the optimization engine. Each critical step of the IMRT/VMAT treatment planning process can be improved and automated by AI methods. As more training data becomes available and more sophisticated models are developed, we can expect that the AI methods in treatment planning will continue to improve accuracy, efficiency, and robustness.

Utilizing pre-determined beam orientation information in dose prediction by 3D fully-connected network for intensity modulated radiotherapy

Although the effect of pre-determined beam orientation on dose distribution of intensity modulated radiotherapy (IMRT) has been well-documented, its impacts on dose prediction are less investigated. In this study, the direction map of beam orientation was incorporated into our proposed deep-learning network and utilized in dose prediction of IMRT plans consisting of multiple static fields. The direction map was used to characterize the radiation path through region of interest along a beam orientation. Besides, the distance map was used to characterize the spatial distribution between organs at risk (OARs) and planning target volume (PTV). The input of prediction model consisted of CT image, mask image (for PTV and OARs), distance map, and direction map. The output of prediction model was the estimated dose distribution in three dimensions. A 3D fully-connected network composed of a down-sampling encoder and an up-sampling pyramid decoder was trained based on the calculated 3D dose distributions obtained from a treatment planning system. The voxel-level mean absolute error (MAE), dosimetric metrics, and dose-volume histogram were employed to assess the quality of the estimated dose distribution. Performance of the prediction model was evaluated in two aspects. First, the effectiveness of the new features, direction map, distance maps, and pyramid decoder on prediction accuracy of model were assessed. Second, the proposed model was compared with the other three published prediction models, 3D UNet, ResNet-anti-ResNet, U-ResNet-D for inter-model evaluation. The improvement of prediction accuracy was 0.38 with the input of direction map and 0.43 with the input of distance map. Our proposed model achieved the least MAE (3.97±1.42) compared with the other three models: (5.37±1.51) for ResNet-anti-ResNet, (4.45±1.52) for U-ResNet-D, and (4.53±1.72) for Unet-3D. The preliminary result demonstrated that the prediction accuracy of the proposed model was higher than those of the other three state-of-the-art prediction models. The introduction of direction maps, distance map, and pyramid decoder can effectively improve the performance of the current deep-learning network-based prediction models.

Predicting Three-Dimensional Dose Distribution of Prostate Volumetric Modulated Arc Therapy Using Deep Learning.

Background: Volumetric modulated arc therapy (VMAT) planning is a time-consuming process of radiation therapy. With a deep learning approach, 3D dose distribution can be predicted without the need for an actual dose calculation. This approach can accelerate the process by guiding and confirming the achievable dose distribution in order to reduce the replanning iterations while maintaining the plan quality. Methods: In this study, three dose distribution predictive models of VMAT for prostate cancer were developed, evaluated, and compared. Each model was designed with a different input data structure to train and test the model: (1) patient CT alone (PCT alone), (2) patient CT and generalized organ structure (PCTGOS), and (3) patient CT and specific organ structure (PCTSOS). The generative adversarial network (GAN) model was used as a core learning algorithm. The models were trained slice-by-slice using 46 VMAT plans for prostate cancer, and then used to predict and evaluate the dose distribution from 8 independent plans. Results: VMAT dose distribution was generated with a mean prediction time of approximately 3.5 s per patient, whereas the PCTSOS model was excluded due to a mean prediction time of approximately 17.5 s per patient. The highest average 3D gamma passing rate was 80.51 ± 5.94, while the lowest overall percentage difference of dose-volume histogram (DVH) parameters was 6.01 ± 5.44% for the prescription dose from the PCTGOS model. However, the PCTSOS model was the most reliable for the evaluation of multiple parameters. Conclusions: This dose prediction model could accelerate the iterative optimization process for the planning of VMAT treatment by guiding the planner with the desired dose distribution.

Further investigation of 3D dose verification in proton therapy utilizing acoustic signal, wavelet decomposition and machine learning

