Deep Q-learning to globally optimize a k-D parameter search for medical imaging.

Background: Estimation of the global optima of multiple model parameters is valuable for precisely extracting parameters that characterize a physical environment. This is especially useful for imaging purposes, to form reliable, meaningful physical images with good reproducibility. However, it is challenging to avoid different local minima when the objective function is nonconvex. The problem of global searching of multiple parameters was formulated to be a k-D move in the parameter space and the parameter updating scheme was converted to be a state-action decision-making problem. Methods: We proposed a novel Deep Q-learning of Model Parameters (DQMP) method for global optimization which updated the parameter configurations through actions that maximized the Q-value and employed a Deep Reward Network (DRN) designed to learn global reward values from both visible fitting errors and hidden parameter errors. The DRN was constructed with Long Short-Term Memory (LSTM) layers followed by fully connected layers and a rectified linear unit (ReLU) nonlinearity. The depth of the DRN depended on the number of parameters. Through DQMP, the k-D parameter search in each step resembled the decision-making of action selections from 3k configurations in a k-D board game. Results: The DQMP method was evaluated by widely used general functions that can express a variety of experimental data and further validated on imaging applications. The convergence of the proposed DRN was evaluated, which showed that the loss values of six general functions all converged after 12 epochs. The parameters estimated by the DQMP method had relative errors of less than 4% for all cases, whereas the relative errors achieved by Q-learning (QL) and the Least Squares Method (LSM) were 17% and 21%, respectively. Furthermore, the imaging experiments demonstrated that the imaging of the parameters estimated by the proposed DQMP method were the closest to the ground truth simulation images when compared to other methods. Conclusions: The proposed DQMP method was able to achieve global optima, thus yielding accurate model parameter estimates. DQMP is promising for estimating multiple high-dimensional parameters and can be generalized to global optimization for many other complex nonconvex functions and imaging of physical parameters.

Enhanced cardiac substructure sparing through knowledge-based treatment planning for non-small cell lung cancer radiotherapy.

Radiotherapy (RT) doses to cardiac substructures from the definitive treatment of locally advanced non-small cell lung cancers (NSCLC) have been linked to post-RT cardiac toxicities. With modern treatment delivery techniques, it is possible to focus radiation doses to the planning target volume while reducing cardiac substructure doses. However, it is often challenging to design such treatment plans due to complex tradeoffs involving numerous cardiac substructures. Here, we built a cardiac-substructure-based knowledge-based planning (CS-KBP) model and retrospectively evaluated its performance against a cardiac-based KBP (C-KBP) model and manually optimized patient treatment plans. CS-KBP/C-KBP models were built with 27 previously-treated plans that preferentially spare the heart. While the C-KBP training plans were created with whole heart structures, the CS-KBP model training plans each have 15 cardiac substructures (coronary arteries, valves, great vessels, and chambers of the heart). CS-KBP training plans reflect cardiac-substructure sparing preferences. We evaluated both models on 28 additional patients. Three sets of treatment plans were compared: (1) manually optimized, (2) C-KBP model-generated, and (3) CS-KBP model-generated. Plans were normalized to receive the prescribed dose to at least 95% of the PTV. A two-tailed paired-sample t-test was performed for clinically relevant dose-volume metrics to evaluate the performance of the CS-KBP model against the C-KBP model and clinical plans, respectively. Overall results show significantly improved cardiac substructure sparing by CS-KBP in comparison to C-KBP and the clinical plans. For instance, the average left anterior descending artery volume receiving 15 Gy (V15 Gy) was significantly lower (p < 0.01) for CS-KBP (0.69 ± 1.57 cc) compared to the clinical plans (1.23 ± 1.76 cc) and C-KBP plans (1.05 ± 1.68 cc). In conclusion, the CS-KBP model significantly improved cardiac-substructure sparing without exceeding the tolerances of other OARs or compromising PTV coverage.

Mutual enhancing learning-based automatic segmentation of CT cardiac substructure.

