Radiomics and its Clinical Application: Artificial Intelligence and Medical Big Data describes the two key aspects of radiomic clinical practice, including precision diagnosis and the therapeutic effect and prognostic evaluation that make radiomics a powerful tool in the clinical setting. This book is a very useful resource for scientists and computer engineers in machine learning and medical image analysis, scientists focusing on antineoplastic drugs, radiologists, pathologists, oncologists and surgeons wanting to understand radiomics and its potential in clinical practice. Provides an introduction to the concepts of radiomics Presents an in-depth discussion on core technologies and methods Summarizes current radiomics research, perspectives on the future of radiomics, and the challenges ahead Includes an introduction to several platforms that are planned to be built, including cooperation, data sharing, software and application platforms
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 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||: Andrew Bush,Diana Bilton,Margaret Hodson|
|Publisher||: CRC Press|
|Release Date||: 2015-07-02|
|ISBN 10||: 0340764252|
|Pages||: 699 pages|
Hodson and Geddes' Cystic Fibrosis provides everything the respiratory clinician, pulmonologist or health professional treating patients needs in a single manageable volume. This international and authoritative work brings together current knowledge and has become established in previous editions as a leading reference in the field. This fourth edition includes a wealth of new information, figures, useful videos, and a companion eBook. The basic science that underlies the disease and its progression is outlined in detail and put into a clinical context. Diagnostic and clinical aspects are covered in depth, as well as promising advances such as gene therapies and other novel molecular based treatments. Patient monitoring and the importance of multidisciplinary care are also emphasized. This edition: Features accessible sections reflecting the multidisciplinary nature of the cystic fibrosis care team Contains a chapter written by patients and families about their experiences with the disease Includes expanded coverage of clinical areas, including chapters covering sleep, lung mechanics and the work of breathing, upper airway disease, insulin deficiency and diabetes, bone disease, and sexual and reproductive issues Discusses management both in the hospital and at home Includes a new section on monitoring and discusses the use of databases to improve patient care Covers monitoring in different age groups, exercise testing and the outcomes of clinical trials in these areas Includes chapters devoted to nursing, physiotherapy, psychology, and palliative and spiritual care Throughout, the emphasis is on providing an up-to-date and balanced review of both the clinical and basic science aspects of the subject and reflecting the multidisciplinary nature of the cystic fibrosis care team.
|Author||: Kenji Suzuki,Yisong Chen|
|Release Date||: 2018-01-09|
|ISBN 10||: 331968843X|
|Pages||: 387 pages|
This book offers the first comprehensive overview of artificial intelligence (AI) technologies in decision support systems for diagnosis based on medical images, presenting cutting-edge insights from thirteen leading research groups around the world. Medical imaging offers essential information on patients’ medical condition, and clues to causes of their symptoms and diseases. Modern imaging modalities, however, also produce a large number of images that physicians have to accurately interpret. This can lead to an “information overload” for physicians, and can complicate their decision-making. As such, intelligent decision support systems have become a vital element in medical-image-based diagnosis and treatment. Presenting extensive information on this growing field of AI, the book offers a valuable reference guide for professors, students, researchers and professionals who want to learn about the most recent developments and advances in the field.
