Evaluation of Radiation Dose Effect on Lung Function Using Iodine Maps Derived From Dual-Energy Computed Tomography.

PURPOSE: There is interest in using dual-energy computed tomography (DECT) to evaluate organ function before and after radiation therapy (RT). The purpose of this study (trial identifier: NCT04863027) is to assess longitudinal changes in lung perfusion using iodine maps derived from DECT in patients with lung cancer treated with conventional or stereotactic RT. METHODS AND MATERIALS: For 48 prospectively enrolled patients with lung cancer, a contrast-enhanced DECT using a dual-source CT simulator was acquired pretreatment and at 6 and 12 months posttreatment. Pulmonary functions tests (PFT) were obtained at baseline and at 6 and 12 months posttreatment. Iodine maps were extracted from the DECT images using a previously described 2-material decomposition framework. Longitudinal iodine maps were normalized using a reference region defined as all voxels with perfusion in the top 10% outside of the 5 Gy isodose volume. Normalized functional responses (NFR) were calculated for 3 dose ranges: <5, 5 to 20, and >20 Gy. Mixed model analysis was used to assess the correlation between dose metrics and NFR. Pearson correlation was used to assess if NFRs were correlated with PFT changes. RESULTS: Out of the 48 patients, 21 (44%) were treated with stereotactic body RT and 27 (56%) were treated with conventionally fractionated intensity-modulated RT. Thirty-one out of these 48 patients were ultimately included in data analysis. It was found that NFR is linearly correlated with dose (P < .001) for both groups. The number of months elapsed post-RT was also found to correlate with NFR (P = .029), although this correlation was not observed for the stereotactic body RT subgroup. The NFR was not found to correlate with PFT changes. CONCLUSIONS: DECT-derived iodine maps are a promising method for detailed anatomic evaluation of radiation effect on lung function, including potentially subclinical changes.

Brachytherapy on-a-chip: a clinically-relevant approach for radiotherapy testing in 3d biology.

We describe the first microfluidic device for in vitro testing of brachytherapy (BT), with applications in translational cancer research. Our PDMS-made BT-on-chip system allows highly precise manual insertion of clinical BT seeds, reliable dose calculation using standard clinically-used TG-43 formalism and easy culture of naturally hypoxic spheroids in less than 3 days, thereby increasing the translational potential of the device. As the BT-on-chip platform is designed to be versatile, we showcase three different gold-standard post-irradiation bioassays and recapitulate, for the first time on-chip, key clinical observations such as dose rate effect and hypoxia-induced radioresistance. Our results suggest that BT-on-chip can be used to safely and efficiently integrate BT and radiotherapy to translational research and drug development pipelines, without expensive equipment or complex workflows.

Extending deterministic transport capabilities for very-high and ultra-high energy electron beams.

Focused Very-High Energy Electron (VHEE, 50-300 MeV) and Ultra-High Energy Electron (UHEE, > 300 MeV) beams can accurately target both large and deeply seated human tumors with high sparing properties, while avoiding the spatial requirements and cost of proton and heavy ion facilities. Advanced testing phases are underway at the CLEAR facilities at CERN (Switzerland), NLCTA at Stanford (USA), and SPARC at INFN (Italy), aiming to accelerate the transition to clinical application. Currently, Monte Carlo (MC) transport is the sole paradigm supporting preclinical trials and imminent clinical deployment. In this paper, we propose an alternative: the first extension of the nuclear-reactor deterministic chain NJOY-DRAGON for VHEE and UHEE applications. We have extended the Boltzmann-Fokker-Planck (BFP) multigroup formalism and validated it using standard radio-oncology benchmarks, complex assemblies with a wide range of atomic numbers, and comprehensive irradiation of the entire periodic table. We report that [Formula: see text] of water voxels exhibit a BFP-MC deviation below [Formula: see text] for electron energies under [Formula: see text]. Additionally, we demonstrate that at least [Formula: see text] of voxels of bone, lung, adipose tissue, muscle, soft tissue, tumor, steel, and aluminum meet the same criterion between [Formula: see text] and [Formula: see text]. For water, the thorax, and the breast intra-operative benchmark, typical average BFP-MC deviations of [Formula: see text] and [Formula: see text] were observed at [Formula: see text] and [Formula: see text], respectively. By irradiating the entire periodic table, we observed similar performance between lithium ([Formula: see text]) and cerium ([Formula: see text]). Deficiencies observed between praseodymium ([Formula: see text]) and einsteinium ([Formula: see text]) have been reported, analyzed, and quantified, offering critical insights for the ongoing development of the Evaluated Nuclear Data File mode in NJOY.

