​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.

Accuracy requirements in radiation oncology have been defined in multiple publications; however, these have been based on differing radiation technologies. In the meantime, the uncertainties in radiation dosimetry reference standards have been reduced and more detailed patient outcome data are available. No comprehensive literature on accuracy and uncertainties in radiotherapy has been published so far. The IAEA has therefore developed a new international consensus document on accuracy requirements and uncertainties in radiation therapy, to promote safer and more effective patient treatments. This publication addresses accuracy and uncertainty issues related to the vast majority of radiotherapy departments including both external beam radiotherapy and brachytherapy. It covers clinical, radiobiological, dosimetric, technical and physical aspects.

Clinical conformal radiotherapy is the holy grail of radiation treatment and is now becoming a reality through the combined efforts of physical scientists and engineers, who have improved the physical basis of radiotherapy, and the interest and concern of imaginative radiotherapists and radiographers. Intensity-Modulated Radiation Therapy de

Modern medical imaging and radiation therapy technologies are so complex and computer driven that it is difficult for physicians and technologists to know exactly what is happening at the point-of-care. Medical physicists responsible for filling this gap in knowledge must stay abreast of the latest advances at the intersection of medical imaging an

“This is a high quality book with directions and guidelines on how to generate valid treatment plans in the modern era of radiation oncology. It is very useful for any student (dosimetry, therapy, physicist, or physician) who is entering a practical treatment planning rotation...It is written as a companion to the Handbook of Treatment Planning in Radiation Oncology, 2nd edition, Videtic et al. (Demos Medical Publishing, 2015), and pairs very well with it.” Score: 88, 3 Stars, Doody’s Medical Reviews “Comparing with earlier published books about radiotherapy treatment planning, which are prone to the pedagogical side as textbooks, this new book serves an unmet need as a pocket-sized book with details and up to date information for user’s quick resource for treatment planning knowledge... “Strategies for Radiation Therapy Treatment Planning” is a handy and essential reference for modern treatment planning. It is therefore recommended as a valuable book for the bookshelf and pocket of everyone involved in radiotherapy treatment planning.” -- Dr. Chengyu Shi of Memorial Sloan Kettering Cancer Center for Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. Strategies for Radiation Therapy Treatment Planning provides radiation oncologists, physicists, and dosimetrists with a step-by-step guide to implementing external beam treatment plans that meet clinical requirements for each major disease site. As a companion book to the Handbook of Treatment Planning in Radiation Oncology Second Edition, this book focuses on the technical aspects of treatment planning and the major challenges in creating highly conformal dose distributions, referenced to as treatment plans, for external beam radiotherapy. To overcome challenges associated with each step, leading experts at the Cleveland Clinic have consolidated their knowledge and experience of treatment planning techniques, potential pitfalls, and other difficulties to develop quality plans across the gamut of clinical scenarios in radiation therapy. The book begins with an overview of external beam treatment planning principles, inverse planning and advanced planning tools, and descriptions of all components in simulation and verification. Following these introductory chapters are disease-site examples, including central nervous system, head and neck, breast, thoracic, gastrointestinal, genitourinary, gynecologic, lymphoma, and soft tissue sarcoma. The book concludes with expert guidance on planning for pediatric cancers and how to tailor palliative plans. Essential for all radiation therapy team members, including trainees, this book is for those who wish to learn or improve their treatment planning skills and understand the different treatment planning processes, plan evaluation, and patient setup. KEY FEATURES: Provides basic principles of treatment planning Contains step-by-step, illustrated descriptions of the treatment planning process Discusses the pros and cons of advanced treatment planning tools, such as auto-planning, knowledge-based planning, and multi-criteria based planning Describes each primary treatment site from simulation, patient immobilization, and creation of various treatment plans to plan evaluations Includes instructive sample plans to highlight best practices

Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data. Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-study Provides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages