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Comparison of Various Online Strategies to Account for Interfractional Variations for Pancreatic Cancer
Purpose: To identify practical techniques to address the large interfractional variations for pancreas irradiation by comparing various used/proposed online strategies. Methods and Materials: The daily computed tomography (CT) images acquired using a respiration-gated in-room CT (CTVision; Siemens) for 10 pancreatic cancer patients treated with image guided radiation therapy (IGRT) were analyzed. The contours of the pancreas and organs at risk on each daily CT set were generated by populating from the planning CT using a deformable registration tool (ABAS; Elekta) with manual editing. Nine online strategies were considered: (1) standard IGRT (ie, IGRT with 0-mm additional margin [AM]); (2) IGRT with 2-mm AM; (3) IGRT with 5-mm AM; (4) IGRT with plan renormalized to maintain 95% planning target volume (PTV) coverage; (5) full-scale reoptimization; (6) reoptimization starting from the original plan; (7) segment aperture morphing (SAM) from the original plan, based on PTV shape change; (8) SAM plus segment weight optimization; and (9) reoptimization starting from the SAM plan. One-way analysis of variance was applied to plan qualities for the 9 strategies to assess statistical significance in difference. Results: The 3 IGRT strategies (1-3) lead to either inadequate PTV coverage or higher doses to critical structures, indicating that the additional margins alone are not adequate to account for the changes. The full-scale reoptimization results in the best plan but requires the delineation of several structures, which is time consuming. The SAM strategy (7) was the fastest one, because it requires delineating only 1 structure (target), and its plan quality was comparable to that for the full-scale reoptimization. Conclusion: Online replanning strategies can lead to either reduced organs-at-risk dose and/or improved target coverage as compared with the current practice of IGRT. The SAM-based online replanning is comparable to full-scale reoptimization and is efficient for practical use.
Doses to Carotid Arteries After Modern Radiation Therapy for Hodgkin Lymphoma: Is Stroke Still a Late Effect of Treatment?
Purpose: Hodgkin lymphoma (HL) survivors are at an increased risk of stroke because of carotid artery irradiation. However, for early-stage HL involved node radiation therapy (INRT) reduces the volume of normal tissue exposed to high doses. Here, we evaluate 3-dimensional conformal radiation therapy (3D-CRT), volumetric-modulated arc therapy (VMAT), and proton therapy (PT) delivered as INRT along with the extensive mantle field (MF) by comparing doses to the carotid arteries and corresponding risk estimates. Methods and Materials: We included a cohort of 46 supradiaphragmatic stage I-II classical HL patients. All patients were initially treated with chemotherapy and INRT delivered as 3D-CRT (30 Gy). For each patient, we simulated MF (36 Gy) and INRT plans using VMAT and PT (30 Gy). Linear dose-response curves for the 20-, 25-, and 30-year risk of stroke were derived from published HL data. Risks of stroke with each technique were calculated for all patients. Statistical analyses were performed with repeated measures analysis of variance. Results: The mean doses to the right and left common carotid artery were significantly lower with modern treatment compared with MF, with substantial patient variability. The estimated excess risk of stroke after 20, 25, and 30 years was 0.6%, 0.86%, and 1.3% for 3D-CRT; 0.67%, 0.96%, and 1.47% for VMAT; 0.61%, 0.96%, and 1.33% for PT; and 1.3%, 1.72%, and 2.61% for MF. Conclusions: INRT reduces the dose delivered to the carotid arteries and corresponding estimated risk of stroke for HL survivors. Even for the subset of patients with lymphoma close to the carotid arteries, the estimated risk is low.
