A robust evaluation of 49 high-dose-rate prostate brachytherapy treatment plans including all major uncertainties.

BACKGROUND: Uncertainties in radiotherapy cause deviation from the planned dose distribution and may result in delivering a treatment that fails to meet clinical objectives. The impact of uncertainties is unique to the patient anatomy and the needle locations in HDR prostate brachytherapy. Evaluating this impact during treatment planning is not common practice, relying on margins around the target or organs-at-risk to account for uncertainties. PURPOSE: A robust evaluation framework for HDR prostate brachytherapy treatment plans was evaluated on 49 patient plans, measuring the range of possible dosimetric outcomes to the patient due to 14 major uncertainties. METHODS: Patient plans were evaluated for their robustness to uncertainties by simulating probable uncertainty scenarios. Five-thousand probabilistic and 1943 worst-case scenarios per patient were simulated by changing the position and size of structures and length of dwell times from their nominal values. For each uncertainty scenario, the prostate D90 and maximum doses to the urethra, D0.01cc , and rectum, D0.1cc , were calculated. RESULTS: The D90 was an average 1.16 ± 0.51% (mean ± SD) below nominal values for the probabilistic scenarios; the D0.01cc metric was 2.24 ± 0.90% higher; and D0.1cc was greater by 0.48 ± 0.30%. The D0.01cc and D90 metrics were more sensitive to uncertainties than D0.1cc , with a median of 79.0% and 84.9% of probabilistic scenarios passing the constraints, compared to 96.5%. The median pass-rate for scenarios that passed all three metrics simultaneously was 63.4%. CONCLUSIONS: Assessing treatment plan robustness improves plan quality assurance, is achievable in less than 1-min, and identifies treatment plans with poor robustness, allowing re-optimization before delivery.

Image harmonization and deep learning automated classification of plus disease in retinopathy of prematurity.

Purpose: Retinopathy of prematurity (ROP) is a retinal vascular disease affecting premature infants that can culminate in blindness within days if not monitored and treated. A disease stage for scrutiny and administration of treatment within ROP is "plus disease" characterized by increased tortuosity and dilation of posterior retinal blood vessels. The monitoring of ROP occurs via routine imaging, typically using expensive instruments ($50 to $140 K) that are unavailable in low-resource settings at the point of care. Approach: As part of the smartphone-ROP program to enable referrals to expert physicians, fundus images are acquired using smartphone cameras and inexpensive lenses. We developed methods for artificial intelligence determination of plus disease, consisting of a preprocessing pipeline to enhance vessels and harmonize images followed by deep learning classification. A deep learning binary classifier (plus disease versus no plus disease) was developed using GoogLeNet. Results: Vessel contrast was enhanced by 90% after preprocessing as assessed by the contrast improvement index. In an image quality evaluation, preprocessed and original images were evaluated by pediatric ophthalmologists from the US and South America with years of experience diagnosing ROP and plus disease. All participating ophthalmologists agreed or strongly agreed that vessel visibility was improved with preprocessing. Using images from various smartphones, harmonized via preprocessing (e.g., vessel enhancement and size normalization) and augmented in physically reasonable ways (e.g., image rotation), we achieved an area under the ROC curve of 0.9754 for plus disease on a limited dataset. Conclusions: Promising results indicate the potential for developing algorithms and software to facilitate the usage of cell phone images for staging of plus disease.

AIR SAC TREMATODES (CYCLOCOELIDAE STOSSICH, 1902) INFECTING BIRDS IN ZOOLOGICAL INSTITUTIONS IN THE UNITED STATES.

Air sac trematodes (Digenea: Cyclocoelidae) were detected in 23 avian species from eight aviaries in the United States. Most of the infected host species were passeriform birds, but a few species in other orders also were infected. Four species of adult flukes were encountered: Circumvitellatrema momota, Morishitium sp., Psophiatrema greineri, and Szidatitrema yamagutii. Findings from retrospective review of medical records, necropsy records, and author observations are presented. Potential terrestrial snail intermediate hosts were collected from three indoor aviaries. A high prevalence (47%) of larval trematode infections was demonstrated in one species of nonnative snail (Prosopeas achatinacea); one larva was isolated and matched to the adult species (C. momota) from birds using PCR. Problems with introducing potentially infected wild-caught birds into aviaries, and exchanging captive individuals between aviaries where they potentially may carry infections, are discussed.

Being certain about uncertainties: a robust evaluation method for high-dose-rate prostate brachytherapy treatment plans including the combination of uncertainties.

In high-dose-rate (HDR) prostate brachytherapy the combined effect of uncertainties cause a range of possible dose distributions deviating from the nominal plan, and which are not considered during treatment plan evaluation. This could lead to dosimetric misses for critical structures and overdosing of organs at risk. A robust evaluation method to assess the combination of uncertainties during plan evaluation is presented and demonstrated on one HDR prostate ultrasound treatment plan retrospectively. A range of uncertainty scenarios are simulated by changing six parameters in the nominal plan and calculating the corresponding dose distribution. Two methods are employed to change the parameters, a probabilistic approach using random number sampling to evaluate the likelihood of variation in dose distributions, and a combination of the most extreme possible values to access the worst-case dosimetric outcomes. One thousand probabilistic scenarios were run on the single treatment plan with 43.2% of scenarios passing seven of the eight clinical objectives. The prostate D90 had a standard deviation of 4.4%, with the worst case decreasing the dose by up to 27.2%. The urethra D10 was up to 29.3% higher than planned in the worst case. All DVH metrics in the probabilistic scenarios were found to be within acceptable clinical constraints for the plan under statistical tests for significance. The clinical significance of the results from the robust evaluation method presented on any individual treatment plan needs to be compared in the context of a historical data set that contains patient outcomes with robustness analysis data to ascertain a baseline acceptance.

Synthetic cranial MRI from 3D optical surface scans using deep learning for radiation therapy treatment planning.

BACKGROUND: Optical scanning technologies are increasingly being utilised to supplement treatment workflows in radiation oncology, such as surface-guided radiotherapy or 3D printing custom bolus. One limitation of optical scanning devices is the absence of internal anatomical information of the patient being scanned. As a result, conventional radiation therapy treatment planning using this imaging modality is not feasible. Deep learning is useful for automating various manual tasks in radiation oncology, most notably, organ segmentation and treatment planning. Deep learning models have also been used to transform MRI datasets into synthetic CT datasets, facilitating the development of MRI-only radiation therapy planning. AIMS: To train a pix2pix generative adversarial network to transform 3D optical scan data into estimated MRI datasets for a given patient to provide additional anatomical data for a select few radiation therapy treatment sites. The proposed network may provide useful anatomical information for treatment planning of surface mould brachytherapy, total body irradiation, and total skin electron therapy, for example, without delivering any imaging dose. METHODS: A 2D pix2pix GAN was trained on 15,000 axial MRI slices of healthy adult brains paired with corresponding external mask slices. The model was validated on a further 5000 previously unseen external mask slices. The predictions were compared with the "ground-truth" MRI slices using the multi-scale structural similarity index (MSSI) metric. A certified neuro-radiologist was subsequently consulted to provide an independent review of the model's performance in terms of anatomical accuracy and consistency. The network was then applied to a 3D photogrammetry scan of a test subject to demonstrate the feasibility of this novel technique. RESULTS: The trained pix2pix network predicted MRI slices with a mean MSSI of 0.831 ± 0.057 for the 5000 validation images indicating that it is possible to estimate a significant proportion of a patient's gross cranial anatomy from a patient's exterior contour. When independently reviewed by a certified neuro-radiologist, the model's performance was described as "quite amazing, but there are limitations in the regions where there is wide variation within the normal population." When the trained network was applied to a 3D model of a human subject acquired using optical photogrammetry, the network could estimate the corresponding MRI volume for that subject with good qualitative accuracy. However, a ground-truth MRI baseline was not available for quantitative comparison. CONCLUSIONS: A deep learning model was developed, to transform 3D optical scan data of a patient into an estimated MRI volume, potentially increasing the usefulness of optical scanning in radiation therapy planning. This work has demonstrated that much of the human cranial anatomy can be predicted from the external shape of the head and may provide an additional source of valuable imaging data. Further research is required to investigate the feasibility of this approach for use in a clinical setting and further improve the model's accuracy.

Can optical scanning technologies replace CT for 3D printed medical devices in radiation oncology?

3D printing is being increasingly adopted in radiation oncology for printing highly conformal medical devices for treatment. Optical surface reconstruction technologies have been shown to be useful for 3D printing applications due to their higher spatial resolution, non-ionising radiation imaging and will likely supplement existing radiographic imaging techniques in the future.

A dosimetric comparison of CT- and photogrammetry- generated 3D printed HDR brachytherapy surface applicators.

In this study, we investigate whether an acceptable dosimetric plan can be obtained for a brachytherapy surface applicator designed using photogrammetry and compare the plan quality to a CT-derived applicator. The nose region of a RANDO anthropomorphic phantom was selected as the treatment site due to its high curvature. Photographs were captured using a Nikon D5600 DSLR camera and reconstructed using Agisoft Metashape while CT data was obtained using a Canon Aquillion scanner. Virtual surface applicators were designed in Blender and printed with PLA plastic. Treatment plans with a prescription dose of 3.85 Gy × 10 fractions with 100% dose to PTV on the bridge of the nose at 2 mm depth were generated separately using AcurosBV in the Varian BrachyVision TPS. PTV D98%, D90% and V100%, and OAR D0.1cc, D2cc and V50% dose metrics and dwell times were evaluated, with the applicator fit assessed by air-gap volume measurements. Both types of surface applicators were printed with minimal defects and visually fitted well to the target area. The measured air-gap volume between the photogrammetry applicator and phantom surface was 44% larger than the CT-designed applicator, with a mean air gap thickness of 3.24 and 2.88 mm, respectively. The largest difference in the dose metric observed for the PTV and OAR was the PTV V100% of - 1.27% and skin D0.1cc of - 0.28%. PTV D98% and D90% and OAR D2cc and V50% for the photogrammetry based plan were all within 0.5% of the CT based plan. Total dwell times were also within 5%. A 3D printed surface applicator for the nose was successfully constructed using photogrammetry techniques. Although it produced a larger air gap between the surface applicator and phantom surface, a clinically acceptable dose plan was created with similar PTV and OAR dose metrics to the CT-designed applicator. Additional future work is required to comprehensively evaluate its suitability in a clinically environment.

DeepWL: Robust EPID based Winston-Lutz analysis using deep learning, synthetic image generation and optical path-tracing.

Radiation therapy requires clinical linear accelerators to be mechanically and dosimetrically calibrated to a high standard. One important quality assurance test is the Winston-Lutz test which localises the radiation isocentre of the linac. In the current work we demonstrate a novel method of analysing EPID based Winston-Lutz QA images using a deep learning model trained only on synthetic image data. In addition, we propose a novel method of generating the synthetic WL images and associated 'ground-truth' masks using an optical path-tracing engine to 'fake' mega-voltage EPID images. The model called DeepWL was trained on 1500 synthetic WL images using data augmentation techniques for 180 epochs. The model was built using Keras with a TensorFlow backend on an Intel Core i5-6500T CPU and trained in approximately 15 h. DeepWL was shown to produce ball bearing and multi-leaf collimator field segmentations with a mean dice coefficient of 0.964 and 0.994 respectively on previously unseen synthetic testing data. When DeepWL was applied to WL data measured on an EPID, the predicted mean displacements were shown to be statistically similar to the Canny Edge detection method. However, the DeepWL predictions for the ball bearing locations were shown to correlate better with manual annotations compared with the Canny edge detection algorithm. DeepWL was demonstrated to analyse Winston-Lutz images with an accuracy suitable for routine linac quality assurance with some statistical evidence that it may outperform Canny Edge detection methods in terms of segmentation robustness and the resultant displacement predictions.

Evaluation of camera settings for photogrammetric reconstruction of humanoid phantoms for EBRT bolus and HDR surface brachytherapy applications.

The fabrication of brachytherapy surface moulds is considered laborious and time consuming that often result in repeated attempts due to incorrect catheter positioning or the presence of air gaps. 3-dimensional printing using low-cost and reliable materials has allowed the rapid creation of patient-specific surface mould applicators to be achieved using patient imaging data obtained via CT scan. In this study we investigate whether an alternative approach using photogrammetry techniques can improve this process and how camera settings and object texture affect the reconstructions. Two humanoid phantoms, an anthropomorphic RANDO phantom and a Laerdal Little Anne CPR training manikin were used in this study. Both were imaged using a Nikon D5600 DSLR and Nokia 3.1 smartphone camera and reconstructed using Agisoft Metashape software. CT scans of both phantoms were taken as references for comparing the photogrammetry reconstructions. Models were reconstructed from different photo sets and assessed by distance to agreement with the CT models. Both phantoms were effectively reconstructed for most experiments. Increasing the number of photos used produced the better reconstructions while in general, reconstructions using video data were poor. The two phantoms were reconstructed at a similar quality. Background light that caused undesirable reflections significantly reduced reconstruction quality. Applying a non-reflective tape to the affected regions provided a suitable method for reducing their effects. Photogrammetry techniques were effectively able to reconstruct 3-dimensional models of both phantom. The camera settings and lighting did have a profound effect on the reconstruction quality and should be chosen appropriately depending on the scene.

Radiation dose calculation in 3D heterogeneous media using artificial neural networks.

PURPOSE: External beam radiotherapy (EBRT) treatment planning requires a fast and accurate method of calculating the dose delivered by a clinical treatment plan. However, existing methods of calculating dose distributions have limitations. Monte Carlo (MC) methods are accurate but can take too long to be clinically viable. Deterministic approaches are quicker but can be inaccurate under certain conditions, particularly near heterogeneities and air interfaces. Neural networks trained on MC-derived data have the potential to reproduce dose distributions that agree closely with the MC method while being significantly quicker to deploy. METHODS: In this work we present a framework for training machine learning models capable of directly calculating the dose delivered to a point in three-dimensional (3D) heterogeneous media given only spatially local information. The framework consists of three parts. First, we describe a novel method of randomly generating 3D heterogeneous geometries using simplex noise. Dose distributions for training were obtained by importing these geometries into a MC simulation. The second and third parts of the framework are precalculated data channels, aligned with the patient computed tomography (CT) image, to be used as input to the model. These data channels are a computationally efficient way of encoding the parameters of an incident radiation beam while also allowing the model to learn from data that would otherwise be outside of its receptive field. RESULTS: We demonstrate the viability of the framework by a training small, fully connected neural network model to reproduce dose distributions from megavoltage photon beams. The trained network displayed excellent agreement with MC dose distributions in randomly generated geometries with an average gamma index (3%/3 mm) pass rate of 94.7% and an average error of 1.45% of peak dose. Finally, the network was used to calculate the dose in a patient CT image, on which the network was not trained, producing similarly impressive results. CONCLUSIONS: A novel method of generating training data for learned radiation dosimetry models has been introduced, along with preprocessing steps that allow even simple models to reproduce accurate dose distributions for EBRT. More importantly, we have demonstrated that a model trained using the proposed framework can generalize from the training data to predicting the therapeutic dose in realistic media.

Individualised selection of left-sided breast cancer patients for proton therapy based on cost-effectiveness.

