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A pilot study of volumetric and density tumor analysis of ACC patients treated with vorinostat in a phase II clinical trial

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HELIYON 2023年第8期9卷 e18680页

作者： Malarkey, Molly Toscano, Alex P. Bagheri, Mohammad Hadi Solomon, Jeffrey Machado, Laura B. Lorusso, Patricia Chen, Alice Folio, Les R. Goncalves, Priscila H. NIH 10 Ctr Dr Bldg 10 Room 1C 340 Bethesda MD 20892 USA NIH Radiol & Imaging Sci Clin Ctr 10 Ctr Dr Bldg 10 Room 1C342 Bethesda MD 20892 USA Frederick Natl Lab Canc Res Clin Monitoring Res Program Directorate Frederick MD 21702 USA Hosp Univ Penn Dept Radiol Div Body Imaging 3400 Spruce St Philadelphia PA 19104 USA Yale Univ 333 Cedar St WWW217 New Haven CT 06510 USA NCI Early Clin Trials Dev Program Div Canc Treatment & Diag NIH 31 Ctr Blvd 3A44 Bethesda MD 20892 USA H Lee Moffitt Canc Ctr & Res Inst 12902 USF Magnolia Dr Tampa FL 33612 USA H Lee Moffitt Canc Ctr & Res Inst Dept Machine Learning 12902 USF Magnolia Dr Tampa FL 33612 USA Univ S Florida Morsani Coll Med Dept Oncol Sci 560 Channelside Dr Tampa FL 33602 USA NIH HIV & AIDS Malignancy Branch Bethesda MD 20892 USA

Rationale and objectives: Adenoid cystic carcinoma (ACC) is a rare salivary gland cancer. The vast majority of clinical trials evaluating systemic therapy efficacy in solid tumors use the Response Evaluation Criteria in Solid Tumors (RECIST) to measure response that is limited to 2 dimensional only evaluations, not taking volume or density into account. The indolent behavior ACC repre-sents a challenge toward an appropriate evaluation of therapy response. Objectives: 1) To describe and contrast volumetric and density changes at each time-point, including changes noted from baseline to best response, to currently used 2 dimensional-only criteria (RECIST) and 2) To report the coefficient of variation in volume measurement among three reviewers on a subset of ACC *** and methods: We retrospectively assessed a cohort of 18 prospectively treated patients with ACC in a phase 2 trial with vorinostat using a volumetric (viable tumor volume, VTV) and density criteria. Three independent and blinded observers segmented target lesions across a sample of randomly selected computed tomography (CT) exams to examine inter-observer ***: We found that the average coefficient of variation among observers for all target lesions was 16.1%, with lung lesions displaying a smaller variation at 14.0% (p-value >0.17). We describe examples of decrease in volume and density in several lesions despite stable disease by ***: This pilot study demonstrates that two-dimensional criteria such as RECIST may not be the best criteria to assess response to therapy, especially with evolving tools within picture archiving and communication system (PACS) that can assess volumetric size, density and texture, however, this should be prospectively studied.

关键词： Adenoid Cystic Carcinoma (ACC) Volume Density Viable Tumor Volume (VTV) Oncology

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Protocol to explain support vector machine predictions via exact Shapley value computation

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STAR Protocols 2024年第2期5卷 103010页

作者： Mastropietro, Andrea Bajorath, Jürgen Deparment of Computer Control and Management Engineering “Antonio Ruberti” Sapienza University of Rome Via Ariosto 25 Rome 00185 Italy Department of Life Science Informatics and Data Science B-IT LIMES Program Unit Chemical Biology and Medicinal Chemistry Rheinische Friedrich-Wilhelms-Universität Friedrich-Hirzebruch-Allee 5/6 Bonn 53115 Germany Lamarr Institute for Machine Learning and Artificial Intelligence Friedrich-Hirzebruch-Allee 5/6 Bonn 53115 Germany