Objective.Current segmentation practice for thoracic cancer RT considers the whole heart as a single organ despite increased risks of cardiac toxicities from irradiation of specific cardiac substructures. Segmenting up to 15 different cardiac substructures can be a very time-intensive process, especially due to their different volume sizes and anatomical variations amongst different patients. In this work, a new deep learning (DL)-based mutual enhancing strategy is introduced for accurate and automatic segmentation, especially of smaller substructures such as coronary arteries.Approach.Our proposed method consists of three subnetworks: retina U-net, classification module, and segmentation module. Retina U-net is used as a backbone network architecture that aims to learn deep features from the whole heart. Whole heart feature maps from retina U-net are then transferred to four different sets of classification modules to generate classification localization maps of coronary arteries, great vessels, chambers of the heart, and valves of the heart. Each classification module is in sync with its corresponding subsequent segmentation module in a bootstrapping manner, allowing them to share their encoding paths to generate a mutual enhancing strategy. We evaluated our method on three different datasets: institutional CT datasets (55 subjects) 2) publicly available Multi-Modality Whole Heart Segmentation (MM-WHS) challenge datasets (120 subjects), and Automated Cardiac Diagnosis Challenge (ACDC) datasets (100 subjects). For institutional datasets, we performed five-fold cross-validation on training data (45 subjects) and performed inference on separate hold-out data (10 subjects). For each subject, 15 cardiac substructures were manually contoured by a resident physician and evaluated by an attending radiation oncologist. For the MM-WHS dataset, we trained the network on 100 datasets and performed an inference on a separate hold-out dataset with 20 subjects, each with 7 cardiac substructures. For ACDC datasets, we performed five-fold cross-validation on 100 datasets, each with 3 cardiac substructures. We compared the proposed method against four different network architectures: 3D U-net, mask R-CNN, mask scoring R-CNN, and proposed network without classification module. Segmentation accuracies were statistically compared through dice similarity coefficient, Jaccard, 95% Hausdorff distance, mean surface distance, root mean square distance, center of mass distance, and volume difference.Main results.The proposed method generated cardiac substructure segmentations with significantly higher accuracy (P < 0.05) for small substructures, especially for coronary arteries such as left anterior descending artery (CA-LADA) and right coronary artery (CA-RCA) in comparison to four competing methods. For large substructures (i.e. chambers of the heart), our method yielded comparable results to mask scoring R-CNN method, resulting in significantly (P < 0.05) improved segmentation accuracy in comparison to 3D U-net and mask R-CNN.Significance.A new DL-based mutual enhancing strategy was introduced for automatic segmentation of cardiac substructures. Overall results of this work demonstrate the ability of the proposed method to improve segmentation accuracies of smaller substructures such as coronary arteries without largely compromising the segmentation accuracies of larger substructures. Fast and accurate segmentations of up to 15 substructures can possibly be used as a tool to rapidly generate substructure segmentations followed by physicians' reviews to improve clinical workflow.

Cascaded mutual enhancing networks for brain tumor subregion segmentation in multiparametric MRI.

Accurate segmentation of glioma and its subregions plays an important role in radiotherapy treatment planning. Due to a very populated multiparameter magnetic resonance imaging image, manual segmentation tasks can be very time-consuming, meticulous, and prone to subjective errors. Here, we propose a novel deep learning framework based on mutual enhancing networks to automatically segment brain tumor subregions. The proposed framework is suitable for the segmentation of brain tumor subregions owing to the contribution of Retina U-Net followed by the implementation of a mutual enhancing strategy between the classification localization map (CLM) module and segmentation module. Retina U-Net is trained to accurately identify view-of-interest and feature maps of the whole tumor (WT), which are then transferred to the CLM module and segmentation module. Subsequently, CLM generated by the CLM module is integrated with the segmentation module to bring forth a mutual enhancing strategy. In this way, our proposed framework first focuses on WT through Retina U-Net, and since WT consists of subregions, a mutual enhancing strategy then further aims to classify and segment subregions embedded within WT. We implemented and evaluated our proposed framework on the BraTS 2020 dataset consisting of 369 cases. We performed a 5-fold cross-validation on 200 datasets and a hold-out test on the remaining 169 cases. To demonstrate the effectiveness of our network design, we compared our method against the networks without Retina U-Net, mutual enhancing strategy, and a recently published Cascaded U-Net architecture. Results of all four methods were compared to the ground truth for segmentation and localization accuracies. Our method yielded significantly (P < 0.01) better values of dice-similarity-coefficient, center-of-mass-distance, and volume difference compared to all three competing methods across all tumor labels (necrosis and non-enhancing, edema, enhancing tumor, WT, tumor core) on both validation and hold-out dataset. Overall quantitative and statistical results of this work demonstrate the ability of our method to both accurately and automatically segment brain tumor subregions.

