Publications by authors named "Xun Jia"

148 Publications

Improving robustness of a deep learning-based lung-nodule classification model of CT images with respect to image noise.

Phys Med Biol 2021 Nov 24. Epub 2021 Nov 24.

Department of Radiation Oncology, UT Southwestern Medical Center, 6363 Forest Park Rd. BL10.202G, MC9315, Dallas, Texas, 75390-9315, UNITED STATES.

Objective: Robustness is an important aspect to consider, when developing methods for medical image analysis. This study investigated robustness properties of deep neural networks (DNNs) for a lung nodule classification problem based on CT images and proposed a solution to improve robustness.

Approach: We firstly constructed a class of four DNNs with different widths, each predicting an output label (benign or malignant) for an input CT image cube containing a lung nodule. These networks were trained to achieve Area Under the Curve of 0.891-0.914 on a testing dataset. We then added to the input CT image cubes noise signals generated randomly using a realistic CT image noise model based on a noise power spectrum at 100 mAs, and monitored the DNN's output change. We defined $SAR_{5} (\%)$ to quantify the robustness of the trained DNN model, indicating that for $5\%$ of CT image cubes, the noise can change the prediction results with a chance of at least $SAR_{5} (\%)$. To understand robustness, we viewed the information processing pipeline by the DNN as a two-step process, with the first step using all but the last layers to extract representations of the input CT image cubes in a latent space, and the second step employing the last fully-connected layer as a linear classifier to determine the position of the sample representations relative to a decision plane. To improve robustness, we proposed to retrain the last layer of the DNN with a Supporting Vector Machine (SVM) hinge loss function to enforce the desired position of the decision plane.

Main Results: $SAR_{5}$ ranged in $47.0\sim 62.0\%$ in different DNNs. The unrobustness behavior may be ascribed to the unfavorable placement of the decision plane in the latent representation space, which made some samples be perturbed to across the decision plane and hence susceptible to noise. The DNN-SVM model improved robustness over the DNN model and reduced $SAR_{5}$ by $8.8\sim 21.0\%$.

Significance: This study provided insights about the potential reason for the unrobustness behavior of DNNs and the proposed DNN-SVM model improved model robustness.
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http://dx.doi.org/10.1088/1361-6560/ac3d16DOI Listing
November 2021

Stereotactic Ablative Radiotherapy for High-Risk Prostate Cancer-a Prospective Multi-level MRI-based Dose Escalation Trial.

Int J Radiat Oncol Biol Phys 2021 Nov 10. Epub 2021 Nov 10.

Department of Radiation Oncology; Department of Neurosurgery Simmons Comprehensive Cancer Center, University of Texas at Southwestern Medical Center, Dallas, Texas.

Purpose: Radiation dose intensification improves outcome in men with high-risk prostate cancer (HR-PCa). A prospective trial was conducted to determine safety, feasibility, and maximal tolerated dose (MTD) of multi-level MRI-based 5-fraction stereotactic radiation (SAbR) in patients with HR-PCa.

Methods And Materials: This phase I clinical trial enrolled HR-PCa patients with grade group ≥4, PSA ≥ 20ng/ml, or radiographic ≥T3, and well-defined prostatic lesions on multi-parametric MRI (mpMRI) into 4 dose-escalation cohorts. The initial cohort received 47.5Gy to the prostate, 50Gy to mpMRI-defined intra-prostatic lesion(s), and 22.5Gy to pelvic lymph nodes in 5 fractions. Radiation doses were escalated for pelvic nodes to 25Gy and mpMRI lesion(s) to 52.5Gy and then 55Gy. Escalation was performed sequentially according to rule-based trial design with 7-15 patients per cohort and a 90-day observation period. All men received peri-rectal hydrogel spacer, intra-prostatic fiducial placement, and 2 years of androgen deprivation. The primary endpoint was MTD according to a 90-day acute dose-limiting toxicity (DLT) rate <33%. DLT was defined as NCI Common Toxicity Criteria for Adverse Events (CTCAE) ≥ grade 3 treatment-related toxicity. Secondary outcomes include acute and delayed gastrointestinal (GI)/genitourinary (GU) toxicity graded with CTCAE.

Results: Fifty-five of the 62 enrolled patients were included in the analysis. Dose was escalated through all 4 cohorts without observing any DLTs. Median overall follow-up was 18 months, with a median follow-up of 42, 24, 12, and 7.5 months for cohorts 1-4 respectively. Acute and late grade 2 GU toxicities were 25% and 20%, while GI were 13% and 7%, respectively. Late grade 3 GU & GI toxicities were 2% and 0%, respectively.

Conclusions: SAbR dose for HR-PCa was safely escalated with multi-level dose painting of 47.5Gy to prostate, 55Gy to mpMRI-defined intra-prostatic lesions, and 25Gy to pelvic nodal region in 5 fractions. Longer and ongoing follow-up will be required to assess late toxicity.
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http://dx.doi.org/10.1016/j.ijrobp.2021.10.137DOI Listing
November 2021

Observation of Nodal-Line Plasmons in ZrSiS.

Phys Rev Lett 2021 Oct;127(18):186802

Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China.

Nodal-line semimetals (NLSMs), a large family of new topological phases of matter with continuous linear band crossing points in the momentum space, attract considerable attention. Here, we report the direct observation of plasmons originating from topological nodal-line states in a prototypical NLSM ZrSiS by high-resolution electron energy loss spectroscopy. There exist three temperature-independent plasmons with energies ranging from the near- to the mid-infrared frequencies. With first-principles calculations of a slab model, these plasmons can be ascribed to the correlations of electrons in the bulk nodal lines and their projected surface states, dubbed nodal-line plasmons. An anomalous surface plasmon has higher excitation energy than the bulk plasmon due to the larger contribution from the nodal-line projected surface states. This work reveals the novel plasmons related to the unique nodal-line states in a NLSM.
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http://dx.doi.org/10.1103/PhysRevLett.127.186802DOI Listing
October 2021

Improving dose calculation accuracy in preclinical radiation experiments using multi-energy element resolved cone beam CT.

Phys Med Biol 2021 Nov 9. Epub 2021 Nov 9.

Department of Radiation Oncology, UT Southwestern Medical Center, 6363 Forest Park Rd. BL10.202G, MC9315, Dallas, Texas, 75390-9315, UNITED STATES.

Cone-beam CT (CBCT) in modern pre-clinical small-animal radiation research platforms provides volumetric images for image guidance and experiment planning purposes. In this work, we implemented multi-energy element-resolved (MEER) CBCT using three scans with different kVps on a SmART platform (Precision X-ray Inc.) We performed comprehensive calibration tasks achieve sufficient accuracy for this quantitative imaging purpose. For geometry calibration, we scanned a ball bearing phantom and used an analytical method together with an optimization approach to derive gantry-angle specific geometry parameters. Intensity calibration and correction included the corrections for detector lag, glare, and beam hardening. The corrected CBCT projection images acquired at 30, 40 and 60 kVp in multiple scans were used to reconstruct CBCT images using the Feldkamp-Davis-Kress reconstruction algorithm. After that, an optimization problem was solved to determine images of relative electron density (rED) and elemental composition (EC) that are needed for Monte Carlo-based radiation dose calculation. We demonstrated effectiveness of our CBCT calibration steps by showing improvements in image quality and successful material decomposition in cases with a small animal CT calibration phantom and a plastinated mouse phantom. It was found that artifacts induced by geometry inaccuracy, detector lag, glare and beam hardening were visually reduced. CT number mean errors were reduced from 19\% to 5\%. In the CT calibration phantom case, median errors in H, O, and Ca fractions for all the inserts were below 1\%, 2\%, and 4\% respectively, and median error in rED was less than 5\%. Compared to standard approach deriving material type and rED via CT number conversion, our approach improved Monte Carlo simulation-based dose calculation accuracy in bone regions. Mean dose error was reduced from 47.5\% to 10.9\%.
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http://dx.doi.org/10.1088/1361-6560/ac37fcDOI Listing
November 2021

Fecal Microbiome Composition Does Not Predict Diet-Induced TMAO Production in Healthy Adults.

J Am Heart Assoc 2021 11 29;10(21):e021934. Epub 2021 Oct 29.

Department of Cardiovascular and Metabolic Sciences Lerner Research Institute Cleveland Clinic Cleveland OH.

Background Trimethylamine--oxide (TMAO) is a small molecule derived from the metabolism of dietary nutrients by gut microbes and contributes to cardiovascular disease. Plasma TMAO increases following consumption of red meat. This metabolic change is thought to be partly because of the expansion of gut microbes able to use nutrients abundant in red meat. Methods and Results We used data from a randomized crossover study to estimate the degree to which TMAO can be estimated from fecal microbial composition. Healthy participants received a series of 3 diets that differed in protein source (red meat, white meat, and non-meat), and fecal, plasma, and urine samples were collected following 4 weeks of exposure to each diet. TMAO was quantitated in plasma and urine, while shotgun metagenomic sequencing was performed on fecal DNA. While the cai gene cluster was weakly correlated with plasma TMAO (rho=0.17, =0.0007), elastic net models of TMAO were not improved by abundances of bacterial genes known to contribute to TMAO synthesis. A global analysis of all taxonomic groups, genes, and gene families found no meaningful predictors of TMAO. We postulated that abundances of known genes related to TMAO production do not predict bacterial metabolism, and we measured choline- and carnitine-trimethylamine lyase activity during fecal culture. Trimethylamine lyase genes were only weakly correlated with the activity of the enzymes they encode. Conclusions Fecal microbiome composition does not predict systemic TMAO because, in this case, gene copy number does not predict bacterial metabolic activity. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT01427855.
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http://dx.doi.org/10.1161/JAHA.121.021934DOI Listing
November 2021

Gut Microbiome-Dependent Metabolic Pathways and Risk of Lethal Prostate Cancer: Prospective Analysis of a PLCO Cancer Screening Trial Cohort.

Cancer Epidemiol Biomarkers Prev 2021 Oct 28. Epub 2021 Oct 28.

Genitourinary Malignancies Research Center, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio.

Background: Diet and the gut microbiome have a complex interaction that generates metabolites with an unclear effect on lethal prostate cancer risk. Identification of modifiable risk factors for lethal prostate cancer is challenging given the long natural history of this disease and difficulty of prospectively identifying lethal cancers.

Methods: Mass spectrometry was performed on baseline serum samples collected from 173 lethal prostate cancer cases and 519 controls enrolled in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening trial. Baseline serum levels of choline, carnitine, betaine, γ-butyrobetaine, crotonobetaine, phenylacetylglutamine, hippuric acid, and p-cresol sulfate were quantified and analyzed by quartile. Conditional multivariable logistic regression analysis associated analyte levels with lethal prostate cancer incidence after adjusting for body mass index and PSA. The Cochran-Armitage test evaluated analyte level trends across quartiles.

Results: Relative to those in the first quartile, cases with the highest baseline levels of choline (Q4 OR: 2.19; 95% CI, 1.23-3.90; -trend: 0.005) and betaine (Q4 OR: 1.86; 95% CI, 1.05-3.30; -trend: 0.11) exhibited increased odds of developing lethal prostate cancer. Higher baseline serum levels of phenylacetylglutamine (Q4 OR: 2.55; 95% CI, 1.40-4.64; -trend: 0.003), a gut microbiome metabolite of phenylalanine with adrenergic activity, were also associated with lethal prostate cancer.

Conclusions: Baseline serum levels of one-carbon methyl donors and adrenergic compounds resulting from human and gut microbiota-mediated metabolism are associated with increased lethal prostate cancer risk.

Impact: Dietary composition, circulating metabolite levels, and downstream signaling pathways may represent modifiable risk factors associated with incident lethal prostate cancer. Beta-adrenergic blockade represents an additional target for oncologic risk reduction.
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http://dx.doi.org/10.1158/1055-9965.EPI-21-0766DOI Listing
October 2021

Emerging technologies in brachytherapy.

Phys Med Biol 2021 Nov 22;66(23). Epub 2021 Nov 22.

Innovative Technology Of Radiotherapy Computations and Hardware (iTORCH) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America.

Brachytherapy is a mature treatment modality. The literature is abundant in terms of review articles and comprehensive books on the latest established as well as evolving clinical practices. The intent of this article is to part ways and look beyond the current state-of-the-art and review emerging technologies that are noteworthy and perhaps may drive the future innovations in the field. There are plenty of candidate topics that deserve a deeper look, of course, but with practical limits in this communicative platform, we explore four topics that perhaps is worthwhile to review in detail at this time. First, intensity modulated brachytherapy (IMBT) is reviewed. The IMBT takes advantage ofradiation profile generated through intelligent high-density shielding designs incorporated onto sources and applicators such to achieve high quality plans. Second, emerging applications of 3D printing (i.e. additive manufacturing) in brachytherapy are reviewed. With the advent of 3D printing, interest in this technology in brachytherapy has been immense and translation swift due to their potential to tailor applicators and treatments customizable to each individual patient. This is followed by, in third, innovations in treatment planning concerning catheter placement and dwell times where new modelling approaches, solution algorithms, and technological advances are reviewed. And, fourth and lastly, applications of a new machine learning technique, called deep learning, which has the potential to improve and automate all aspects of brachytherapy workflow, are reviewed. We do not expect that all ideas and innovations reviewed in this article will ultimately reach clinic but, nonetheless, this review provides a decent glimpse of what is to come. It would be exciting to monitor as IMBT, 3D printing, novel optimization algorithms, and deep learning technologies evolve over time and translate into pilot testing and sensibly phased clinical trials, and ultimately make a difference for cancer patients. Today's fancy is tomorrow's reality. The future is bright for brachytherapy.
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http://dx.doi.org/10.1088/1361-6560/ac344dDOI Listing
November 2021

Passive sampling of chlorophenols in water and soils using diffusive gradients in thin films based on β-cyclodextrin polymers.

Sci Total Environ 2022 Feb 4;806(Pt 4):150739. Epub 2021 Oct 4.

