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Canine malignant melanoma(CMM) is an aggressive neoplasm treated with surgery and/or fractionated RT; however, metastatic disease is common and chemoresistant. Preclinical and clinical studies by our laboratory and others have shown that xenogeneic DNA vaccination with tyrosinase family members can produce immune responses resulting in tumor rejection or protection and prolongation of survival. The potency of DNA vaccines can be further enhanced by adding DNA encoding cytokine genes. We have shown in preclinical mouse models that GM-CSF DNA enhances immune responses and tumor protection. These studies provided the impetus for murine tyrosinase(muTyr) ± human GM-CSF(huGM-CSF) DNA vaccination in CMM.Two groups of five dogs each with advanced(WHO stage II-IV) CMM received four biweekly IM injections(100 ug or 500 ug, respectively/vaccination) of plasmid DNA encoding muTyr via the Biojector2000 jet delivery device. Subsequently, three groups of nine dogs each with advanced CMM received four biweekly IM injections of plasmid DNA encoding muTyr(50 ug), huGM-CSF(3 dogs each at 100/400/800 ug) or both.Minimal to mild pain was noted on vaccination and no toxicity or induction of autoimmunity was seen. The KM median survival time was 224 days(100/500 ug muTyr), 278 days(50 ug muTyr), 140 days(huGM-CSF) and>265 days(muTyr&huGM-CSF; 6 dogs still alive).The results of these trials demonstrate that xenogeneic DNA vaccination continues to be a safe and potentially therapeutic modality for CMM. These results also warrant further evaluation of this novel therapeutic in a Phase II setting. [ABSTRACT FROM AUTHOR]

The bioMérieux BIOFIRE Joint Infection (JI) Panel is a multiplex in vitro diagnostic test for the simultaneous and rapid (~1 h) detection of 39 potential pathogens and antimicrobial resistance (AMR) genes directly from synovial fluid (SF) samples. Thirty-one species or groups of microorganisms are included in the kit, as well as several AMR genes. This study, performed to evaluate the BIOFIRE JI Panel for regulatory clearance, provides data from a multicenter evaluation of 1,544 prospectively collected residual SF samples with performance compared to standard-of-care (SOC) culture for organisms or polymerase chain reaction (PCR) and sequencing for AMR genes. The BIOFIRE JI Panel demonstrated a sensitivity of 90.9% or greater for all but six organisms and a positive percent agreement (PPA) of 100% for all AMR genes. The BIOFIRE JI Panel demonstrated a specificity of 98.5% or greater for detection of all organisms and a negative percent agreement (NPA) of 95.7% or greater for all AMR genes. The BIOFIRE JI Panel provides an improvement over SOC culture, with a substantially shorter time to result for both organisms and AMR genes with excellent sensitivity/PPA and specificity/NPA, and is anticipated to provide timely and actionable diagnostic information for joint infections in a variety of clinical scenarios., Competing Interests: J.E. has received research funding from bioMérieux and has served as a consultant for bioMérieux. AL has received research funding from BioFire, Cepheid, and Luminex. J.M.B.L. has served as a consultant for bioMérieux and has also participated in other BioFire clinical studies. B.S. has received research funding from BioFire, Becton, Dickinson and Company, and DiaSorin.

Aberrant alternative pre-mRNA splicing plays a critical role in MYC-driven cancers and therefore may represent a therapeutic vulnerability. Here, we show that neuroblastoma, a MYC-driven cancer characterized by splicing dysregulation and spliceosomal dependency, requires the splicing factor RBM39 for survival. Indisulam, a “molecular glue” that selectively recruits RBM39 to the CRL4-DCAF15 E3 ubiquitin ligase for proteasomal degradation, is highly efficacious against neuroblastoma, leading to significant responses in multiple high-risk disease models, without overt toxicity. Genetic depletion or indisulam-mediated degradation of RBM39 induces significant genome-wide splicing anomalies and cell death. Mechanistically, the dependency on RBM39 and high-level expression of DCAF15 determine the exquisite sensitivity of neuroblastoma to indisulam. Our data indicate that targeting the dysregulated spliceosome by precisely inhibiting RBM39, a vulnerability in neuroblastoma, is a valid therapeutic strategy.

Bacteriophages (phages) are ubiquitous in nature. These viruses play a number of central roles in microbial ecology and evolution by, for instance, promoting horizontal gene transfer (HGT) among bacterial species. The ability of phages to mediate HGT through transduction has been widely exploited as an experimental tool for the genetic study of bacteria. As such, bacteriophage P1 represents a prototypical generalized transducing phage with a broad host range that has been extensively employed in the genetic manipulation of Escherichia coli and a number of other model bacterial species. Here we demonstrate that P1 is capable of infecting, lysogenizing, and promoting transduction in members of the bacterial genus Sodalis , including the maternally inherited insect endosymbiont Sodalis glossinidius While establishing new tools for the genetic study of these bacterial species, our results suggest that P1 may be used to deliver DNA to many Gram-negative endosymbionts in their insect host, thereby circumventing a culturing requirement to genetically manipulate these organisms. IMPORTANCE A large number of economically important insects maintain intimate associations with maternally inherited endosymbiotic bacteria. Due to the inherent nature of these associations, insect endosymbionts cannot be usually isolated in pure culture or genetically manipulated. Here we use a broad-host-range bacteriophage to deliver exogenous DNA to an insect endosymbiont and a closely related free-living species. Our results suggest that broad-host-range bacteriophages can be used to genetically alter insect endosymbionts in their insect host and, as a result, bypass a culturing requirement to genetically alter these bacteria., (Copyright © 2021 Keller et al.)

Evidence has been presented that moving beyond the binary definition of clinical target volume (CTV) towards a probabilistic CTV can result in better treatment plans. The probabilistic CTV takes the likelihood of disease spread outside of the gross tumor into account. An open question is: how to optimize tumor control probability (TCP) based on the probabilistic CTV. We derive expressions for TCP under the assumptions of voxel independence and dependence. For the dependent case, we make the assumption that tumors grow outward from the gross tumor volume. We maximize the (non-convex) TCP under convex dose constraints for all models. For small numbers of voxels, and when a dose-influence matrix is not used, we use exhaustive search or Lagrange multiplier theory to compute optimal dose distributions. For larger cases we present (1) a multi-start strategy using linear programming with a random cost vector to provide random feasible starting solutions, followed by a local search, and (2) a heuristic strategy that greedily selects which subvolumes to dose, and then for each subvolume assignment runs a convex approximation of the optimization problem. The optimal dose distributions are in general different for the independent and dependent models even though the probabilities of each voxel being tumorous are set to the same in both cases. We observe phase transitions, where a subvolume is either dosed to a high level, or it gets 'sacrificed' by not dosing it at all. The greedy strategy often yields solutions indistinguishable from the multi-start solutions, but for the 2D case involving organs-at-risk and the dependent TCP model, discrepancies of around 5% (absolute) for TCP are observed. For realistic geometries, although correlated voxels is a more reasonable assumption, the correlation function is in general unknown. We demonstrate a tractable heuristic that works very well for the independent models and reasonably well for the dependent models. All data are provided.

Aims: The objective of this study was to determine the effects of Ca-dipicolinic acid (CaDPA), cortex-lytic enzymes (CLEs), the inner membrane (IM) CaDPA channel and coat on spore killing by dodecylamine., Methods and Results: Bacillus subtilis spores, wild-type, CaDPA-less due to the absence of DPA synthase or the IM CaDPA channel, or lacking CLEs, were dodecylamine-treated and spore viability and vital staining were all determined. Dodecylamine killed intact wild-type and CaDPA-less B. subtilis spores similarly, and also killed intact Clostridiodes difficile spores ± CaDPA, with up to 99% killing with 1 mol l -1 dodecylamine in 4 h at 45°C with spores at ~10 8  ml -1 . Dodecylamine killing of decoated wild type and CLE-less B. subtilis spores was similar, but ~twofold faster than for intact spores, and much faster for decoated CaDPA-less spores, with ≥99% killing in 5 min. Propidium iodide stained intact spores ± CaDPA minimally, decoated CaDPA-replete spores or dodecylamine-killed CLE-less spores peripherally, and cores of decoated CaDPA-less spores and dodecylamine-killed intact spores with CLEs. The IM of some decoated CaDPA-less spores was greatly reorganized., Conclusions: Dodecylamine spore killing does not require CaDPA channels, CaDPA or CLEs. The lack of CaDPA in decoated spores allowed strong PI staining of the spore core, indicating loss of these spores IM permeability barrier., Significance and Impact of the Study: This work gives new information on killing bacterial spores by dodecylamine, and how spore IM's relative impermeability is maintained., (© 2020 The Society for Applied Microbiology.)

The discovery of drivers of cancer has traditionally focused on protein-coding genes 1-4 . Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium 5 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers 6,7 , raise doubts about others and identify novel candidates, including point mutations in the 5' region of TP53, in the 3' untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that although point mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available.

Motivation: In a predictive modeling setting, if sufficient details of the system behavior are known, one can build and use a simulation for making predictions. When sufficient system details are not known, one typically turns to machine learning, which builds a black-box model of the system using a large dataset of input sample features and outputs. We consider a setting which is between these two extremes: some details of the system mechanics are known but not enough for creating simulations that can be used to make high quality predictions. In this context we propose using approximate simulations to build a kernel for use in kernelized machine learning methods, such as support vector machines. The results of multiple simulations (under various uncertainty scenarios) are used to compute similarity measures between every pair of samples: sample pairs are given a high similarity score if they behave similarly under a wide range of simulation parameters. These similarity values, rather than the original high dimensional feature data, are used to build the kernel., Results: We demonstrate and explore the simulation-based kernel (SimKern) concept using four synthetic complex systems-three biologically inspired models and one network flow optimization model. We show that, when the number of training samples is small compared to the number of features, the SimKern approach dominates over no-prior-knowledge methods. This approach should be applicable in all disciplines where predictive models are sought and informative yet approximate simulations are available., Availability and Implementation: The Python SimKern software, the demonstration models (in MATLAB, R), and the datasets are available at https://github.com/davidcraft/SimKern., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press.)