INTRODUCTION: The significantly greater cost of proton therapy compared with X-ray therapy is frequently justified by the expected reduction in normal tissue toxicity. This is often true for indications such as paediatric and skull base cancers. However, the benefit is less clear for other more common indications such as breast cancer, and it is possible that the degree of benefit may vary widely between these patients. The aim of this work was to demonstrate a method of individualised selection of left-sided breast cancer patients for proton therapy based on cost-effectiveness of treatment. METHODS: 16 left-sided breast cancer patients had a treatment plan generated for the delivery of intensity-modulated proton therapy (IMPT) and of intensity-modulated photon therapy (IMRT) with the deep inspiration breath-hold (DIBH) technique. The resulting dosimetric data was used to predict probabilities of tumour control and toxicities for each patient. These probabilities were used in a Markov model to predict costs and the number of quality-adjusted life years expected as a result of each of the two treatments. RESULTS: IMPT was not cost-effective for the majority of patients but was cost-effective where there was a greater risk reduction of second malignancies with IMPT. CONCLUSION: The Markov model predicted that IMPT with DIBH was only cost-effective for selected left-sided breast cancer patients where IMRT resulted in a significantly greater dose to normal tissue. The presented model may serve as a means of evaluating the cost-effectiveness of IMPT on an individual patient basis.

Patient selection for proton therapy: a radiobiological fuzzy Markov model incorporating robust plan analysis.

While proton therapy can offer increased sparing of healthy tissue compared with X-ray therapy, it can be difficult to predict whether a benefit can be expected for an individual patient. Predictive modelling may aid in this respect. However, the predictions of these models can be affected by uncertainties in radiobiological model parameters and in planned dose. The aim of this work is to present a Markov model that incorporates these uncertainties to compare clinical outcomes for individualised proton and X-ray therapy treatments. A time-inhomogeneous fuzzy Markov model was developed which estimates the response of a patient to a given treatment plan in terms of quality adjusted life years. These are calculated using the dose-dependent probabilities of tumour control and toxicities as transition probabilities in the model. Dose-volume data representing multiple isotropic patient set-up uncertainties and range uncertainties (for proton therapy) are included to model dose delivery uncertainties. The model was retrospectively applied to an example patient as a demonstration. When uncertainty in the radiobiological model parameter was considered, the model predicted that proton therapy would result in an improved clinical outcome compared with X-ray therapy. However, when dose delivery uncertainty was included, there was no difference between the two treatments. By incorporating uncertainties in the predictive modelling calculations, the fuzzy Markov concept was found to be well suited to providing a more holistic comparison of individualised treatment outcomes for proton and X-ray therapy. This may prove to be useful in model-based patient selection strategies.

Modelling Spatial Scales of Dose Deposition and Radiolysis Products from Gold Nanoparticle Sensitisation of Proton Therapy in A Cell: From Intracellular Structures to Adjacent Cells.

Gold nanoparticle (GNP) enhanced proton therapy is a promising treatment concept offering increased therapeutic effect. It has been demonstrated in experiments which provided indications that reactive species play a major role. Simulations of the radiolysis yield from GNPs within a cell model were performed using the Geant4 toolkit. The effect of GNP cluster size, distribution and number, cell and nuclear membrane absorption and intercellular yields were evaluated. It was found that clusters distributed near the nucleus increased the nucleus yield by 91% while reducing the cytoplasm yield by 7% relative to a disperse distribution. Smaller cluster sizes increased the yield, 200 nm clusters had nucleus and cytoplasm yields 117% and 35% greater than 500 nm clusters. Nuclear membrane absorption reduced the cytoplasm and nucleus yields by 8% and 35% respectively to a permeable membrane. Intercellular enhancement was negligible. Smaller GNP clusters delivered near sub-cellular targets maximise radiosensitisation. Nuclear membrane absorption reduces the nucleus yield, but can damage the membrane providing another potential pathway for biological effect. The minimal effect on adjacent cells demonstrates that GNPs provide a targeted enhancement for proton therapy, only effecting cells with GNPs internalised. The provided quantitative data will aid further experiments and clinical trials.

Gold nanoparticle enhanced proton therapy: A Monte Carlo simulation of the effects of proton energy, nanoparticle size, coating material, and coating thickness on dose and radiolysis yield.