State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China; Key Laboratory of Poyang Lake Environment and Resource Utilization, Ministry of Education, School of Resources Environmental & Chemical Engineering, Nanchang University, Nanchang 330031, PR China. Electronic address:

Chlorophenols (CPs) have been listed as priority control pollutants because of their high toxicity and wide range. An In-situ monitoring technique using diffusive gradients in thin films based on porous β-cyclodextrin polymers as binding materials (CDP-DGT), was established to monitor four typical CPs, namely, 4-Chlorophenol (4-CP), 2,4-Dichlorophenol (2,4-DCP), 2,4,5-Trichlorophenol (2,4,5-TCP), 2,4,6-Trichlorophenol (2,4,6-TCP) in water and soils. The performance of CDP-DGT are stable under the conditions of pH 3.5-9.3, ionic strength 0.001-0.500 mol L and dissolved organic matter concentration 0-20 mol L. The adsorption capacities of CDP-DGT for 4-CP, 2,4-DCP, 2,4,5-TCP, 2,4,6-TCP were 57.80 μg cm, 98.82 μg cm, 95.69 μg cm and 98.91 μg cm, respectively. The time-average weighted concentrations of four CPs determined by CDP-DGT at Sanjiangkou wharf (Yangtze river, China) were consistent with the results of grab sampling, indicating the feasibility of CDP-DGT application in actual water. In addition, the distribution of CPs in the red soil of Kunming and paddy soil of Yixing were also studied by CDP-DGT, and the desorption kinetics in the two soils were analyzed with the DIFS model. The higher the soil organic matter content is, the more CPs are distributed in the soil solid phase. CPs in both soils can be partially resupplied to soil solution from the soil solid phase and the higher the partition coefficient for labile CPs is, the stronger the supplement capacity is.
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http://dx.doi.org/10.1016/j.scitotenv.2021.150739DOI Listing
February 2022

Design and experimental validation of a unilateral magnet for MRI-guided small animal radiation experiments.

J Magn Reson 2021 11 16;332:107062. Epub 2021 Sep 16.

innovative Technology Of Radiotherapy Computations and Hardware (iTORCH) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas 75390, TX, USA. Electronic address:

Small animal radiation experiments are of paramount importance for the advancement of human radiation therapy. These experiments use a dedicated radiation platform to deliver radiation to small animals, such as mice and rats, similar to how human radiation therapy is performed. By acquiring images immediately before radiation delivery to guide positioning of the animals, image guidance plays a critical role to ensure accuracy of the experiments. Recently, MR-based image guidance has been enabled in human radiation therapy. This paper proposes a new concept using a unilateral magnet-based MRI scanner to realize image guidance for small animal radiation experiments. We reported our design, optimization, construction, and characterization of the magnet. The magnet was designed using eight 2-inch neodymium magnet cubes arranged in a modified Halbach ring configuration. The ring has an opening to allow for animal positioning. We considered a spherical region of interest (ROI) located outside of the ring's plane to allow radiation delivery to the ROI without obstruction of the magnet. An optimization problem was formulated and solved to determine the positions and orientations of the magnet cubes to generate a magnetic field with desired properties in the ROI. The optimization improved the average magnetic flux density from 55 mT to 72 mT and reduced variation from 1.2 T/m to 1.0 T/m. We constructed the magnet using 3D-printed templates to hold the neodymium magnet cubes with the optimized positions and orientations. We measured the spatial distribution of the magnetic flux density. The measurement results and computed results agreed with an average difference of 0.35% through the ROI.
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http://dx.doi.org/10.1016/j.jmr.2021.107062DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8546776PMC
November 2021

Insights into the photocatalytic activation of peroxymonosulfate by visible light over BiOBr-cyclodextrin polymer complexes for efficient degradation of dye pollutants in water.

Environ Res 2021 Sep 30:112160. Epub 2021 Sep 30.

School of Environment, Nanjing Normal University, Nanjing, 210023, China. Electronic address:

The combination of adsorption-photocatalysis and advanced oxidation processes (AOP) based on sulfate (SO) for the treatment of organic pollution has the advantages of a high degradation rate, affordability, and an absence of secondary pollution. This study combined amphiphilic super-crosslinked porous cyclodextrin resin (PBCD-B-D), bismuth oxybromide (BiOBr), a composite material with dual functions of adsorption and photocatalysis, and AOP based on SO for the treatment of Acid Orange 7 (AO7) in water. The combination of BiOBr/PBCD-B-D (BOP-24) with peroxymonosulfate (PMS) showed an optimal adsorption-photocatalytic effect. Compared to the 24% PBCD-B-D (BOP-24)/visible light system, the degradation efficiency of BOP-24/PMS system for AO7 is increased from 64.1% to 99.2% within shorter time (∼60 min). Moreover, the BOP-24/PMS system showed a wide range of pH application (pH = 3-11). The addition of Cl, SO, and NO promoted the photodegradation of AO7, whereas the addition of CO did not. The free radical capture experiments of the BOP-24/PMS AO7 degradation system showed that •O, h, •OH, and SO are reactive species. The proposed BOP-24 system used adsorption and a unique cavity structure to enrich AO7 near the active site, thereby reducing the path for PMS activation. PMS also acted as an electron (e) acceptor to promote the transfer of part of e to PMS, thereby further improving the efficiency of carrier separation. The proposed system is an effective method to improve the degradation of pollutants and broadens the range of application of SO-based AOP technology.
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http://dx.doi.org/10.1016/j.envres.2021.112160DOI Listing
September 2021

Monte Carlo methods for device simulations in radiation therapy.

Phys Med Biol 2021 Sep 14;66(18). Epub 2021 Sep 14.

Department of Radiation Convergence Engineering, Yonsei University, Wonju, Republic of Korea.

Monte Carlo (MC) simulations play an important role in radiotherapy, especially as a method to evaluate physical properties that are either impossible or difficult to measure. For example, MC simulations (MCSs) are used to aid in the design of radiotherapy devices or to understand their properties. The aim of this article is to review the MC method for device simulations in radiation therapy. After a brief history of the MC method and popular codes in medical physics, we review applications of the MC method to model treatment heads for neutral and charged particle radiation therapy as well as specific in-room devices for imaging and therapy purposes. We conclude by discussing the impact that MCSs had in this field and the role of MC in future device design.
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http://dx.doi.org/10.1088/1361-6560/ac1d1fDOI Listing
September 2021

Recent Developments on gMicroMC: Transport Simulations of Proton and Heavy Ions and Concurrent Transport of Radicals and DNA.

Int J Mol Sci 2021 Jun 21;22(12). Epub 2021 Jun 21.

Department of Physics, University of Texas at Arlington, Arlington, TX 76019, USA.

Mechanistic Monte Carlo (MC) simulation of radiation interaction with water and DNA is important for the understanding of biological responses induced by ionizing radiation. In our previous work, we employed the Graphical Processing Unit (GPU)-based parallel computing technique to develop a novel, highly efficient, and open-source MC simulation tool, gMicroMC, for simulating electron-induced DNA damages. In this work, we reported two new developments in gMicroMC: the transport simulation of protons and heavy ions and the concurrent transport of radicals in the presence of DNA. We modeled these transports based on electromagnetic interactions between charged particles and water molecules and the chemical reactions between radicals and DNA molecules. Various physical properties, such as Linear Energy Transfer (LET) and particle range, from our simulation agreed with data published by NIST or simulation results from other CPU-based MC packages. The simulation results of DNA damage under the concurrent transport of radicals and DNA agreed with those from nBio-Topas simulation in a comprehensive testing case. GPU parallel computing enabled high computational efficiency. It took 41 s to simultaneously transport 100 protons with an initial kinetic energy of 10 MeV in water and 470 s to transport 105 radicals up to 1 µs in the presence of DNA.
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http://dx.doi.org/10.3390/ijms22126615DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8233829PMC
June 2021

A hierarchical deep reinforcement learning framework for intelligent automatic treatment planning of prostate cancer intensity modulated radiation therapy.

Phys Med Biol 2021 Jun 23;66(13). Epub 2021 Jun 23.

Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America.

We have previously proposed an intelligent automatic treatment planning (IATP) framework that builds a virtual treatment planner network (VTPN) to operate a treatment planning system (TPS) to generate high-quality radiation therapy (RT) treatment plans. While the potential of IATP in automating RT treatment planning has been demonstrated, its poor scalability caused by an almost linear growth of network size with the number of treatment planning parameters (TPPs) is a bottleneck, preventing its application in complicate, but clinically relevant treatment planning problems. The decision-making behavior of the trained network is hard to understand. Motivated by the decision-making process of a human planner, this study proposes a hierarchical IATP framework.The hierarchical VTPN (HieVTPN) consists of three networks, i.e. Structure-Net, Parameter-Net, and Action-Net. When interacting with a TPS, the networks are employed in a sequential order in each step to decide the structure to adjust, the TPP to adjust for the selected structure, and the specific adjustment manner for the parameter, respectively. We developed an end-to-end hierarchical deep reinforcement learning scheme to simultaneously train the three networks. We then evaluated the effectiveness of the proposed framework in the treatment planning problems for prostate cancer intensity modulated RT (IMRT) and stereotactic body RT (SBRT). We benchmarked the performance of our approach by comparing plans made by VTPN of a parallel architecture, and the human plans submitted for competition in the 2016 American Association of Medical Dosimetrist (AAMD)/Radiosurgery Society (RSS) Plan Study. We analyzed scalability of the network size with respect to the number of TPPs. Numerical experiments were also performed to understand the rationale of the decision-making behaviors of the trained HieVTPN.Both HieVTPNs for prostate IMRT and SBRT were trained successfully using 10 training patient cases and 5 validation cases. For IMRT, HieVTPN was able to generate high-quality plans for 59 testing patient cases that were not included in training process, achieving an average plan score of 8.62 (±0.83), with 9 being the maximal score. The score was comparable to that of the VTPN, 8.45 (±0.48). For SBRT planning, HieVTPN achieved an average plan score of 139.07 on five testing patient cases compared to the score of 132.21 averaged over the human plans summited for competition in AAMD/RSS plan study. Different from VTPN with network size linearly scaling with the number of TPPs, the network size of HieVTPN is almost independent of the number of TPPs. It was also observed that the decision-making behaviors of HieVTPN were understandable and generally agreed with the human experience.With the scalability and explainability, the hierarchical IATP framework is more favorable than the previous framework in terms of handling treatment planning problems involving a large number of TPPs.
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http://dx.doi.org/10.1088/1361-6560/ac09a2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8406431PMC
June 2021

A feasibility study on deep learning-based individualized 3D dose distribution prediction.

Med Phys 2021 Aug 11;48(8):4438-4447. Epub 2021 Jul 11.

Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.

Purpose: Radiation therapy treatment planning is a trial-and-error, often time-consuming process. An approximately optimal dose distribution corresponding to a specific patient's anatomy can be predicted by using pre-trained deep learning (DL) models. However, dose distributions are often optimized based not only on patient-specific anatomy but also on physicians' preferred trade-offs between planning target volume (PTV) coverage and organ at risk (OAR) sparing or among different OARs. Therefore, it is desirable to allow physicians to fine-tune the dose distribution predicted based on patient anatomy. In this work, we developed a DL model to predict the individualized 3D dose distributions by using not only the patient's anatomy but also the desired PTV/OAR trade-offs, as represented by a dose volume histogram (DVH), as inputs.

Methods: In this work, we developed a modified U-Net network to predict the 3D dose distribution by using patient PTV/OAR masks and the desired DVH as inputs. The desired DVH, fine-tuned by physicians from the initially predicted DVH, is first projected onto the Pareto surface, then converted into a vector, and then concatenated with feature maps encoded from the PTV/OAR masks. The network output for training is the dose distribution corresponding to the Pareto optimal DVH. The training/validation datasets contain 77 prostate cancer patients, and the testing dataset has 20 patients.

Results: The trained model can predict a 3D dose distribution that is approximately Pareto optimal while having the DVH closest to the input desired DVH. We calculated the difference between the predicted dose distribution and the optimized dose distribution that has a DVH closest to the desired one for the PTV and for all OARs as a quantitative evaluation. The largest absolute error in mean dose was about 3.6% of the prescription dose, and the largest absolute error in the maximum dose was about 2.0% of the prescription dose.

Conclusions: In this feasibility study, we have developed a 3D U-Net model with the patient's anatomy and the desired DVH curves as inputs to predict an individualized 3D dose distribution that is approximately Pareto optimal while having the DVH closest to the desired one. The predicted dose distributions can be used as references for dosimetrists and physicians to rapidly develop a clinically acceptable treatment plan.
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http://dx.doi.org/10.1002/mp.15025DOI Listing
August 2021

Loop Diuretics Inhibit Renal Excretion of Trimethylamine -Oxide.

JACC Basic Transl Sci 2021 Feb 27;6(2):103-115. Epub 2021 Jan 27.

Center for Microbiome and Human Health, Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA.

This study demonstrates, for the first time, that renal tubular excretion of trimethylamine -oxide (TMAO) is inhibited by concomitant loop diuretic administration. The observed marked accumulation in the renal parenchyma, and to lesser extent, plasma, implies differential distributions of TMAO across various tissues and/or systems as a consequence of efflux channel control. A better understanding of TMAO renal clearance and its potential interactions with current and future therapies in patients with heart failure are warranted.
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http://dx.doi.org/10.1016/j.jacbts.2020.11.010DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7907536PMC
February 2021

Inhibition of microbiota-dependent TMAO production attenuates chronic kidney disease in mice.

Sci Rep 2021 01 12;11(1):518. Epub 2021 Jan 12.

Division of Cardiology, Department of Medicine, University of California, 10833 Le Conte Avenue, A2-237 CHS, Los Angeles, CA, 90095-1679, USA.

Patients with chronic kidney disease (CKD) have elevated circulating levels of trimethylamine N-oxide (TMAO), a metabolite derived from gut microbes and associated with cardiovascular diseases. High circulating levels of TMAO and its dietary precursor, choline, predict increased risk for development of CKD in apparently healthy subjects, and studies in mice fed TMAO or choline suggest that TMAO can contribute to kidney impairment and renal fibrosis. Here we examined the interactions between TMAO, kidney disease, and cardiovascular disease in mouse models. We observed that while female hyperlipidemic apoE KO mice fed a 0.2% adenine diet for 14 weeks developed CKD with elevated plasma levels of TMAO, provision of a non-lethal inhibitor of gut microbial trimethylamine (TMA) production, iodomethylcholine (IMC), significantly reduced multiple markers of renal injury (plasma creatinine, cystatin C, FGF23, and TMAO), reduced histopathologic evidence of fibrosis, and markedly attenuated development of microalbuminuria. In addition, while the adenine-induced CKD model significantly increased heart weight, a surrogate marker for myocardial hypertrophy, this was largely prevented by IMC supplementation. Surprisingly, adenine feeding did not increase atherosclerosis and significantly decreased the expression of inflammatory genes in the aorta compared to the control groups, effects unrelated to TMAO levels. Our data demonstrate that inhibition of TMAO production attenuated CKD development and cardiac hypertrophy in mice, suggesting that TMAO reduction may be a novel strategy in treating CKD and its cardiovascular disease complications.
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http://dx.doi.org/10.1038/s41598-020-80063-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7804188PMC
January 2021

Improving efficiency of training a virtual treatment planner network via knowledge-guided deep reinforcement learning for intelligent automatic treatment planning of radiotherapy.

