[PDF] Big Data In Radiation Oncology eBook

Author : Jun Deng
Publisher : CRC Press
Page : 289 pages
File Size : 16,29 MB
Release : 2019-03-07
Category : Science
ISBN : 1351801120

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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.

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Publisher :
Page : 0 pages
File Size : 29,22 MB
Release : 2019
Category :
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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.

Author : Neeraj Kumar Fuloria
Publisher : CRC Press
Page : 415 pages
File Size : 18,49 MB
Release : 2023-12-21
Category : Medical
ISBN : 1000965260

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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.

Author : Kerstin A. Kessel
Publisher : Frontiers Media SA
Page : 109 pages
File Size : 49,15 MB
Release : 2018-04-12
Category : Cancer
ISBN : 288945438X

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In radiation oncology as in many other specialties clinical trials are essential to investigate new therapy approaches. Usually, preparation for a prospective clinical trial is extremely time consuming until ethics approval is obtained. To test a new treatment usually many years pass before it can be implemented in the routine care. During that time, already new interventions emerge, new drugs appear on the market, technical & physical innovations are being implemented, novel biology driven concepts are translated into clinical approaches while we are still investigating the ones from years ago. Another problem is associated with molecular diagnostics and the growing amount of tumor specific biomarkers which allows for a better stratification of patient subgroups. On the other side, this may result in a much longer time for patient recruiting and consequently in larger multicenter trials. Moreover, all of the relevant data must be readily available for treatment decision making, treatment as well as follow-up, and ultimately for trial evaluation. This challenges even more for agreed standards in data acquisition, quality and management. How could we change the way currently clinical trials are performed in a way they are safe and ethically justifiable and speed up the initiation process, so we can provide new and better treatments faster for our patients? Further, while we rely on various quantitative information handling distributed, large heterogeneous amounts of data efficiently is very important. Thus data management becomes a strong focus. A good infrastructure helps to plan, tailor and conduct clinical trials in a way they are easy and quickly analyzable. In this research topic we want to discuss new ideas for intelligent trial designs and concepts for data management.

Author : Alexander F. I. Osman
Publisher :
Page : 0 pages
File Size : 27,10 MB
Release : 2022
Category : Computers
ISBN :

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Machine learning (ML) applications in medicine represent an emerging field of research with the potential to revolutionize the field of radiation oncology, in particular. With the era of big data, the utilization of machine learning algorithms in radiation oncology research is growing fast with applications including patient diagnosis and staging of cancer, treatment simulation, treatment planning, treatment delivery, quality assurance, and treatment response and outcome predictions. In this chapter, we provide the interested reader with an overview of the ongoing advances and cutting-edge applications of state-of-the-art ML techniques in radiation oncology process from the radiotherapy workflow perspective, starting from patient,Äôs diagnosis to follow-up. We present with discussion the areas where ML has presently been used and also areas where ML could be applied to improve the efficiency (i.e., optimizing and automating the clinical processes) and quality (i.e., potentials for decision-making support toward a practical application of precision medicine in radiation therapy) of patient care.

Author : Barry S. Rosenstein
Publisher : Academic Press
Page : 480 pages
File Size : 32,88 MB
Release : 2023-12-02
Category : Science
ISBN : 0128220015

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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

Author : Ruijiang Li
Publisher : CRC Press
Page : 420 pages
File Size : 42,96 MB
Release : 2019-07-09
Category : Science
ISBN : 1351208268

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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

Author : Issam El Naqa
Publisher : CRC Press
Page : 368 pages
File Size : 13,82 MB
Release : 2018-04-19
Category : Science
ISBN : 0429840357

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This book explores outcome modeling in cancer from a data-centric perspective to enable a better understanding of complex treatment response, to guide the design of advanced clinical trials, and to aid personalized patient care and improve their quality of life. It contains coverage of the relevant data sources available for model construction (panomics), ranging from clinical or preclinical resources to basic patient and treatment characteristics, medical imaging (radiomics), and molecular biological markers such as those involved in genomics, proteomics and metabolomics. It also includes discussions on the varying methodologies for predictive model building with analytical and data-driven approaches. This book is primarily intended to act as a tutorial for newcomers to the field of outcome modeling, as it includes in-depth how-to recipes on modeling artistry while providing sufficient instruction on how such models can approximate the physical and biological realities of clinical treatment. The book will also be of value to seasoned practitioners as a reference on the varying aspects of outcome modeling and their current applications. Features: Covers top-down approaches applying statistical, machine learning, and big data analytics and bottom-up approaches using first principles and multi-scale techniques, including numerical simulations based on Monte Carlo and automata techniques Provides an overview of the available software tools and resources for outcome model development and evaluation, and includes hands-on detailed examples throughout Presents a diverse selection of the common applications of outcome modeling in a wide variety of areas: treatment planning in radiotherapy, chemotherapy and immunotherapy, utility-based and biomarker applications, particle therapy modeling, oncological surgery, and the design of adaptive and SMART clinical trials

Author : Maximilian Werner Hohensinner
Publisher :
Page : 60 pages
File Size : 39,34 MB
Release : 2018
Category :
ISBN :

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Modern radiation oncology, the extensive monitoring and subsequent follow-up of patients creates a massive amount of partly heterogeneous data. Thus, it does not only consist of demographic data, but rather comprises additional information about the treatment position, dose distribution, volumetric information and other medical data. The further processing of this information for the purpose of advanced patient care is challenging if executed manually by the physician. Big Data analytics can be a powerful solution for this issue, since it enables the development of predictive models with the aim to guide treatment decisions. However, much of the data generated around patients is collected and stored in a heterogeneous form and is not suitable for documentation and analysis. The main aim of this thesis will be the collection and pre-processing of radiotherapy related data. In a second step, the data will be analysed using machine learning techniques with the aim of investigating possible autonomous support systems for treatment decisions.*****Modern radiation oncology, the extensive monitoring and subsequent follow-up of patients creates a massive amount of partly heterogeneous data. Thus, it does not only consist of demographic data, but rather comprises additional information about the treatment position, dose distribution, volumetric information and other medical data. The further processing of this information for the purpose of advanced patient care is challenging if executed manually by the physician. Big Data analytics can be a powerful solution for this issue, since it enables the development of predictive models with the aim to guide treatment decisions. However, much of the data generated around patients is collected and stored in a heterogeneous form and is not suitable for documentation and analysis. The main aim of this thesis will be the collection and pre-processing of radiotherapy related data. In a second step, the data will be analysed using machine