Lung tumor segmentation in 4D CT images using motion convolutional neural networks.

PURPOSE: Manual delineation on all breathing phases of lung cancer 4D CT image datasets can be challenging, exhaustive, and prone to subjective errors because of both the large number of images in the datasets and variations in the spatial location of tumors secondary to respiratory motion. The purpose of this work is to present a new deep learning-based framework for fast and accurate segmentation of lung tumors on 4D CT image sets. METHODS: The proposed DL framework leverages motion region convolutional neural network (R-CNN). Through integration of global and local motion estimation network architectures, the network can learn both major and minor changes caused by tumor motion. Our network design first extracts tumor motion information by feeding 4D CT images with consecutive phases into an integrated backbone network architecture, locating volume-of-interest (VOIs) via a regional proposal network and removing irrelevant information via a regional convolutional neural network. Extracted motion information is then advanced into the subsequent global and local motion head network architecture to predict corresponding deformation vector fields (DVFs) and further adjust tumor VOIs. Binary masks of tumors are then segmented within adjusted VOIs via a mask head. A self-attention strategy is incorporated in the mask head network to remove any noisy features that might impact segmentation performance. We performed two sets of experiments. In the first experiment, a five-fold cross-validation on 20 4D CT datasets, each consisting of 10 breathing phases (i.e., 200 3D image volumes in total). The network performance was also evaluated on an additional unseen 200 3D images volumes from 20 hold-out 4D CT datasets. In the second experiment, we trained another model with 40 patients' 4D CT datasets from experiment 1 and evaluated on additional unseen nine patients' 4D CT datasets. The Dice similarity coefficient (DSC), center of mass distance (CMD), 95th percentile Hausdorff distance (HD95 ), mean surface distance (MSD), and volume difference (VD) between the manual and segmented tumor contour were computed to evaluate tumor detection and segmentation accuracy. The performance of our method was quantitatively evaluated against four different methods (VoxelMorph, U-Net, network without global and local networks, and network without attention gate strategy) across all evaluation metrics through a paired t-test. RESULTS: The proposed fully automated DL method yielded good overall agreement with the ground truth for contoured tumor volume and segmentation accuracy. Our model yielded significantly better values of evaluation metrics (p < 0.05) than all four competing methods in both experiments. On hold-out datasets of experiment 1 and 2, our method yielded DSC of 0.86 and 0.90 compared to 0.82 and 0.87, 0.75 and 0.83, 081 and 0.89, and 0.81 and 0.89 yielded by VoxelMorph, U-Net, network without global and local networks, and networks without attention gate strategy. Tumor VD between ground truth and our method was the smallest with the value of 0.50 compared to 0.99, 1.01, 0.92, and 0.93 for between ground truth and VoxelMorph, U-Net, network without global and local networks, and networks without attention gate strategy, respectively. CONCLUSIONS: Our proposed DL framework of tumor segmentation on lung cancer 4D CT datasets demonstrates a significant promise for fully automated delineation. The promising results of this work provide impetus for its integration into the 4D CT treatment planning workflow to improve the accuracy and efficiency of lung radiotherapy.

Knowledge-based radiation treatment planning: A data-driven method survey.

