Normal Constraints Research Articles

Multi-objective operation of microgrids based on electrical and thermal flexibility metrics using the NNC and IGDT methods

This paper addressed the optimal operation of the microgrids (MGs) containing both the electrical and thermal loads. Combined Heat and Power (CHP) units, boilers, wind turbines, storage devices, Demand Response Resources (DRRs), as well as the power exchange possibility with the upstream wholesale market are the energy resources that have been considered as the portfolio of the decision maker. The aim of the MG operator is to provide the electrical and thermal loads of the network in the most economic and flexible way. Therefore, suitable metrics are developed in this paper in order to evaluate the flexibility of the system for both the electrical and thermal units. Then, considering both the flexibility and financial concerns, a multiobjective framework is proposed. The Information Gap Decision Theory (IGDT) method is employed to handle the uncertainties of the proposed problem. Since the objectives of the MG operator might be conflicting with each other, an efficient optimization approach should be employed to properly satisfy the operator’s policies. The Normalized Normal Constraint (NNC) approach is employed here as an efficient method that is able to find the evenly distributed Pareto solutions. The IEEE 33-bus test system is utilized to simulate the proposed problem and analyze the results. The performance of the proposed problem is approved using different scenarios. The simulation results justify the advantages and necessities of the proposed problem.

Implicit Surface Reconstruction via RBF Interpolation: A Review

Background: Implicit surface is a kind of surface modeling tool, which is widely used in point cloud reconstruction, deformation and fusion due to its advantages of good smoothness and Boolean operation. The most typical method is the surface reconstruction with Radial Basis Functions (RBF) under normal constraints. RBF has become one of the main methods of point cloud fitting because it has a strong mathematical foundation, an advantage of computation simplicity, and the ability of processing nonuniform points. Objective: Techniques and patents of implicit surface reconstruction interpolation with RBF are surveyed. Theory, algorithm, and application are discussed to provide a comprehensive summary for implicit surface reconstruction in RBF and Hermite Radial Basis Functions (HRBF) interpolation. Methods: RBF implicit surface reconstruction interpolation can be divided into RBF interpolation under the constraints of points and HRBF interpolation under the constraints of points and corresponding normals. Results: A total of 125 articles were reviewed, in which more than 30% were related to RBF in the last decade. The continuity properties and application fields of the popular global supported radial basis functions and compactly supported radial basis functions are analyzed. Different methods of RBF and HRBF implicit surface reconstruction are evaluated, and the challenges of these methods are discussed. Conclusion: In future work, implicit surface reconstruction via RBF and HRBF should be further studied in fitting accuracy, computation speed, and other fundamental problems. In addition, it is a more challenging but valuable research direction to construct a new RBF with both compact support and improved fitting accuracy.

Margin Line Deformation Network Based on Graph Convolution Combined with with Geometric Consistency

To address the problem of the high computational complexity of the curved surface deformation method, ignoring the relevance of non-local information on the curve deformation method, and the regardless of the geometric consistency between the position and normal, a graph convolutional layer that simultaneously models local and non-local information is proposed, and a geometry aided prediction module that uses position and normal geometric constraints is designed. Firstly, the <italic>k</italic>-nearest neighbor algorithm and attention mechanism are used to mine local and non-local information to predict the position and normal information of points in space. Secondly, the position of the predicted points is input into the geometry aided prediction module, then the geometric consistency constraint is used to calculate geometric normal information. Finally, the geometric normal information is fused with the normal information predicted by the graph convolution network, then the graph convolution network is reused to obtain the final deformation prediction result. The experimental results of self-built margin line dataset comprising of 876 samples show that this method reduces the mean square error measurement by 16.7% when it compares with other mainstream methods.