Purpose: To provide a method for optimizing the multileaf collimator angle trajectory in volumetric modulated arc therapy (VMAT) in order to make VMAT delivery and planning more efficient., Methods: Static fluence maps are optimized at a 10-degree spacing around the patient. Sliding window delivery time of each of these fields is computed for a large set of possible collimator orientations. An optimal trajectory, which selects a collimator angle for each field and assures that the collimator angles do not differ excessively between adjacent fields, is computed by solving a network flow model of a shortest path problem., Results: For four clinical cases (two brains, an anal, and a spine), we demonstrate time reductions from 6% to 32% (average: 24%) for the optimal static angle vs the worst static angle. Further reductions from 3% to 17% (average 9%) are achievable when dynamic collimator trajectories are allowed., Conclusions: Dynamic collimator trajectories, which can be computed with an efficient linear programming formulation, can improve the efficiency of VMAT delivery., (© 2019 American Association of Physicists in Medicine.)

Setup and range uncertainties compromise radiotherapy plan robustness. We introduce a method to evaluate the clinical effect of these uncertainties on the population using tumor control probability (TCP) and normal tissue complication probability (NTCP) models. Eighteen oropharyngeal cancer patients treated with curative intent were retrospectively included. Both photon (VMAT) and proton (IMPT) plans were created using a planning target volume as planning objective. Plans were recalculated for uncertainty scenarios: two for range over/undershoot (IMPT) or CT-density scaling (VMAT), six for shifts. An average shift scenario ([Formula: see text]) was calculated to assess random errors. Dose differences between nominal and scenarios were translated to TCP (2 models) and NTCP (15 models). A weighted average (W_Avg) of the TCP\NTCP based on Gaussian distribution over the variance scenarios was calculated to assess the clinical effect of systematic errors on the population. TCP/NTCP uncertainties were larger in IMPT compared to VMAT. Although individual perturbations showed risks of plan deterioration, the [Formula: see text] scenario did not show a substantial decrease in any of the TCP endpoints suggesting evaluated plans in this cohort were robust for random errors. Evaluation of the W_Avg scenario to assess systematic errors showed in VMAT no substantial decrease in TCP endpoints and in IMPT a limited decrease. In IMPT, the W_Avg scenario had a mean TCP loss of 0%-2% depending on plan type and primary or nodal control. The W_Avg for NTCP endpoints was around 0%, except for mandible necrosis in IMPT (W_Avg: 3%). The estimated population impact of setup and range uncertainties on TCP/NTCP following VMAT or IMPT of oropharyngeal cancer patients was small for both treatment modalities. The use of TCP/NTCP models allows for clinical interpretation of the population effect and could be considered for incorporation in robust evaluation methods. Highlights: - TCP/NTCP models allow for a clinical evaluation of uncertainty scenarios. - For this cohort, in silico-PTV based IMPT plans and VMAT plans were robust for random setup errors. - Effect of systematic errors on the population was limited: mean TCP loss was 0%-2%.

Objectives: Underfilling of blood culture bottles decreases the sensitivity of the culture. We attempt to increase average blood culture fill volumes (ABCFVs) through an educational program., Methods: Partnerships were established with four hospital units (surgical intensive care unit [SICU], medical intensive care unit [MICU], medical intermediate care unit [MIMCU], and hematology and oncology unit [HEME/ONC]). ABCFVs were continuously tracked and communicated to each unit monthly. Educational sessions were provided to each unit., Results: ABCFVs for the SICU, MICU, MIMCU, and HEME/ONC were 4.8, 5.0, 5.0, and 6.3 mL/bottle, respectively. After the final education session, the SICU, MICU, MIMCU, and HEME/ONC were able to maintain an ABCFV of 6.8, 8.1, 7.9, and 8.2 mL/bottle, respectively., Conclusions: Partnering with a specific unit and providing monthly volume reports with educational sessions has a direct positive correlation on increasing ABCFVs. Increasing ABCFVs has the potential to decrease false-negative blood cultures, time to detection of positive blood cultures, and time to appropriate and specific antimicrobial therapy, as well as improve patient outcomes in high-acuity patient care units.

Definition of the clinical target volume (CTV) is one of the weakest links in the radiation therapy chain. In particular, inability to account for uncertainties is a severe limitation in the traditional CTV delineation approach. Here, we introduce and test a new concept for tumor target definition, the clinical target distribution (CTD). The CTD is a continuous distribution of the probability of voxels to be tumorous. We describe an approach to incorporate the CTD in treatment plan optimization algorithms, and implement it in a commercial treatment planning system. We test the approach in two synthetic and two clinical cases, a sarcoma and a glioblastoma case. The CTD is straightforward to implement in treatment planning and comes with several advantages. It allows one to find the most suitable tradeoff between target coverage and sparing of surrounding healthy organs at the treatment planning stage, without having to modify or redraw a CTV. Owing to the variable probabilities afforded by the CTD, a more flexible and more clinically meaningful sparing of critical structure becomes possible. Finally, the CTD is expected to reduce the inter-user variability of defining the traditional CTV.

