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Author: Ruijiang Li Publisher: CRC Press ISBN: 1351208268 Category : Science Languages : en Pages : 484
Book Description
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: Ruijiang Li Publisher: CRC Press ISBN: 1351208268 Category : Science Languages : en Pages : 484
Book Description
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: Hassan Mohy-ud-Din Publisher: Springer Nature ISBN: 3030401243 Category : Computers Languages : en Pages : 100
Book Description
This book constitutes the proceedings of the First International Workshop on Radiomics and Radiogenomics in Neuro-oncology, RNO-AI 2019, which was held in conjunction with MICCAI in Shenzhen, China, in October 2019. The 10 full papers presented in this volume were carefully reviewed and selected from 15 submissions. They deal with the development of tools that can automate the analysis and synthesis of neuro-oncologic imaging.
Author: Sanjay Saxena Publisher: Elsevier ISBN: 0443185107 Category : Medical Languages : en Pages : 377
Book Description
Radiomics and Radiogenomics in Neuro-Oncology: An Artificial Intelligence Paradigm—Volume 2: Genetics and Clinical Applications provides readers with a broad and detailed framework for radiomics and radiogenomics (R-n-R) approaches with AI in neuro-oncology. It delves into the study of cancer biology and genomics, presenting methods and techniques for analyzing these elements. The book also highlights current solutions that R-n-R can offer for personalized patient treatments, as well as discusses the limitations and future prospects of AI technologies. Volume 1: Radiogenomics Flow Using Artificial Intelligence covers the genomics and molecular study of brain cancer, medical imaging modalities and their analysis in neuro-oncology, and the development of prognostic and predictive models using radiomics. Volume 2: Genetics and Clinical Applications extends the discussion to imaging signatures that correlate with molecular characteristics of brain cancer, clinical applications of R-n-R in neuro-oncology, and the use of Machine Learning and Deep Learning approaches for R-n-R in neuro-oncology. - Includes coverage of foundational concepts of the emerging fields of Radiomics and Radiogenomics - Covers imaging signatures for brain cancer molecular characteristics, including Isocitrate Dehydrogenase Mutations (IDH), TP53 Mutations, ATRX loss, MGMT gene, Epidermal Growth Factor Receptor (EGFR), and other mutations - Presents clinical applications of R-n-R in neuro-oncology such as risk stratification, survival prediction, heterogeneity analysis, as well as early and accurate prognosis - Provides in-depth technical coverage of radiogenomics studies for difference brain cancer types, including glioblastoma, astrocytoma, CNS lymphoma, meningioma, acoustic neuroma, and hemangioblastoma
Author: Whitney B. Pope Publisher: Springer Nature ISBN: 3030273598 Category : Medical Languages : en Pages : 289
Book Description
This book covers physiologic, metabolic and molecular imaging for gliomas. Gliomas are the most common primary brain tumors. Imaging is critical for glioma management because of its ability to noninvasively define the anatomic location and extent of disease. While conventional MRI is used to guide current treatments, multiple studies suggest molecular features of gliomas may be identified with noninvasive imaging, including physiologic MRI and amino acid positron emission tomography (PET). These advanced imaging techniques have the promise to help elucidate underlying tumor biology and provide important information that could be integrated into routine clinical practice. The text outlines current clinical practice including common scenarios in which imaging interpretation impacts patient management. Gaps in knowledge and potential areas of advancement based on the application of more experimental imaging techniques will be discussed. In reviewing this book, readers will learn: current standard imaging methodologies used in clinical practice for patients undergoing treatment for glioma and the implications of emerging treatment modalities including immunotherapy the theoretical basis for advanced imaging techniques including diffusion and perfusion MRI, MR spectroscopy, CEST and amino acid PET the relationship between imaging and molecular/genomic glioma features incorporated in the WHO 2016 classification update and the potential application of machine learning about the recently adopted and FDA approved standard brain tumor protocol for multicenter drug trials of the gaps in knowledge that impede optimal patient management and the cutting edge imaging techniques that could address these deficits
Author: Ana M. Franceschi Publisher: Springer Nature ISBN: 3030823679 Category : Medical Languages : en Pages : 848
Book Description
This book serves as a reference and comprehensive guide for PET/MR neuroimaging. The field of PET/MR is rapidly evolving, however, there is no standard resource summarizing the vast information and its potential applications. This book will guide neurological molecular imaging applications in both clinical practice and the research setting. Experts from multiple disciplines, including radiologists, researchers, and physicists, have collaborated to bring their knowledge and expertise together. Sections begin by covering general considerations, including public health and economic implications, the physics of PET/MR systems, an overview of hot lab and cyclotron, and radiotracers used in neurologic PET/MRI. There is then coverage of each major disease/systemic category, including dementia and neurodegenerative disease, epilepsy localization, brain tumors, inflammatory and infectious CNS disorders, head and neck imaging, as well as vascular hybrid imaging. Together, we have created a thorough, concise and up-to-date textbook in a unique, user-friendly format. This is an ideal guide for neuroradiologists, nuclear medicine specialists, medical physicists, clinical trainees and researchers.