PURPOSE: Radiosensitizer enhanced radiotherapy provides the possibility of improved treatment outcomes by preferentially increasing the effectiveness of radiation within the tumor. Proton therapy offers improved sparing of tissue distal of the tumor along the beam path and reduced integral dose compared to conventional photon therapy. The combination of proton therapy with radiosensitizers offers the potential for an enhanced therapy with increased effect within the tumor and low integral dose. The simulations performed in this work determine the effect of nanoparticle characteristics and proton energy on the nanoscale dose and radiolysis yield enhancement for a single gold nanoparticle irradiated with a proton beam. This data can be used to determine optimal nanoparticle characteristics to enhance proton therapy. METHODS: A two-stage Monte Carlo simulation was performed using Geant4. In the first stage of the simulation, the physical interactions of protons within a gold nanoparticle were modeled and the secondary electrons escaping the nanoparticle's surface were scored in a phase space file. In the second stage of the simulation, the phase space file was used as an input to model the physical interactions of the secondary electrons in water and the resulting production and chemical interactions of reactive species. By comparing a gold nanoparticle with an equivalent water nanoparticle, the nanoscale enhancement of dose and radiolysis yield was calculated. RESULTS: A large nanoscale enhancement of both the dose and radiolysis yield of up to a factor of 11 due to gold nanoparticles was found for most simulated conditions. For 50 nm gold nanoparticles, a large enhancement factor of 9-11 was observed for high proton energies; however, the enhancement was reduced for proton energies below 10 MeV. For 5 MeV incident protons, it was found that the enhancement factor was approximately 9 for gold nanoparticles of sizes 5-25 nm with a reduction in enhancement observed for nanoparticle sizes outside this range. Additionally, it was found that larger nanoparticle sizes resulted in greater total energy deposition and radiolysis yields per proton flux but with reduced efficiency per nanoparticle mass. It was observed that a large loss of enhancement occurred for thick nanoparticle coatings. However, for polyethylene glycol (PEG) coatings, coating density had a minimal effect on enhancement. CONCLUSIONS: A large enhancement in dose and radiolysis yield was observed. However, the low-energy secondary electrons produced within the gold for lower energy protons are susceptible to self-absorption and result in the loss of enhancement observed for larger nanoparticles and thicker coatings. The radiolysis yield and dose increase with nanoparticle size; however, the yield and dose per gold mass decrease due to self-absorption. Therefore, an intermediate nanoparticle size of approximately 10-25 nm maximizes both the radiolysis yield and dose as well as the enhancement. Coatings should be kept to the minimum effective thickness to limit the loss of enhancement. For molecular coatings such as PEG, coating density should be maximized as this increases the coating's effectiveness with only a minimal effect on enhancement.

Stochastic multicellular modeling of x-ray irradiation, DNA damage induction, DNA free-end misrejoining and cell death.

The repair or misrepair of DNA double-strand breaks (DSBs) largely determines whether a cell will survive radiation insult or die. A new computational model of multicellular, track structure-based and pO2-dependent radiation-induced cell death was developed and used to investigate the contribution to cell killing by the mechanism of DNA free-end misrejoining for low-LET radiation. A simulated tumor of 1224 squamous cells was irradiated with 6 MV x-rays using the Monte Carlo toolkit Geant4 with low-energy Geant4-DNA physics and chemistry modules up to a uniform dose of 1 Gy. DNA damage including DSBs were simulated from ionizations, excitations and hydroxyl radical interactions along track segments through cell nuclei, with a higher cellular pO2 enhancing the conversion of DNA radicals to strand breaks. DNA free-ends produced by complex DSBs (cDSBs) were able to misrejoin and produce exchange-type chromosome aberrations, some of which were asymmetric and lethal. A sensitivity analysis was performed and conditions of full oxia and anoxia were simulated. The linear component of cell killing from misrejoining was consistently small compared to values in the literature for the linear component of cell killing for head and neck squamous cell carcinoma (HNSCC). This indicated that misrejoinings involving DSBs from the same x-ray (including all associated secondary electrons) were rare and that other mechanisms (e.g. unrejoined ends) may be important. Ignoring the contribution by the indirect effect toward DNA damage caused the DSB yield to drop to a third of its original value and the cDSB yield to drop to a tenth of its original value. Track structure-based cell killing was simulated in all 135306 viable cells of a 1 mm3 hypoxic HNSCC tumor for a uniform dose of 1 Gy.