This paper surveys the data-driven dose prediction methods investigated for knowledge-based planning (KBP) in the last decade. These methods were classified into two major categories-traditional KBP methods and deep-learning (DL) methods-according to their techniques of utilizing previous knowledge. Traditional KBP methods include studies that require geometric or anatomical features to either find the best-matched case(s) from a repository of prior treatment plans or to build dose prediction models. DL methods include studies that train neural networks to make dose predictions. A comprehensive review of each category is presented, highlighting key features, methods, and their advancements over the years. We separated the cited works according to the framework and cancer site in each category. Finally, we briefly discuss the performance of both traditional KBP methods and DL methods, then discuss future trends of both data-driven KBP methods to dose prediction.

Fully automated segmentation of brain tumor from multiparametric MRI using 3D context deep supervised U-Net.

PURPOSE: Owing to histologic complexities of brain tumors, its diagnosis requires the use of multimodalities to obtain valuable structural information so that brain tumor subregions can be properly delineated. In current clinical workflow, physicians typically perform slice-by-slice delineation of brain tumor subregions, which is a time-consuming process and also more susceptible to intra- and inter-rater variabilities possibly leading to misclassification. To deal with this issue, this study aims to develop an automatic segmentation of brain tumor in MR images using deep learning. METHOD: In this study, we develop a context deep-supervised U-Net to segment brain tumor subregions. A context block which aggregates multiscale contextual information for dense segmentation was proposed. This approach enlarges the effective receptive field of convolutional neural networks, which, in turn, improves the segmentation accuracy of brain tumor subregions. We performed the fivefold cross-validation on the Brain Tumor Segmentation Challenge (BraTS) 2020 training dataset. The BraTS 2020 testing datasets were obtained via BraTS online website as a hold-out test. For BraTS, the evaluation system divides the tumor into three regions: whole tumor (WT), tumor core (TC), and enhancing tumor (ET). The performance of our proposed method was compared against two state-of-the-arts CNN networks in terms of segmentation accuracy via Dice similarity coefficient (DSC) and Hausdorff distance (HD). The tumor volumes generated by our proposed method were compared with manually contoured volumes via Bland-Altman plots and Pearson analysis. RESULTS: The proposed method achieved the segmentation results with a DSC of 0.923 ± 0.047, 0.893 ± 0.176, and 0.846 ± 0.165 and a 95% HD95 of 3.946 ± 7.041, 3.981 ± 6.670, and 10.128 ± 51.136 mm on WT, TC, and ET, respectively. Experimental results demonstrate that our method achieved comparable to significantly (p < 0.05) better segmentation accuracies than other two state-of-the-arts CNN networks. Pearson correlation analysis showed a high positive correlation between the tumor volumes generated by proposed method and manual contour. CONCLUSION: Overall qualitative and quantitative results of this work demonstrate the potential of translating proposed technique into clinical practice for segmenting brain tumor subregions, and further facilitate brain tumor radiotherapy workflow.

Learning-based dose prediction for pancreatic stereotactic body radiation therapy using dual pyramid adversarial network.

Treatment planning for pancreatic cancer stereotactic body radiation therapy (SBRT) is very challenging owing to vast spatial variations and close proximity of many organs-at-risk. Recently, deep learning (DL) based methods have been applied in dose prediction tasks of various treatment sites with the aim of relieving planning challenges. However, its effectiveness on pancreatic cancer SBRT is yet to be fully explored due to limited investigations in the literature. This study aims to further current knowledge in DL-based dose prediction tasks by implementing and demonstrating the feasibility of a new dual pyramid networks (DPNs) integrated DL model for predicting dose distributions of pancreatic SBRT. The proposed framework is composed of four parts: CT-only feature pyramid network (FPN), contour-only FPN, late fusion network and an adversarial network. During each phase of the network, combination of mean absolute error, gradient difference error, histogram matching, and adversarial loss is used for supervision. The performance of proposed model was demonstrated for pancreatic cancer SBRT plans with doses prescribed between 33 and 50 Gy across as many as three planning target volumes (PTVs) in five fractions. Five-fold cross validation was performed on 30 patients, and another 20 patients were used as holdout tests of trained model. Predicted plans were compared with clinically approved plans through dose volume parameters and two-paired t-test. For the same sets, our results were compared with three different DL architectures: 3D U-Net, 3D U-Net with adversarial learning, and DPN without adversarial learning. The proposed framework was able to predict 87% and 91% of clinically relevant dose parameters for cross validation sets and holdout sets, respectively, without any significant differences (P > 0.05). Dose distribution predicted by our framework was also able to predict the intentional hotspots as feature characteristics of SBRT plans. Our method achieved higher correlation coefficients with the ground truth in 22/26, 24/26 and 20/26 dose volume parameters compared to the network without adversarial learning, 3D U-Net, and 3D U-Net with adversarial learning, respectively. Overall, the proposed model was able to predict doses to cases with both single and multiple PTVs. In conclusion, the DPN integrated DL model was successfully implemented, and demonstrated good dose prediction accuracy and dosimetric characteristics for pancreatic cancer SBRT.

Photon beam energy dependent single-arc volumetric modulated arc optimization.

PURPOSE: The purpose of this work was to present a new single-arc mixed photon (6&18MV) VMAT (SAMP) optimization framework that concurrently optimizes for two photon energies with corresponding partial arc lengths. METHODS AND MATERIALS: Owing to simultaneous optimization of energy dependent intensity maps and corresponding arc locations, the proposed model poses nonlinearity. Unique relaxation constraints based on McCormick approximations were introduced for linearization. Energy dependent intensity maps were then decomposed to generate apertures. Feasibility of the proposed framework was tested on a sample of ten prostate cancer cases with lateral separation ranging from 34 cm (case no.1) to 52 cm (case no.6). The SAMP plans were compared against single energy (6MV) VMAT (SE) plans through dose volume histograms (DVHs) and radiobiological parameters including normal tissue complication probability (NTCP) and equivalent uniform dose (EUD). RESULTS: The contribution of higher energy photon beam optimized by the algorithm demonstrated an increase for cases with a lateral separation >40 cm. SAMP-VMAT notably improved bladder and rectum sparing in large size cases. Compared to single energy, SAMP-VMAT plans reduced bladder and rectum NTCP in cases with large lateral separation. With the exception of one case, SAMP-VMAT either improved or maintained femoral heads compared to SE-VMAT. SAMP-VMAT reduced the nontarget tissue integral dose in all ten cases. CONCLUSIONS: A single-arc VMAT optimization framework comprising mixed photon energy partial arcs was presented. Overall results underline the feasibility and potential of the proposed approach for improving OAR sparing in large size patients without compromising the target homogeneity and coverage.

Simultaneous optimization of mixed photon energy beams in volumetric modulated arc therapy.