Efficacy of Synbiotics to Reduce Acute Radiation Proctitis Symptoms and Improve Quality of Life: A Randomized, Double-Blind, Placebo-Controlled Pilot Trial
Purpose: To evaluate whether the daily intake of synbiotics interferes in radiation-induced acute proctitis symptoms and in quality of life in patients with prostate cancer. Methods and Materials: Twenty patients who underwent 3-dimensional conformal radiation therapy for prostate cancer were randomized to intake either a synbiotic powder containing Lactobacillus reuteri 10{sup 8} colony-forming units and 4.3 g of soluble fiber (Nestlé) or placebo. The questionnaire EORTC QLQ-PRT23 was applied before the beginning of radiation therapy and in every week for the first 4 weeks of treatment. The sum of both the complete (proctitis symptoms plus quality of life) and partial (proctitis symptoms) scores of the EORTC QLQ-PRT23 (European Organization for Research and Treatment of Cancer Quality of Life Module for Proctitis–23 items) questionnaire were the main endpoints. Results: This pilot study showed that the complete questionnaire score (median [range]) was higher in the second (23 [21-30] vs 26.5 [22-34], P<.05) and third (23 [21-32] vs 27.5 [24-33], P<.01) weeks in the placebo group. Proctitis symptoms were highest scored in the placebo group in both the second (19.5 [16-25]) and third (19 [17-24]) weeks than in the synbiotic group (week 2: 16.5 [15-20], P<.05; week 3: 17 [15-23], P<.01). In both scores the placebo group had a significantly higher result (P<.01) than the synbiotic group (repeated-measures analysis of variance). Conclusions: Synbiotics reduce proctitis symptoms and improve quality of life in radiation-induced acute proctitis during radiation therapy for prostate cancer.
Deformable Image Registration for Adaptive Radiation Therapy of Head and Neck Cancer: Accuracy and Precision in the Presence of Tumor Changes
Purpose: To compare deformable image registration (DIR) accuracy and precision for normal and tumor tissues in head and neck cancer patients during the course of radiation therapy (RT). Methods and Materials: Thirteen patients with oropharyngeal tumors, who underwent submucosal implantation of small gold markers (average 6, range 4-10) around the tumor and were treated with RT were retrospectively selected. Two observers identified 15 anatomical features (landmarks) representative of normal tissues in the planning computed tomography (pCT) scan and in weekly cone beam CTs (CBCTs). Gold markers were digitally removed after semiautomatic identification in pCTs and CBCTs. Subsequently, landmarks and gold markers on pCT were propagated to CBCTs, using a b-spline-based DIR and, for comparison, rigid registration (RR). To account for observer variability, the pair-wise difference analysis of variance method was applied. DIR accuracy (systematic error) and precision (random error) for landmarks and gold markers were quantified. Time trend of the precisions for RR and DIR over the weekly CBCTs were evaluated. Results: DIR accuracies were submillimeter and similar for normal and tumor tissue. DIR precision (1 SD) on the other hand was significantly different (P<.01), with 2.2 mm vector length in normal tissue versus 3.3 mm in tumor tissue. No significant time trend in DIR precision was found for normal tissue, whereas in tumor, DIR precision was significantly (P<.009) degraded during the course of treatment by 0.21 mm/week. Conclusions: DIR for tumor registration proved to be less precise than that for normal tissues due to limited contrast and complex non-elastic tumor response. Caution should therefore be exercised when applying DIR for tumor changes in adaptive procedures.
Lung Texture in Serial Thoracic Computed Tomography Scans: Correlation of Radiomics-based Features With Radiation Therapy Dose and Radiation Pneumonitis Development
Purpose: To assess the relationship between radiation dose and change in a set of mathematical intensity- and texture-based features and to determine the ability of texture analysis to identify patients who develop radiation pneumonitis (RP). Methods and Materials: A total of 106 patients who received radiation therapy (RT) for esophageal cancer were retrospectively identified under institutional review board approval. For each patient, diagnostic computed tomography (CT) scans were acquired before (0-168 days) and after (5-120 days) RT, and a treatment planning CT scan with an associated dose map was obtained. 32- × 32-pixel regions of interest (ROIs) were randomly identified in the lungs of each pre-RT scan. ROIs were subsequently mapped to the post-RT scan and the planning scan dose map by using deformable image registration. The changes in 20 feature values (ΔFV) between pre- and post-RT scan ROIs were calculated. Regression modeling and analysis of variance were used to test the relationships between ΔFV, mean ROI dose, and development of grade ≥2 RP. Area under the receiver operating characteristic curve (AUC) was calculated to determine each feature's ability to distinguish between patients with and those without RP. A classifier was constructed to determine whether 2- or 3-feature combinations could improve RP distinction. Results: For all 20 features, a significant ΔFV was observed with increasing radiation dose. Twelve features changed significantly for patients with RP. Individual texture features could discriminate between patients with and those without RP with moderate performance (AUCs from 0.49 to 0.78). Using multiple features in a classifier, AUC increased significantly (0.59-0.84). Conclusions: A relationship between dose and change in a set of image-based features was observed. For 12 features, ΔFV was significantly related to RP development. This study demonstrated the ability of radiomics to provide a quantitative, individualized measurement of patient lung tissue reaction to RT and assess RP development.
