June 18, 2021

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Radiomics and Its Clinical Application

Radiomics and Its Clinical Application
Author : Jie Tian,Di Dong,Zhenyu Liu,Jingwei Wei
Publisher : Elsevier
Release Date : 2021-07-02
Category : Computers
Total pages :300
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The rapid development of artificial intelligence technology in medical data analysis has led to the concept of radiomics. This book introduces the essential and latest technologies in radiomics, such as imaging segmentation, quantitative imaging feature extraction, and machine learning methods for model construction and performance evaluation, providing invaluable guidance for the researcher entering the field. It fully describes three key aspects of radiomic clinical practice: precision diagnosis, the therapeutic effect, and prognostic evaluation, which 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, and radiologists, pathologists, oncologists, as well as surgeons wanting to understand radiomics and its potential in clinical practice. An introduction to the concepts of radiomics In-depth presentation of the core technologies and methods Summary of current radiomics research, perspective on the future of radiomics and the challenges ahead An introduction to several platforms that are planned to be built: cooperation, data sharing, software, and application platforms

Radiomics and Its Clinical Application

Radiomics and Its Clinical Application
Author : Jie Tian,Di Dong,Zhenyu Liu,Jingwei Wei
Publisher : Academic Press
Release Date : 2021-06-18
Category : Computers
Total pages :300
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The rapid development of artificial intelligence technology in medical data analysis has led to the concept of radiomics. This book introduces the essential and latest technologies in radiomics, such as imaging segmentation, quantitative imaging feature extraction, and machine learning methods for model construction and performance evaluation, providing invaluable guidance for the researcher entering the field. It fully describes three key aspects of radiomic clinical practice: precision diagnosis, the therapeutic effect, and prognostic evaluation, which 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, and radiologists, pathologists, oncologists, as well as surgeons wanting to understand radiomics and its potential in clinical practice. An introduction to the concepts of radiomics In-depth presentation of the core technologies and methods Summary of current radiomics research, perspective on the future of radiomics and the challenges ahead An introduction to several platforms that are planned to be built: cooperation, data sharing, software, and application platforms

Radiomics and Radiogenomics

Radiomics and Radiogenomics
Author : Ruijiang Li,Lei Xing,Sandy Napel,Daniel L. Rubin
Publisher : CRC Press
Release Date : 2019-07-09
Category : Science
Total pages :420
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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

Radiomics and Radiogenomics

Radiomics and Radiogenomics
Author : Taylor & Francis Group
Publisher : Unknown
Release Date : 2021-03-31
Category :
Total pages :129
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Molecular Imaging in Oncology

Molecular Imaging in Oncology
Author : Otmar Schober,Fabian Kiessling,Jürgen Debus
Publisher : Springer Nature
Release Date : 2020-06-27
Category : Medical
Total pages :918
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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.

Artificial Intelligence in Medicine

Artificial Intelligence in Medicine
Author : Lei Xing,Maryellen L. Giger,James K Min
Publisher : Academic Press
Release Date : 2020-09-16
Category : Business & Economics
Total pages :568
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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

Big Data in Radiation Oncology

Big Data in Radiation Oncology
Author : Jun Deng,Lei Xing
Publisher : CRC Press
Release Date : 2019-03-07
Category : Science
Total pages :289
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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.

Increasing 18f-Fdg Pet/ct Capabilities In Radiotherapy For Lung And Esophageal Cancer Via Image Feature Analysis

Increasing 18f-Fdg Pet/ct Capabilities In Radiotherapy For Lung And Esophageal Cancer Via Image Feature Analysis
Author : Jasmine Alexandria Oliver
Publisher : Unknown
Release Date : 2016
Category : Diagnostic imaging
Total pages :129
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Also, certain feature groups were more affected by noise than others. For instance, contour-dependent shape features exhibited the least change with noise. Comparatively, GLSZM features exhibited the greatest change with added noise. Discordance was discovered between the inferior and superior tumor fiducial markers and metabolic tumor volume (MTV). This demonstrated a need for both fiducial markers and MTV to provide a comprehensive view of a tumor. These studies called attention to the differences in features caused by factors such as motion, acquisition parameters, and noise, etc. Investigators should be aware of these effects. PET/CT radiomic features are indeed highly affected by noise and motion. For accurate clinical use, these effects must be account by investigators and future clinical users. Further investigation is warranted towards the standardization of PET/CT radiomic feature acquisition and clinical application.