A flexible strategy for choosing samples nonuniformly from a Nyquist grid using the concept of statistical quantiles is presented for broad classes of NMR experimentation. Quantile-directed scheduling is intuitive and flexible for any weighting function, promotes reproducibility and seed independence, and is generalizable to multiple dimensions. In brief, weighting functions are divided into regions of equal probability, which define the samples to be acquired. Quantile scheduling therefore achieves close adherence to a probability distribution function, thereby minimizing gaps for any given degree of subsampling of the Nyquist grid. A characteristic of quantile scheduling is that one-dimensional, weighted NUS schedules are deterministic, however higher dimensional schedules are similar within a user-specified jittering parameter. To develop unweighted sampling, we investigated the minimum jitter needed to disrupt subharmonic tracts, and show that this criterion can be met in many cases by jittering within 25-50% of the subharmonic gap. For nD-NUS, three supplemental components to choosing samples by quantiles are proposed in this work: (i) forcing the corner samples to ensure sampling to specified maximum values in indirect evolution times, (ii) providing an option to triangular backfill sampling schedules to promote dense/uniform tracts at the beginning of signal evolution periods, and (iii) providing an option to force the edges of nD-NUS schedules to be identical to the 1D quantiles. Quantile-directed scheduling meets the diverse needs of current NUS experimentation, but can also be used for future NUS implementations such as off-grid NUS and more. A computer program implementing these principles (a.k.a. QSched) in 1D- and 2D-NUS is available under the general public license., (Copyright © 2018 Elsevier Inc. All rights reserved.)

We introduce a new method called NoVo (Noncoplanar VMAT Optimization) to produce volumetric modulated arc therapy (VMAT) treatment plans with noncoplanar trajectories. While the use of noncoplanar beam arrangements for intensity modulated radiation therapy (IMRT), and in particular high fraction stereotactic radiosurgery (SRS), is common, noncoplanar beam trajectories for VMAT are less common as the availability of treatment machines handling these is limited. For both IMRT and VMAT, the beam angle selection problem is highly nonconvex in nature, which is why automated beam angle selection procedures have not entered mainstream clinical usage. NoVo determines a noncoplanar VMAT solution (i.e. the simultaneous trajectories of the gantry and the couch) by first computing a [Formula: see text] solution (beams from every possible direction, suitably discretized) and then eliminating beams by examing fluence contributions. Also all beam angles are scored via geometrical considerations only to find out the usefulness of the whole beam space in a very short time. A custom path finding algorithm is applied to find an optimized, continuous trajectory through the most promising beam angles using the calculated score of the beam space. Finally, using this trajectory a VMAT plan is optimized. For three clinical cases, a lung, brain, and liver case, we compare NoVo to the ideal [Formula: see text] solution, nine beam noncoplanar IMRT, coplanar VMAT, and a recently published noncoplanar VMAT algorithm. NoVo comes closest to the [Formula: see text] solution considering the lung case (brain and liver case: second), as well as improving the solution time by using geometrical considerations, followed by a time effective iterative process reducing the [Formula: see text] solution. Compared to a recently published noncoplanar VMAT algorithm, using NoVo the computation time is reduced by a factor of 2-3 (depending on the case). Compared to coplanar VMAT, NoVo reduces the objective function value by 24%, 49% and 6% for the lung, brain and liver cases, respectively.

Motivation: Our overall goal is to develop machine-learning approaches based on genomics and other relevant accessible information for use in predicting how a patient will respond to a given proposed drug or treatment. Given the complexity of this problem, we begin by developing, testing and analyzing learning methods using data from simulated systems, which allows us access to a known ground truth. We examine the benefits of using prior system knowledge and investigate how learning accuracy depends on various system parameters as well as the amount of training data available., Results: The simulations are based on Boolean networks-directed graphs with 0/1 node states and logical node update rules-which are the simplest computational systems that can mimic the dynamic behavior of cellular systems. Boolean networks can be generated and simulated at scale, have complex yet cyclical dynamics and as such provide a useful framework for developing machine-learning algorithms for modular and hierarchical networks such as biological systems in general and cancer in particular. We demonstrate that utilizing prior knowledge (in the form of network connectivity information), without detailed state equations, greatly increases the power of machine-learning algorithms to predict network steady-state node values ('phenotypes') and perturbation responses ('drug effects')., Availability and Implementation: Links to codes and datasets here: https://gray.mgh.harvard.edu/people-directory/71-david-craft-phd., Contact: dcraft@broadinstitute.org., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com)