Evaluation of a Machine-Learning Algorithm for Treatment Planning in Prostate Low-Dose-Rate Brachytherapy
Purpose: This work presents the application of a machine learning (ML) algorithm to automatically generate high-quality, prostate low-dose-rate (LDR) brachytherapy treatment plans. The ML algorithm can mimic characteristics of preoperative treatment plans deemed clinically acceptable by brachytherapists. The planning efficiency, dosimetry, and quality (as assessed by experts) of preoperative plans generated with an ML planning approach was retrospectively evaluated in this study. Methods and Materials: Preimplantation and postimplantation treatment plans were extracted from 100 high-quality LDR treatments and stored within a training database. The ML training algorithm matches similar features from a new LDR case to those within the training database to rapidly obtain an initial seed distribution; plans were then further fine-tuned using stochastic optimization. Preimplantation treatment plans generated by the ML algorithm were compared with brachytherapist (BT) treatment plans in terms of planning time (Wilcoxon rank sum, α = 0.05) and dosimetry (1-way analysis of variance, α = 0.05). Qualitative preimplantation plan quality was evaluated by expert LDR radiation oncologists using a Likert scale questionnaire. Results: The average planning time for the ML approach was 0.84 ± 0.57 minutes, compared with 17.88 ± 8.76 minutes for the expert planner (P=.020). Preimplantation plans were dosimetrically equivalent to the BT plans; the average prostate V150% was 4% lower for ML plans (P=.002), although the difference was not clinically significant. Respondents ranked the ML-generated plans as equivalent to expert BT treatment plans in terms of target coverage, normal tissue avoidance, implant confidence, and the need for plan modifications. Respondents had difficulty differentiating between plans generated by a human or those generated by the ML algorithm. Conclusions: Prostate LDR preimplantation treatment plans that have equivalent quality to plans created by brachytherapists can be rapidly generated using ML. The adoption of ML in the brachytherapy workflow is expected to improve LDR treatment plan uniformity while reducing planning time and resources.
Radiation therapy of synchronous bilateral breast carcinoma (SBBC) using multiple techniques
The purpose of this study was to establish intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT) treatment plans for synchronous bilateral breast cancer (SBBC) and to compare those plans with the previous treatment plans using 3D conformal radiation therapy (3DCRT). The differences among the treatments were also statistically compared regarding dosimetry distribution and treatment efficiency. The research was conducted with 10 SBBC patients. The study established IMRT (12 fields with a single isocenter) and VMAT (2 partial arcs with a single isocenter) treatment plans for SBBC patients and then compared those plans with 3DCRT (8 fields with multiple isocenters). The plans were evaluated based on a dose-volume histogram analysis. For planning target volumes (PTVs), the mean doses and the values of V{sub 95%}, V{sub 105%}, conformity index, and homogeneity index were reported. For the organs at risk, the analysis included the mean dose, maximum dose, and V{sub XGy}, depending on the organs (lungs, heart, and liver). To objectively evaluate the efficiency of the treatment plans, each plan's beam times, treatment times (including set-up time), and monitor units were compared. Tukey test and one-way analysis of variance were used to compare the PTV and organs at risk values of the 3 techniques. Additionally, the independent-samples t-test was used to compare the 2 techniques (IMRT and VMAT) based on the values of Rt. PTV and Lt. PTV (p mean and of approximately 5.7% in V{sub 95%} dose distribution compared with 3DCRT. In comparison to VMAT, 3DCRT showed about 3.0% higher dose distribution in D{sub mean} and V{sub 95%}. IMRT was the best in terms of conformity index and homogeneity index (p 30Gy and V{sub 40Gy} was approximately 70% lower for IMRT and approximately 40% lower for VMAT than for 3DCRT. For distribution volumes of low doses such as V{sub 5%} and V{sub 10%}, that for 3DCRT was approximately 60% smaller than for IMRT and approximately 70% smaller than for VMAT. Comparison between IMRT and VMAT showed that the IMRT was superior in all distribution factors. VMAT showed better treatment efficiency than 3DCRT or IMRT. Among the SBBC radiotherapy treatment plans, IMRT was superior to 3DCRT and VMAT in terms of PTV dose distribution, whereas VMAT showed the most outstanding treatment efficiency.