Cost-effectiveness of proton therapy in treating base of skull chordoma.

While proton beam therapy (PBT) can offer increased sparing of healthy tissue, it is associated with large capital costs and as such, has limited availability. Furthermore, it has not been well established whether PBT has significant clinical advantages over conventional volumetric modulated arc therapy (VMAT) for all tumour types. PBT can potentially offer improved clinical outcomes for base of skull chordoma (BOSCh) patients compared with photon (X-ray) therapy, however the cost-effectiveness of these treatments is unclear. In this study, the cost-effectiveness of PBT in the treatment of BOSCh patients is assessed, based on an analysis of comparative radiotherapy treatment plans using a radiobiological Markov model. Seven BOSCh patients had treatment plans for the delivery of intensity modulated proton therapy and VMAT retrospectively analysed. The patient outcome (in terms of tumour local control and normal tissue complications) after receiving each treatment was estimated with a radiobiological Markov model. In addition, the model estimated the cost of both the primary treatment and treating any resultant adverse events. The incremental cost-effectiveness ratio (ICER) was obtained for each patient. PBT was found to be cost-effective for 5 patients and cost-saving for 2. The mean ICER was AUD$1,990 per quality adjusted life year gained. Variation of model parameters resulted in the proton treatments remaining cost-effective for these patients. Based on this cohort, PBT is a cost-effective treatment for patients with BOSCh. This supports the inclusion of PBT for BOSCh in the Medicare Services Advisory Committee 1455 application.

Gold Nanoparticle Enhanced Proton Therapy: Monte Carlo Modeling of Reactive Species' Distributions Around a Gold Nanoparticle and the Effects of Nanoparticle Proximity and Clustering.

Gold nanoparticles (GNPs) are promising radiosensitizers with the potential to enhance radiotherapy. Experiments have shown GNP enhancement of proton therapy and indicated that chemical damage by reactive species plays a major role. Simulations of the distribution and yield of reactive species from 10 ps to 1 µs produced by a single GNP, two GNPs in proximity and a GNP cluster irradiated with a proton beam were performed using the Geant4 Monte Carlo toolkit. It was found that the reactive species distribution at 1 µs extended a few hundred nm from a GNP and that the largest enhancement occurred over 50 nm from the nanoparticle. Additionally, the yield for two GNPs in proximity and a GNP cluster was reduced by up to 17% and 60% respectively from increased absorption. The extended range of action from the diffusion of the reactive species may enable simulations to model GNP enhanced proton therapy. The high levels of absorption for a large GNP cluster suggest that smaller clusters and diffuse GNP distributions maximize the total radiolysis yield within a cell. However, this must be balanced against the high local yields near a cluster particularly if the cluster is located adjacent to a biological target.

Application of optical photogrammetry in radiation oncology: HDR surface mold brachytherapy.

PURPOSE: We propose a novel method of designing surface mold brachytherapy applicators using optical photogrammetry. The accuracy of this technique for the purpose of 3D-printing surface mold brachytherapy applicators is investigated. METHODS AND MATERIALS: Photogrammetry was used to generate a 3D model of a patient's right arm. The geometric accuracy of the model was evaluated against CT in terms of volume, surface area, and the Hausdorff distance. A surface mold applicator was then 3D printed using this reconstructed model. The accuracy was evaluated by analyzing the displacement and air-gap volumes between the applicator and plaster cast on a CT image. This technique was subsequently applied to generate a 3D-printed applicator of the author's hand directly, as a proof of principle, using only photographic images. RESULTS: The volume and surface area of the model were within 0.1% and 2.6% of the CT-obtained values, respectively. Using the Hausdorff distance metric, it was determined that 93% of the visible vertices present in the CT-derived model had a matching vertex on the photogrammetry-derived model within 1 mm, indicating a high level of similarity. The maximum displacement between the plaster cast of the patient's arm and the photo-derived 3D-printed applicator was 1.2 mm with a total air-gap volume of approximately 0.05 cm3. CONCLUSIONS: Photogrammetry has been applied to the task of generating 3D-printed brachytherapy surface mold applicators. The current work demonstrates the feasibility and accuracy of this technique and how it may be incorporated into a 3D-printing brachytherapy workflow.

