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.

Neuro-oncology broadly encompasses life-threatening brain and spinal cord malignancies, including primary lesions and lesions metastasizing to the central nervous system. It is well suited for diagnosis, classification, and prognosis as well as assessing treatment response. Radiomics and Radiogenomics (R-n-R) have become two central pillars in precision medicine for neuro-oncology.Radiomics is an approach to medical imaging used to extract many quantitative imaging features using different data characterization algorithms, while Radiogenomics, which has recently emerged as a novel mechanism in neuro-oncology research, focuses on the relationship of imaging phenotype and genetics of cancer. Due to the exponential progress of different computational algorithms, AI methods are composed to advance the precision of diagnostic and therapeutic approaches in neuro-oncology.The field of radiomics has been and definitely will remain at the lead of this emerging discipline due to its efficiency in the field of neuro-oncology. Several AI approaches applied to conventional and advanced medical imaging data from the perspective of radiomics are very efficient for tasks such as survival prediction, heterogeneity analysis of cancer, pseudo progression analysis, and infiltrating tumors. Radiogenomics advances our understanding and knowledge of cancer biology, letting noninvasive sampling of the molecular atmosphere with high spatial resolution along with a systems-level understanding of causal heterogeneous molecular and cellular processes. These AI-based R-n-R tools have the potential to stratify patients into more precise initial diagnostic and therapeutic pathways and permit better dynamic treatment monitoring in this period of personalized medicine. While extremely promising, the clinical acceptance of R-n-R methods and approaches will primarily hinge on their resilience to non-standardization across imaging protocols and their capability to show reproducibility across large multi-institutional cohorts.Radiomics and Radiogenomics in Neuro-Oncology: An Artificial Intelligence Paradigm provides readers with a broad and detailed framework for R-n-R approaches with AI in neuro-oncology, the description of cancer biology and genomics study of cancer, and the methods usually implemented for analyzing. Readers will also learn about the current solutions R-n-R can offer for personalized treatments of patients, limitations, and prospects. There is comprehensive coverage of information based on radiomics, radiogenomics, cancer biology, and medical image analysis viewpoints on neuro-oncology, so this in-depth coverage is divided into two Volumes.Volume 1: Radiogenomics Flow Using Artificial Intelligence provides coverage of genomics and molecular study of brain cancer, medical imaging modalities and analysis in neuro-oncology, and prognostic and predictive models using radiomics.Volume 2: Genetics and Clinical Applications provides coverage of imaging signatures for brain cancer molecular characteristics, clinical applications of R-n-R in neuro-oncology, and Machine Learning and Deep Learning AI approaches for R-n-R in neuro-oncology. - Includes coverage on the foundational concepts of the emerging fields of radiomics and radiogenomics - Covers neural engineering modeling and AI algorithms for the imaging, diagnosis, and predictive modeling of neuro-oncology - Presents crucial technologies and software platforms, along with advanced brain imaging techniques such as quantitative imaging using CT, PET, and MRI - Provides in-depth technical coverage of computational modeling techniques and applied mathematics for brain tumor segmentation and radiomics features such as extraction and selection

This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2020, and the Second International Workshop on Radiogenomics in Neuro-oncology, RNO-AI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020.* For MLCN 2020, 18 papers out of 28 submissions were accepted for publication. The accepted papers present novel contributions in both developing new machine learning methods and applications of existing methods to solve challenging problems in clinical neuroimaging. For RNO-AI 2020, all 8 submissions were accepted for publication. They focus on addressing the problems of applying machine learning to large and multi-site clinical neuroimaging datasets. The workshop aimed to bring together experts in both machine learning and clinical neuroimaging to discuss and hopefully bridge the existing challenges of applied machine learning in clinical neuroscience. *The workshops were held virtually due to the COVID-19 pandemic.

