Big Data In Radiation Oncology
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Big Data In Radiation Oncology
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Author : Jun Deng
language : en
Publisher: CRC Press
Release Date : 2019-03-07
Big Data In Radiation Oncology written by Jun Deng and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-07 with Science categories.
Big Data in Radiation Oncology gives readers an in-depth look into how big data is having an impact on the clinical care of cancer patients. While basic principles and key analytical and processing techniques are introduced in the early chapters, the rest of the book turns to clinical applications, in particular for cancer registries, informatics, radiomics, radiogenomics, patient safety and quality of care, patient-reported outcomes, comparative effectiveness, treatment planning, and clinical decision-making. More features of the book are: Offers the first focused treatment of the role of big data in the clinic and its impact on radiation therapy. Covers applications in cancer registry, radiomics, patient safety, quality of care, treatment planning, decision making, and other key areas. Discusses the fundamental principles and techniques for processing and analysis of big data. Address the use of big data in cancer prevention, detection, prognosis, and management. Provides practical guidance on implementation for clinicians and other stakeholders. Dr. Jun Deng is a professor at the Department of Therapeutic Radiology of Yale University School of Medicine and an ABR board certified medical physicist at Yale-New Haven Hospital. He has received numerous honors and awards such as Fellow of Institute of Physics in 2004, AAPM Medical Physics Travel Grant in 2008, ASTRO IGRT Symposium Travel Grant in 2009, AAPM-IPEM Medical Physics Travel Grant in 2011, and Fellow of AAPM in 2013. Lei Xing, Ph.D., is the Jacob Haimson Professor of Medical Physics and Director of Medical Physics Division of Radiation Oncology Department at Stanford University. His research has been focused on inverse treatment planning, tomographic image reconstruction, CT, optical and PET imaging instrumentations, image guided interventions, nanomedicine, and applications of molecular imaging in radiation oncology. Dr. Xing is on the editorial boards of a number of journals in radiation physics and medical imaging, and is recipient of numerous awards, including the American Cancer Society Research Scholar Award, The Whitaker Foundation Grant Award, and a Max Planck Institute Fellowship.
Machine Learning With Radiation Oncology Big Data
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Author : Jun Deng
language : en
Publisher: Frontiers Media SA
Release Date : 2019-01-21
Machine Learning With Radiation Oncology Big Data written by Jun Deng and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-21 with categories.
Machine Learning With Radiation Oncology Big Data
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Author :
language : en
Publisher:
Release Date : 2019
Machine Learning With Radiation Oncology Big Data written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Radiation oncology is uniquely positioned to harness the power of big data as vast amounts of data are generated at an unprecedented pace for individual patients in imaging studies and radiation treatments worldwide. The big data encountered in the radiotherapy clinic may include patient demographics stored in the electronic medical record (EMR) systems, plan settings and dose volumetric information of the tumors and normal tissues generated by treatment planning systems (TPS), anatomical and functional information from diagnostic and therapeutic imaging modalities (e.g., CT, PET, MRI and kVCBCT) stored in picture archiving and communication systems (PACS), as well as the genomics, proteomics and metabolomics information derived from blood and tissue specimens. Yet, the great potential of big data in radiation oncology has not been fully exploited for the benefits of cancer patients due to a variety of technical hurdles and hardware limitations. With recent development in computer technology, there have been increasing and promising applications of machine learning algorithms involving the big data in radiation oncology. This research topic is intended to present novel technological breakthroughs and state-of-the-art developments in machine learning and data mining in radiation oncology in recent years.
Big Data In Oncology Impact Challenges And Risk Assessment
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Author : Neeraj Kumar Fuloria
language : en
Publisher: CRC Press
Release Date : 2023-12-21
Big Data In Oncology Impact Challenges And Risk Assessment written by Neeraj Kumar Fuloria and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-21 with Medical categories.
We are in the era of large-scale science. In oncology there is a huge number of data sets grouping information on cancer genomes, transcriptomes, clinical data, and more. The challenge of big data in cancer is to integrate all this diversity of data collected into a unique platform that can be analyzed, leading to the generation of readable files. The possibility of harnessing information from all the accumulated data leads to an improvement in cancer patient treatment and outcome. Solving the big data problem in oncology has multiple facets. Big data in Oncology: Impact, Challenges, and Risk Assessment brings together insights from emerging sophisticated information and communication technologies such as artificial intelligence, data science, and big data analytics for cancer management. This book focuses on targeted disease treatment using big data analytics. It provides information about targeted treatment in oncology, challenges and application of big data in cancer therapy. Recent developments in the fields of artificial intelligence, machine learning, medical imaging, personalized medicine, computing and data analytics for improved patient care. Description of the application of big data with AI to discover new targeting points for cancer treatment. Summary of several risk assessments in the field of oncology using big data. Focus on prediction of doses in oncology using big data The most targeted or relevant audience is academics, research scholars, health care professionals, hospital management, pharmaceutical chemists, the biomedical industry, software engineers and IT professionals.
Machine Learning And Artificial Intelligence In Radiation Oncology
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Author : Barry S. Rosenstein
language : en
Publisher: Academic Press
Release Date : 2023-12-02
Machine Learning And Artificial Intelligence In Radiation Oncology written by Barry S. Rosenstein and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-02 with Science categories.