Precipitation hardening of laser powder bed fusion Ti-6Al-4V
Here, the laser powder bed fusion (PBF-L) additive manufacturing (AM) community has dedicated significant efforts into process optimization and control for defect-free Ti-6Al-4V. As defects become less of an issue for PBF-L Ti-6Al-4V, the processing-structure-properties (PSP) relationships between AM microstructures can now be explored to optimize mechanical properties. Lower temperature aging treatments around 550 °C in wrought Ti-6A-4V have been historically understood to precipitation harden the α phase with an ordered, hexagonal close packed (HCP) α 2 phase. The α 2 phase existed as nanoscale Ti 3 Al precipitates coherent with the parent α phase. The goal of the present investigation was to implement a vacuum heat treatment of 545 °C for 100 hours on PBF-L Ti-6Al-4V. This vacuum heat treatment took place after an initial hot isostatic pressure (HIP) treatment that decomposed the as-built, martensitic microstructure. The vacuum aging successfully produced nanoscale precipitates of α 2 phase within α-laths, confirmed via atom probe tomography (APT). Microstructural-length scale and quasi-static mechanical properties were investigated by nanoindentation and uniaxial tensile tests of miniaturized test specimens. Given the sensitivity of Ti-6Al-4V mechanical properties to small changes in chemistry, all test specimens originated from the same build. The α 2 precipitation resulted in significantly harder α-laths (≈ 2 GPa) as measured via nanoindentation, as well as a relative yield and ultimate tensile strength increase of 60 MPa and 38 MPa, respectively. Analysis of Variance (ANOVA) of the datasets revealed no statistical differences in total elongation between the HIPed and HIPed + aged specimens, indicative of the aging treatment producing a net benefit of strength with no loss in ductility.
More buck-per-shot: Why learning trumps mitigation in noisy quantum sensing
Quantum sensing is one of the most promising applications for quantum technologies. However, reaching the ultimate sensitivities enabled by the laws of quantum mechanics can be a challenging task in realistic scenarios where noise is present. While several strategies have been proposed to deal with the detrimental effects of noise, these come at the cost of an extra shot budget. Given that shots are a precious resource for sensing – as infinite measurements could lead to infinite precision – care must be taken to truly guarantee that any shot not being used for sensing is actually leading to some metrological improvement. In this work, we study whether investing shots in error-mitigation, inference techniques, or combinations thereof, can improve the sensitivity of a noisy quantum sensor on a (shot) budget. We present a detailed bias–variance error analysis for various sensing protocols. Our results show that the costs of zero-noise extrapolation techniques outweigh their benefits. We also find that pre-characterizing a quantum sensor via inference techniques leads to the best performance, under the assumption that the sensor is sufficiently stable.