Big Data in Radiation Oncology

Big Data in Radiation Oncology
Author : Taylor & Francis Group
Publisher : Unknown
Release Date : 2021-03-31
Category :
Total pages :129
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Imaging in Clinical Oncology

Imaging in Clinical Oncology
Author : Athanasios Gouliamos,John A. Andreou,Paris A. Kosmidis
Publisher : Springer
Release Date : 2018-04-11
Category : Medical
Total pages :129
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This is the second edition of a well-received book reflecting the state of the art in oncologic imaging research and promoting mutual understanding and collaboration between radiologists and clinical oncologists. It presents all currently available imaging modalities and covers a broad spectrum of oncologic diseases for most organ systems. Today, oncologic imaging faces the challenge of improving and refining concepts for precise tumor delineation and biologic/functional tumor characterization, as well as for purposes of creating individual treatment plans. The concept of radiomics has further advanced the conversion of images into mineable data and subsequent analysis of said data for decision-making support. Since the release of the book’s first edition, radiomics has been introduced in oncology studies and can be performed with tomographic images from CT, MRI and PET/CT studies. The combination of radiomic data with genomic features is known as radiogenomics, and can potentially offer additional decision-making support. This book will be of interest to clinical oncologists with regard to the diagnosis, staging, treatment and follow-up on various tumors affecting the CNS, chest, abdomen, urogenital and musculoskeletal systems.

Radiomics

Radiomics
Author : Martin Carrier-Vallières
Publisher : Unknown
Release Date : 2018
Category :
Total pages :129
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"In this thesis, the major aim is to develop radiomic-based models for the accurate prediction of tumour outcomes via advanced machine learning. We first showed that the optimization of how texture features are extracted from medical images (different isotropic voxel sizes, image quantization schemes, etc.) is fundamental for best tumour outcome prediction. We then integrated the texture optimization process into a robust multivariable modeling methodology developed for the construction of radiomic-based prediction models. This multivariable modeling methodology employs logistic regression to linearly combine radiomic features. Using this methodology, we were able to develop a model that can predict the development of lung metastases in soft-tissue sarcomas with high accuracy. This model combines texture features extracted from functional FDG-PET and anatomical MRI pre-treatment images. Following this initial work, we demonstrated how the predictive properties of imaging textures composing such prediction models could be further enhanced by optimizing the way images are acquired. The proof of concept for the enhancement of the prediction of lung metastases in soft-tissue sarcomas was carried out using computerized simulations of FDG-PET and MR image acquisitions with tumour and clinical scanner models, by varying different physical parameters employed during image acquisitions. Next, in another study, we developed a strategy for personalizing treatments for soft-tissue sarcoma patients identified at diagnosis to be at higher risks of developing lung metastases (using radiomic-based prediction models); specifically, we verified the feasibility of applying double nested radiation dose boosting to the hypermetabolic and hypoxic soft-tissue sarcoma sub-regions to counteract the progression of more aggressive parts of tumours. For the purpose of radiation treatment planning, contours defining the hypermetabolic and hypoxic tumour sub-regions were obtained from FDG-PET and low-perfusion DCE-MRI functional images. Finally, in our last study, we developed a methodology allowing to integrate radiomic imaging data with clinical prognostic factors into comprehensive prediction models using a random forest algorithm. We tested our methodology in head-and-neck cancer to better assess the risk of locoregional recurrences and distant metastases, this time using functional FDG-PET and anatomical CT pre-treatment images in conjunction to clinical data. The clinically-integrated radiomic models that we developed possess high prognostic power, leading to patient stratification into two sub-groups for the risk assessment of locoregional recurrences (low, high) in head-and-neck cancer, and into three groups for distant metastases (low, medium, high).Overall, in this thesis, we demonstrated that radiomics analysis is an enabling method towards precision medicine. The different radiomic techniques and models developed in this work could have a major impact on the design of new clinical trials aiming at a better personalization of cancer treatments. One can envision different treatment regimens being delivered to patients based on different radiomic-based risk assessments of specific tumour outcomes." --