|Author||: S. Kevin Zhou,Daniel Rueckert,Gabor Fichtinger|
|Publisher||: Academic Press|
|Release Date||: 2019-10-18|
|ISBN 10||: 0128165863|
|Pages||: 1072 pages|
Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention. Presents the key research challenges in medical image computing and computer-assisted intervention Written by leading authorities of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society Contains state-of-the-art technical approaches to key challenges Demonstrates proven algorithms for a whole range of essential medical imaging applications Includes source codes for use in a plug-and-play manner Embraces future directions in the fields of medical image computing and computer-assisted intervention
Artificial Intelligence Medicine: Technical Basis and Clinical Applications presents a comprehensive overview of the field, ranging from its history and technical foundations, to specific clinical applications and finally to prospects. Artificial Intelligence (AI) is expanding across all domains at a breakneck speed. Medicine, with the availability of large multidimensional datasets, lends itself to strong potential advancement with the appropriate harnessing of AI. The integration of AI can occur throughout the continuum of medicine: from basic laboratory discovery to clinical application and healthcare delivery. Integrating AI within medicine has been met with both excitement and scepticism. By understanding how AI works, and developing an appreciation for both limitations and strengths, clinicians can harness its computational power to streamline workflow and improve patient care. It also provides the opportunity to improve upon research methodologies beyond what is currently available using traditional statistical approaches. On the other hand, computers scientists and data analysts can provide solutions, but often lack easy access to clinical insight that may help focus their efforts. This book provides vital background knowledge to help bring these two groups together, and to engage in more streamlined dialogue to yield productive collaborative solutions in the field of medicine. Provides history and overview of artificial intelligence, as narrated by pioneers in the field Discusses broad and deep background and updates on recent advances in both medicine and artificial intelligence that enabled the application of artificial intelligence Addresses the ever-expanding application of this novel technology and discusses some of the unique challenges associated with such an approach
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.
The impact of molecular imaging on diagnostics, therapy, and follow-up in oncology is increasing steadily. Many innovative molecular imaging probes have already entered clinical practice, and there is no doubt that the future emphasis will be on multimodality imaging in which morphological, functional, and molecular imaging techniques are combined in a single clinical investigation. This handbook addresses all aspects of molecular imaging in oncology, from basic research to clinical applications. The first section is devoted to technology and probe design, and examines a variety of PET and SPECT tracers as well as multimodality probes. Preclinical studies are then discussed in detail, with particular attention to multimodality imaging. In the third section, diverse clinical applications are presented, and the book closes by looking at future challenges. This handbook will be of value to all who are interested in the revolution in diagnostic oncology that is being brought about by molecular imaging.
This book discusses the most significant recent advances in oncological molecular imaging, covering the full spectrum from basic and preclinical research to clinical practice. The content is divided into five sections, the first of which is devoted to standardized and emerging technologies and probe designs for different modalities, such as PET, SPECT, optical and optoacoustic imaging, ultrasound, CT, and MRI. The second section focuses on multiscale preclinical applications ranging from advanced microscopy and mass spectroscopy to whole-body imaging. In the third section, various clinical applications are presented, including image-guided surgery and the radiomic analysis of multiple imaging features. The final two sections are dedicated to the emerging, crucial role that molecular imaging can play in the planning and monitoring of external and internal radiotherapy, and to future challenges and prospects in multimodality imaging. Given its scope, the handbook will benefit all readers who are interested in the revolution in diagnostic and therapeutic oncology that is now being brought about by molecular imaging.
Biomedical Texture Analysis: Fundamentals, Applications, Tools and Challenges describes the fundamentals and applications of biomedical texture analysis (BTA) for precision medicine. It defines what biomedical textures (BTs) are and why they require specific image analysis design approaches when compared to more classical computer vision applications. The fundamental properties of BTs are given to highlight key aspects of texture operator design, providing a foundation for biomedical engineers to build the next generation of biomedical texture operators. Examples of novel texture operators are described and their ability to characterize BTs are demonstrated in a variety of applications in radiology and digital histopathology. Recent open-source software frameworks which enable the extraction, exploration and analysis of 2D and 3D texture-based imaging biomarkers are also presented. This book provides a thorough background on texture analysis for graduate students, and biomedical engineers from both industry and academia who have basic image processing knowledge. Medical doctors and biologists with no background in image processing will also find available methods and software tools for analyzing textures in medical images. •Defines biomedical texture precisely and describe how it is different from general texture information considered in computer vision •Define the general problem to translate 2D and 3D texture patterns from biomedical images to visually and biologically relevant measurements •Describes, using intuitive concepts, how the most popular biomedical texture analysis approaches (e.g., gray-level matrices, fractals, wavelets, deep convolutional neural networks) work, what they have in common, and how they are different •Identifies the strengths, weaknesses, and current challenges of existing methods including both handcrafted and learned representations, as well as deep learning. The goal is to establish foundations for building the next generation of biomedical texture operators •Showcases applications where biomedical texture analysis has succeeded and failed •Provides details on existing, freely available texture analysis software, helping experts in medicine or biology develop and test precise research hypothesis
This book offers a comprehensive and topical depiction of advances in CT imaging. CT has become a leading medical imaging modality, thanks to its superb spatial and temporal resolution to depict anatomical details. New advances have further extended the technology to provide physiological information, enabling a wide and expanding range of clinical applications. The text covers the latest advancements in CT technology and clinical applications for a variety of CT types and imaging methods. The content is presented in seven parts to offer a structure across a board coverage of CT: CT Systems, CT Performance, CT Practice, Spectral CT, Quantitative CT, Functional CT, and Special Purpose CT. Each contain chapters written by leading experts in the field, covering CT hardware and software innovations, CT operation, CT performance characterization, functional and quantitative applications, and CT systems devised for specific anatomical applications. This book is an ideal resource for practitioners of CT applications in medicine, including physicians, trainees, engineers, and scientists.