Shapley values from cooperative game theory are adapted for explaining machine learning predictions. For large feature sets used in machine learning, Shapley values are approximated. We present a protocol for two techniques for explaining support vector machine predictions with exact Shapley value computation. We detail the application of these algorithms and provide ready-to-use Python scripts and custom code. The final output of the protocol includes quantitative feature analysis and mapping of important features for visualization. For complete details on the use and execution of this protocol, please refer to Feldmann and Bajorath¹ and Mastropietro et al.² © 2024 The Author(s)

关键词： Bioinformatics Chemistry Computer sciences

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Systematic identification and characterization of compounds with reliable activity against multiple target proteins from different classes

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RESULTS IN CHEMISTRY 2024年 7卷

作者： Srinivasan, Sanjana Bajorath, Juergen Rhein Friedrich Wilhelms Univ Dept Life Sci Informat & Data Sci LIMES Program Unit Chem Biol & Med Chem B IT Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany Rhein Friedrich Wilhelms Univ Lamarr Inst Machine Learning & Artificial Intellig Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany Rhein Friedrich Wilhelms Univ Dept Life Sci Informat & Data Sci LIMES Program Unit Chem Biol & Med Chem B IT Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany

Polypharmacology provides a basis for the treatment of diseases originating from perturbed target and pathway networks. It depends on the use of compounds with suitable multi-target activity and pharmacological effects resulting from multi-target engagement. In addition to their central role for polypharmacology, multi-target compounds are also of interest for basic research, for example, to better understand their binding characteristics or interaction patterns. While it is not unlikely that active compounds might bind to closely related targets, we have been interested in identifying compounds that are active against distantly or unrelated proteins, representing more unexpected instances of multi-target activity. Therefore, we have carried out a systematic search for compounds with activity against proteins from different classes, termed multi-class compounds (MC-CPDs). To this end, currently available compound activity data from medicinal chemistry were carefully curated. Subsequent analysis of similar to 120,000 qualifying compounds with high-confidence target annotations identified 689 MC-CPDs with activity against at least three targets from two different protein classes. The target distribution of these compounds was determined and structural relationships were explored together with activity profiles. MCCPDs covered many target combinations and established a wealth of compound-based target relationships, some of which could be rationalized by considering ligand binding characteristics of distinct targets. As a part of our study, the collection of newly identified MC-CPDs and associated target information is made publicly available.

关键词： Multi-target compounds Polypharmacology Structural relationships Protein classes Unrelated targets Compound-based target relationships

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Multimodal Sentiment Analysis using Kernel Based Support Vector machine

Multimodal Sentiment Analysis using Kernel Based Support Vec...

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Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE), International Conference on

作者： V Sunil Kumar Piyush Kumar Pareek Victor Hugo Costa De Albuquerque Ashish Khanna Deepak Gupta Madhumala R B Department Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceará Fortaleza Brazil Department of Teleinformatics Engineering Federal University of Ceará Brazil CSE Dept Maharaja Agrasen Institute of Technology GGSIPU Department of ISE Dayananda Sagar Academy of Technology and Management Bangalore

ISBN: (纸本)9781665493970

There has been a steady expansion in the number of social websites, electronic media outlets, and blogging services, all of which contribute a large number of user messages such as comments, reviews, and opinions from customers. The quantity of these signals, which are used to evaluate the quality of a wide variety of goods and services, is rather substantial. As a result, it is challenging to monitor and identify the feelings of customers. As a result, a method known as Sentiment Analysis (SA), also known as Opinion Mining (OM), was developed. This method gathers data from the internet by using Natural Language Processing (NLP) strategies and then analyses people's thoughts. It does this by extracting subjective information from the words, assessing opinions, and quantifying positive and negative evaluations. The end result is a conclusion about the specific entity, which may be an organisation, the public, or a product. The NLP first gathers the views, and then those opinions are sorted into positive, neutral, and negative categories. Both the marketing promotion and the customer's ability to choose the most appropriate product for their needs may be improved by doing an analysis. The purpose of this study is to evaluate the use of different machine learning and swarm intelligence optimization methods to perform sentiment analysis on data derived from movie reviews. This study helps many businesses increase their profits while simultaneously lowering the number of problems caused by a certain product. Within the scope of this investigation, three distinct models of sentiment analysis were investigated. An effective method for doing sentiment analysis known as a Hybrid Kernel based Support Vector machine (HKSVM) has been presented. The preprocessing that is described here is meant to cut down on the data inconsistencies and get rid of the data that isn't needed. After that, the Expectation Maximization (EM) technique is used in order to identify an outlier. After t

关键词： Support vector machines Sentiment analysis Analytical models Sensitivity Veins Tagging Probabilistic logic

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Churn Prediction in Financial Risk Management using Deep learning Techniques

Churn Prediction in Financial Risk Management using Deep Lea...

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Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE), International Conference on

作者： V Sunil Kumar Sowmyashree S Piyush Kumar Pareek Ashish Khanna Victor Hugo Costa de Albuquerque Deepak Gupta Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru Computer Science & Engineering East West Institute of Technology Bengaluru Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Cearά Fortaleza Brazil CSE dept Maharaja Agrasen Institute of Technology GGSIPU Department of Teleinformatics Engineering Federal University of Cearά Brazil

ISBN: (纸本)9781665493970

It is essential to develop an efficient Client Relationship Model in order to make use of the estimations about customer turnover (CRM). Finding and breaking down the facts surrounding the business information understanding is necessary for client produce prediction, which should be made possible effectively by altering the strategies used for business insight. The tools that are provided by company insight allow for the historical, current, and prospective perspectives of business activity to be anticipated and dissected. It is possible to deal with it in an efficient way by providing the information mining approaches that choose the data from the created informative index that is the most helpful. Fundamental leadership should be possible to achieve in a fruitful way if one has this information. The actions that consumers have taken in the recent past are going to be analyzed, and an accurate forecast is going to be formed about those customers who are likely going to get unhappy in the near future based on the results of that prediction. The problem is that deciding on a certain period to investigate the client's activities and also establishing on a specific day and age for each of the clients won't be appropriate6. This is due to the fact that the problem is caused by the fact that deciding on a certain period to investigate the client's activities will take too long. The challenge lies just in this aspect. How to decide on a certain amount of time to spend researching the activities of the customer. For instance, when planning a model, 100 customers are used, 70 of those customers are considered “active,” which indicates that they are still conducting business with the organisation, and the remaining 30 customers are considered “agitate,” which indicates that they have severed their ties with the organisation. The primary purpose of the work that has been suggested is to carry out exploratory research and management as a multicriteria basic leadership issue, a

关键词： Leadership Roads Companies Predictive models Media Planning Data mining

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Swarm Intelligence in Internet of Medical Things: A Review

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SENSORS 2023年第3期23卷 1466-1466页

作者： Alizadehsani, Roohallah Roshanzamir, Mohamad Izadi, Navid Hoseini Gravina, Raffaele Kabir, H. M. Dipu Nahavandi, Darius Alinejad-Rokny, Hamid Khosravi, Abbas Acharya, U. Rajendra Nahavandi, Saeid Fortino, Giancarlo Deakin Univ Inst Intelligent Syst Res & Innovat Geelong Vic 3216 Australia Fasa Univ Fac Engn Dept Comp Engn Vali asr Blvd Fasa *** Iran Isfahan Univ Technol Dept Elect & Comp Engn Daneshgah e Sanati Hwy Esfahan 83111 Iran Univ Calabria Dept Informat Modeling Elect & Syst DIMES I-87036 Cosenza Italy UNSW Grad Sch Biomed Engn Biomed Machine Learning Lab BML Sydney NSW 2052 Australia Univ New South Wales UNSW Sydney UNSW Data Sci Hub Sydney NSW 2052 Australia Macquarie Univ AI Enabled Proc AIP Res Ctr Hlth Data Analyt Program Sydney NSW 2109 Australia Ngee Ann Polytech Dept Elect & Comp Engn Singapore 599489 Singapore SUSS Univ Sch Sci & Technol Dept Biomed Engn Singapore 599494 Singapore Asia Univ Dept Biomed Informat & Med Engn Taichung 41354 Taiwan Harvard Univ Harvard Paulson Sch Engn & Appl Sci Allston MA 02134 USA