Image reconstruction using UNET-transformer network for fast and low-dose PET scans.

INTRODUCTION: Low-dose and fast PET imaging (low-count PET) play a significant role in enhancing patient safety, healthcare efficiency, and patient comfort during medical imaging procedures. To achieve high-quality images with low-count PET scans, effective reconstruction models are crucial for denoising and enhancing image quality. The main goal of this paper is to develop an effective and accurate deep learning-based method for reconstructing low-count PET images, which is a challenging problem due to the limited amount of available data and the high level of noise in the acquired images. The proposed method aims to improve the quality of reconstructed PET images while preserving important features, such as edges and small details, by combining the strengths of UNET and Transformer networks. MATERIAL AND METHODS: The proposed TrUNET-MAPEM model integrates a residual UNET-transformer regularizer into the unrolled maximum a posteriori expectation maximization (MAPEM) algorithm for PET image reconstruction. A loss function based on a combination of structural similarity index (SSIM) and mean squared error (MSE) is utilized to evaluate the accuracy of the reconstructed images. The simulated dataset was generated using the Brainweb phantom, while the real patient dataset was acquired using a Siemens Biograph mMR PET scanner. We also implemented state-of-the-art methods for comparison purposes: OSEM, MAPOSEM, and supervised learning using 3D-UNET network. The reconstructed images are compared to ground truth images using metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and relative root mean square error (rRMSE) to quantitatively evaluate the accuracy of the reconstructed images. RESULTS: Our proposed TrUNET-MAPEM approach was evaluated using both simulated and real patient data. For the patient data, our model achieved an average PSNR of 33.72 dB, an average SSIM of 0.955, and an average rRMSE of 0.39. These results outperformed other methods which had average PSNRs of 36.89 dB, 34.12 dB, and 33.52 db, average SSIMs of 0.944, 0.947, and 0.951, and average rRMSEs of 0.59, 0.49, and 0.42. For the simulated data, our model achieved an average PSNR of 31.23 dB, an average SSIM of 0.95, and an average rRMSE of 0.55. These results also outperformed other state-of-the-art methods, such as OSEM, MAPOSEM, and 3DUNET-MAPEM. The model demonstrates the potential for clinical use by successfully reconstructing smooth images while preserving edges. The comparison with other methods demonstrates the superiority of our approach, as it outperforms all other methods for all three metrics. CONCLUSION: The proposed TrUNET-MAPEM model presents a significant advancement in the field of low-count PET image reconstruction. The results demonstrate the potential for clinical use, as the model can produce images with reduced noise levels and better edge preservation compared to other reconstruction and post-processing algorithms. The proposed approach may have important clinical applications in the early detection and diagnosis of various diseases.

Development of a stand-alone precalculated Monte Carlo code to calculate the dose by alpha and beta emitters from the Ra-224 decay chain.

BACKGROUND: Recent developments in alpha and beta emitting radionuclide therapy highlight the importance of developing efficient methods for patient-specific dosimetry. Traditional tabulated methods such as Medical Internal Radiation Dose (MIRD) estimate the dose at the organ level while more recent numerical methods based on Monte Carlo (MC) simulations are able to calculate dose at the voxel level. A precalculated MC (PMC) approach was developed in this work as an alternative to time-consuming fully simulated MC. Once the spatial distribution of alpha and beta emitters is determined using imaging and/or numerical methods, the PMC code can be used to achieve an accurate voxelized 3D distribution of the deposited energy without relying on full MC calculations. PURPOSE: To implement the PMC method to calculate energy deposited by alpha and beta particles emitted from the Ra-224 decay chain. METHODS: The GEANT4 (version 10.7) MC toolkit was used to generate databases of precalculated tracks to be integrated in the PMC code as well as to benchmark its output. In this regard, energy spectra of alpha and beta particles emitted by the Ra-224 decay chain were generated using GAMOS (version 6.2.0) and imported into GEANT4 macro files. Either alpha or beta emitting sources were defined at the center of a homogeneous phantom filled with various materials such as soft tissue, bone, and lung where particles were emitted either mono-directionally (for database generation) or isotropically (for benchmarking). Two heterogeneous phantoms were used to demonstrate PMC code compatibility with boundary crossing events. Each precalculated database was generated step-by-step by storing particle track information from GEANT4 simulations followed by its integration in a PMC code developed in MATLAB. For a user-defined number of histories, one of the tracks in a given database was selected randomly and rotated randomly to reflect an isotropic emission. Afterward, deposited energy was divided between voxels based on step length in each voxel using a ray-tracing approach. The radial distribution of deposited energy was benchmarked against fully simulated MC calculations using GEANT4. The effect of the GEANT4 parameter StepMax on the accuracy and speed of the code was also investigated. RESULTS: In the case of alpha decay, primary alpha particles show the highest contribution (>99%) in deposited energy compared to their secondary particles. In most cases, protons act as the main secondary particles in the deposition of energy. However, for a lung phantom, using a range cutoff parameter of 10 µm on primary alpha particles yields a higher contribution of secondary electrons than protons. Differences between deposited energy calculated by PMC and fully simulated MC are within 2% for all alpha and beta emitters in homogeneous and heterogeneous phantoms. Additionally, statistical uncertainties are less than 1% for voxels with doses higher than 5% of the maximum dose. Moreover, optimization of the parameter StepMax is necessary to achieve the best tradeoff between code accuracy and speed. CONCLUSIONS: The PMC code shows good performance for dose calculations deposited by alpha and beta emitters. As a stand-alone algorithm, it is suitable to be integrated into clinical treatment planning systems.