Author: Makoto Hashizume Publisher: Springer Nature ISBN: 9811643253 Category : Medical Languages : en Pages : 370
Book Description
This volume thoroughly describes the fundamentals of a new multidisciplinary field of study that aims to deepen our understanding of the human body by combining medical image processing, mathematical analysis, and artificial intelligence. Multidisciplinary Computational Anatomy (MCA) offers an advanced diagnosis and therapeutic navigation system to help detect or predict human health problems from the micro-level to macro-level using a four-dimensional, dynamic approach to human anatomy: space, time, function, and pathology. Applying this dynamic and “living” approach in the clinical setting will promote better planning for – and more accurate, effective, and safe implementation of – medical management. Multidisciplinary Computational Anatomy will appeal not only to clinicians but also to a wide readership in various scientific fields such as basic science, engineering, image processing, and biomedical engineering. All chapters were written by respected specialists and feature abundant color illustrations. Moreover, the findings presented here share new insights into unresolved issues in the diagnosis and treatment of disease, and into the healthy human body.
Author: Elke Hattingen Publisher: Springer ISBN: 3642450407 Category : Medical Languages : en Pages : 166
Book Description
This book describes the basics, the challenges and the limitations of state of the art brain tumor imaging and examines in detail its impact on diagnosis and treatment monitoring. It opens with an introduction to the clinically relevant physical principles of brain imaging. Since MR methodology plays a crucial role in brain imaging, the fundamental aspects of MR spectroscopy, MR perfusion and diffusion-weighted MR methods are described, focusing on the specific demands of brain tumor imaging. The potential and the limits of new imaging methodology are carefully addressed and compared to conventional MR imaging. In the main part of the book, the most important imaging criteria for the differential diagnosis of solid and necrotic brain tumors are delineated and illustrated in examples. A closing section is devoted to the use of MR methods for the monitoring of brain tumor therapy. The book is intended for radiologists, neurologists, neurosurgeons, oncologists and other scientists in the biomedical field with an interest in neuro-oncology.
Author: Jun Deng Publisher: CRC Press ISBN: 1351801120 Category : Science Languages : en Pages : 311
Book Description
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.
Author: Ke-Lin Du Publisher: Springer Nature ISBN: 1447174526 Category : Mathematics Languages : en Pages : 996
Book Description
This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing. Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include: • multilayer perceptron; • the Hopfield network; • associative memory models;• clustering models and algorithms; • t he radial basis function network; • recurrent neural networks; • nonnegative matrix factorization; • independent component analysis; •probabilistic and Bayesian networks; and • fuzzy sets and logic. Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.
Author: Ruijiang Li
Author: Ruijiang Li
Author: Hassan Mohy-ud-Din
Author: Sanjay Saxena
Author: Whitney B. Pope
Author: Katrin Scheinemann
Author: Ana M. Franceschi
Author: Makoto Hashizume
Author: Elke Hattingen
Author: Jun Deng
Author: Ke-Lin Du