Alcohol‐Associated Liver Disease Before and After COVID‐19 – An Overview and Call for Ongoing Investigation

The Coronavirus Disease-2019 (COVID-19) pandemic has exacted a heavy toll on patients with alcohol-associated liver disease (ALD) and alcohol use disorder (AUD). The collective burden of ALD and AUD was large and growing prior to the COVID-19 pandemic. There is accumulating evidence that this pandemic has had a large direct effect on these patients and is likely to produce indirect effects via delays in care, psychological strain, and increased alcohol use. Now a year into the pandemic, it is important that clinicians fully understand the effects of the COVID-19 pandemic on patients with ALD and AUD. To fill existing gaps in knowledge, the scientific community must set research priorities for patients with ALD regarding their risk of COVID-19, prevention/treatment of COVID-19, changes in alcohol use during the pandemic, best use of AUD treatments in the COVID-19 era, and downstream effects of this pandemic on ALD. Conclusion: The COVID-19 pandemic has already inflicted disproportionate harms on patients with ALD and ongoing, focused research efforts will be critical to better understand the direct and collateral effects of this pandemic on ALD

Development and Pilot of a Checklist for Management of Acute Liver Failure in the Intensive Care Unit

Introduction Acute liver failure (ALF) is an ideal condition for use of a checklist. Our aims were to develop a checklist for the management of ALF in the intensive care unit (ICU) and assess the usability of the checklist among multiple providers. Methods The initial checklist was developed from published guidelines and expert opinion. The checklist underwent pilot testing at 11 academic liver transplant centers in the US and Canada. An anonymous, written survey was used to assess the usability and quality of the checklist. Written comments were used to improve the checklist following the pilot testing period. Results We received 81 surveys involving the management of 116 patients during the pilot testing period. The overall quality of the checklist was judged to be above average to excellent by 94% of users. On a 5-point Likert scale, the majority of survey respondents agreed or agreed strongly with the following checklist characteristics: the checklist was easy to read (99% agreed/agreed strongly), easy to use (97%), items are categorized logically (98%), time to complete the checklist did not interfere with delivery of appropriate and safe patient care (94%) and was not excessively burdensome (92%), the checklist allowed the user the freedom to use his or her clinical judgment (80%), it is a useful tool in the management of acute liver failure (98%). Web-based and mobile apps were developed for use of the checklist at the point of care. Conclusion The checklist for the management of ALF in the ICU was shown in this pilot study to be easy to use, helpful and accepted by a wide variety of practitioners at multiple sites in the US and Canada

Assessing dose rate distributions in VMAT plans

Dose rate is an essential factor in radiobiology. As modern radiotherapy delivery techniques such as volumetric modulated arc therapy (VMAT) introduce dynamic modulation of the dose rate, it is important to assess the changes in dose rate. Both the rate of monitor units per minute (MU rate) and collimation are varied over the course of a fraction, leading to different dose rates in every voxel of the calculation volume at any point in time during dose delivery. Given the radiotherapy plan and machine specific limitations, a VMAT treatment plan can be split into arc sectors between Digital Imaging and Communications in Medicine control points (CPs) of constant and known MU rate. By calculating dose distributions in each of these arc sectors independently and multiplying them with the MU rate, the dose rate in every single voxel at every time point during the fraction can be calculated. Independently calculated and then summed dose distributions per arc sector were compared to the whole arc dose calculation for validation. Dose measurements and video analysis were performed to validate the calculated datasets. A clinical head and neck, cranial and liver case were analyzed using the tool developed. Measurement validation of synthetic test cases showed linac agreement to precalculated arc sector times within ±0.4 s and doses ±0.1 MU (one standard deviation). Two methods for the visualization of dose rate datasets were developed: the first method plots a two-dimensional (2D) histogram of the number of voxels receiving a given dose rate over the course of the arc treatment delivery. In similarity to treatment planning system display of dose, the second method displays the dose rate as color wash on top of the corresponding computed tomography image, allowing the user to scroll through the variation over time. Examining clinical cases showed dose rates spread over a continuous spectrum, with mean dose rates hardly exceeding 100 cGy min(-1) for conventional fractionation. A tool to analyze dose rate distributions in VMAT plans with sub-second accuracy was successfully developed and validated. Dose rates encountered in clinical VMAT test cases show a continuous spectrum with a mean less than or near 100 cGy min(-1) for conventional fractionation

A hybrid column generation and simulated annealing algorithm for direct aperture optimization.