Conditional-Based Transformer Network With Learnable Queries for 4D Deformation Forecasting and Tracking.

Real-time motion management for image-guided radiation therapy interventions plays an important role for accurate dose delivery. Forecasting future 4D deformations from in-plane image acquisitions is fundamental for accurate dose delivery and tumor targeting. However, anticipating visual representations is challenging and is not exempt from hurdles such as the prediction from limited dynamics, and the high-dimensionality inherent to complex deformations. Also, existing 3D tracking approaches typically need both template and search volumes as inputs, which are not available during real-time treatments. In this work, we propose an attention-based temporal prediction network where features extracted from input images are treated as tokens for the predictive task. Moreover, we employ a set of learnable queries, conditioned on prior knowledge, to predict future latent representation of deformations. Specifically, the conditioning scheme is based on estimated time-wise prior distributions computed from future images available during the training stage. Finally, we propose a new framework to address the problem of temporal 3D local tracking using cine 2D images as inputs, by employing latent vectors as gating variables to refine the motion fields over the tracked region. The tracker module is anchored on a 4D motion model, which provides both the latent vectors and the volumetric motion estimates to be refined. Our approach avoids auto-regression and leverages spatial transformations to generate the forecasted images. The tracking module reduces the error by 63% compared to a conditional-based transformer 4D motion model, yielding a mean error of 1.5± 1.1 mm. Furthermore, for the studied cohort of abdominal 4D MRI images, the proposed method is able to predict future deformations with a mean geometrical error of 1.2± 0.7 mm.

Feasibility of a multigroup Boltzmann-Fokker-Planck solution for electron beam dose calculations.

Legacy nuclear-reactor Boltzmann solvers start clinical deployment as an alternative to Monte Carlo (MC) codes and Fermi-Eyges semiemprical models in radiation oncology treatment planning. Today's certified clinical solvers are limited to photon beams. In this paper, ELECTR, a state-of-the-art multigroup electron cross sections generation module in NJOY is presented and validated against Lockwood's calorimetric measurements, EGS-nrc and GEANT-4 for 1-20 MeV unidirectional electron beams. The nuclear-reactor DRAGON-5 solver is upgraded to access the library and solve the Boltzmann-Fokker-Planck (BFP) equation. A variety of heterogeneous radiotherapy and radiosurgery phantom configurations were used for validation purpose. Case studies include a thorax benchmark, that of a typical breast Intra-Operative Radiotherapy and a high-heterogeneity patient-like benchmark. For all beams, [Formula: see text] of the water voxels satisfied the American Association of Physicists in Medicine accuracy criterion for a BFP-MC dose error below [Formula: see text]. At least, [Formula: see text] of adipose, muscle, bone, lung, tumor and breast voxels satisfied the [Formula: see text] criterion. The average BFP-MC relative error was about [Formula: see text] for all voxels, beams and materials combined. By irradiating homogeneous slabs from [Formula: see text] (hydrogen) to [Formula: see text] (einsteinium), we reported performance and defects of the CEPXS mode [US. Sandia National Lab., SAND-89-1685] in ELECTR for the entire periodic table. For all Lockwood's benchmarks, NJOY-DRAGON dose predictions are within the experimental data precision for [Formula: see text] of voxels.

The Montreal split ring applicator: Towards highly adaptive gynecological brachytherapy using 3D-printed biocompatible patient-specific interstitial caps.