Online dose verification in proton therapy is a critical task for quality assurance. We further studied the feasibility of using a wavelet-based machine learning framework to accomplishing that goal in three dimensions, built upon our previous work in 1D. The wavelet decomposition was utilized to extract features of acoustic signals and a bidirectional long-short-term memory (Bi-LSTM) recurrent neural network (RNN) was used. The 3D dose distributions of mono-energetic proton beams (multiple beam energies) inside a 3D CT phantom, were generated using Monte-Carlo simulation. The 3D propagation of acoustic signal was modeled using the k-Wave toolbox. Three different beamlets (i.e. acoustic pathways) were tested, one with its own model. The performance was quantitatively evaluated in terms of mean relative error (MRE) of dose distribution and positioning error of Bragg peak (), for two signal-to-noise ratios (SNRs). Due to the lack of experimental data for the time being, two SNR conditions were modeled (SNR = 1 and 5). The model is found to yield good accuracy and noise immunity for all three beamlets. The results exhibit an MRE below 0.6% (without noise) and 1.2% (SNR = 5), and below 1.2 mm (without noise) and 1.3 mm (SNR = 5). For the worst-case scenario (SNR = 1), the MRE and are below 2.3% and 1.9 mm, respectively. It is encouraging to find out that our model is able to identify the correlation between acoustic waveforms and dose distributions in 3D heterogeneous tissues, as in the 1D case. The work lays a good foundation for us to advance the study and fully validate the feasibility with experimental results.

Biological dosiomic features for the prediction of radiation pneumonitis in esophageal cancer patients

ObjectiveThe purpose of this study was to develop a model using dose volume histogram (DVH) and dosiomic features to predict the risk of radiation pneumonitis (RP) in the treatment of esophageal cancer with radiation therapy and to compare the performance of DVH and dosiomic features after adjustment for the effect of fractionation by correcting the dose to the equivalent dose in 2 Gy (EQD2).Materials and methodsDVH features and dosiomic features were extracted from the 3D dose distribution of 101 esophageal cancer patients. The features were extracted with and without correction to EQD2. A predictive model was trained to predict RP grade ≥ 1 by logistic regression with L1 norm regularization. The models were then evaluated by the areas under the receiver operating characteristic curves (AUCs).ResultThe AUCs of both DVH-based models with and without correction of the dose to EQD2 were 0.66 and 0.66, respectively. Both dosiomic-based models with correction of the dose to EQD2 (AUC = 0.70) and without correction of the dose to EQD2 (AUC = 0.71) showed significant improvement in performance when compared to both DVH-based models. There were no significant differences in the performance of the model by correcting the dose to EQD2.ConclusionDosiomic features can improve the performance of the predictive model for RP compared with that obtained with the DVH-based model.

Dose Prediction Using a Three-Dimensional Convolutional Neural Network for Nasopharyngeal Carcinoma With Tomotherapy.

PurposeThis study focused on predicting 3D dose distribution at high precision and generated the prediction methods for nasopharyngeal carcinoma patients (NPC) treated with Tomotherapy based on the patient-specific gap between organs at risk (OARs) and planning target volumes (PTVs).MethodsA convolutional neural network (CNN) is trained using the CT and contour masks as the input and dose distributions as output. The CNN is based on the “3D Dense-U-Net”, which combines the U-Net and the Dense-Net. To evaluate the model, we retrospectively used 124 NPC patients treated with Tomotherapy, in which 96 and 28 patients were randomly split and used for model training and test, respectively. We performed comparison studies using different training matrix shapes and dimensions for the CNN models, i.e., 128 ×128 ×48 (for Model I), 128 ×128 ×16 (for Model II), and 2D Dense U-Net (for Model III). The performance of these models was quantitatively evaluated using clinically relevant metrics and statistical analysis.ResultsWe found a more considerable height of the training patch size yields a better model outcome. The study calculated the corresponding errors by comparing the predicted dose with the ground truth. The mean deviations from the mean and maximum doses of PTVs and OARs were 2.42 and 2.93%. Error for the maximum dose of right optic nerves in Model I was 4.87 ± 6.88%, compared with 7.9 ± 6.8% in Model II (p=0.08) and 13.85 ± 10.97% in Model III (p<0.01); the Model I performed the best. The gamma passing rates of PTV60 for 3%/3 mm criteria was 83.6 ± 5.2% in Model I, compared with 75.9 ± 5.5% in Model II (p<0.001) and 77.2 ± 7.3% in Model III (p<0.01); the Model I also gave the best outcome. The prediction error of D95 for PTV60 was 0.64 ± 0.68% in Model I, compared with 2.04 ± 1.38% in Model II (p<0.01) and 1.05 ± 0.96% in Model III (p=0.01); the Model I was also the best one.ConclusionsIt is significant to train the dose prediction model by exploiting deep-learning techniques with various clinical logic concepts. Increasing the height (Y direction) of training patch size can improve the dose prediction accuracy of tiny OARs and the whole body. Our dose prediction network model provides a clinically acceptable result and a training strategy for a dose prediction model. It should be helpful to build automatic Tomotherapy planning.