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

Foundational Handbook of Artificial Intelligence in Healthcare and Bioscience: A User Friendly Guide for IT Professionals, Healthcare Providers, Researchers, and Clinicians uses color-coded illustrations to explain AI from its basics to modern technologies. Other sections cover extensive, current literature research and citations regarding AI's role in the business and clinical aspects of health care. The book provides readers with a unique opportunity to appreciate AI technology in practical terms, understand its applications, and realize its profound influence on the clinical and business aspects of health care. Artificial Intelligence is a disruptive technology that is having a profound and growing influence on the business of health care as well as medical diagnosis, treatment, research and clinical delivery. The AI relationships in health care are complex, but understandable, especially when discussed and developed from their foundational elements through to their practical applications in health care. - Provides an illustrated, foundational guide and comprehensive descriptions of what Artificial Intelligence is and how it functions - Integrates a comprehensive discussion of AI applications in the business of health care - Presents in-depth clinical and AI-related discussions on diagnostic medicine, therapeutic medicine, and prevalent disease categories with an emphasis on immunology and genetics, the two categories most influenced by AI - Includes comprehensive coverage of a variety of AI treatment applications, including medical/pharmaceutical care, nursing care, stem cell therapies, robotics, and 10 common disease categories with AI applications

With treatment approaches and the field of neuro-oncology neuroimaging changing rapidly, this third edition of the Handbook of Neuro-Oncology Neuroimaging is very relevant to those in the field, providing a single-source, comprehensive, reference handbook of the most up-to-date clinical and technical information regarding the application of neuroimaging techniques to brain tumor and neuro-oncology patients. This new volume will have updates on all of the material from the second edition, and in addition features several new important chapters covering diverse topics such as imaging for the use of Laser Interstitial Thermal Therapy, advanced imaging techniques in radiation therapy, therapeutic treatment fields, response assessment in clinical trials, surgical planning of neoplastic disease of the spine, and more. Sections first overview neuro-oncological disorders before delving into the physics and basic science of neuroimaging and great focus on CT and MRI. The book then focuses on advances in the neuroimaging of brain tumors and neuroimaging of specific tumor types. There is also discussion of neuroimaging of other neuro-oncological syndromes. This book will serve as a resource of background information to neuroimaging researchers and basic scientists with an interest in brain tumors and neuro-oncology. - Summarizes translational research on brain imaging for brain tumors - Discusses limitations of neuroimaging for diagnosis and treatment - Presents advanced imaging technologies, including CT, MRI, and PET - Contains new coverage on Laser Interstitial Thermal Therapy, radiation therapy, clinical trials, and more

The Conference was established for the first time in 2023 as part of a programme of activities to sustain research culture environment and dissemination activities at the School of Engineering, Cardiff University, Cardiff United Kingdom. The conference served as a platform to celebrate advancements in various engineering domains researched at our School, and to explore and discuss further advancements in the diverse fields that define contemporary engineering. The structure of the conference programme reflected the multidimensional nature of our research and was built around the priority research areas for the school. 1. Sustainable Energy stands as a testament to our commitment to a greener, more efficient future. We aim to advance energy technology and play a key role in addressing the increasing demand for sustainable and low carbon technologies while reducing environmental impact and ensuring a sustainable environment. Our work helps to drive forward net-zero solutions for achieving the government carbon targets. 2. Advanced Manufacturing represents cutting-edge research into materials, systems and transformative technologies to transform engineering and economic performance in the transport, energy generation and manufacturing industries. Our research in this area focuses on developing smart materials and structures, and sustainable manufacturing processes that help create a sustainable and greener economy. 3. Civil Infrastructure takes centre stage as we improve the sustainability and resilience of infrastructure across the UK and the globe. We work on developing sustainable and resilient total lifecycle solutions across a wide range of domains including construction, structures, energy, geo-environmental and water infrastructure systems. From creating new nano-scale smart materials to macro-scale urban interventions. 4. Compound Semiconductors and Applications represents the cutting edge of electronics, a critical driver of progress in the digital age. We explore the latest developments in compound semiconductor materials, advanced characterisation techniques, quantum optics and novel circuit design methodologies and their diverse applications. We anticipate breakthroughs that will power the next generation of computing, communication, and sensing technologies. 5. Engineering for Health forms a cornerstone of our discussions, recognizing the pivotal role technology plays in revolutionizing healthcare. We are applying the latest research in medical engineering to push the boundaries in areas where innovation has the potential to transform patient care.