Continuous advancements of technologies such as machine-to-machine interactions and big data analysis have led to the internet of things (IoT) making information sharing and smart decision-making possible using everyday devices. On the other hand, swarm intelligence (SI) algorithms seek to establish constructive interaction among agents regardless of their intelligence level. In SI algorithms, multiple individuals run simultaneously and possibly in a cooperative manner to address complex nonlinear problems. In this paper, the application of SI algorithms in IoT is investigated with a special focus on the internet of medical things (IoMT). The role of wearable devices in IoMT is briefly reviewed. Existing works on applications of SI in addressing IoMT problems are discussed. Possible problems include disease prediction, data encryption, missing values prediction, resource allocation, network routing, and hardware failure management. Finally, research perspectives and future trends are outlined.

关键词： internet of things internet of medical things internet of things in health swarm intelligence algorithm wearable devices wireless sensor network

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A Developer Recommendation Method Based on Code Quality

A Developer Recommendation Method Based on Code Quality

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International Joint Conference on Neural Networks (IJCNN)

作者： Matheus Camilo da Silva André Armstrong Janino Cizotto Emerson Cabrera Paraiso Graduate Program in Informatics Pontificia Universidade Catolica do Parana Curitiba Brazil Machine Learning Committee Siemens Ltd Curitiba Brazil

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

During the development cycle of a project, it is common for software requirements and functionality to change and for code errors to occur. To deal with these unforeseen changes, the artifact known as change request, which is a formal proposal to alter a system, is used. Its assignment is an important step in the development process. Projects can receive a very high number of requests daily, which makes the automation of this process compelling. This work proposes a method for assigning unresolved requests, based on developer's profiles. The proposed method consists of three steps. The first step is to extract code quality metrics, commit data and previously resolved requests, in order to model developers through the mining of repositories. The second step concerns with the selection of the profile of potential developers through the application of natural language processing and information retrieval techniques. And finally, in the third step the appropriate developers are selected based on the quality of their code and the impact of their commits. Results from experimental evaluation show that the method is able to recommend more developers with a positive impact on the repository quality if compared to the iMacPro method.

关键词： Tools Data mining Measurement Computer bugs Task analysis Software Maintenance engineering

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Heterogeneity-driven phenotypic plasticity and treatment response in branched-organoid models of pancreatic ductal adenocarcinoma

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NATURE BIOMEDICAL ENGINEERING 2025年第6期9卷 1-29页