“VULVA STUDY. hidden but not undiscovered” in Conversation with “Manufacturing the Vulva”

Cosmetic surgery and techno-medical manufacturing of the body are booming. The transformative potential of cosmetic surgery is used to shape and enhance physical appearance, gender identity and sexuality. Among the cosmetic procedures that have become popular is intimate surgery for women, which is oriented towards an ideal shape of the vulva. Almost in parallel with this trend, vulva-positive websites highlighting the diversity of the vulva are becoming ever more widespread in order to enlighten women and contribute to women’s health. This Art-Science Interaction consists of two parts that are in conversation with one another. Artist Merve Şahinol presents in her part “VULVA STUDY. hidden but not undiscovered” photographs of clay vulvas she modeled during therapy sessions to use in talks with women who are challenged by their own perception of their body image. She shares her thoughts and experience of the modeling and learning process in form of prose. In “Manufacturing the Vulva”, sociologist Melike Şahinol explores the rhizomatic dilemma between self/body awareness and empowerment, and the normalization and beauty constraints of manufacturing the vulva. This contribution emphasizes the awareness of the vulva as well as the lack of specific analyses of post-surgery embodiment experiences that include the “new” corporeal experience of sexuality and the fact that these experiences in general have been addressed but not sufficiently analyzed.

Multicriteria Course Mode Selection and Classroom Assignment Under Sudden Space Scarcity

Problem definition: Although physical (or “social”) distancing is an important public health intervention during airborne pandemics, physical distancing dramatically reduces the effective capacity of classrooms. Academic/practical relevance: During the COVID-19 pandemic, this presented a unique problem to campus planners who hoped to deliver a meaningful amount of in-person instruction in a way that respected physical distancing. This process involved (1) assigning a mode to each offered class as remote, residential (in-person), or hybrid and (2) reassigning classrooms under severely reduced capacities to the non-remote classes. These decisions need to be made quickly and under several constraints and competing priorities, such as restrictions on changes to the timetable of classes, trade-offs between classroom density and educational benefits of in-person versus online instruction, and administrative preferences for course modes and classrooms reassignments. Methodology: We solve a flexible integer program and use hierarchical optimization to handle the multiple criteria according to priorities. We apply our methods using actual Georgia Institute of Technology (GT) student registration data, COVID-19–adjusted classroom and laboratory capacities, and departmental course mode delivery preferences. We generate optimal classroom assignments for all GT classes at the Atlanta campus and quantify the trade-offs among the competing priorities. Results: When classroom capacities decreased to 20%–25% of their normal seating capacities, optimization afforded students 15.5% more in-person contact hours compared with no room reassignments (NRRs). Among sections with an in-person preference, our model satisfies 87% of mode preferences, whereas only 47% are satisfied under NRR. Additionally, in a scenario in which all classes are preferred to be delivered in person, our model can satisfy 90% of mode preferences compared with 37% under NRR. Managerial implications: Multiobjective optimization is well suited for classroom assignment problems that campus planners usually manage sequentially and manually. Our models are computationally efficient and flexible, with the ability to handle multiple objectives with different priorities and build a new class-classrooms assignment or optimize an existing one, and they can apply under normal or sudden capacity scarcity constraints. Funding: This work was supported by a grant from the Thos and Clair Muller Research Endowment Fund of the Georgia Institute of Technology’s H. Milton Stewart School of Industrial and Systems Engineering and a grant from the Georgia Institute of Technology Executive Vice President for Research COVID-19 Rapid Response Seed Grant Program. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1131 .

A Novel Method for Fault Diagnosis of Bearings with Small and Imbalanced Data Based on Generative Adversarial Networks

The data-driven intelligent fault diagnosis method of rolling bearings has strict requirements regarding the number and balance of fault samples. However, in practical engineering application scenarios, mechanical equipment is usually in a normal state, and small and imbalanced (S &amp; I) fault samples are common, which seriously reduces the accuracy and stability of the fault diagnosis model. To solve this problem, an auxiliary classifier generative adversarial network with spectral normalization (ACGAN-SN) is proposed in this paper. First, a generation module based on a deconvolution layer is built to generate false data from Gaussian noise. Second, to enhance the training stability of the model, the data label information is used to make label constraints on the generated fake data under the basic GAN framework. Spectral normalization constraints are imposed on the output of each layer of the neural network of the discriminator to realize the Lipschitz continuity condition so as to avoid vanishing or exploding gradients. Finally, based on the generated data and the original S &amp; I dataset, seven kinds of bearing fault datasets are made, and the prediction results of the Bi-directional Long Short-Term Memory (BiLSTM) model is verified. The results show that the data generated by ACGAN-SN can significantly promote the performance of the fault diagnosis model under the S &amp; I fault samples.

Normalized solution to the Schrödinger equation with potential and general nonlinear term: Mass super-critical case

In present paper, we prove the existence of solutions (λ,u)∈R×H1(RN) to the following Schrödinger equation{−Δu(x)+V(x)u(x)+λu(x)=g(u(x))inRN0≤u(x)∈H1(RN),N≥3 satisfying the normalization constraint ∫RNu2dx=a. We treat the so-called mass super-critical case here. Under an explicit smallness assumption on V with lim|x|→∞⁡V(x)=supx∈RN⁡V(x) and some Ambrosetti-Rabinowitz type conditions on g, we can prove the existence of ground state normalized solutions for prescribed mass a>0. Furthermore, we emphasize that the mountain pass characterization of a minimizing solution of the probleminf⁡{∫[12|∇u|2+12V(x)u2−G(u)]dx:‖u‖L2(RN)2=a,P[u]=0}, where G(s)=∫0sg(τ)dτ andP[u]=∫[|∇u|2−12〈∇V(x),x〉u2−N(12g(u)u−G(u))]dx.

Feature-preserving Mumford–Shah mesh processing via nonsmooth nonconvex regularization

Motivated by the success in image processing, the Mumford–Shah functional has attracted extensive attentions in geometry processing. Existing methods, mainly focusing on discretizations on the triangulated mesh, either over-smooth sharp features or are sensitive to noises or outliers. In this paper, we first introduce a nonsmooth nonconvex Mumford–Shah model for a feature-preserving filtering of face normal field to ameliorate the staircasing artifacts that appear in the original Mumford–Shah total variation (MSTV) and develop an alternating minimization scheme based on alternating direction method of multipliers to realize the proposed model. After restoring the face normal field, vertex updating is then employed by incorporating the oriented normal constraints and discontinuities to achieve a detail-preserving reconstruction of mesh geometry. Extensive experimental results demonstrate the effectiveness of the above shape optimization routine for various geometry processing applications such as mesh denoising, mesh inpainting and mesh segmentation.

EA2183: A phase III study of consolidative radiotherapy in patients with oligometastatic HER2-negative esophageal and gastric adenocarcinoma.

TPS4162 Background: Advanced esophageal and gastric adenocarcinomas (EGA) have poor prognosis. Doublet chemotherapy with fluoropyrimidine and platinum agents in combination with nivolumab for PD-L1 positive tumors is now the standard first-line approach, but overall survival (OS) remains &lt; 1.5 years. A subset of EGA patients have limited burden of metastatic disease. There is accumulating evidence that patients with oligometastatic states across disease types may benefit from locoregional ablative therapies during the course of their treatment. EA2183 is the first prospective study to evaluate the potential benefits of consolidative radiotherapy (XRT) in oligometastatic EGA. Methods: This is a prospective, randomized phase 3 study evaluating the role of consolidative XRT in oligometastatic EGA. Patients with ≤3 metastases at the time of diagnosis of advanced disease are eligible for enrollment. After completion of 4 months of systemic therapy, patients whose disease has not progressed are randomized to consolidation with XRT to all sites of disease followed by continuation of systemic therapy or continuation of systemic therapy alone. Patients are able to enroll in the study at the time of diagnosis of advanced disease or after completion of induction therapy. Systemic therapy is left to the discretion of the treating physician and can include FOLFOX (5-fluorouracil, leucovorin, and oxaliplatin) or CAPOX (capecitabine, oxaliplatin) in combination with nivolumab. Unless there are contraindications to immunotherapy or a history of prior treatment with immune checkpoint inhibitors, nivolumab use is mandatory if tumor has PD-L1 combined positive score (CPS) ≥5. The goal of radiation is to consolidate gains made by chemotherapy by delivering the highest dose that maintains the greatest tumor control probability that is also safe to deliver given the anatomic and normal tissue constraints. Specific radiation dose and fractionation are recommended in the protocol but is left to the discretion of the treating radiation oncologist to choose the course that is most suitable. Radiation must be administered over a maximum of 15 treatment days to minimize systemic therapy treatment breaks. Primary endpoint is OS from the time of randomization. Secondary endpoints include progression free survival from the time of randomization and safety and tolerability of consolidative XRT. We hypothesize that consolidative XRT will prolong OS from 10 to 15.6 months (an increase in median OS of 55.6%). The study is planning to enroll 314 patients with the goal of randomizing 204 patients in a 2:1 fashion. Stratification factors include number of metastatic sites, choice of immunotherapy with relation to PD-L1 CPS, as well as time of registration to the protocol (before or after systemic therapy initiation). Enrollment to the study is ongoing. Clinical trial information: NCT04248452.