Purpose: The addition of interstitial (IS) needles to intra-cavitary (IC) brachytherapy applicators is associated with improved outcomes in locally advanced cervical cancers involving parametrial tumor extensions. The purpose of this work was to validate a clinical workflow involving 3D-printed caps for a commercial IC split ring applicator that enable using IS needle trajectories tailored to each treatment. Material and methods: A dedicated software module was developed in this work allowing users to design patient-specific IS caps without knowledge of computer-aided design (CAD) software. This software module was integrated to 3D Brachy, a commercial software developed by Adaptiiv Medical Technologies Inc. For validation of the workflow, CAD models of ground truth caps with five IS needle trajectories were designed with Fusion 360™, 3D-printed, assembled with a split ring applicator, and CT-scanned with radio-opaque markers. 3D Brachy was then applied to generate a replica based on trajectories reconstructed from the radio-opaque markers. A comparison between ground truth and replicated IS needle trajectories was done using intersection points with planes at the level of the cervix (z = 0 cm) and a representative needle depth (z = 3 cm). Results: Prototypes of interstitial caps 3D-printed in both BioMed Amber and BioMed Clear SLA resins were tested to be functional both pre- and post-sterilization for IS needles with obliquity angles ≤ 45°. Distance-to-agreement at z = 0 cm and 3 cm as well as deviations in pitch and yaw angles of the five IS needle trajectories were found to have mean values of 3.3 ±2.1 mm, 7.3 ±2.0 mm, 2.9° ±2.3°, and 7.0° ±7.0°, respectively. Conclusions: The clinical workflow for image-guided adaptive cervical cancer brachytherapy using the Montreal split ring applicator was validated.

Image-guided Raman spectroscopy navigation system to improve transperineal prostate cancer detection. Part 2: in-vivo tumor-targeting using a classification model combining spectral and MRI-radiomics features.

SIGNIFICANCE: The diagnosis and treatment of prostate cancer (PCa) are limited by a lack of intraoperative information to accurately target tumors with needles for biopsy and brachytherapy. An innovative image-guidance technique using optical devices could improve the diagnostic yield of biopsy and efficacy of radiotherapy. AIM: To evaluate the performance of multimodal PCa detection using biomolecular features from in-situ Raman spectroscopy (RS) combined with image-based (radiomics) features from multiparametric magnetic resonance images (mpMRI). APPROACH: In a prospective pilot clinical study, 18 patients were recruited and underwent high-dose-rate brachytherapy. Multimodality image fusion (preoperative mpMRI with intraoperative transrectal ultrasound) combined with electromagnetic tracking was used to navigate an RS needle in the prostate prior to brachytherapy. This resulting dataset consisted of Raman spectra and co-located radiomics features from mpMRI. Feature selection was performed with the constraint that no more than 10 features were retained overall from a combination of inelastic scattering spectra and radiomics. These features were used to train support vector machine classifiers for PCa detection based on leave-one-patient-out cross-validation. RESULTS: RS along with biopsy samples were acquired from 47 sites along the insertion trajectory of the fiber-optics needle: 26 were confirmed as benign or grade group = 1, and 21 as grade group >1, according to histopathological reports. The combination of the fingerprint region of the RS and radiomics showed an accuracy of 83% (sensitivity = 81 % and a specificity = 85 % ), outperforming by more than 9% models trained with either spectroscopic or mpMRI data alone. An optimal number of features was identified between 6 and 8 features, which have good potential for discriminating grade group ≥1 / grade group <1 (accuracy = 87 % ) or grade group >1 / grade group ≤1 (accuracy = 91 % ). CONCLUSIONS: In-situ Raman spectroscopy combined with mpMRI radiomics features can lead to highly accurate PCa detection for improved in-vivo targeting of biopsy sample collection and radiotherapy seed placement.

Radiotherapy on-chip: microfluidics for translational radiation oncology.

The clinical importance of radiotherapy in the treatment of cancer patients justifies the development and use of research tools at the fundamental, pre-clinical, and ultimately clinical levels, to investigate their toxicities and synergies with systemic agents on relevant biological samples. Although microfluidics has prompted a paradigm shift in drug discovery in the past two decades, it appears to have yet to translate to radiotherapy research. However, the materials, dimensions, design versatility and multiplexing capabilities of microfluidic devices make them well-suited to a variety of studies involving radiation physics, radiobiology and radiotherapy. This review will present the state-of-the-art applications of microfluidics in these fields and specifically highlight the perspectives offered by radiotherapy on-a-chip in the field of translational radiobiology and precision medicine. This body of knowledge can serve both the microfluidics and radiotherapy communities by identifying potential collaboration avenues to improve patient care.