This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.
|Author||: Issam El Naqa|
|Publisher||: CRC Press|
|Release Date||: 2018-04-19|
|ISBN 10||: 0429840349|
|Pages||: 368 pages|
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
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
This work has true international scope, being a unique European/American joint venture that focuses on the state of the art in both diagnostic and therapeutic radionuclide methodology. Pertinent clinical applications are emphasized rather than attempting to cover everything included in the several large comprehensive texts available in our field. This "practical" approach should make it an essential guide to nuclear medicine physicians, technologists, students and interested clinicians alike.
"Molecular Imaging: Fundamentals and Applications" is a comprehensive monograph which describes not only the theory of the underlying algorithms and key technologies but also introduces a prototype system and its applications, bringing together theory, technology and applications. By explaining the basic concepts and principles of molecular imaging, imaging techniques, as well as research and applications in detail, the book provides both detailed theoretical background information and technical methods for researchers working in medical imaging and the life sciences. Clinical doctors and graduate students will also benefit from this book. Jie Tian is a professor at the Institute of Automation, Chinese Academy of Sciences, China.
This book, written by authors with more than a decade of experience in the design and development of artificial intelligence (AI) systems in medical imaging, will guide readers in the understanding of one of the most exciting fields today. After an introductory description of classical machine learning techniques, the fundamentals of deep learning are explained in a simple yet comprehensive manner. The book then proceeds with a historical perspective of how medical AI developed in time, detailing which applications triumphed and which failed, from the era of computer aided detection systems on to the current cutting-edge applications in deep learning today, which are starting to exhibit on-par performance with clinical experts. In the last section, the book offers a view on the complexity of the validation of artificial intelligence applications for commercial use, describing the recently introduced concept of software as a medical device, as well as good practices and relevant considerations for training and testing machine learning systems for medical use. Open problematics on the validation for public use of systems which by nature continuously evolve through new data is also explored. The book will be of interest to graduate students in medical physics, biomedical engineering and computer science, in addition to researchers and medical professionals operating in the medical imaging domain, who wish to better understand these technologies and the future of the field. Features: An accessible yet detailed overview of the field Explores a hot and growing topic Provides an interdisciplinary perspective
The book is an on-the-spot reference for residents and medical students seeking diagnostic radiology fast facts. Its question-and-answer format makes it a perfect quick-reference for personal review and studying for board examinations and re-certification. Readers can read the text from cover to cover to gain a general foundation of knowledge that can be built upon through practice or can use choice chapters to review a specific subspecialty before starting a new rotation or joining a new service. With hundreds of high-yield questions and answer items, this resource addresses both general and subspecialty topics and provides accurate, on-the-spot answers. Sections are organized by subspecialty and body area, including chest, abdomen, and trauma, and chapters cover the anatomy, pathophysiology, differential diagnosis, hallmark signs, and image features of major diseases and conditions. Key example images and illustrations enhance the text throughout and provide an ideal, pocket-sized resource for residents and medical students.