Influence of living grass Roots and endophytic fungal hyphae on soil hydraulic properties
Soil hydraulic properties are often estimated based on laboratory data or pedotransfer functions dependent on soil physical properties, which often do not consider potential impacts of soil roots or fungal hyphae. Here, we first review current knowledge of how these soil biotic components affect hydraulic properties, then we conducted laboratory experiments to specifically test if the presence of roots and mycorrhizal fungi had a significant effect on the hydraulic properties of two soils with contrasting textures: Flint sand and Hamblen silt loam. Soil cores were seeded with (Panicum virgatum) and grown in a greenhouse over three separate growth periods. The endophytic fungus Serendipita indica was injected as liquid inoculant into designated mycorrhizal cores. Saturated hydraulic conductivity (K sat ) measurements were made with a constant head permeameter, and soil water retention curves were obtained by the evaporation method, supplemented at the dry end for Hamblen silt loam with water activity meter data. Retention curve parameters were obtained by fitting the van Genuchten equation to the resulting measurements. Mean root volume ratios were higher in the mycorrhizal inoculated treatment than in the uninoculated treatment for both soils. For Flint sand, analysis of variance revealed that K sat was reduced by the presence of roots as compared to bare soil. This was likely due to roots clogging soil pores. Results also indicated the presence of roots changed the shape of the water retention curve for Flint sand by increasing water content at saturation and by reducing the slope of the curve. These changes suggested roots created additional porosity and broadened the pore-size distribution. The presence of mycorrhizal fungi accentuated the root effects. The influence of roots and mycorrhizal fungi on hydraulic properties was less obvious for the Hamblen silt loam, as none of the treatments differed from each other at p < 0.05. The results highlight the necessity to consider the impact of root and fungal structures on models of soil hydraulic properties.
Distribution–Based Global Sensitivity Analysis in Hydrology
Global sensitivity analysis (GSA) is routinely used in academic setting to quantify the influence of input variability and uncertainty on predictions of a quantity of interest. Practical applications of GSA are hampered by its high computational cost, which arises from the need to run large (e.g., groundwater) models multiple times, and by its reliance on the analysis of variance, which formally requires input parameters to be uncorrelated. The former difficulty can be alleviated by replacing expensive models with inexpensive (e.g., polynomial) surrogates, while adoption of distribution-based (rather than variance-based) metrics can, in principle, overcome the latter but at significantly increased computational cost. To make use of distribution-based GSA feasible for regional-scale models with a large number of degrees of freedom, we supplement it with a surrogate model built with polynomial chaos expansions with analytically updated coefficients. Here, we demonstrate the computational efficiency of our algorithm on a case study dealing with evaluation of the effects of temperature variability on annual evapotranspiration at the regional scale.
Vegetation type is an important predictor of the arctic summer land surface energy budget
Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm –2 ) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.
Multi-model Hydroclimate Projections for the Alabama-Coosa-Tallapoosa River Basin in the Southeastern United States
Abstract This study uses a high-resolution, process-based modeling framework to assess the impacts of changing climate on water resources for the Alabama-Coosa-Tallapoosa River Basin in the southeastern United States. A 33-member ensemble of hydrologic projections was generated using 3 distributed hydrologic models (Precipitation-Runoff Modeling System, Variable Infiltration Capacity, and Distributed Hydrology Soil Vegetation Model) of different complexity. These hydrologic models were driven by dynamically downscaled and bias-corrected future climate simulations from 11 Coupled Model Intercomparison Project Phase 5 global climate models under Representative Concentration Pathway 8.5 emission scenario, with 40 years each in baseline (1966–2005) and future (2011–2050) periods. The hydroclimate response, in general, projects an increase in mean seasonal precipitation, runoff, and streamflow. The high and low flows are projected to increase and decrease, respectively, in general, suggesting increased likelihood of extreme rainfall events and intensification of the hydrologic cycle. The uncertainty associated with the ensemble hydroclimate response, analyzed through an analysis of variance technique, suggests that the choice of climate model is more critical than the choice of hydrologic model for the studied region. This study provides in-depth insights of hydroclimate response and associated uncertainties to support informed decisions by water resource managers.