Background: The purpose of this study was to demonstrate the feasibility of physician driven planning in intensity modulated radiotherapy (IMRT) with a multicriteria optimization (MCO) treatment planning system and template based plan optimization. Exploiting the full planning potential of MCO navigation, this alternative planning approach intends to improve planning efficiency and individual plan quality., Methods: Planning was retrospectively performed on 12 brain tumor and 10 post-prostatectomy prostate patients previously treated with MCO-IMRT. For each patient, physicians were provided with a template-based generated Pareto surface of optimal plans to navigate, using the beam angles from the original clinical plans. We compared physician generated plans to clinically delivered plans (created by dosimetrists) in terms of dosimetric differences, physician preferences and planning times., Results: Plan qualities were similar, however physician generated and clinical plans differed in the prioritization of clinical goals. Physician derived prostate plans showed significantly better sparing of the high dose rectum and bladder regions (p(D1) < 0.05; D1: dose received by 1% of the corresponding structure). Physicians' brain tumor plans indicated higher doses for targets and brainstem (p(D1) < 0.05). Within blinded plan comparisons physicians preferred the clinical plans more often (brain: 6:3 out of 12, prostate: 2:6 out of 10) (not statistically significant). While times of physician involvement were comparable for prostate planning, the new workflow reduced the average involved time for brain cases by 30%. Planner times were reduced for all cases. Subjective benefits, such as a better understanding of planning situations, were observed by clinicians through the insight into plan optimization and experiencing dosimetric trade-offs., Conclusions: We introduce physician driven planning with MCO for brain and prostate tumors as a feasible planning workflow. The proposed approach standardizes the planning process by utilizing site specific templates and integrates physicians more tightly into treatment planning. Physicians' navigated plan qualities were comparable to the clinical plans. Given the reduction of planning time of the planner and the equal or lower planning time of physicians, this approach has the potential to improve departmental efficiencies.

In this article we provide a method to generate the trade-off between delivery time and fluence map matching quality for dynamically delivered fluence maps. At the heart of our method lies a mathematical programming model that, for a given duration of delivery, optimizes leaf trajectories and dose rates such that the desired fluence map is reproduced as well as possible. We begin with the single fluence map case and then generalize the model and the solution technique to the delivery of sequential fluence maps. The resulting large-scale, non-convex optimization problem was solved using a heuristic approach. We test our method using a prostate case and a head and neck case, and present the resulting trade-off curves. Analysis of the leaf trajectories reveals that short time plans have larger leaf openings in general than longer delivery time plans. Our method allows one to explore the continuum of possibilities between coarse, large segment plans characteristic of direct aperture approaches and narrow field plans produced by sliding window approaches. Exposing this trade-off will allow for an informed choice between plan quality and solution time. Further research is required to speed up the optimization process to make this method clinically implementable.

We report a rare case of sacroileitis in a teenager resulting from Streptobacillus moniliformis bacteremia, identified by matrix-assisted laser desorption/ionization-time of flight. We discuss the challenges of making this diagnosis and review the literature on rat bite fever.

Proton pencil beam scanning (PBS) treatment plans are made of numerous unique spots of different weights. These weights are optimized by the treatment planning systems, and sometimes fall below the deliverable threshold set by the treatment delivery system. The purpose of this work is to investigate a Greedy reassignment algorithm to mitigate the effects of these low weight pencil beams. The algorithm is applied during post-processing to the optimized plan to generate deliverable plans for the treatment delivery system. The Greedy reassignment method developed in this work deletes the smallest weight spot in the entire field and reassigns its weight to its nearest neighbor(s) and repeats until all spots are above the minimum monitor unit (MU) constraint. Its performance was evaluated using plans collected from 190 patients (496 fields) treated at our facility. The Greedy reassignment method was compared against two other post-processing methods. The evaluation criteria was the γ-index pass rate that compares the pre-processed and post-processed dose distributions. A planning metric was developed to predict the impact of post-processing on treatment plans for various treatment planning, machine, and dose tolerance parameters. For fields with a pass rate of 90  ±  1% the planning metric has a standard deviation equal to 18% of the centroid value showing that the planning metric and γ-index pass rate are correlated for the Greedy reassignment algorithm. Using a 3rd order polynomial fit to the data, the Greedy reassignment method has 1.8 times better planning metric at 90% pass rate compared to other post-processing methods. As the planning metric and pass rate are correlated, the planning metric could provide an aid for implementing parameters during treatment planning, or even during facility design, in order to yield acceptable pass rates. More facilities are starting to implement PBS and some have spot sizes (one standard deviation) smaller than 5 mm, hence would require small spot spacing. While this is not the only parameter that affects the optimized plan, the perturbation due to the minimum MU constraint increases with decreasing spot spacing. This work could help to design the minimum MU threshold with the goal to keep the γ-index pass rate above an acceptable value.

Purpose: In this planning study, we studied the benefit of intensity-modulated radiation therapy (IMRT) with multi-criteria optimization (MCO) in locally advanced non-small cell lung carcinoma (NSCLC)., Methods: We selected 10 consecutive patients with gross tumor within 1cm of the esophagus eligible for RTOG 1308, randomized phase II trial of 70 Gy protons vs photons. Planning was performed per protocol. In addition, a novel approach for esophagus sparing was applied by making the contralateral esophagus (CE) an avoidance structure. MCO and non-MCO plans underwent double-blinded review. Plan differences in dose-volume histogram parameters were analyzed., Results: Median plan differences were mean lung dose=0.8 Gy (p=0.01), lung V20=1.1% (p=0.06), heart V30=1.0% (p=0.03), heart V45=0.6% (p=0.03), esophagus V60=1.2% (p=0.04), and CE V45=3.2% (p=0.01), all favoring MCO over non-MCO. PTV coverage with 95% dose was ⩾98.0% for both plans. There were 5 minor protocol deviations with non-MCO plans and 2 with MCO. Median improvement of active planning time with MCO was 88 min (p<0.01). Physicians preferred 8 MCO and 2 non-MCO plans (p=0.04)., Conclusions: MCO plans yielded significant improvements in organ-at-risk sparing without compromising target coverage, consumed less dosimetrist time, and were preferred by physicians. We suggest incorporating MCO into prospective clinical trials., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