Med Phys 2021 Apr 16;48(4):1909-1920. Epub 2021 Feb 16.

Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.

Purpose: We previously proposed an intelligent automatic treatment planning framework for radiotherapy, in which a virtual treatment planner network (VTPN) is built using deep reinforcement learning (DRL) to operate a treatment planning system (TPS) by adjusting treatment planning parameters in it to generate high-quality plans. We demonstrated the potential feasibility of this idea in prostate cancer intensity-modulated radiation therapy (IMRT). Despite the success, the process to train a VTPN via the standard DRL approach with an ϵ-greedy algorithm was time-consuming. The required training time was expected to grow with the complexity of the treatment planning problem, preventing the development of VTPN for more complicated but clinically relevant scenarios. In this study, we proposed a novel knowledge-guided DRL (KgDRL) approach that incorporated knowledge from human planners to guide the training process to improve the efficiency of training a VTPN.

Method: Using prostate cancer IMRT as a test bed, we first summarized a number of rules in the actions of adjusting treatment planning parameters of our in-house TPS. During the training process of VTPN, in addition to randomly navigating the large state-action space, as in the standard DRL approach using the ϵ-greedy algorithm, we also sampled actions defined by the rules. The priority of sampling actions from rules decreased over the training process to encourage VTPN to explore new policy on parameter adjustment that were not covered by the rules. To test this idea, we trained a VTPN using KgDRL and compared its performance with another VTPN trained using the standard DRL approach. Both networks were trained using 10 training patient cases and five additional cases for validation, while another 59 cases were employed for the evaluation purpose.

Results: It was found that both VTPNs trained via KgDRL and standard DRL spontaneously learned how to operate the in-house TPS to generate high-quality plans, achieving plan quality scores of 8.82 (±0.29) and 8.43 (±0.48), respectively. Both VTPNs outperformed treatment planning purely based on the rules, which had a plan score of 7.81 (±1.59). VTPN trained with eight episodes using KgDRL was able to perform similar to that trained using DRL with 100 epochs. The training time was reduced from more than a week to ~13 hrs.

Conclusion: The proposed KgDRL framework was effective in accelerating the training process of a VTPN by incorporating human knowledge, which will facilitate the development of VTPN for more complicated treatment planning scenarios.
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http://dx.doi.org/10.1002/mp.14712DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8058266PMC
April 2021

Semi-automatic sigmoid colon segmentation in CT for radiation therapy treatment planning via an iterative 2.5-D deep learning approach.

Med Image Anal 2021 02 16;68:101896. Epub 2020 Dec 16.

innovative Technology of Radiotherapy Computation and Hardware (iTORCH) Laboratory. Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA. Electronic address:

Automatic sigmoid colon segmentation in CT for radiotherapy treatment planning is challenging due to complex organ shape, close distances to other organs, and large variations in size, shape, and filling status. The patient bowel is often not evacuated, and CT contrast enhancement is not used, which further increase problem difficulty. Deep learning (DL) has demonstrated its power in many segmentation problems. However, standard 2-D approaches cannot handle the sigmoid segmentation problem due to incomplete geometry information and 3-D approaches often encounters the challenge of a limited training data size. Motivated by human's behavior that segments the sigmoid slice by slice while considering connectivity between adjacent slices, we proposed an iterative 2.5-D DL approach to solve this problem. We constructed a network that took an axial CT slice, the sigmoid mask in this slice, and an adjacent CT slice to segment as input and output the predicted mask on the adjacent slice. We also considered other organ masks as prior information. We trained the iterative network with 50 patient cases using five-fold cross validation. The trained network was repeatedly applied to generate masks slice by slice. The method achieved average Dice similarity coefficients of 0.82 0.06 and 0.88 0.02 in 10 test cases without and with using prior information.
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http://dx.doi.org/10.1016/j.media.2020.101896DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7847132PMC
February 2021

Experimental and numerical studies on kV scattered x-ray imaging for real-time image guidance in radiation therapy.

Phys Med Biol 2021 02 11;66(4):045022. Epub 2021 Feb 11.

Innovative Technology Of Radiotherapy Computations and Hardware (iTORCH) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75235, United States of America. Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75235, United States of America.

Motion management is a critical component of image guided radiotherapy for lung cancer. We previously proposed a scheme using kV scattered x-ray photons for marker-less real-time image guidance in lung cancer radiotherapy. This study reports our recent progress using the photon counting detection technique to demonstrate potential feasibility of this method and using Monte Carlo (MC) simulations and ray-tracing calculations to characterize the performance. In our scheme, a thin slice of x-ray beam was directed to the target and we measured the outgoing scattered photons using a photon counting detector with a parallel-hole collimator to establish the correspondence between detector pixels and scatter positions. Image corrections of geometry, beam attenuation and scattering angle were performed to convert the raw image to the actual image of Compton attenuation coefficient. We set up a MC simulation system using an in-house developed GPU-based MC package modeling the image formation process. We also performed ray-tracing calculations to investigate the impacts of imaging system geometry on resulting image resolution. The experiment demonstrated feasibility of using a photon counting detector to measure scattered x-ray photons and generate the proposed scattered x-ray image. After correction, x-ray scattering image intensity and Compton scattering attenuation coefficient were linearly related, with R greater than 0.9. Contrast to noise ratios of different objects were improved and the values in experimental results and MC simulation results agreed with each other. Ray-tracing calculations revealed the dependence of image resolution on imaging geometry. The image resolution increases with reduced source to object distance and increased collimator height. The study demonstrated potential feasibility of using scattered x-ray imaging as a real-time image guidance method in radiation therapy.
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http://dx.doi.org/10.1088/1361-6560/abd66cDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8283910PMC
February 2021

Simultaneous needle catheter selection and dwell time optimization for preplanning of high-dose-rate brachytherapy of prostate cancer.

Phys Med Biol 2021 03 1;66(5):055028. Epub 2021 Mar 1.

Innovative Technology Of Radiotherapy Computation and Hardware (iTORCH) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America. Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75287, United States of America.

Purpose: Needle catheter positions critically affect the quality of treatment plans in prostate cancer high-dose-rate (HDR) brachytherapy. The current standard needle positioning approach is based on human intuition, which cannot guarantee a high-quality plan. This study proposed a method to simultaneously select needle catheter positions and determine dwell time for preplanning of HDR brachytherapy of prostate cancer.

Methods: We formulated the needle catheter selection problem and inverse dwell time optimization problem in a unified framework. In addition to the dose objectives of the planning target volume (PTV) and organs at risk (OARs), the objective function incorporated a group-sparsity term with a needle-specific adaptive weighting scheme to generate high-quality plans with the minimal number of needle catheters. The optimization problem was solved by a fast-iterative shrinkage-thresholding algorithm. For validation purposes, we tested the proposed algorithm on 10 patient cases previously treated at our institution and compared the resulting plans with plans generated using needle catheters selected manually.

Results: Compared to the plan with manually selected needle catheters, when normalizing both plans to the same PTV coverage V  = 95%, the plans generated by the proposed algorithm reduced median V from 65% to 64%, but increased median V from 35% to 38%, and V from 14% to 16%. All planning objectives were met. All clinically important dosimetric parameters of OARs were reduced. D of bladder and rectum were reduced from 8.57 Gy to 8.50 Gy and from 7.24 Gy to 6.80 Gy, respectively. D of urethra was reduced from 15.85 Gy to 15.77 Gy. The median number of selected needle catheters was reduced by two. The computational time for solving the proposed optimization problem was ∼90 s using MATLAB.