2D stochastic-integral models for characterizing random grain noise in titanium alloys
We extend our previous work, in which we applied high-dimensional model representation (HDMR) and analysis of variance (ANOVA) concepts to the characterization of a metallic surface that has undergone a shot-peening treatment to reduce residual stresses, and has, therefore, become a random conductivity field. That example was treated as a onedimensional problem, because those were the only data available. In this study, we develop a more rigorous two-dimensional model for characterizing random, anisotropic grain noise in titanium alloys. Such a model is necessary if we are to accurately capture the 'clumping' of crystallites into long chains that appear during the processing of the metal into a finished product. The mathematical model starts with an application of the Karhunen-Loève (K-L) expansion for the random Euler angles, θ and φ, that characterize the orientation of each crystallite in the sample. The random orientation of each crystallite then defines the stochastic nature of the electrical conductivity tensor of the metal. We study two possible covariances, Gaussian and double-exponential, which are the kernel of the K-L integral equation, and find that the double-exponential appears to satisfy measurements more closely of the two. Results based on data from a Ti-7Al sample will be given, and further applications of HDMR and ANOVA will be discussed.
Fabrication of FCC-SiO{sub 2} colloidal crystals using the vertical convective self-assemble method
In order to determine the optimal conditions for the growth of high-quality 250 nm-SiO{sub 2} colloidal crystals by the vertical convective self-assemble method, the Design of Experiments (DoE) methodology is applied. The influence of the evaporation temperature, the volume fraction, and the pH of the colloidal suspension is studied by means of an analysis of variance (ANOVA) in a 3{sup 3} factorial design. Characteristics of the stacking lattice of the resulting colloidal crystals are determined by scanning electron microscopy and angle-resolved transmittance spectroscopy. Quantitative results from the statistical test show that the temperature is the most critical factor influencing the quality of the colloidal crystal, obtaining highly ordered structures with FCC stacking lattice at a growth temperature of 40°C.
Optimization of the pyrolysis process of empty fruit bunch (EFB) in a fixed-bed reactor through a central composite design (CCD)
The production of crude palm oil from the processing of palm fresh fruit bunches in the palm oil mills in Malaysia hs resulted in a huge quantity of empty fruit bunch (EFB) accumulated. The EFB was used as a feedstock in the pyrolysis process using a fixed-bed reactor in the present study. The optimization of process parameters such as pyrolysis temperature (factor A), biomass particle size (factor B) and holding time (factor C) were investigated through Central Composite Design (CCD) using Stat-Ease Design Expert software version 7 with bio-oil yield considered as the response. Twenty experimental runs were conducted. The results were completely analyzed by Analysis of Variance (ANOVA). The model was statistically significant. All factors studied were significant with p-values < 0.05. The pyrolysis temperature (factor A) was considered as the most significant parameter because its F-value of 116.29 was the highest. The value of R{sup 2} was 0.9564 which indicated that the selected factors and its levels showed high correlation to the production of bio-oil from EFB pyrolysis process. A quadratic model equation was developed and employed to predict the highest theoretical bio-oil yield. The maximum bio-oil yield of 46.2 % was achieved at pyrolysis temperature of 442.15 °C using the EFB particle size of 866 μm which corresponded to the EFB particle size in the range of 710–1000 μm and holding time of 483 seconds.
Effect of bentonite modification on hardness and mechanical properties of natural rubber nanocomposites
The effect of sodium activation, ion-exchange with tertiary amine salt, surface treatment with non-ionic surfactant, and wet grinding of bentonite on hardness and mechanical properties of natural rubber nanocomposites (NRN) was studied using full factorial design of experiment. Results of X-ray diffraction (XRD) show increase in basal spacing d of bentonite due to modification, while attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) confirm the organic modification of bentonite. Analysis of variance (ANOVA) shows that the main effect of surface treatment increases the hardness and decreases the tensile modulus of the NRN. The surface treatment and wet grinding of bentonite decrease the tensile stresses at 100, 200 and 300% strain of NRN. Sodium activation and ion-exchange negatively affect the compressive properties, while surface treatment significantly improves the compressive properties of NRN.