The purpose of this work was to develop a hybrid column generation (CG) and simulated annealing (SA) algorithm for direct aperture optimization (H-DAO) and to show its effectiveness in generating high quality treatment plans for intensity modulated radiation therapy (IMRT) and mixed photon-electron beam radiotherapy (MBRT). The H-DAO overcomes limitations of the CG-DAO with two features improving aperture selection (branch-feature) and enabling aperture shape changes during optimization (SA feature). The H-DAO algorithm iteratively adds apertures to the plan. At each iteration, a branch is created for each field provided. First, each branch determines the most promising aperture of its assigned field and adds it to a copy of the current apertures. Afterwards, the apertures of each branch undergo an MU-weight optimization followed by an SA-based simultaneous shape and MU-weight optimization and a second MU-weight optimization. The next H-DAO iteration continues the branch with the lowest objective function value. IMRT and MBRT treatment plans for an academic, a brain and a head and neck case generated using the CG DAO and H DAO were compared. For every investigated case and both IMRT and MBRT, the H-DAO leads to a faster convergence of the objective function value with number of apertures compared to the CG-DAO. In particular, the H DAO needs on average half the apertures to reach the same objective function value as the CG DAO for a specifically selected number of apertures. The average aperture areas are 27% smaller for H-DAO than for CG-DAO leading to a slightly larger discrepancy between optimized and final dose. However, a dosimetric benefit remains. The H-DAO was successfully developed and applied to IMRT and MBRT. The faster convergence with number of apertures of the H-DAO compared to the CG-DAO allows to select a better compromise between plan quality and number of apertures

Efficiency enhancements of a Monte Carlo beamlet based treatment planning process: implementation and parameter study.

OBJECTIVE The computational effort to perform beamlet calculation, plan optimization and final dose calculation of a treatment planning process (TPP) generating intensity modulated treatment plans is enormous, especially if Monte Carlo (MC) simulations are used for dose calculation. The goal of this work is to improve the computational efficiency of a fully MC based TPP for static and dynamic photon, electron and mixed photon-electron treatment techniques by implementing multiple methods and studying the influence of their parameters. APPROACH A framework is implemented calculating MC beamlets efficiently in parallel on each available CPU core. The user can specify the desired statistical uncertainty of the beamlets, a fractional sparse dose threshold to save beamlets in a sparse format and minimal distances to the PTV surface from which 2x2x2=8 (medium) or even 4x4x4=64 (large) voxels are merged. The compromise between final plan quality and computational efficiency of beamlet calculation and optimization is studied for several parameter values to find a reasonable trade-off. For this purpose, four clinical and one academic case are considered with different treatment techniques. MAIN RESULTS Setting the statistical uncertainty to 5% (photon beamlets) and 15% (electron beamlets), the fractional sparse dose threshold relative to the maximal beamlet dose to 0.1% and minimal distances for medium and large voxels to the PTV to 1 cm and 2 cm, respectively, does not lead to substantial degradation in final plan quality. Only OAR sparing is slightly degraded. Furthermore, computation times are reduced by about 58% (photon beamlets), 88% (electron beamlets) and 96% (optimization) compared to using 2.5% (photon beamlets) and 5% (electron beamlets) statistical uncertainty and no sparse format nor voxel merging. SIGNIFICANCE Several methods are implemented improving computational efficiency of beamlet calculation and plan optimization of a fully MC based TPP without substantial degradation in final plan quality

Measurement of the invariant mass distributions for the pp -> ppeta' reaction at excess energy of Q = 16.4 MeV

The proton-proton and proton-eta' invariant mass distributions have been determined for the pp -> ppeta' reaction at an excess energy of Q = 16.4 MeV. The measurement was carried out using the COSY-11 detector setup and the proton beam of the cooler synchrotron COSY. The shapes of the determined invariant mass distributions are similar to those of the pp -> ppeta reaction and reveal an enhancement for large relative proton-proton momenta. This result, together with the fact that the proton-eta interaction is much stronger that the proton-eta' interaction, excludes the hypothesis that the observed enhancement is caused by the interaction between the proton and the meson

Impact of random outliers in auto-segmented targets on radiotherapy treatment plans for glioblastoma.

AIMS To save time and have more consistent contours, fully automatic segmentation of targets and organs at risk (OAR) is a valuable asset in radiotherapy. Though current deep learning (DL) based models are on par with manual contouring, they are not perfect and typical errors, as false positives, occur frequently and unpredictably. While it is possible to solve this for OARs, it is far from straightforward for target structures. In order to tackle this problem, in this study, we analyzed the occurrence and the possible dose effects of automated delineation outliers. METHODS First, a set of controlled experiments on synthetically generated outliers on the CT of a glioblastoma (GBM) patient was performed. We analyzed the dosimetric impact on outliers with different location, shape, absolute size and relative size to the main target, resulting in 61 simulated scenarios. Second, multiple segmentation models where trained on a U-Net network based on 80 training sets consisting of GBM cases with annotated gross tumor volume (GTV) and edema structures. On 20 test cases, 5 different trained models and a majority voting method were used to predict the GTV and edema. The amount of outliers on the predictions were determined, as well as their size and distance from the actual target. RESULTS We found that plans containing outliers result in an increased dose to healthy brain tissue. The extent of the dose effect is dependent on the relative size, location and the distance to the main targets and involved OARs. Generally, the larger the absolute outlier volume and the distance to the target the higher the potential dose effect. For 120 predicted GTV and edema structures, we found 1887 outliers. After construction of the planning treatment volume (PTV), 137 outliers remained with a mean distance to the target of 38.5 ± 5.0 mm and a mean size of 1010.8 ± 95.6 mm3. We also found that majority voting of DL results is capable to reduce outliers. CONCLUSIONS This study shows that there is a severe risk of false positive outliers in current DL predictions of target structures. Additionally, these errors will have an evident detrimental impact on the dose and therefore could affect treatment outcome

Impact of the gradient in gantry-table rotation on dynamic trajectory radiotherapy plan quality.