Plasma hormone peptides, including GLP-1, GIP, Glucagon, and OXM, possess multiple physiological roles and potential therapeutic and diagnostic utility as biomarkers in the research of metabolic disorders. These peptides are subject to proteolytic degradation causing preanalytical variations. Stabilization for accurate quantitation of these active peptides in ex vivo blood specimens is essential for drug and biomarker development. We investigated the protease-driven instability of these peptides in conventional serum, plasma, anticoagulated whole blood, as well as whole blood and plasma stabilized with protease inhibitors. The peptide was monitored by both time-course Matrix-Assisted Laser Desorption Ionization Time-to-Flight Mass Spectrometry (MALDI -TOF MS) and Ab-based assay (ELISA or RIA). MS enabled the identification of proteolytic fragments. In non-stabilized blood samples, the results clearly indicated that dipeptidyl peptidase-IV (DPP-IV) removed the N-terminal two amino acid residues from GLP-1, GIP and OXM(1-37) and not-yet identified peptidase(s) cleave(s) the full-length OXM(1-37) and its fragments. DPP-IV also continued to remove two additional N-terminal residues of processed OXM(3-37) to yield OXM(5-37). Importantly, both DPP-IV and other peptidase(s) activities were inhibited efficiently by the protease inhibitors included in the BD P800* tube. There was preservation of GLP-1, GIP, OXM and glucagon in the P800 plasma samples with half-lives > 96, 96, 72, and 45 hours at room temperature (RT), respectively. In the BD P700* plasma samples, the stabilization of GLP-1 was also achieved with half-life > 96 hours at RT. The stabilization of these variable peptides increased their utility in drug and/or biomarker development. While stability results of GLP-1 obtained with Ab-based assay were consistent with those obtained by MS analysis, the Ab-based results of GIP, Glucagon, and OXM did not reflect the time-dependent degradations revealed by MS analysis. Therefore, we recommended characterizing the degradation of the peptide using the MS-based method when investigating the stability of a specific peptide.

Volumetric modulated arc therapy (VMAT) has found widespread clinical application in recent years. A large number of treatment planning studies have evaluated the potential for VMAT for different disease sites based on the currently available commercial implementations of VMAT planning. In contrast, literature on the underlying mathematical optimization methods used in treatment planning is scarce. VMAT planning represents a challenging large scale optimization problem. In contrast to fluence map optimization in intensity-modulated radiotherapy planning for static beams, VMAT planning represents a nonconvex optimization problem. In this paper, the authors review the state-of-the-art in VMAT planning from an algorithmic perspective. Different approaches to VMAT optimization, including arc sequencing methods, extensions of direct aperture optimization, and direct optimization of leaf trajectories are reviewed. Their advantages and limitations are outlined and recommendations for improvements are discussed.

Purpose: The purpose of this study was to evaluate automated multicriteria optimization (MCO), which is designed for intensity modulated radiation therapy (IMRT) but invoked with limited segmentation, to efficiently produce high-quality 3-dimensional (3D) conformal radiation therapy (3D-CRT) plans., Methods and Materials: Treatment for 10 patients previously planned with 3D-CRT to various disease sites (brain, breast, lung, abdomen, pelvis) was replanned with a low-segment inverse MCO technique. The MCO-3D plans used the same beam geometry of the original 3D plans but were limited to an energy of 6 MV. The MCO-3D plans were optimized with fluence-based MCO IMRT and then, after MCO navigation, segmented with a low number of segments. The 3D and MCO-3D plans were compared by evaluating mean dose for all structures, D95 (dose that 95% of the structure receives) and homogeneity indexes for targets, D1 and clinically appropriate dose-volume objectives for individual organs at risk (OARs), monitor units, and physician preference., Results: The MCO-3D plans reduced the mean doses to OARs (41 of a total of 45 OARs had a mean dose reduction; P << .01) and monitor units (7 of 10 plans had reduced monitor units; the average reduction was 17% [P = .08]) while maintaining clinical standards for coverage and homogeneity of target volumes. All MCO-3D plans were preferred by physicians over their corresponding 3D plans., Conclusions: High-quality 3D plans can be produced by use of MCO-IMRT optimization, resulting in automated field-in-field-type plans with good monitor unit efficiency. Adoption of this technology in a clinic could improve plan quality and streamline treatment plan production by using a single system applicable to both IMRT and 3D planning., (Copyright © 2015 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.)

Background: We provide common datasets (which we call the CORT dataset: common optimization for radiation therapy) that researchers can use when developing and contrasting radiation treatment planning optimization algorithms. The datasets allow researchers to make one-to-one comparisons of algorithms in order to solve various instances of the radiation therapy treatment planning problem in intensity modulated radiation therapy (IMRT), including beam angle optimization, volumetric modulated arc therapy and direct aperture optimization., Results: We provide datasets for a prostate case, a liver case, a head and neck case, and a standard IMRT phantom. We provide the dose-influence matrix from a variety of beam/couch angle pairs for each dataset. The dose-influence matrix is the main entity needed to perform optimizations: it contains the dose to each patient voxel from each pencil beam. In addition, the original Digital Imaging and Communications in Medicine (DICOM) computed tomography (CT) scan, as well as the DICOM structure file, are provided for each case., Conclusions: Here we present an open dataset - the first of its kind - to the radiation oncology community, which will allow researchers to compare methods for optimizing radiation dose delivery.