Conclusion: The proposed algorithm was able to generate plans for prostate cancer HDR brachytherapy preplanning with increased median conformity index (0.73-0.77) and slightly lower median homogeneity index (0.64-0.62) with the number of selected needles reduced by two compared to the manual needle selection approach.
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http://dx.doi.org/10.1088/1361-6560/abd00eDOI Listing
March 2021

Modeling the effect of oxygen on the chemical stage of water radiolysis using GPU-based microscopic Monte Carlo simulations, with an application in FLASH radiotherapy.

Phys Med Biol 2021 01 26;66(2):025004. Epub 2021 Jan 26.

Department of Physics, University of Texas at Arlington, Arlington, TX 76019, United States of America. innovative Technology Of Radiotherapy Computation and Hardware (iTORCH) laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75287, United States of America.

Oxygen plays a critical role in determining the initial DNA damages induced by ionizing radiation. It is important to mechanistically model the oxygen effect in the water radiolysis process. However, due to the computational costs from the many body interaction problem, oxygen is often ignored or treated as a constant continuum radiolysis-scavenger background in the simulations using common microscopic Monte Carlo tools. In this work, we reported our recent progress on the modeling of the chemical stage of the water radiolysis with an explicit consideration of the oxygen effect, based upon our initial development of an open-source graphical processing unit (GPU)-based MC simulation tool, gMicroMC. The inclusion of oxygen mainly reduces the yields of [Formula: see text] and [Formula: see text] chemical radicals, turning them into highly toxic [Formula: see text] and [Formula: see text] species. To demonstrate the practical value of gMicroMC in large scale simulation problems, we applied the oxygen-simulation-enabled gMicroMC to compute the yields of chemical radicals under a high instantaneous dose rate [Formula: see text] to study the oxygen depletion hypothesis in FLASH radiotherapy. A decreased oxygen consumption rate (OCR) was found associated with a reduced initial oxygen concentration level due to reduced probabilities of reactions. With respect to dose rate, for the oxygen concentration of 21% and electron energy of 4.5 [Formula: see text], OCR remained approximately constant (∼0.22 [Formula: see text]) for [Formula: see text]'s of [Formula: see text], [Formula: see text] and reduced to 0.19 [Formula: see text] at [Formula: see text], because the increased dose rate improved the mutual reaction frequencies among radicals, hence reducing their reactions with oxygen. We computed the time evolution of oxygen concentration under the FLASH irradiation setups. At the dose rate of [Formula: see text] and initial oxygen concentrations from 0.01% to 21%, the oxygen is unlikely to be fully depleted with an accumulative dose of 30 Gy, which is a typical dose used in FLASH experiments. The computational efficiency of gMicroMC when considering oxygen molecules in the chemical stage was evaluated through benchmark work to GEANT4-DNA with simulating an equivalent number of radicals. With an initial oxygen concentration of 3% (∼10 molecules), a speedup factor of 1228 was achieved for gMicroMC on a single GPU card when comparing with GEANT4-DNA on a single CPU.
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http://dx.doi.org/10.1088/1361-6560/abc93bDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8236313PMC
January 2021

On the robustness of deep learning-based lung-nodule classification for CT images with respect to image noise.

Phys Med Biol 2020 12 22;65(24):245037. Epub 2020 Dec 22.

Innovative Technology Of Radiotherapy Computations and Hardware (iTORCH) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75235, United States of America. Medical Artificial Intelligence and Automation (MAIA) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX 75235, United States of America.

Robustness is an important aspect when evaluating a method of medical image analysis. In this study, we investigated the robustness of a deep learning (DL)-based lung-nodule classification model for CT images with respect to noise perturbations. A deep neural network (DNN) was established to classify 3D CT images of lung nodules into malignant or benign groups. The established DNN was able to predict malignancy rate of lung nodules based on CT images, achieving the area under the curve of 0.91 for the testing dataset in a tenfold cross validation as compared to radiologists' prediction. We then evaluated its robustness against noise perturbations. We added to the input CT images noise signals generated randomly or via an optimization scheme using a realistic noise model based on a noise power spectrum for a given mAs level, and monitored the DNN's output. The results showed that the CT noise was able to affect the prediction results of the established DNN model. With random noise perturbations at 100 mAs, DNN's predictions for 11.2% of training data and 17.4% of testing data were successfully altered by at least once. The percentage increased to 23.4% and 34.3%, respectively, for optimization-based perturbations. We further evaluated robustness of models with different architectures, parameters, number of output labels, etc, and robustness concern was found in these models to different degrees. To improve model robustness, we empirically proposed an adaptive training scheme. It fine-tuned the DNN model by including perturbations in the training dataset that successfully altered the DNN's perturbations. The adaptive scheme was repeatedly performed to gradually improve DNN's robustness. The numbers of perturbations at 100 mAs affecting DNN's predictions were reduced to 10.8% for training and 21.1% for testing by the adaptive training scheme after two iterations. Our study illustrated that robustness may potentially be a concern for an exemplary DL-based lung-nodule classification model for CT images, indicating the needs for evaluating and ensuring model robustness when developing similar models. The proposed adaptive training scheme may be able to improve model robustness.
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http://dx.doi.org/10.1088/1361-6560/abc812DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7870572PMC
December 2020

Predicting which patients may benefit from the hybrid intracavitary+interstitial needle (IC/IS) applicator for advanced cervical cancer: A dosimetric comparison and toxicity benefit analysis.

Brachytherapy 2021 Jan-Feb;20(1):136-145. Epub 2020 Oct 29.

Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX. Electronic address:

Purpose: The purpose of this study is to compare the predicted rate of local control and bladder and rectum toxicity rates for image-guided adaptive brachytherapy plans using a tandem and ovoid (T/O) applicator versus using a simulated hybrid intracavitary/interstitial tandem and ring applicator with needles (T/R + N) for patients with locally advanced cervical cancer (LACC).

Methods And Materials: Patients with ≥ FIGO Stage IIB locally advanced cervical cancer treated with T/O from a single institution were included. Simulated treatment plans were created with a T/R + N applicator for the best high-risk clinical target volume (CTV) coverage and minimal dose to organs at risk. Three-year local control rate was estimated using published dose-volume effect relationships. Next, the high-risk CTV EQD2 D90 of T/R + N plans were calculated, and bladder and rectum toxicity rates were estimated. Analysis was performed in subpatient groups defined based on tumor volume and ratio of maximal and minimal tumor radii (RR) that reflects tumor shape asymmetry.

Results: Improvements in predicted local control rate for the T/R + N were 0.8, 4.1, 1.6, and 3.9% for groups with tumor volume <35 cc, ≥35 cc, RR < 2.0, and ≥2.0, respectively, with the latter three being statistically significant. Predicted reductions in Grade 2-4 toxicity rates of bladder and rectum were significant in all groups except bladder toxicity in tumor volume <35 cc, when T/R + N plans were normalized to the same CTV coverage as the T/O plans. Comparing unnormalized T/R + N plans and T/O plans, predicted toxicity reductions were significant in all groups except rectum toxicity in RR ≥ 2.0. Predicted reduction of toxicity rate was larger for patients with large tumor or large tumor RR, although some reductions were relatively small.