Integrating computer simulation and the normalized normal constraint method to plan a temporary hospital for COVID-19 patients

Although Temporary Hospitals (THs) usually present improvised structures, they are considered necessary solutions in extreme situations, such as critical moments of the COVID-19 pandemic. Timely planning of hospital resources is a challenging task for public health decision-makers and essential to caring for patients to ensure the healthcare system's sustainability. For this reason, we propose the integration of Discrete Event Simulation (DES), Response Surface Methodology (RSM), and Normalized Normal Constraint (NNC) for planning a TH in Brazil amid the COVID-19 pandemic. While DES is an outstanding process modelling approach, RSM and NNC are methods for planning and analysing experiments and generating a representative set of optimal solutions for decision-makers. This study defined the number of beds in the Short Stay Unit (SSU) that maximizes the number of patients attended and the patient conformity rate while minimizing the SSU daily cost. Moreover, two uncontrollable input factors are also considered: the number of patients arriving at the TH and the percentage of these patients who have COVID-19. We developed complete and evenly distributed Pareto frontiers considering controllable and uncontrollable factors, which decision-makers can use according to the metrics prioritization and the pandemic status.

State-space Bloch mode synthesis for fast band-structure calculations of non-classically damped phononic materials

Bloch mode synthesis (BMS) techniques enable efficient band-structure calculations of periodic media by forming reduced-order models of the unit cell. Rooted in the framework of the Craig–Bampton component mode synthesis methodology, these techniques decompose the unit cell into interior and boundary degrees-of-freedom that are nominally described, respectively, by sets of normal modes and constraint modes. In this paper, we generalize the BMS approach by state-space transformation to extend its applicability to generally damped periodic materials that violate the Caughey–O’Kelly condition for classical damping. In non-classically damped periodic models, the fixed-interface eigenvalue problem may, in general, produce a mixture of underdamped and overdamped modes. We examine two mode-selection schemes for the reduced-order model and demonstrate the underlying accuracy–efficiency trade-offs when qualitatively distinct mixtures of underdamped and overdamped modes are incorporated. The proposed approach provides a highly effective computational tool for analysis of large models of phononic crystals and acoustic/elastic metamaterials with complex damping properties.

A Solution Method for Many-Objective Security-Constrained Unit Commitment Considering Flexibility

With the increasing penetration of wind power into power systems and the random fluctuation of the wind farm (WF) output, system flexibility must be considered in the optimal generation dispatch. Based on the extreme scenarios of the WF output, we proposed a flexibility risk index to evaluate the system flexibility of each time interval. We established a five-objective security-constrained unit commitment (SCUC) model of a power system with WFs, thermal generation plants, battery energy storage stations, and pumped storage hydro stations. The objectives were to minimize the system flexibility risk, total network loss, operation cost, power purchase cost, and pollutant gas emissions. To obtain the Pareto optimal solutions of the model, based on the objective selection and ε-constraint methods, we proposed two methods to reduce the dimension of objectives and transformed the five-objective optimization model into a series of three-objective optimization models. Then, we used the normalized normal constraint method to solve the Pareto frontier surface of each three-objective optimization model. We used a color column to represent the value change of the two upper layer objectives and visualized the Pareto frontier of the five-objective SCUC model in the three-dimensional ordinate space. Case studies on the modified IEEE-9 bus system and an actual power grid demonstrated the effectiveness and high computational efficiency of the proposed method.