Performance of an integrated multimodality image guidance and dose-planning system supporting tumor-targeted HDR brachytherapy for prostate cancer.

BACKGROUND AND PURPOSE: Advances in high-dose-rate brachytherapy to treat prostate cancer hinge on improved accuracy in navigation and targeting while optimizing a streamlined workflow. Multimodal image registration and electromagnetic (EM) tracking are two technologies integrated into a prototype system in the early phase of clinical evaluation. We aim to report on the system's accuracy and workflow performance in support of tumor-targeted procedures. MATERIALS AND METHODS: In a prospective study, we evaluated the system in 43 consecutive procedures after clinical deployment. We measured workflow efficiency and EM catheter reconstruction accuracy. We also evaluated the system's MRI-TRUS registration accuracy with/without deformation, and with/without y-axis rotation for urethral alignment at initialization. RESULTS: The cohort included 32 focal brachytherapy and 11 integrated boost whole-gland implants. Mean procedure time excluding dose delivery was 38 min (range: 21-83) for focal, and 56 min (range: 38-89) for whole-gland implants; stable over time. EM catheter reconstructions achieved a mean difference between computed and measured free-length of 0.8 mm (SD 0.8, no corrections performed), and mean axial manual corrections 1.3 mm (SD 0.7). EM also enabled the clinical use of a non or partially visible catheter in 21% of procedures. Registration accuracy improved with y-axis rotation for urethral alignment at initialization and with the elastic registration (mTRE 3.42 mm, SD 1.49). CONCLUSION: The system supported tumor-targeting and was implemented with no demonstrable learning curve. EM reconstruction errors were small, correctable, and improved with calibration and control of external distortion sources; increasing confidence in the use of partially visible catheters. Image registration errors remained despite rotational alignment and deformation, and should be carefully considered.

Probabilistic 4D predictive model from in-room surrogates using conditional generative networks for image-guided radiotherapy.

Shape and location organ variability induced by respiration constitutes one of the main challenges during dose delivery in radiotherapy. Providing up-to-date volumetric information during treatment can improve tumor tracking, thereby increasing treatment efficiency and reducing damage to healthy tissue. We propose a novel probabilistic model to address the problem of volumetric estimation with scalable predictive horizon from image-based surrogates during radiotherapy treatments, thus enabling out-of-plane tracking of targets. This problem is formulated as a conditional learning task, where the predictive variables are the 2D surrogate images and a pre-operative static 3D volume. The model learns a distribution of realistic motion fields over a population dataset. Simultaneously, a seq-2-seq inspired temporal mechanism acts over the surrogate images yielding extrapolated-in-time representations. The phase-specific motion distributions are associated with the predicted temporal representations, allowing the recovery of dense organ deformation in multiple times. Due to its generative nature, this model enables uncertainty estimations by sampling the latent space multiple times. Furthermore, it can be readily personalized to a new subject via fine-tuning, and does not require inter-subject correspondences. The proposed model was evaluated on free-breathing 4D MRI and ultrasound datasets from 25 healthy volunteers, as well as on 11 cancer patients. A navigator-based data augmentation strategy was used during the slice reordering process to increase model robustness against inter-cycle variability. The patient data was used as a hold-out test set. Our approach yields volumetric prediction from image surrogates with a mean error of 1.67 ± 1.68 mm and 2.17 ± 0.82 mm in unseen cases of the patient MRI and US datasets, respectively. Moreover, model personalization yields a mean landmark error of 1.4 ± 1.1 mm compared to ground truth annotations in the volunteer MRI dataset, with statistically significant improvements over state-of-the-art.

Evaluation of the high affinity [18F]fluoropyridine-candesartan in rats for PET imaging of renal AT1 receptors.