Conclusions: Cases with large tumor (volume ≥35 cc) or large tumor asymmetry (RR ≥ 2.0) would probably benefit more from the use of hybrid applicators.
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http://dx.doi.org/10.1016/j.brachy.2020.09.004DOI Listing
August 2021

Dose rate determination for preclinical total body irradiation.

Phys Med Biol 2020 09 8;65(17):175018. Epub 2020 Sep 8.

Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75287, United States of America. Innovative Technologies Of Radiotherapy Computations and Hardware (iTORCH) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75287, United States of America.

The accuracy of delivered radiation dose and the reproducibility of employed radiotherapy methods are key factors for preclinical radiobiology applications and research studies. In this work, ionization chamber (IC) measurements and Monte Carlo (MC) simulations were used to accurately determine the dose rate for total body irradiation (TBI), a classic radiobiologic and immunologic experimental method. Several phantom configurations, including large solid water slab, small water box and rodentomorphic mouse and rat phantoms were simulated and measured for TBI setup utilizing a preclinical irradiator XRad320. The irradiator calibration and the phantom measurements were performed using an ADCL calibrated IC N31010 following the AAPM TG-61 protocol. The MC simulations were carried out using Geant4/GATE to compute absorbed dose distributions for all phantom configurations. All simulated and measured geometries had favorable agreement. On average, the relative dose rate difference was 2.3%. However, the study indicated large dose rate deviations, if calibration conditions are assumed for a given experimental setup as commonly done for a quick determination of irradiation times utilizing lookup tables and hand calculations. In a TBI setting, the reference calibration geometry at an extended source-to-surface distance and a large reference field size is likely to overestimate true photon scatter. Consequently, the measured and hand calculated dose rates, for TBI geometries in this study, had large discrepancies: 16% for a large solid water slab, 27% for a small water box, and 31%, 36%, and 30% for mouse phantom, rat phantom, and mouse phantom in a pie cage, respectively. Small changes in TBI experimental setup could result in large dose rate variations. MC simulations and the corresponding measurements specific to a designed experimental setup are vital for accurate preclinical dosimetry and reproducibility of radiobiological findings. This study supports the well-recognized need for physics consultation for all radiobiological investigations.
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http://dx.doi.org/10.1088/1361-6560/aba40fDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7717670PMC
September 2020

Scatter correction based on adaptive photon path-based Monte Carlo simulation method in Multi-GPU platform.

Comput Methods Programs Biomed 2020 Oct 11;194:105487. Epub 2020 May 11.

School of Biomedical Engineering, Southern Medical University, Guangzhou, China, 510515. Electronic address:

Monte Carlo (MC)-based simulation is the most precise method in scatter correction for Cone-beam CT (CBCT). Nonetheless, the existing MC methods cannot be fully applied in clinical due to its low efficiency. The traditional MC simulations perform calculations via a particle-by-particle scheme, which leads to high computation costs because abundant photons do not reach the X-ray detector in transport. The conventional approaches cannot control where the particle ends. Hence, it unavoidably waste lots of time in transporting numerous photons that have no contribution to the signal at the detector, yielding a low computational efficiency. To solve the problem, an innovative GPU-based Metropolis MC (gMMC) method was proposed. Compared with the traditional ones, the Metropolis based algorithm utilizes a path-by-path sampling method. The method can automatically control each particle path and eventually accelerate the convergence. In this paper, we firstly take planning CT image as prior information because of its precise CT value, and utilize gMMC to estimate scatter signal. Then the scatter signals are removed from the raw CBCT projections. Afterwards, FDK reconstruction is performed to obtain the corrected image,some accelerating strategies including reducing photon history number, pixels sampling, projection angles sampling and reconstructed image down-sampling achieve adaptive fast CBCT image reconstruction. For having high computational efficiency, we implemented the whole workflow on a 4-GPU workstation. In order to verify the feasibility of the the method, the experiment of several cases are conducted including simulation, phantom, and real patient cases. Results indicate that the image contrast becomes better, the scatter artifacts are eliminated. The maximum error (e), the minimum error (e), the 95th percentile error (e), average error (¯e) are reduced from 264, 56, 14 and 21 HU to 28, 10, 3 and 7 HU in full-fan case, and from 387, 5, 19 and 95 HU to 39, 2, 2 and 6 HU in the half-fan case. In terms of computation time, the MC simulation time of all cases is within 2.5 seconds, and the total time is within 15 seconds.
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http://dx.doi.org/10.1016/j.cmpb.2020.105487DOI Listing
October 2020

Development of a real-time indoor location system using bluetooth low energy technology and deep learning to facilitate clinical applications.

Med Phys 2020 Aug 11;47(8):3277-3285. Epub 2020 May 11.

Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75235, USA.

Purpose: An indoor, real-time location system (RTLS) can benefit both hospitals and patients by improving clinical efficiency through data-driven optimization of procedures. Bluetooth-based RTLS systems are cost-effective but lack accuracy because Bluetooth signal is subject to significant fluctuation. We aim to improve the accuracy of RTLS using the deep learning technique.

Methods: We installed a Bluetooth sensor network in a three-floor clinic building to track patients, staff, and devices. The Bluetooth sensors measured the strength of the signal broadcasted from Bluetooth tags, which was fed into a deep neural network to calculate the location of the tags. The proposed deep neural network consists of a long short-term memory (LSTM) network and a deep classifier for tracking moving objects. Additionally, a spatial-temporal constraint algorithm was implemented to further increase the accuracy and stability of the results. To train the neural network, we divided the building into 115 zones and collected training data in each zone. We further augmented the training data to generate cross-zone trajectories, mimicking the real-world scenarios. We tuned the parameters for the proposed neural network to achieve relatively good accuracy.

Results: The proposed deep neural network achieved an overall accuracy of about 97% for tracking objects in each individual zone in the whole three-floor building, 1.5% higher than the baseline neural network that was proposed in an earlier paper, when using 10 s of signals. The accuracy increased with the density of Bluetooth sensors. For tracking moving objects, the proposed neural network achieved stable and accurate results. When latency is less of a concern, we eliminated the effect of latency from the accuracy and gained an accuracy of 100% for our testing trajectories, significantly improved from the baseline method.

Conclusions: The proposed deep neural network composed of a LSTM, a deep classifier and a posterior constraint algorithm significantly improved the accuracy and stability of RTLS for tracking moving objects.
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http://dx.doi.org/10.1002/mp.14198DOI Listing
August 2020

Simultaneous Measurement of Urinary Trimethylamine (TMA) and Trimethylamine -Oxide (TMAO) by Liquid Chromatography-Mass Spectrometry.

Molecules 2020 Apr 17;25(8). Epub 2020 Apr 17.

Department of Cardiovascular & Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH 44195, USA.