Merging slime mould with whale optimization algorithm for optimal allocation of hybrid power flow controller in power system

ABSTRACT This manuscript proposes the optimal allocation of hybrid power flow controller (HPFC) using hybrid technique. The proposed technique is the implementation of Integrated Slime Mould Algorithm (ISMA). The searching behaviour of Slime Mould Algorithm (SMA) is enhanced by the position updating behaviour of the whale optimisation algorithm (WOA). HPFC, a hybrid topology, has VAR compensator or an impedance-type FACTS device, most probably obtainable at power system, and two voltage source converters depend on controllers share a general DC link. The novel contributions of allocating HPFC at optimal location for multi-objective fitness functions denote minimal real power loss of system as well as minimal generation cost using ISMA method. Here, ISMA method optimises maximum line of power loss as appropriate location of unified power flow controller (UPFC). The optimal location parameters and dynamic stability restrictions are restored with normal constraints, employing UPFC optimal capacity has been optimised to decreased cost with the help of ISMA technique.

Efficient band-structure calculations of non-classically damped phononic materials by Bloch mode synthesis in state space

Bloch mode synthesis (BMS) techniques enable efficient band-structure calculations of periodic media by forming reduced-order models of the unit cell. Rooted in the framework of the Craig-Bampton component mode synthesis methodology, these techniques decompose the unit cell into interior and boundary degrees-of-freedom that are nominally described, respectively, by sets of normal modes and constraint modes. In this paper, we generalize the BMS approach by state-space transformation to extend its applicability to generally damped periodic materials that violate the Caughey-O’Kelly condition for classical damping. In non-classically damped periodic models, the fixed-interface eigenvalue problem may, in general, produce a mixture of underdamped and overdamped modes. We examine two mode-selection schemes for the reduced order model and demonstrate the underlying accuracy-efficiency trade-offs when qualitatively distinct mixtures of underdamped and overdamped modes are incorporated. The proposed approach provides a highly effective computational tool for analysis of large models of phononic crystals and acoustic/elastic metamaterials with complex damping properties. This investigation does not only extend the applicability of BMS techniques to the most generally damped models of periodic media, it also advances our understanding of the nature of damping modes and the non-trivial manner by which they contribute to the wave propagation properties.

Sustainable supply chain network design: A case of the glass manufacturer in Asia

Traditionally, profit maximization was the major objective for many businesses. However, due to growing stakeholders’ pressure and awareness about the social and environmental impacts of supply chain activities, many companies are now focused on optimizing their profits, while reducing negative impacts of their activities on planet and society. While sustainable development requires concurrent and balanced consideration of social, environmental and economic dimensions, unbalanced approaches have been adopted both in real world practices, and in academic literature. We contribute to the stream of research in the literature that tries to address this gap. We propose a multi-objective mixed-integer programming (MOMIP) model for sustainable supply chain network design (SSCND), encompassing economic, environmental, and social objectives. Both strategic and tactical network design decision variables are considered, including location, size, and technology type of facilities, along with determining the flow of materials and transportation modes. The Normalized Normal Constraint Method is applied to generate evenly distributed Pareto frontiers. The case of a glass manufacturing company in Asia is used to demonstrate applicability of the model and solution procedure to real-world problems. Non-dominated solutions are analyzed, and a more sustainable and balanced network design alternative is proposed, which outperforms the current network configuration in terms of all three dimensions of sustainability. Managerial implications and future research directions are discussed.

An information gap decision theory-based decision-making model for complementary operation of hydro-wind-solar system considering wind and solar output uncertainties