INTRODUCTION: Alterations in the expression of the Angiotensin II type 1 receptors (AT1R) have been demonstrated in the development of several heart and renal diseases. The aim of this study was to evaluate the novel compound [18F]fluoropyridine-candesartan as a PET imaging tracer of AT1R in rat kidneys. METHODS: Competition binding assays were carried out with membranes from CHO-K1 cells expressing human AT1R. Binding to plasma proteins was assessed by ultrafiltration. Radiolabeled metabolites in rat plasma and kidneys of control and pretreated animals (candesartan 10 mg/kg or losartan 30 mg/kg) were analyzed by column-switch HPLC. Dynamic PET/CT images of [18F]fluoropyridine-candesartan in male Sprague-Dawley rats were acquired for 60 min at baseline, pre-treatment with the AT1R antagonist losartan (30 mg/kg) or the AT2R antagonist PD123,319 (5 mg/kg). RESULTS: Fluoropyridine-candesartan bound with a high affinity for AT1R (Ki = 5.9 ± 1.1 nM), comparable to fluoropyridine-losartan but lower than the parent compound candesartan (Ki = 0.4 ± 0.1 nM). [18F]Fluoropyridine-candesartan bound strongly to plasma proteins (99.3%) and was mainly metabolized to radiolabeled hydrophilic compounds, displaying minimal interference on renal AT1R binding with 82% of unchanged tracer in the kidneys at 20 min post-injection. PET imaging displayed high renal and liver accumulations and slow clearances, with maximum tissue-to-blood ratios of 14 ± 3 and 54 ± 12 in kidney cortex and liver, respectively, at 10 min post-injection. Binding specificity for AT1R was demonstrated with marked reductions in kidney cortex (-84%) and liver (-93%) tissue-to-blood ratios at 20 min post-injection, when blocking with AT1R antagonist losartan (30 mg/kg). No change was observed in kidney cortex of rats pre-treated with AT2R antagonist PD 123,319 (5 mg/kg), confirming binding selectivity for AT1 over AT2 receptors. CONCLUSION: High kidney-to-blood ratios and binding selectivity to renal AT1R combined with tracer in vivo stability displaying minimal interference from labeled metabolites support further PET imaging studies with [18F]fluoropyridine-candesartan.

X-ray on chip: Quantifying therapeutic synergies between radiotherapy and anticancer drugs using soft tissue sarcoma tumor spheroids.

PURPOSE: Radioresistance, tumor microenvironment, and normal tissue toxicity from radiation limit the efficacy of radiotherapy in treating cancers. These challenges can be tackled by the discovery of new radiosensitizing and radioprotecting agents aimed at increasing the therapeutic efficacy of radiotherapy. The goal of this work was to develop a miniaturized microfluidic platform for the discovery of drugs that could be used in combination with radiotherapy. The microfluidic system will allow the toxicity testing of cancer spheroids to different combinations of radiotherapy and molecular agents. MATERIALS AND METHODS: An orthovoltage-based technique was used to expose the devices to multiple X-ray radiation doses simultaneously. Radiation dose-dependent DNA double-strand breaks in soft tissue sarcoma (STS) spheroids were quantified using comet assays. Analysis of proliferative death using clonogenic assays was also performed, and synergy between treatments with Talazoparib, Pazopanib, AZD7762, and radiotherapy was quantified using dedicated statistical tests. RESULTS: The developed microfluidic system with simple magnetic valves was capable of growing 336 homogeneous STS spheroids. The irradiation of the microfluidic system with an orthovoltage-based technique enabled the screening of sixteen drug-radiotherapy combinations with minimal reagent consumption. Using this framework, we predicted a therapeutic synergy between a novel anticancer drug Talazoparib and radiotherapy for STS. No synergy was found between RT and either Pazopanib or AZD7762, as the combinations were found to be additive. CONCLUSION: This methodology lays the basis for the systemic search for molecular agent/radiotherapy synergies among preexisting pharmaceutical compounds libraries, in the hope to identify failed drug candidates in monotherapy that, in the presence of radiotherapy, would make it through clinical trials.

Synthesis of the Novel AT1 Receptor Tracer [18F]Fluoropyridine-Candesartan via Click Chemistry.

A novel 7-((4-(3-((2-[18F]fluoropyridin-3-yl)oxy)propyl)-1H-1,2,3-triazol-1-yl)methyl)-1H-benzo[d]imidazole derivative of the angiotensin II type-1 receptor (AT1R) blocker candesartan, [18F]fluoropyridine-candesartan, was synthesized via the copper-catalyzed azide-alkyne cycloaddition click reaction between 2-[18F]fluoro-3-(pent-4-yn-1-yloxy)pyridine ([18F]FPyKYNE) and the tetrazole-protected azido-candesartan derivative, followed by acid deprotection. This three-step, two-pot, and two-step purification synthesis was done within 2 h. The use of tris[(1-hydroxypropyl-1H-1,2,3-triazol-4-yl)methyl]amine (THPTA) as a Cu(I) stabilizing agent increased the overall radiochemical yield by 4-fold (10 ± 2%, n = 13) compared to the reaction without THPTA (2.4 ± 0.2%, n = 3; decay-corrected from 18F produced at the end-of-beam). Complete separation of [18F]FPyKYNE from its nitro precursor and [18F]fluoropyridine-candesartan from the deprotected azido-candesartan allowed for high molar activities (>380 GBq/µmol) of the tracer. The use of 0.1% trifluoroacetic acid in water for reformulation and the addition of sodium ascorbate to the final formulation (1.6 ± 0.2 GBq/mL, n = 3) prevented tracer radiolysis with >97% radiochemical purity for a period of up to 10 h after the end-of-synthesis. A significant reduction in the uptake (86 ± 3%, n = 8) of the tracer was observed ex vivo in rats (at 20 min postinjection) in the AT1R-rich kidney cortex following pretreatment with saturating doses of the AT1R antagonist candesartan or losartan. This specific binding to AT1R was confirmed in vitro in the rat renal cortex (autoradiography) by a reduction of 26 ± 5% (n = 12) with losartan coincubation (10 µM). These favorable binding properties support further studies to assess the potential of [18F]fluoropyridine-candesartan as a tracer for the positron emission tomography imaging of renal AT1R.