Trimethylamine (TMA) is a gut microbial metabolite-rendered by the enzymatic cleavage of nutrients containing a TMA moiety in their chemical structure. TMA can be oxidized as trimethylamine -oxide (TMAO) catalyzed by hepatic flavin monooxygenases. Circulating TMAO has been demonstrated to portend a pro-inflammatory state, contributing to chronic diseases such as cardiovascular disease and chronic kidney disease. Consequently, TMAO serves as an excellent candidate biomarker for a variety of chronic inflammatory disorders. The highly positive correlation between plasma TMAO and urine TMAO suggests that urine TMAO has the potential to serve as a less invasive biomarker for chronic disease compared to plasma TMAO. In this study, we validated a method to simultaneously measure urine TMA and TMAO concentrations by liquid chromatography-mass spectrometry (LC/MS). Urine TMA and TMAO can be extracted by hexane/butanol under alkaline pH and transferred to the aqueous phase following acidification for LC/MS quantitation. Importantly, during sample processing, none of the nutrients with a chemical structure containing a TMA moiety were spontaneously cleaved to yield TMA. Moreover, we demonstrated that the acidification of urine prevents an increase of TMA after prolonged storage as was observed in non-acidified urine. Finally, here we demonstrated that TMAO can spontaneously degrade to TMA at a very slow rate.
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http://dx.doi.org/10.3390/molecules25081862DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7222018PMC
April 2020

Operating a treatment planning system using a deep-reinforcement learning-based virtual treatment planner for prostate cancer intensity-modulated radiation therapy treatment planning.

Med Phys 2020 Jun 28;47(6):2329-2336. Epub 2020 Mar 28.

Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.

Purpose: In the treatment planning process of intensity-modulated radiation therapy (IMRT), a human planner operates the treatment planning system (TPS) to adjust treatment planning parameters, for example, dose volume histogram (DVH) constraints' locations and weights, to achieve a satisfactory plan for each patient. This process is usually time-consuming, and the plan quality depends on planer's experience and available planning time. In this study, we proposed to model the behaviors of human planners in treatment planning by a deep reinforcement learning (DRL)-based virtual treatment planner network (VTPN), such that it can operate the TPS in a human-like manner for treatment planning.

Methods And Materials: Using prostate cancer IMRT as an example, we established the VTPN using a deep neural network developed. We considered an in-house optimization engine with a weighted quadratic objective function. Virtual treatment planner network was designed to observe an intermediate plan DVHs and decide the action to improve the plan by changing weights and threshold dose in the objective function. We trained the VTPN in an end-to-end DRL process in 10 patient cases. A plan score was used to measure plan quality. We demonstrated the feasibility and effectiveness of the trained VTPN in another 64 patient cases.

Results: Virtual treatment planner network was trained to spontaneously learn how to adjust treatment planning parameters to generate high-quality treatment plans. In the 64 testing cases, with initialized parameters, quality score was 4.97 (±2.02), with 9.0 being the highest possible score. Using VTPN to perform treatment planning improved quality score to 8.44 (±0.48).

Conclusions: To our knowledge, this was the first time that intelligent treatment planning behaviors of human planner in external beam IMRT are autonomously encoded in an artificial intelligence system. The trained VTPN is capable of behaving in a human-like way to produce high-quality plans.
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http://dx.doi.org/10.1002/mp.14114DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7903320PMC
June 2020

Upfront radiotherapy with brachytherapy for medically inoperable and unresectable patients with high-risk endometrial cancer.

Brachytherapy 2020 Mar - Apr;19(2):139-145. Epub 2020 Feb 12.

Department of Radiation Oncology, Harold C. Simmons Comprehensive Cancer Center, Dallas, TX. Electronic address:

Objectives: Comprehensive surgery with adjuvant therapy is standard of care for high-risk endometrial cancers, whereas upfront radiotherapy with brachytherapy is indicated for inoperable/unresectable patients, irrespective of risk. We evaluated outcomes for inoperable/unresectable patients with high-risk endometrial cancer (HREC: stage III and/or grade 3) and low-risk endometrial cancer (LREC: stage I/II and grade 1/2) treated with upfront radiotherapy.

Methods: Twenty-nine patients with inoperable/unresectable endometrial cancer were treated with upfront radiotherapy at an academic medical center from 2012 to 2019. Cancer-specific survival (CSS), overall survival (OS), and recurrence rates between patients with HREC and LREC were compared.

Results: Median follow-up was 17.0 months (range 3.7-54.0). Twenty cancers were stage I + II and nine were stage III. Twenty-one cancers were grade 1 + 2 and eight were grade 3. Thirteen patients (45%) had HREC. Twenty-five patients received radiotherapy/chemoradiotherapy for primary treatment, while 4 patients received chemoradiotherapy before surgery. All patients underwent high dose rate brachytherapy (HDR) with 7 receiving HDR alone and 22 receiving external beam radiation and HDR. Two-year CSS was 100% for both HREC and LREC patients (log-rank p = 0.32). There was no OS difference between HREC and LREC patients (2-year: 73% vs. 77%; log-rank p = 0.33). Four HREC and 1 LREC patients recurred with one local recurrence in each group. There were no acute grade ≥3 and two late grade ≥3 gastrointestinal/genitourinary toxicities.

Conclusions: Upfront radiotherapy for inoperable/unresectable HREC patients was well tolerated with high local control and CSS rates. Upfront radiotherapy with brachytherapy remains important even for high-risk inoperable and unresectable endometrial cancer patients.
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http://dx.doi.org/10.1016/j.brachy.2020.01.003DOI Listing
December 2020

A new open-source GPU-based microscopic Monte Carlo simulation tool for the calculations of DNA damages caused by ionizing radiation - Part II: sensitivity and uncertainty analysis.

Med Phys 2020 Apr 14;47(4):1971-1982. Epub 2020 Feb 14.

Innovative Technology Of Radiotherapy Computation and Hardware (iTORCH) laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75287, USA.

Purpose: Calculations of deoxyribonucleic acid (DNA) damages involve many parameters in the computation process. As these parameters are often subject to uncertainties, it is of central importance to comprehensively quantify their impacts on DNA single-strand break (SSB) and double-strand break (DSB) yields. This has been a challenging task due to the required large number of simulations and the relatively low computational efficiency using CPU-based MC packages. In this study, we present comprehensive evaluations on sensitivities and uncertainties of DNA SSB and DSB yields on 12 parameters using our GPU-based MC tool, gMicroMC.

Methods: We sampled one electron at a time in a water sphere containing a human lymphocyte nucleus and transport the electrons and generated radicals until 2 Gy dose was accumulated in the nucleus. We computed DNA damages caused by electron energy deposition events in the physical stage and the hydroxyl radicals at the end of the chemical stage. We repeated the computations by varying 12 parameters: (a) physics cross section, (b) cutoff energy for electron transport, (c)-(e) three branching ratios of hydroxyl radicals in the de-excitation of excited water molecules, (f) temporal length of the chemical stage, (g)-(h) reaction radii for direct and indirect damages, (i) threshold energy defining the threshold damage model to generate a physics damage, (j)-(k) minimum and maximum energy values defining the linear-probability damage model to generate a physics damage, and (l) probability to generate a damage by a radical. We quantified sensitivity of SSB and DSB yields with respect to these parameters for cases with 1.0 and 4.5 keV electrons. We further estimated uncertainty of SSB and DSB yields caused by uncertainties of these parameters.

Results: Using a threshold of 10% uncertainty as a criterion, threshold energy in the threshold damage model, maximum energy in the linear-probability damage model, and probability for a radical to generate a damage were found to cause large uncertainties in both SSB and DSB yields. The scaling factor of the cross section, cutoff energy, physics reaction radius, and minimum energy in the linear-probability damage model were found to generate large uncertainties in DSB yields.

Conclusions: We identified parameters that can generate large uncertainties in the calculations of SSB and DSB yields. Our study could serve as a guidance to reduce uncertainties of parameters and hence uncertainties of the simulation results.
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http://dx.doi.org/10.1002/mp.14036DOI Listing
April 2020
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