Radiomics and Radiogenomics
Radiomics and Radiogenomics

Author: Ruijiang Li

Publisher: CRC Press

Published: 2019-07-09

Total Pages: 484

ISBN-13: 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

Artificial Intelligence in Medical Imaging
Artificial Intelligence in Medical Imaging

Author: Erik R. Ranschaert

Publisher: Springer

Published: 2019-01-29

Total Pages: 369

ISBN-13: 3319948784

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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.

Radiomics and Radiogenomics in Neuro-oncology
Radiomics and Radiogenomics in Neuro-oncology

Author: Hassan Mohy-ud-Din

Publisher: Springer Nature

Published: 2020-02-24

Total Pages: 100

ISBN-13: 3030401243

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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.

Neural Networks and Statistical Learning
Neural Networks and Statistical Learning

Author: Ke-Lin Du

Publisher: Springer Science & Business Media

Published: 2013-12-09

Total Pages: 834

ISBN-13: 1447155718

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Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included. Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence, and data mining.

Advances in CNS Tumors Treatment and Diagnosis: Obstacles, Challenges, and Opportunities
Advances in CNS Tumors Treatment and Diagnosis: Obstacles, Challenges, and Opportunities

Author: Sheng Zhong

Publisher: Frontiers Media SA

Published: 2024-03-26

Total Pages: 174

ISBN-13: 283254617X

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Central nervous system (CNS) tumors are the eighth leading cause of cancer death and the second most common childhood cancer worldwide. Current treatment methods include surgery, radiation therapy, chemotherapy, targeted therapy, immune therapy etc. However, patients with CNS tumors usually have an unsatisfactory prognosis accompanied by impaired neurologic functions when compared with other cancers. Few drugs or treatments have been proven effective in CNS tumors due to the presence of blood-brain barriers and its distinctive tumor microenvironment.

Computational Intelligence And Its Applications: Evolutionary Computation, Fuzzy Logic, Neural Network And Support Vector Machine Techniques
Computational Intelligence And Its Applications: Evolutionary Computation, Fuzzy Logic, Neural Network And Support Vector Machine Techniques

Author: Hung Tan Nguyen

Publisher: World Scientific

Published: 2012-07-17

Total Pages: 318

ISBN-13: 1908977078

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This book focuses on computational intelligence techniques and their applications — fast-growing and promising research topics that have drawn a great deal of attention from researchers over the years. It brings together many different aspects of the current research on intelligence technologies such as neural networks, support vector machines, fuzzy logic and evolutionary computation, and covers a wide range of applications from pattern recognition and system modeling, to intelligent control problems and biomedical applications. Fundamental concepts and essential analysis of various computational techniques are presented to offer a systematic and effective tool for better treatment of different applications, and simulation and experimental results are included to illustrate the design procedure and the effectiveness of the approaches./a

Machine Learning in Radiation Oncology
Machine Learning in Radiation Oncology

Author: Issam El Naqa

Publisher: Springer

Published: 2015-06-19

Total Pages: 336

ISBN-13: 3319183052

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​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.

Radiomics and Radiogenomics in Neuro-Oncology
Radiomics and Radiogenomics in Neuro-Oncology

Author: Sanjay Saxena

Publisher: Elsevier

Published: 2024-10-15

Total Pages: 377

ISBN-13: 0443185107

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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