PURPOSE: Despite the availability of multiple energy photon beams on clinical linear accelerators, volumetric modulated arc therapy (VMAT) optimization is currently limited to a single photon beam. The purpose of this work was to present a proof-of-principle study on an algorithm for simultaneous optimization of mixed photon beams for VMAT (MP - VMAT), utilizing an additional photon energy as an additional degree of freedom. METHODS: The MP - VMAT optimization algorithm is presented as a two-step heuristic approach. First, a convex linear programming problem is solved for simultaneous optimization of nonuniform dual energy intensity maps (DEIMs) for an angular resolution of 36 equi-spaced beam segments. Subsequently, for a given gantry speed schedule, the second step aims to best replicate each DEIM by dispersing MP - VMAT apertures along with their corresponding intensities over their respective beam segment. This constitutes a nonlinear problem, which is linearized using McCormick relaxation. The final large-scale mixed integer linear programming (MILP) dispersion model ensures a contiguous and smooth transition of multileaf collimators (MLCs) from one beam segment to the next. To demonstrate the proof-of-principle, we first compared the quality of dose volume histograms (DVHs) of MP - VMAT to the ones calculated from 36 DEIMs following the step 1 of MP - VMAT model. Additionally, the MLCs motion violations were evaluated for the complete 360° gantry rotation for gantry speeds ranging from 1 to 6° per second. The quality of MP - VMAT plans were also compared to conventional single energy VMAT plans via DVH, homogeneity index (HI), and conformity number (CN) for two prostate cases. RESULTS: The MP - VMAT model resulted in a successful convergence of DVHs relative to the ones from DEIMs with HI and CN of 0.05 and 0.9, respectively, for 1 and 2° per second gantry speed schedules. In replicating the DEIMs, the MILP dispersion model was able to achieve optimality for almost all segments at 1° per second and for majority of segments at 2° per second. Although, DVHs quality was slightly inferior for 3° per second gantry speed, the target conformity of 0.9 and heterogeneity of 0.08 were achievable even for the suboptimal solutions. No violations of the MLC constraints were observed throughout the complete 360 degree arc rotation for any gantry speed schedule, thereby confirming MILP dispersion model. For the two prostate cases, the results showed MP - VMAT's ability to achieve substantial dose reduction in rectum and bladder while yielding similar target coverage compared to single energy VMAT. Bladder volume was mostly spared in low-to-intermediate dose region. Rectal volume sparing (3 % to 12 %) was observed in the intermediate (from 25 to 50 Gy) dose region. CONCLUSION: We demonstrate the first formalism of a large-scale simultaneous optimization of mixed photon energy beams for VMAT. Dosimetric comparison of MP - VMAT to single energy VMAT demonstrated potential advantages of using mixed photon energy beams for prostate plans, thus providing an impetus for further testing on a large clinical cohort.

Characterizing a Geant4 Monte Carlo model of a multileaf collimator for a TrueBeam™ linear accelerator.

PURPOSE: The purpose of this work was to develop and validate a multileaf collimator (MLC) model for a TrueBeam™ linac using Geant4 Monte Carlo (MC) simulation kit. METHODS: A Geant4 application was developed to accurately represent TrueBeam™ linac. Pre-computed phase-space file in a plane just above the jaws was used for radiation transport. A Varian 120 leaf Millennium™ MLC was modeled using geometry and material specifications provided by the manufacturer using Geant4 constructs. Leaf characteristics e.g. tongue-groove design, variable thickness, interleaf gap were simulated. The linac model was validated by comparing simulated dose profiles and depth-doses with experimental data using an ionization chamber in water. Dosimetric characteristics of the MLC such as inter- and intra-leaf leakage, penumbra effect, MLC leaf positioning, and dynamic characteristics were also investigated. RESULTS: For the depth dose curves, 99% of the calculated data points agree within 1% of the experimental values for the 4 × 4 cm2 and 10 × 10 cm2 and within 2% of the experimental values for 20 × 20, 30 × 30 and 40 × 40 cm2 jaw defined fields. The cross-plane dose profiles show agreement <2% for depths up to 10 cm and to within 4% beyond 10 cm. MLC dosimetric characterization with MC agree well with film measurements. The rounded leaf penumbra remained constant throughout the range of leaf motion. CONCLUSIONS: The TrueBeam™ linac equipped with 120-leaf MLC was successfully modeled using Geant4. The accuracy of the model was verified by comparing the simulations with experiments. The model may be utilized for independent dose verification and QA of IMRT.

Evaluation of mixed energy partial arcs for volumetric modulated arc therapy for prostate cancer.