作者： Papargyriou, Aristeidis Najajreh, Mulham Cook, David P. Maurer, Carlo H. Baerthel, Stefanie Messal, Hendrik A. Ravichandran, Sakthi K. Richter, Till Knolle, Moritz Metzler, Thomas Shastri, Akul R. Oellinger, Rupert Jasper, Jacob Schmidleitner, Laura Wang, Surui Schneeweis, Christian Ishikawa-Ankerhold, Hellen Engleitner, Thomas Mataite, Laura Semina, Mariia Trabulssi, Hussein Lange, Sebastian Ravichandra, Aashreya Schuster, Maximilian Mueller, Sebastian Peschke, Katja Schaefer, Arlett Dobiasch, Sophie Combs, Stephanie E. Schmid, Roland M. Bausch, Andreas R. Braren, Rickmer Heid, Irina Scheel, Christina H. Schneider, Guenter Zeigerer, Anja Luecken, Malte D. Steiger, Katja Kaissis, Georgios van Rheenen, Jacco Theis, Fabian J. Saur, Dieter Rad, Roland Reichert, Maximilian Tech Univ Munich Translat Pancreat Canc Res Ctr Klin & Klinikum Innere Med 2 Klinikum Rechts Isar Munich Germany Tech Univ Munich Klinikum Rechts Isar Klin & Poliklin Innere Med 2 Munich Germany Tech Univ Munich Ctr Funct Prot Assemblies Garching Germany Tech Univ Munich Ctr Organoid Syst COS Garching Germany Bavarian Canc Res Ctr BZKF Munich Germany Helmholtz Ctr Munich Inst Stem Cell Res Neuherberg Germany Univ Ottawa Fac Med Dept Cellular & Mol Med Ottawa ON Canada Ottawa Hosp Res Inst Canc Therapeut Program Ottawa ON Canada Tech Univ Munich Ctr Translat Canc Res TranslaTUM Sch Med Munich Germany Tech Univ Munich Chair Translat Canc Res Munich Germany Tech Univ Munich Inst Expt Canc Therapy Sch Med Klinikum Rechts Isar Munich Germany German Canc Res Ctr German Canc Consortium DKTK Div Canc Genome Res Heidelberg Germany Netherlands Canc Inst Oncode Inst Div Mol Pathol Amsterdam Netherlands Helmholtz Ctr Munich Inst Computat Biol Neuherberg Germany Tech Univ Munich TUM Sch Comp Informat & Technol Dept Math Informat & Technol D-80333 Munich Germany Tech Univ Munich Inst Diagnost & Intervent Radiol Klinikum Rechts Isar Munich Germany Tech Univ Munich Artificial Intelligence Med & Healthcare Munich Germany Tech Univ Munich Inst Allgemeine Pathol & Pathol Anat Sch Med & Hlth Munich Germany Tech Univ Munich Inst Mol Oncol & Funct Genom Sch Med Munich Germany Helmholtz Ctr Munich Inst Diabet & Canc Neuherberg Germany Heidelberg Univ Joint Heidelberg IDC Translat Diabet Program Heidelberg Germany Univ Munich Univ Hosp Ludwig Maximilians Dept Med 1 Munich Germany Tech Univ Munich Dept Radiat Oncol Munich Germany Helmholtz Zentrum Munchen Inst Radiat Med IRM Neuherberg Germany Tech Univ Munich Phys Dept Lehrstuhl Zell Biophys E27 Garching Germany Ruhr Univ Bochum Dept Dermatol Bochum Germany Univ Med Ctr Gottingen Dept Gen Visceral & Pediat Surg Gottingen Germany Heidelberg Univ Med Fac Mannheim European Ctr Angiosci ECAS

In patients with pancreatic ductal adenocarcinoma (PDAC), intratumoural and intertumoural heterogeneity increases chemoresistance and mortality rates. However, such morphological and phenotypic diversities are not typically captured by organoid models of PDAC. Here we show that branched organoids embedded in collagen gels can recapitulate the phenotypic landscape seen in murine and human PDAC, that the pronounced molecular and morphological intratumoural and intertumoural heterogeneity of organoids is governed by defined transcriptional programmes (notably, epithelial-to-mesenchymal plasticity), and that different organoid phenotypes represent distinct tumour-cell states with unique biological features in vivo. We also show that phenotype-specific therapeutic vulnerabilities and modes of treatment-induced phenotype reprogramming can be captured in phenotypic heterogeneity maps. Our methodology and analyses of tumour-cell heterogeneity in PDAC may guide the development of phenotype-targeted treatment strategies.

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Assessing the Alignment of Large Language Models With Human Values for Mental Health Integration: Cross-Sectional Study Using Schwartz's Theory of Basic Values

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JMIR MENTAL HEALTH 2024年第1期11卷 e55988页

作者： Hadar-Shoval, Dorit Asraf, Kfir Mizrachi, Yonathan Haber, Yuval Elyoseph, Zohar Max Stern Yezreel Valley Coll Psychol Dept Tel Adashim Israel Max Stern Yezreel Valley Coll Jane Goodall Inst Tel Adashim Israel Tel Aviv Univ Lab AI Machine Learning Business & Data Analyt Tel Aviv Israel Bar Ilan Univ PhD Program Hermeneut & Cultural Studies Interdisciplinary Studies Unit Ramat Gan Israel Max Stern Yezreel Valley Coll Ctr Psychobiol Res Psychol Dept Tel Adashim Israel Imperial Coll London Fac Med Dept Brain Sci Fulham Palace Rd London W6 8RF England