Artificial Intelligence in Medical Imaging

Artificial Intelligence in Medical Imaging
Author : Lia Morra,Silvia Delsanto,Loredana Correale
Publisher : CRC Press
Release Date : 2019-11-25
Category : Science
Total pages :152
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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 Basic Science of Oncology, Sixth Edition

The Basic Science of Oncology, Sixth Edition
Author : Lea Harrington,Robert E. Bristow,Ian F. Tannock,Richard Hill
Publisher : McGraw Hill Professional
Release Date : 2021-01-08
Category : Medical
Total pages :608
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Complete coverage of the basis of cancer and molecular biology – from globally recognized experts The Basic Science of Oncology is an accessible and thorough introduction to cancer causation, cancer biology, and the biology underlying cancer treatment. You’ll find everything you need to know about the latest critical thinking in oncology, as well ready to apply information about state-of-the-art science and therapeutic applications. Written by leading oncology researchers and clinicians, this is an essential resource for health professionals, students, advanced undergraduates and graduates in biological sciences, and clinicians needing an understanding of cancer cells. Presented in full-color, The Basic Science of Oncology reflects the latest research and developments in the field. Features NEW chapters: Epigenetics and Principles of Genome Regulation and Targeted Cancer Diagnosis and Treatment Thoroughly revised content, with expanded coverage of key topics such as immune system and immunotherapy, tumor growth and metabolism, vaccine development, methods of molecular analysis, tumor environment, and more The most current, evidence-based oncology primer—one that encapsulates the science of cancer causation, cancer biology, and cancer therapy Key insights into molecular and genetic aspects of cancer familiarize you with cancer biology as applied to prognosis and personalized cancer medicine In-depth focus on the discovery, evaluation, and biology of anti-cancer drugs, immunotherapy, and molecularly-targeted agents Up-to-date coverage of the basic science of radiation therapy

Understanding COVID-19: The Role of Computational Intelligence

Understanding COVID-19: The Role of Computational Intelligence
Author : Janmenjoy Nayak,Bighnaraj Naik,Ajith Abraham
Publisher : Springer
Release Date : 2021-09-24
Category : Technology & Engineering
Total pages :480
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This book provides a comprehensive description of the novel coronavirus infection, spread analysis, and related challenges for the effective combat and treatment. With a detailed discussion on the nature of transmission of COVID-19, few other important aspects such as disease symptoms, clinical application of radiomics, image analysis, antibody treatments, risk analysis, drug discovery, emotion and sentiment analysis, virus infection, and fatality prediction are highlighted. The main focus is laid on different issues and futuristic challenges of computational intelligence techniques in solving and identifying the solutions for COVID-19. The book drops radiance on the reasons for the growing profusion and complexity of data in this sector. Further, the book helps to focus on further research challenges and directions of COVID-19 for the practitioners as well as researchers.

Auto-Segmentation for Radiation Oncology

Auto-Segmentation for Radiation Oncology
Author : Jinzhong Yang,Gregory C Sharp,Mark J Gooding
Publisher : Unknown
Release Date : 2021
Category : Cancer
Total pages :248
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This book provides a comprehensive introduction to current state-of-the-art auto-segmentation approaches used in radiation oncology for auto-delineation of organs-of-risk for thoracic radiation treatment planning. Containing the latest, cutting edge technologies and treatments, it explores deep-learning methods, multi-atlas-based methods, and model-based methods that are currently being developed for clinical radiation oncology applications. Each chapter focuses on a specific aspect of algorithm choices and discusses the impact of the different algorithm modules to the algorithm performance as well as the implementation issues for clinical use (including data curation challenges and auto-contour evaluations). This book is an ideal guide for radiation oncology centers looking to learn more about potential auto-segmentation tools for their clinic in addition to medical physicists commissioning auto-segmentation for clinical use. Features: Up-to-date with the latest technologies in the field Edited by leading authorities in the area, with chapter contributions from subject area specialists All approaches presented in this book are validated using a standard benchmark dataset established by the Thoracic Auto-segmentation Challenge held as an event of the 2017 Annual Meeting of American Association of Physicists in Medicine