BACKGROUND To improve organ at risk (OAR) sparing, dynamic trajectory radiotherapy (DTRT) extends VMAT by dynamic table and collimator rotation during beam-on. However, comprehensive investigations regarding the impact of the gantry-table (GT) rotation gradient on the DTRT plan quality have not been conducted. PURPOSE To investigate the impact of a user-defined GT rotation gradient on plan quality of DTRT plans in terms of dosimetric plan quality, dosimetric robustness, deliverability, and delivery time. METHODS The dynamic trajectories of DTRT are described by GT and gantry-collimator paths. The GT path is determined by minimizing the overlap of OARs with planning target volume (PTV). This approach is extended to consider a GT rotation gradient by means of a maximum gradient of the path ( ) between two adjacent control points ( ) and maximum absolute change of G ( ). Four DTRT plans are created with different maximum G&∆G: &  = 0.5&0.125 (DTRT-1), 1&0.125 (DTRT-2), 3&0.125 (DTRT-3) and 3&1‍(DTRT-4), including 3-4 dynamic trajectories, for three clinically motivated cases in the head and neck and brain region (A, B, and C). A reference VMAT plan for each case is created. For all plans, plan quality is assessed and compared. Dosimetric plan quality is evaluated by target coverage, conformity, and OAR sparing. Dosimetric robustness is evaluated against systematic and random patient-setup uncertainties between in the lateral, longitudinal, and vertical directions, and machine uncertainties between in the dynamically rotating machine components (gantry, table, collimator rotation). Delivery time is recorded. Deliverability and delivery accuracy on a TrueBeam are assessed by logfile analysis for all plans and additionally verified by film measurements for one case. All dose calculations are Monte Carlo based. RESULTS The extension of the DTRT planning process with user-defined to investigate the impact of the GT rotation gradient on plan quality is successfully demonstrated. With increasing , slight (case C, : up to‍-1‍Gy) and substantial (case A, : up to -9.3 Gy, case‍B, : up to -4.7‍Gy) improvements in OAR sparing are observed compared to VMAT, while maintaining similar target coverage. All plans are delivered on the TrueBeam. Expected and actual machine position values recorded in the logfiles deviated by 96% (2%‍global/2 mm Gamma passing rate) with the dose calculation. With increasing , delivery time is prolonged by <2 min/trajectory (DTRT-4) compared to VMAT and DTRT-1. The DTRT plans for case A and B and the VMAT plan for case C plan reveal the best dosimetric robustness for the considered uncertainties. CONCLUSION The impact of the GT rotation gradient on DTRT plan quality is comprehensively investigated for three cases in the head and neck and brain region. Increasing freedom in this gradient improves dosimetric plan quality at the cost of increased delivery time for the investigated cases. No clear dependency of GT rotation gradient on dosimetric robustness is observed

Technical note: A collision prediction tool using Blender.

Non-coplanar radiotherapy treatment techniques on C-arm linear accelerators have the potential to reduce dose to organs-at-risk in comparison with coplanar treatment techniques. Accurately predicting possible collisions between gantry, table and patient during treatment planning is needed to ensure patient safety. We offer a freely available collision prediction tool using Blender, a free and open-source 3D computer graphics software toolset. A geometric model of a C-arm linear accelerator including a library of patient models is created inside Blender. Based on the model, collision predictions can be used both to calculate collision-free zones and to check treatment plans for collisions. The tool is validated for two setups, once with and once without a full body phantom with the same table position. For this, each gantry-table angle combination with a 2° resolution is manually checked for collision interlocks at a TrueBeam system and compared to simulated collision predictions. For the collision check of a treatment plan, the tool outputs the minimal distance between the gantry, table and patient model and a video of the movement of the gantry and table, which is demonstrated for one use case. A graphical user interface allows user-friendly input of the table and patient specification for the collision prediction tool. The validation resulted in a true positive rate of 100%, which is the rate between the number of correctly predicted collision gantry-table combinations and the number of all measured collision gantry-table combinations, and a true negative rate of 89%, which is the ratio between the number of correctly predicted collision-free combinations and the number of all measured collision-free combinations. A collision prediction tool is successfully created and able to produce maps of collision-free zones and to test treatment plans for collisions including visualisation of the gantry and table movement