Background: Large language models (LLMs) hold potential for mental health applications. However, their opaque alignment processes may embed biases that shape problematic perspectives. Evaluating the values embedded within LLMs that guide their decision -making have ethical importance. Schwartz's theory of basic values (STBV) provides a framework for quantifying cultural value orientations and has shown utility for examining values in mental health contexts, including cultural, diagnostic, and therapist -client dynamics. Objective: This study aimed to (1) evaluate whether the STBV can measure value -like constructs within leading LLMs and (2) determine whether LLMs exhibit distinct value -like patterns from humans and each other. Methods: In total, 4 LLMs (Bard, Claude 2, Generative Pretrained Transformer [GPT]-3.5, GPT-4) were anthropomorphized and instructed to complete the Portrait Values Questionnaire-Revised (PVQ-RR) to assess value -like constructs. Their responses over 10 trials were analyzed for reliability and validity. To benchmark the LLMs' value profiles, their results were compared to published data from a diverse sample of 53,472 individuals across 49 nations who had completed the PVQ-RR. This allowed us to assess whether the LLMs diverged from established human value patterns across cultural groups. Value profiles were also compared between models via statistical tests. Results: The PVQ-RR showed good reliability and validity for quantifying value -like infrastructure within the LLMs. However, substantial divergence emerged between the LLMs' value profiles and population data. The models lacked consensus and exhibited distinct motivational biases, reflecting opaque alignment processes. For example, all models prioritized universalism and self -direction, while de-emphasizing achievement, power, and security relative to humans. Successful discriminant analysis differentiated the 4 LLMs' distinct value profiles. Further examination found the biased value

关键词： large language models LLMs large language model LLM machine learning ML natural language processing NLP deep learning ChatGPT Chat-GPT chatbot chatbots chat-bot chat-bots Claude values Bard artificial intelligence AI algorithm algorithms predictive model predictive models predictive analytics predictive system practical model practical models mental health mental illness mental illnesses mental disease mental diseases mental disorder mental disorders mobile health mHealth eHealth mood disorder mood disorders

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Software Support Tools for Reirradiation: A Report From Reirradiation Collaborative Group (ReCOG) 2024

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INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS 2025年第1期122卷 181-185页

作者： Matrosic, Charles K. Andrzejewski, Piotr Bergman, Alanah Chng, Nick El Naqa, Issam Freislederer, Philipp Hoffmann, Lone Hope, Andrew Jacqmin, Dustin Kelly, Colin Kuo, Licheng Lin, Haibo Mayo, Charles Murrell, Donna H. Oden, Jakob Svensson, Stina Thompson, Stephen Osorio, Eliana Vasquez West, Nick Wicha, Roman Yorke, Ellen Appelt, Ane L. Paradis, Kelly C. Univ Michigan Dept Radiat Oncol Ann Arbor MI 48109 USA MedAustron Ion Therapy Ctr Wiener Neustadt Austria Univ British Columbia Dept Radiat Oncol BC Canc Vancouver Vancouver BC Canada Univ British Columbia Dept Med Phys BC Canc Prince George Prince George BC Canada H Lee Moffitt Canc Ctr & Res Inst Machine Learning Dept Tampa FL USA Brainlab AG Munich Germany Aarhus Univ Hosp Dept Oncol Aarhus Denmark Univ Toronto Univ Hlth Network Dept Radiat Oncol Radiat Med ProgramPrincess Margaret Canc Ctr Toronto ON Canada Univ Wisconsin Dept Human Oncol Madison WI USA St Lukes Radiat Oncol Network Dublin Ireland Mem Sloan Kettering Canc Ctr Dept Med Phys New York NY USA New York Proton Ctr New York NY USA Western Univ Dept Oncol London ON Canada London Hlth Sci Ctr London ON Canada RaySearch Labs AB Stockholm Sweden Varian Med Syst Palo Alto CA USA Univ Manchester Div Canc Sci Manchester England Newcastle Tyne Hosp Trust Northern Ctr Canc Care Newcastle Upon Tyne England Univ Leeds Leeds Inst Med Res St Jamess Leeds England

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