Machine Learning and Artificial Intelligence in Radiation Oncology: A Guide for Clinicians is designed for the application of practical concepts in machine learning to clinical radiation oncology. It addresses the existing void in a resource to educate practicing clinicians about how machine learning can be used to improve clinical and patient-centered outcomes. This book is divided into three sections: the first addresses fundamental concepts of machine learning and radiation oncology, detailing techniques applied in genomics; the second section discusses translational opportunities, such as in radiogenomics and autosegmentation; and the final section encompasses current clinical applications in clinical decision making, how to integrate AI into workflow, use cases, and cross-collaborations with industry. The book is a valuable resource for oncologists, radiologists and several members of biomedical field who need to learn more about machine learning as a support for radiation oncology. Presents content written by practicing clinicians and research scientists, allowing a healthy mix of both new clinical ideas as well as perspectives on how to translate research findings into the clinic Provides perspectives from artificial intelligence (AI) industry researchers to discuss novel theoretical approaches and possibilities on academic collaborations Brings diverse points-of-view from an international group of experts to provide more balanced viewpoints on a complex topic
Applying Big Data To Address The Social Determinants Of Health In Oncology
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Author : National Academies of Sciences, Engineering, and Medicine
language : en
Publisher: National Academies Press
Release Date : 2020-09-14
Applying Big Data To Address The Social Determinants Of Health In Oncology written by National Academies of Sciences, Engineering, and Medicine and has been published by National Academies Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-14 with Medical categories.
The National Academies of Sciences, Engineering, and Medicine held the workshop Applying Big Data to Address the Social Determinants of Health in Oncology on October 28â€"29, 2019, in Washington, DC. This workshop examined social determinants of health (SDOH) in the context of cancer, and considered opportunities to effectively leverage big data to improve health equity and reduce disparities. The workshop featured presentations and discussion by experts in technology, oncology, and SDOH, as well as representatives from government, industry, academia, and health care systems. This publication summarizes the presentations and discussions from the workshop.
Radiomics And Radiogenomics
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Author : Ruijiang Li
language : en
Publisher: CRC Press
Release Date : 2019-07-09
Radiomics And Radiogenomics written by Ruijiang Li and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-09 with Science categories.
Radiomics and Radiogenomics: Technical Basis and Clinical Applications provides a first summary of the overlapping fields of radiomics and radiogenomics, showcasing how they are being used to evaluate disease characteristics and correlate with treatment response and patient prognosis. It explains the fundamental principles, technical bases, and clinical applications with a focus on oncology. The book’s expert authors present computational approaches for extracting imaging features that help to detect and characterize disease tissues for improving diagnosis, prognosis, and evaluation of therapy response. This book is intended for audiences including imaging scientists, medical physicists, as well as medical professionals and specialists such as diagnostic radiologists, radiation oncologists, and medical oncologists. Features Provides a first complete overview of the technical underpinnings and clinical applications of radiomics and radiogenomics Shows how they are improving diagnostic and prognostic decisions with greater efficacy Discusses the image informatics, quantitative imaging, feature extraction, predictive modeling, software tools, and other key areas Covers applications in oncology and beyond, covering all major disease sites in separate chapters Includes an introduction to basic principles and discussion of emerging research directions with a roadmap to clinical translation
Big Data On Real World Applications
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Author : Sebastian Ventura Soto
language : en
Publisher: BoD – Books on Demand
Release Date : 2016-07-20
Big Data On Real World Applications written by Sebastian Ventura Soto and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-20 with Computers categories.
As technology advances, high volumes of valuable data are generated day by day in modern organizations. The management of such huge volumes of data has become a priority in these organizations, requiring new techniques for data management and data analysis in Big Data environments. These environments encompass many different fields including medicine, education data, and recommender systems. The aim of this book is to provide the reader with a variety of fields and systems where the analysis and management of Big Data are essential. This book describes the importance of the Big Data era and how existing information systems are required to be adapted to face up the problems derived from the management of massive datasets.
Precision Medicine In Oncology
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Author : Bulent Aydogan
language : en
Publisher: John Wiley & Sons
Release Date : 2020-11-02
Precision Medicine In Oncology written by Bulent Aydogan and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-02 with Medical categories.
A FRESH EXAMINATION OF PRECISION MEDICINE'S INCREASINGLY PROMINENT ROLE IN THE FIELD OF ONCOLOGY Precision medicine takes into account each patient's specific characteristics and requirements to arrive at treatment plans that are optimized towards the best possible outcome. As the field of oncology continues to advance, this tailored approach is becoming more and more prevalent, channelling data on genomics, proteomics, metabolomics and other areas into new and innovative methods of practice. Precision Medicine in Oncology draws together the essential research driving the field forward, providing oncology clinicians and trainees alike with an illuminating overview of the technology and thinking behind the breakthroughs currently being made. Topics covered include: Biologically-guided radiation therapy Informatics for precision medicine Molecular imaging Biomarkers for treatment assessment Big data Nanoplatforms Casting a spotlight on this emerging knowledge base and its impact upon the management of tumors, Precision Medicine in Oncology opens up new possibilities and ways of working – not only for oncologists, but also for molecular biologists, radiologists, medical geneticists, and others.
Machine Learning In Radiation Oncology
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Author : Issam El Naqa
language : en
Publisher: Springer
Release Date : 2015-06-19
Machine Learning In Radiation Oncology written by Issam El Naqa and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-19 with Medical categories.
This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.