In multi-stage radiotherapy, a patient is treated in several stages separated by weeks or months. This regimen has been motivated mostly by radiobiological considerations, but also provides an approach to reduce normal tissue dose by exploiting tumor shrinkage. The paper considers the optimal design of multi-stage treatments, motivated by the clinical management of large liver tumors for which normal liver dose constraints prohibit the administration of an ablative radiation dose in a single treatment. We introduce a dynamic tumor model that incorporates three factors: radiation induced cell kill, tumor shrinkage, and tumor cell repopulation. The design of multi-stage radiotherapy is formulated as a mathematical optimization problem in which the total dose to the normal tissue is minimized, subject to delivering the prescribed dose to the tumor. Based on the model, we gain insight into the optimal administration of radiation over time, i.e. the optimal treatment gaps and dose levels. We analyze treatments consisting of two stages in detail. The analysis confirms the intuition that the second stage should be delivered just before the tumor size reaches a minimum and repopulation overcompensates shrinking. Furthermore, it was found that, for a large range of model parameters, approximately one-third of the dose should be delivered in the first stage. The projected benefit of multi-stage treatments in terms of normal tissue sparing depends on model assumptions. However, the model predicts large dose reductions by more than a factor of 2 for plausible model parameters. The analysis of the tumor model suggests that substantial reduction in normal tissue dose can be achieved by exploiting tumor shrinkage via an optimal design of multi-stage treatments. This suggests taking a fresh look at multi-stage radiotherapy for selected disease sites where substantial tumor regression translates into reduced target volumes.

Purpose: To describe a method for combining sliding window plans [intensity modulated radiation therapy (IMRT) or volumetric modulated arc therapy (VMAT)] for use in treatment plan averaging, which is needed for Pareto surface navigation based multicriteria treatment planning., Methods: The authors show that by taking an appropriately defined average of leaf trajectories of sliding window plans, the authors obtain a sliding window plan whose fluence map is the exact average of the fluence maps corresponding to the initial plans. In the case of static-beam IMRT, this also implies that the dose distribution of the averaged plan is the exact dosimetric average of the initial plans. In VMAT delivery, the dose distribution of the averaged plan is a close approximation of the dosimetric average of the initial plans., Results: The authors demonstrate the method on three Pareto optimal VMAT plans created for a demanding paraspinal case, where the tumor surrounds the spinal cord. The results show that the leaf averaged plans yield dose distributions that approximate the dosimetric averages of the precomputed Pareto optimal plans well., Conclusions: The proposed method enables the navigation of deliverable Pareto optimal plans directly, i.e., interactive multicriteria exploration of deliverable sliding window IMRT and VMAT plans, eliminating the need for a sequencing step after navigation and hence the dose degradation that is caused by such a sequencing step.

Stereotactic body radiation therapy (SBRT) is characterized by delivering a high amount of dose in a short period of time. In SBRT the dose is delivered using open fields (e.g., beam's-eye-view) known as 'apertures'. Mathematical methods can be used for optimizing treatment planning for delivery of sufficient dose to the cancerous cells while keeping the dose to surrounding organs at risk (OARs) minimal. Two important elements of a treatment plan are quality and delivery time. Quality of a plan is measured based on the target coverage and dose to OARs. Delivery time heavily depends on the number of beams used in the plan as the setup times for different beam directions constitute a large portion of the delivery time. Therefore the ideal plan, in which all potential beams can be used, will be associated with a long impractical delivery time. We use the dose to OARs in the ideal plan to find the plan with the minimum number of beams which is guaranteed to be epsilon-optimal (i.e., a predetermined maximum deviation from the ideal plan is guaranteed). Since the treatment plan optimization is inherently a multi-criteria-optimization problem, the planner can navigate the ideal dose distribution Pareto surface and select a plan of desired target coverage versus OARs sparing, and then use the proposed technique to reduce the number of beams while guaranteeing epsilon-optimality. We use mixed integer programming (MIP) for optimization. To reduce the computation time for the resultant MIP, we use two heuristics: a beam elimination scheme and a family of heuristic cuts, known as 'neighbor cuts', based on the concept of 'adjacent beams'. We show the effectiveness of the proposed technique on two clinical cases, a liver and a lung case. Based on our technique we propose an algorithm for fast generation of epsilon-optimal plans.