As large-scale wind and solar power sharing hydropower transmission channels are put into operation in river basins to lower investment costs in China, joint operations of hydro-wind-solar energy sources have become an effective operating strategy in power systems. However, wind and solar output with uncertainties and volatilities are problematic and pose challenges for power grid optimal scheduling, especially for peak shaving of grid system. In this paper, a novel decision-making model based on the information gap decision theory method is formulated to optimize the complementary operation of multi-energy systems. First, with the objective of minimizing the variance of the residual load, a deterministic short-term peak shaving model for a hydro-wind-solar system as a base case is established. Then, to model uncertainties of wind and solar output in the hybrid system and analyze the impact of grid peak shaving, the risk averse and opportunity seeker strategies of information gap decision theory-based technique are developed. Finally, as a proficient multi-objective optimization method, the normalized normal constraint method is exploited to solve the information gap decision theory-based bilevel optimal problem, and a set of Pareto solutions is obtained for the decision maker. A hydro-wind-solar hybrid system in Guizhou Province in China, with seven hydropower plants, a virtual wind power plant and a virtual solar power plant, is taken as an example to demonstrate the effectiveness of the proposed model. The results indicate that, by considering the uncertainties of wind and solar output, the presented model not only reduces the load fluctuations but also increases the flexibility response to the system against operational risk. In addition, the calculation time and load variance of the system are greatly reduced in comparison with stochastic programming.

Stricter Postoperative Oropharyngeal Cancer Radiation Therapy Normal Tissue Dose Constraints Are Feasible

PurposeAlthough dose de-escalation is one proposed strategy to mitigate long-term toxicity in human papillomavirus associated oropharyngeal cancer, applying more stringent normal tissue constraints may be a complementary approach to further reduce toxicity. Our study demonstrates that in a postoperative setting, improving upon nationally accepted constraints is achievable and leads to reductions in normal tissue complication probabilities (NTCP) without compromising disease control. Methods and MaterialsWe identified 92 patients at our institution between 2015 and 2019 with p16+ oropharyngeal cancer who were treated with adjuvant volumetric modulated arc therapy. We included patients treated to postoperative doses and standard volumes (including bilateral neck). Doses delivered to organs at risk were compared with recommended dose constraints from a recent cooperative group head and neck cancer trial of radiation therapy to 60 Gy. We applied validated and published NTCP models for dysphagia, dysgeusia, esophagitis, oral mucositis, and xerostomia relevant to oropharyngeal cancer. ResultsAchievable and delivered mean doses to most normal head and neck tissues were well below national recommended constraints. This translates to notable absolute NTCP reductions for salivary flow (10% improvement in contralateral parotid, 35% improvement in submandibular gland), grade ≥ 2 esophagitis (23% improvement), grade ≥ 3 mucositis (17% improvement), dysgeusia (10% improvement), and dysphagia (8% improvement). Locoregional control at a median follow-up of 26.3 months was 96.7%, with only 3 patients experiencing locoregional recurrence (1 local, 2 regional). ConclusionsModern radiation therapy planning techniques allow for improved normal tissue sparing compared with currently established dose constraints without compromising disease control. These improvements may lead to reduced toxicity in a patient population expected to have favorable long-term outcomes. Stricter constraints can be easily achieved and should be used in conjunction with other evolving efforts to mitigate toxicity.

Procurement of Deceased Donor Parathyroid Glands With the Aid of Near-infrared Autofluorescence Imaging

Background.Parathyroid allotransplantation is a burgeoning treatment for severe hypoparathyroidism. Deceased donor parathyroid gland (PTG) procurement can be technically challenging due to lack of normal intraoperative landmarks and exposure constraints in the neck of organ donors. In this study, we assessed standard 4-gland exposure in situ and en bloc surgical techniques for PTG procurement and ex vivo near-infrared autofluorescence (NIRAF) imaging for identification of PTGs during organ recovery.Methods.Research tissue consent was obtained from organ donors or donor families for PTG procurement. All donors were normocalcemic, brain-dead, solid organ donors between 18 and 65 y of age. PTGs were procured initially using a standard 4-gland exposure technique in situ and subsequently using a novel en bloc resection technique after systemic organ preservation flushing. Parathyroid tissue was stored at 4 °C in the University of Wisconsin solution up to 48 h post-procurement. Fluoptics Fluobeam NIRAF camera and Image J software were utilized for quantification of NIRAF signal.Results.Thirty-one brain-dead deceased donor PTG procurements were performed by abdominal transplant surgeons. In the initial 8 deceased donors, a mean of 1.75 glands (±1.48 glands SD) per donor were recovered using the 4-gland in situ technique. Implementation of combined en bloc resection with ex vivo NIRAF imaging in 23 consecutive donors yielded a mean of 3.60 glands (±0.4 SD) recovered per donor (P < 0.0001). Quantification of NIRAF integrated density signal demonstrated >1-fold log difference in PTG (2.13 × 105 pixels) versus surrounding anterior neck structures (1.9 × 104 pixels; P < 0.0001). PTGs maintain distinct NIRAF signal from the time of recovery (1.88 × 105 pixels) up to 48 h post-procurement (1.55 × 105 pixels) in organ preservation cold storage (P = 0.34).Conclusions.The use of an en bloc surgical technique with ex vivo NIRAF imaging significantly enhances the identification and recovery of PTG from deceased donors.