Prediction of in-plane organ deformation during free-breathing radiotherapy via discriminative spatial transformer networks.

External beam radiotherapy is a commonly used treatment option for patients with cancer in the thoracic and abdominal regions. However, respiratory motion constitutes a major limitation during the intervention. It may stray the pre-defined target and trajectories determined during planning from the actual anatomy. We propose a novel framework to predict the in-plane organ motion. We introduce a recurrent encoder-decoder architecture which leverages feature representations at multiple scales. It simultaneously learns to map dense deformations between consecutive images from a given input sequence and to extrapolate them through time. Subsequently, several cascade-arranged spatial transformers use the predicted deformation fields to generate a future image sequence. We propose the use of a composite loss function which minimizes the difference between ground-truth and predicted images while maintaining smooth deformations. Our model is trained end-to-end in an unsupervised manner, thus it does not require additional information beyond image data. Moreover, no pre-processing steps such as segmentation or registration are needed. We report results on 85 different cases (healthy subjects and patients) belonging to multiples datasets across different imaging modalities. Experiments were aimed at investigating the importance of the proposed multi-scale architecture design and the effect of increasing the number of predicted frames on the overall accuracy of the model. The proposed model was able to predict vessel positions in the next temporal image with a median accuracy of 0.45 (0.55) mm, 0.45 (0.74) mm and 0.28 (0.58) mm in MRI, US and CT datasets, respectively. The obtained results show the strong potential of the model by achieving accurate matching between the predicted and target images on several imaging modalities.

On-chip combined radiotherapy and chemotherapy testing on soft-tissue sarcoma spheroids to study cell death using flow cytometry and clonogenic assay.

Radiotherapy (RT) and chemotherapy (CT) are the major therapeutics to treat cancer patients. Conventional in vitro 2D models are insufficient to study the combined effects of RT and CT towards optimized dose selection or drug screening. Soft-tissue sarcomas (STS) are rare cancers with profound social impacts as they affect patients of all ages. We developed a microfluidic device to form and culture STS spheroids to study the combined cytotoxicities of RT and CT. Uniformly-sized spheroids of two different cell lines, STS 93 and STS 117, were formed in the device. RT doses of 0.5 Gy, 2 Gy, and 8 Gy were used in combination with CT, doxorubicin at 2 µM and 20 µM. The spheroids culture chambers within the device were arranged in a 3 × 5 matrix form. The device was made "peelable", which enabled us to collect spheroids from each treatment condition separately. Collected spheroids were dissociated into single cells and evaluated using flow cytometry and clonogenic assays. Through this workflow, we observed that STS 93 spheroids treated with doxorubicin die through apoptosis, whereas RT induced death through other pathways. Spheroids from the p53 mutant STS 117 cell line were more resistant to RT and doxorubicin. The developed device could be used for the discovery of new drugs and RT synergies.

Fusion of Intraoperative Transrectal Ultrasound Images with Post-implant Computed Tomography and Magnetic Resonance Imaging.

Purpose To compare the impact of the fusion of intraoperative transrectal ultrasound (TRUS) images with day 30 computed tomography (CT) and magnetic resonance imaging (MRI) on prostate volume and dosimetry. Methods and materials Seventy-five consecutive patients with CT and MRI obtained on day 30 with a Fast Spin Echo T2-weighted magnetic resonance (MR) sequence were analyzed. A rigid manual registration was performed between the intraoperative TRUS and day-30 CT based on the prostate volume. A second manual rigid registration was performed between the intraoperative TRUS and the day-30 MRI. The prostate contours were manually modified on CT and MRI. The difference in prostate volume and dosimetry between CT and MRI were compared. Results Prostate volume was on average 8% (standard deviation (SD) ± 16%) larger on intraoperative TRUS than on CT and 6% (18%) larger than on MRI. In 48% of the cases, the difference in volume on CT was > 10% compared to MRI. The difference in prostate volume between CT and MRI was inversely correlated to the difference in D90 (minimum dose that covers 90% of the prostate volume) between CT and MRI (r = -0.58, P < .001). A D90 < 90% was found in 5% (n = 4) on MRI and in 10% (n = 7) on CT (Fisher exact test one-sided P = .59), but in no patient was the D90 < 90% on both MRI and CT. Conclusions When fusing TRUS images with CT and MRI, the differences in prostate volume between those modalities remain clinically important in nearly half of the patients, and this has a direct influence on how implant quality is evaluated.