Cross-Correlative Single-Cell Analysis Reveals Biological Mechanisms of Nanoparticle Radiosensitization.

Nanoparticle radiosensitization has been demonstrated well to enhance the effects of radiotherapy, motivate the improvement of therapeutic ratios, and decrease morbidity in cancer treatment. A significant challenge exists in optimizing formulations and translation due to insufficient knowledge of the associated mechanisms, which have historically been limited to physical concepts. Here, we investigated a concept for the role of biological mechanisms. The mere presence of gold nanoparticles led to a down-regulation of thymidylate synthase, important for DNA damage repair in the radioresistant S-phase cells. By developing a cross-correlative methodology to reveal probabilistic gold nanoparticle uptake by cell sub-populations and the associated sensitization as a function of the uptake, a number of revealing observations have been achieved. Surprisingly, for low numbers of nanoparticles, a desensitization action was observed. Sensitization was discovered to preferentially impact S-phase cells, in which impairment of the DNA damage response by the homologous recombination pathway dominates. This small but radioresistant cell population correlates with much greater proliferative ability. Thus, a paradigm is presented whereby enhanced DNA damage is not necessarily due to an increase in the number of DNA double-strand breaks (DSBs) created but can be from a nanoparticle-induced impairment of the damage response by down-regulating repair proteins such as thymidylate synthase.

Validation and investigation of reactive species yields of Geant4-DNA chemistry models.

PURPOSE: Indirect biological damage due to reactive species produced in water radiolysis reactions is responsible for the majority of biological effect for low linear energy transfer (LET) radiation. Modeling water radiolysis and the subsequent interactions of reactive species, as well as track structures, is essential to model radiobiology on the microscale. Recently, chemistry models have been developed for Geant4-DNA to be used in combination with the comprehensive existing physics models. In the current work, the first detailed, independent, in silico validation of all species yields with published experimental observations and comparison with other radiobiological simulations is presented. Additionally, the effect of LET of protons and heavier ions on reactive species yield in the model was examined, as well as the completeness of the chemical reactions following the radiolysis within the time after physical interactions simulated in the model. METHODS: Yields over time of reactive species were simulated for water radiolysis by incident electrons, protons, alpha particles, and ions with various LETs using Geant4 and RITRACKS simulation tools. Water dissociation and recombination was simulated using Geant4 to determine the completeness of chemical reactions at the end of the simulation. Yield validation was performed by comparing yields simulated using Geant4 with experimental observations and other simulations. Validation was performed for all species for low LET radiation and the solvated electron and hydroxyl radical for high LET ions. RESULTS: It was found that the Geant4-DNA chemistry yields were generally in good agreement with experimental observations and other simulations. However, the Geant4-DNA yields for the hydroxyl radical and hydrogen peroxide at the end of the chemistry stage were found to be respectively considerably higher and lower than the experimentally observed yields. Increasing the LET of incident hadrons increased the yield of secondary species and decreased the yield of primary species. The effect of LET on the yield of the hydroxyl radical at 100 ns simulated with Geant4 was in good agreement with experimental measurements. Additionally, by the end of the simulation only 40% of dissociated water molecules had been recombined and the rate of recombination was slowing. CONCLUSIONS: The yields simulated using Geant4 are within reasonable agreement with experimental observations. Higher LET radiation corresponds with increased yields of secondary species and decreased yields of primary species. These trends combined with the LET having similar effects on the 100 ns hydroxyl radical yield for Geant4 and experimental measurements indicate that Geant4 accurately models the effect of LET on radiolysis yields. The limited recombination within the modeled chemistry stage and the slowing rate of recombination at the end of the stage indicate potential long-range indirect biological damage.