UK 2022 Consensus on Normal Tissue Dose-Volume Constraints for Oligometastatic, Primary Lung and Hepatocellular Carcinoma Stereotactic Ablative Radiotherapy

The use of stereotactic ablative radiotherapy (SABR) in the UK has expanded over the past decade, in part as the result of several UK clinical trials and a recent NHS England Commissioning through Evaluation programme. A UK SABR Consortium consensus for normal tissue constraints for SABR was published in 2017, based on the existing literature at the time. The published literature regarding SABR has increased in volume over the past 5 years and multiple UK centres are currently working to develop new SABR services. A review and update of the previous consensus is therefore appropriate and timely. It is hoped that this document will provide a useful resource to facilitate safe and consistent SABR practice.

Radiation technique and outcomes following moderately hypofractionated treatment of low risk prostate cancer: A secondary analysis of the NRG oncology RTOG 0415 randomized clinical trial.

243 Background: While intensity-modulated radiotherapy (IMRT) is commonly used to deliver moderately hypofractionated radiotherapy (MHRT) for prostate cancer (PC), IMRT has not been prospectively compared to three-dimensional conformal radiotherapy (3D-CRT) in the context of MHRT. This secondary analysis of the phase III RTOG 0415 trial compares survival outcomes and toxicity across RT technique between IMRT and 3D-CRT for low-risk PC. Methods: The phase III, non-inferiority trial RTOG 0415 randomized patients with low risk PC to either MHRT (70Gy at 2.5Gy/fraction) or conventionally fractionated radiation (CFRT; 73.8Gy at 1.8Gy/fraction) with stratification by RT technique. A secondary analysis for differences in overall (OS), biochemical recurrence free survival (BRFS), or toxicity by EPIC scores and Common Terminology Criteria for Adverse Events (CTCAE) was performed. For patient and tumor characteristics, continuous data were compared with Wilcoxon rank sum test and categorical data with Chi-squared test, as appropriate. Rates of BRFS and overall survival (OS) were calculated using the Kaplan-Meier method. Results: 1079 patients received the allocated intervention with a median follow up of 5.8 years. RT technique was balanced between treatment arms, with 79.1% of patients receiving IMRT. RT protocol compliance was &gt; 95% for both IMRT and 3D-CRT. There were no significant differences in BRFS between patients treated with 3D-CRT versus IMRT for all patients (p = 0.33), those randomized to CFRT (p = 0.78), or those randomized to MHRT (p = 0.24). Overall survival did not differ by RT technique as well. For all patients, there was no difference in acute and late GI and GU toxicity rates across RT technique. For patients treated with MHRT, late grade 2 GU toxicity was more common with IMRT than 3D-CRT (31.3% vs 23.4%; p = 0.004). On logistic regression analysis, only poor baseline urinary function, defined as an EPIC score of 90 or below, correlated with acute (p &lt; 0.001) or late (p &lt; 0.001) GU toxicity. Baseline bowel function did not correlate to GI toxicity. Conclusions: RT technique did not impact survival outcomes or toxicity rates following MHRT for low risk PC. Higher rates of late CTCAE grade 2+ GU and GI toxicity observed within the RTOG 0415 MHRT arm were not disproportionally observed following 3D-CRT than IMRT. These data highlight the need for careful consideration of target delineation and normal tissue constraints in the selection and delivery of appropriate RT technique.