Stopping power and CSDA range calculations of electrons and positrons over the 20 eV–1 GeV energy range in some water equivalent polymer gel dosimeters

In this study, the stopping power, CSDA range and radiation yield calculations of electrons and positrons over the 20 eV–1 GeV energy range in some water equivalent polymer gel dosimeters were performed. For collision stopping power calculations of electrons and positrons the effective charge concept proposed by Sugiyama were considered. Here, both the effective charge of incident electrons and positrons (z*) and the effective charge (Z*) and the effective mean excitation energy (I*) of the target material were calculated. For the density effect correction the Fano model was selected. For the radiative stopping power analytical model based on the ratio between the collision and the radiative stopping power discribed by Attix was considered. For CSDA range and radition yield the continuous slowing down approximation (CSDA) was considered and the calculations were performed using numerical integral methods. Because of their water equivalent and 3D dose distribution properties, MAGIC and MAGAS polymer gels were selected as a target materials. The calculations were performed by programming all the equations discribed in this study as a computational code. The results of the stopping power, range and radiation yield were compared with those of ESTAR program and PENELOPE Monte Carlo modelling. Some deviations in low and high energy region between the calculated and reference data were observed. However, the similarity between calculated and reference data is remarkable. For the collision stopping power and CSDA range a good agreement between the calculated and reference data was observed for energies >1 keV. Whereas, for the radiative stopping power a good agreement was observed for energies >100 keV.

Technical Note: Field size analysis of patient-specific quality assurance in scanned carbon ion radiotherapy.

To evaluate the dose difference between measurement and double Gaussian beam model prediction according to the field size and correct the measurements in patient-specific quality assurance (QA). The field size dependence of the dose was evaluated with volumes of 20× 20× 80mm3 , 40× 40× 80 mm3 , 60× 60× 80 mm3 , and 80× 80× 80mm3 of 1Gy uniform dose at three depths. Additional two 80× 80× 80 mm3 volumes of nonuniform fields were created: one high-dose field was given 1Gy at the central 40× 40 mm2 and 0.5Gy in its surrounding, and the other low-dose field was given 0.5Gy in the middle and 1Gy at the periphery. The dose in the center of the spread-out Bragg peak (SOBP) was measured in a water phantom and compared with the treatment planning system (TPS) predication. A field factor based on the two-dimensional (2D) dose distribution was proposed to estimate the field size. The field factor was first evaluated against the dose difference in the square fields, and then used to analyze and correct the patient-specific QA results. TPS overestimated dose for fields smaller than 80× 80 mm2 . A practically positive correlation was observed between the measured dose and the field factor. In the patient-specific QA, measured doses were lower than the TPS predication as they were calculated a relatively small field factor. The corrected dose differences were no longer field factor dependent. Using the proposed field factor, we have shown that all the measurements with a large dose deviation were due to the small-sized field. It is clinically relevant to take into consideration the field size in the QA analysis as long as the double Gaussian beam model being used for the dose calculation. Correction to the measurement can be made based on the field factor.

Feasibility of a Reusable Radiochromic Dosimeter

The current practice for patient-specific quality assurance (QA) uses ion chambers or diode arrays primarily because of their ease of use and reliability. A standard routine compares the dose distribution measured in a phantom with the dose distribution calculated by the treatment planning system for the same experimental conditions. For the particular problems encountered in the treatment planning of complex radiotherapy techniques, such as small fields/segments and dynamic delivery systems, additional tests are required to verify the accuracy of dose calculations. The dose distribution verification should be throughout the total 3D dose distribution for a high dose gradient in a small, irradiated volume, instead of the standard practice of one to several planes with 2D radiochromic (GAFChromic) film. To address this issue, we have developed a 3D radiochromic dosimeter that improves the rigor of current QA techniques by providing high-resolution, complete 3D verification for a wide range of clinical applications. The dosimeter is composed of polyurethane, a radical initiator, and a leuco dye, which is radiolytically oxidized to a dye absorbing at 633 nm. Since this chemical dosimeter is single-use, it represents a significant expense. The purpose of this research is to develop a cost-effective reusable dosimeter formulation. Based on prior reusability studies, three promising dosimeter formulations were studied using small volume optical cuvettes and irradiated to known clinically relevant doses of 0.5–10 Gy. After irradiation, the change in optical density was measured in a spectrophotometer. All three formulations retained linearity of optical density response to radiation upon re-irradiations. However, only one formulation retained dose sensitivity upon at least five re-irradiations, making it ideal for further evaluation as a 3D dosimeter.