PURPOSE: The purpose of this work was to investigate the dosimetric impact of mixed energy (6-MV, 15-MV) partial arcs (MEPAs) technique on prostate cancer VMAT plans. METHODS: This work involved prostate only patients, planned with 79.2 Gy in 44 fractions to the planning target volume (PTV). Femoral heads, bladder, and rectum were considered organs at risk. This study was performed in two parts. For each of the 25 patients in Part 1, two single-energy single-arc plans, a 6 MV-SA plan and a 15 MV-SA plan, and a third MEPA plan involving composite of 6-MV anterior-posterior partial arcs and a 15-MV lateral partial arc weighted 1:2 were created. The dosimetric difference between MEPA(6/15 MV 1:2 weighted) and 6 MV-SA plans, and MEPA(6/15 MV 1:2 weighted) and 15 MV-SA plans were measured. In the Part 2 of this study, a second MEPAs plan (6 MV anterior-posterior arcs and 15 MV lateral arcs weighted 1:1), (MEPA 6/15 MV 1:1 weighted), was generated for 15 patients and compared only with two single-energy partial arcs plans, a 6 and a 15 MV-PA, to investigate the influence of the energy only. Dosimetric parameters of each structure, total monitor-units (MUs), homogeneity index (HI), and conformity number (CN) were analyzed. RESULTS: In Part 1, no statistically significant differences were observed for mean dose to PTV and CN for MEPAs (6/15 MV 1:2 weighted) vs 6 and 15 MV-SA. MEPAs (6/15 MV 1:2 weighted) increased HI compared to 6 and 15 MV-SA (P < 0.0005; P < 0.0005). MEPAs (6/15 MV 1:2 weighted) produced significantly lower mean doses to rectum, bladder, and MUs/fraction, but higher mean doses to femoral heads, compared to 6 MV-SA (P < 0.0005) and 15 MV-SA (P < 0.0005). The results of Part 2 of this study showed that, in comparison to 6 and 15 MV-PA, MEPAs (6/15 MV 1:1 weighted) plans significantly improved CNs (P < 0.0005; P < 0.0005) and produced significantly lower mean doses to the rectum and bladder (P < 0.0005; P < 0.0005). While mean doses to the PTV and femoral heads of MEPAs (6/15 MV 1:1 weighted) plans were statistically comparable to 6 MV-PA (P > 0.05), MEPAs (6/15 MV 1:1 weighted) increased mean doses to left (P = 0.04) and right (P = 0.04) femoral heads compared to 15 MV-PA. MEPAs (6/15 MV 1:1 weighted) resulted in significantly lower total MUs compared to 6 MV-PA (P < 0.0005) and 15 MV-PA (P = 0.04). CONCLUSION: The study for prostate radiotherapy demonstrated that a choice of MEPAs for VMAT has the potential to minimize doses to OARs and improve dose conformity to PTV, at the expense of a moderate increase in mean dose to the femoral heads.

Analysis of the cytotoxic effects of combined ultrasound, microbubble and nucleoside analog combinations on pancreatic cells in vitro.

Ultrasonically-stimulated microbubbles enhance the therapeutic effects of various chemotherapy drugs. However, the application of ultrasound and microbubbles (USMB) for enhancing the therapeutic effect of nucleoside analogs, which are used as front-line treatments in a range of cancers, and its underlying mechanism is not well understood. This study investigated the effect of gemcitabine, a nucleoside analog drug, in combination with USMB in increasing cell cytotoxicity relative to either treatment alone in BxPC3 pancreatic cancer cells. Cells were sonicated using low frequency (0.5 MHz) ultrasound in combination with Definity® microbubbles (1.7% v/v) in the presence of 1 µM of gemcitabine for a total of 2 h. USMB in combination with gemcitabine decreased cell viability (48 h) to 44.7 ±â€¯5.2%, 27.7 ±â€¯4.3%, and 12.5 ±â€¯3.4% with increasing ultrasound peak negative pressures (220, 360, 530 kPa) from 84.7 ±â€¯3.6%, 54.2 ±â€¯3.8%, and 26.8 ±â€¯3.0%, respectively, when USMB was applied in the absence of drug. We further confirmed that USMB did not enhance the internalization of 1 µM of a radiolabeled nucleoside analog (2-chloroadenosine) at each of the three chosen ultrasound PNPs, determined by radiolabeled scintillation counting. These data suggest that USMB in combination with nucleoside analog drugs leads to an additive effect on cell toxicity and that USMB does not impair transporter-mediated uptake of nucleoside analogs.