Purpose: To investigate the feasibility and potential clinical benefit of linear energy transfer (LET) guided plan optimization in intensity modulated proton therapy (IMPT)., Methods and Materials: A multicriteria optimization (MCO) module was used to generate a series of Pareto-optimal IMPT base plans (BPs), corresponding to defined objectives, for 5 patients with head-and-neck cancer and 2 with pancreatic cancer. A Monte Carlo platform was used to calculate dose and LET distributions for each BP. A custom-designed MCO navigation module allowed the user to interpolate between BPs to produce deliverable Pareto-optimal solutions. Differences among the BPs were evaluated for each patient, based on dose-volume and LET-volume histograms and 3-dimensional distributions. An LET-based relative biological effectiveness (RBE) model was used to evaluate the potential clinical benefit when navigating the space of Pareto-optimal BPs., Results: The mean LET values for the target varied up to 30% among the BPs for the head-and-neck patients and up to 14% for the pancreatic cancer patients. Variations were more prominent in organs at risk (OARs), where mean LET values differed by a factor of up to 2 among the BPs for the same patient. An inverse relation between dose and LET distributions for the OARs was typically observed. Accounting for LET-dependent variable RBE values, a potential improvement on RBE-weighted dose of up to 40%, averaged over several structures under study, was noticed during MCO navigation., Conclusions: We present a novel strategy for optimizing proton therapy to maximize dose-averaged LET in tumor targets while simultaneously minimizing dose-averaged LET in normal tissue structures. MCO BPs show substantial LET variations, leading to potentially significant differences in RBE-weighted doses. Pareto-surface navigation, using both dose and LET distributions for guidance, provides the means for evaluating a large variety of deliverable plans and aids in identifying the clinically optimal solution., (Copyright © 2013 Elsevier Inc. All rights reserved.)

We examine the quality of plans created using multicriteria optimization (MCO) treatment planning in intensity-modulated radiation therapy (IMRT) in treatment of localized prostate cancer. Nine random cases of patients receiving IMRT to the prostate were selected. Each case was associated with a clinically approved plan created using Corvus. The cases were replanned using MCO-based planning in RayStation. Dose-volume histogram data from both planning systems were presented to 2 radiation oncologists in a blinded evaluation, and were compared at a number of dose-volume points. Both physicians rated all 9 MCO plans as superior to the clinically approved plans (p<10(-5)). Target coverage was equivalent (p = 0.81). Maximum doses to the prostate and bladder and the V50 and V70 to the anterior rectum were reduced in all MCO plans (p<0.05). Treatment planning time with MCO took approximately 60 minutes per case. MCO-based planning for prostate IMRT is efficient and produces high-quality plans with good target homogeneity and sparing of the anterior rectum, bladder, and femoral heads, without sacrificing target coverage., (Copyright © 2013 American Association of Medical Dosimetrists. Published by Elsevier Inc. All rights reserved.)

We consider Pareto surface based multi-criteria optimization for step and shoot IMRT planning. By analyzing two navigation algorithms, we show both theoretically and in practice that the number of plans needed to form convex combinations of plans during navigation can be kept small (much less than the theoretical maximum number needed in general, which is equal to the number of objectives for on-surface Pareto navigation). Therefore a workable approach for directly deliverable navigation in this setting is to segment the underlying Pareto surface plans and then enforce the mild restriction that only a small number of these plans are active at any time during plan navigation, thus limiting the total number of segments used in the final plan.

We consider the fractionation problem in radiation therapy. Tumor sites in which the dose-limiting organ at risk (OAR) receives a substantially lower dose than the tumor, bear potential for hypofractionation even if the α/β-ratio of the tumor is larger than the α/β-ratio of the OAR. In this work, we analyze the interdependence of the optimal fractionation scheme and the spatial dose distribution in the OAR. In particular, we derive a criterion under which a hypofractionation regimen is indicated for both a parallel and a serial OAR. The approach is based on the concept of the biologically effective dose (BED). For a hypothetical homogeneously irradiated OAR, it has been shown that hypofractionation is suggested by the BED model if the α/β-ratio of the OAR is larger than α/β-ratio of the tumor times the sparing factor, i.e. the ratio of the dose received by the tumor and the OAR. In this work, we generalize this result to inhomogeneous dose distributions in the OAR. For a parallel OAR, we determine the optimal fractionation scheme by minimizing the integral BED in the OAR for a fixed BED in the tumor. For a serial structure, we minimize the maximum BED in the OAR. This leads to analytical expressions for an effective sparing factor for the OAR, which provides a criterion for hypofractionation. The implications of the model are discussed for lung tumor treatments. It is shown that the model supports hypofractionation for small tumors treated with rotation therapy, i.e. highly conformal techniques where a large volume of lung tissue is exposed to low but nonzero dose. For larger tumors, the model suggests hyperfractionation. We further discuss several non-intuitive interdependencies between optimal fractionation and the spatial dose distribution. For instance, lowering the dose in the lung via proton therapy does not necessarily provide a biological rationale for hypofractionation.

Global dissemination of imipenem-resistant (IR) clones of Acinetobacter baumannii - A. calcoaceticus complex (ABC) have been frequently reported but the molecular epidemiological features of IR-ABC in military treatment facilities (MTFs) have not been described. We characterized 46 IR-ABC strains from a dataset of 298 ABC isolates collected from US service members hospitalized in different US MTFs domestically and overseas during 2003-2008. All IR strains carried the bla(OXA-51) gene and 40 also carried bla(OXA-23) on plasmids and/or chromosome; one carried bla(OXA-58) and four contained ISAbal located upstream of bla(OXA-51). Strains tended to cluster by pulsed-field gel electrophoresis profiles in time and location. Strains from two major clusters were identified as international clone I by multilocus sequence typing.