Robust quantitative contrast-enhanced dual-energy CT for radiotherapy applications.

PURPOSE: The purpose of this study was to develop and validate accurate methods for determining iodine content and virtual noncontrast maps of physical parameters, such as electron density, in the context of radiotherapy. METHODS: A simulation environment is developed to compare three methods allowing extracting iodine content and virtual noncontrast composition: (a) two-material decomposition, (b) three-material decomposition with the conservation of volume constraint, and (c) eigentissue decomposition. The simulation allows comparing the performance of the methods using iodine-based contrast agent contents in tissues from a reference dataset with variable density and elemental composition. The comparison is performed in two ways: (a) with a priori knowledge on the composition of the targeted tissue, and (b) without a priori knowledge on the base tissue. The three methods are tested with patient images scanned with dual-energy CT and iodine-based contrast agent. An experimental calibration adapted to the presence of iodine is performed by imaging tissue equivalent materials and diluted contrast agent solutions with known atomic composition. RESULTS: Results show that in the case of known a priori on the composition of the targeted tissue, the two-material decomposition is robust to variable densities and atomic compositions without biasing the results. In the absence of a priori knowledge on the target tissue composition, the eigentissue decomposition method yields minimal bias and higher robustness to variations. Results from the experimental calibration and the images of two patients show that the extracted quantities are accurate and the bias is negligible for both methods with respect to clinical applications in their respective scope of use. For the patient imaged with a contrast agent, virtual noncontrast electron densities are found in good agreement with values extracted from the scan without contrast agent. CONCLUSION: This study identifies two accurate methods to quantify iodine-based contrast agents and virtual noncontrast composition images with dual-energy CT. One is the two-material decomposition with a priori knowledge of the constituent components focused on organ-specific applications, such as kidney or lung function assessment. The other method is the eigentissue decomposition and is useful for general radiotherapy applications, such as treatment planning where accurate dose calculations are needed in the absence of contrast agent.

Dosimetric impact of dual-energy CT tissue segmentation for low-energy prostate brachytherapy: a Monte Carlo study.

The purpose of this study is to evaluate the impact of a novel tissue characterization method using dual-energy over single-energy computed tomography (DECT and SECT) on Monte Carlo (MC) dose calculations for low-dose rate (LDR) prostate brachytherapy performed in a patient like geometry. A virtual patient geometry is created using contours from a real patient pelvis CT scan, where known elemental compositions and varying densities are overwritten in each voxel. A second phantom is made with additional calcifications. Both phantoms are the ground truth with which all results are compared. Simulated CT images are generated from them using attenuation coefficients taken from the XCOM database with a 100 kVp spectrum for SECT and 80 and 140Sn kVp for DECT. Tissue segmentation for Monte Carlo dose calculation is made using a stoichiometric calibration method for the simulated SECT images. For the DECT images, Bayesian eigentissue decomposition is used. A LDR prostate brachytherapy plan is defined with 125I sources and then calculated using the EGSnrc user-code Brachydose for each case. Dose distributions and dose-volume histograms (DVH) are compared to ground truth to assess the accuracy of tissue segmentation. For noiseless images, DECT-based tissue segmentation outperforms the SECT procedure with a root mean square error (RMS) on relative errors on dose distributions respectively of 2.39% versus 7.77%, and provides DVHs closest to the reference DVHs for all tissues. For a medium level of CT noise, Bayesian eigentissue decomposition still performs better on the overall dose calculation as the RMS error is found to be of 7.83% compared to 9.15% for SECT. Both methods give a similar DVH for the prostate while the DECT segmentation remains more accurate for organs at risk and in presence of calcifications, with less than 5% of RMS errors within the calcifications versus up to 154% for SECT. In a patient-like geometry, DECT-based tissue segmentation provides dose distributions with the highest accuracy and the least bias compared to SECT. When imaging noise is considered, benefits of DECT are noticeable if important calcifications are found within the prostate.