An Optimization Framework To Improve Patient Safety In The Radiation Therapy Process

Radiation therapy is a complex process where a given target volume receives a given dose of radiation divided over one or multiple treatments. Every step in this process can introduce some types of uncertainties into the problem which may compromise the quality of the treatment. Typically, a volume larger than the actual tumor is irradiated to make the treatment more robust against these uncertainties. This comes at the cost of normal tissue irradiation and an increased risk of toxicity. In this dissertation, we investigate approaches to managing uncertainties in radiation therapy treatments for lung cancer patients. In the first part of the dissertation, we focus on the process of designing a treatment plan which involves selecting appropriate beam angles and deciding the right amount of radiation dose to the tumor cells, while sparing the normal tissue surrounding the tumor. Selecting the optimal set of treatment beam angles, called beam angle optimization (BAO), involves a very large-scale combinatorial optimization problem with many local minima. In order to identify an efficient approach to obtain high quality beam angles, we first examine the strengths and weaknesses of some existing BAO optimization methods including both global and local search algorithms. We then propose a hybrid framework to overcome some of the weaknesses observed in these methods. Next, we perform an in-depth study into the impact of interplay effect, which results from relative motion of the tumor and proton beam, on the dose distribution in the patient with lung cancer. The dynamic dose distribution, that provides an estimation of delivered dose under the influence of interplay effect, is calculated by simulating the machine delivery processes on the moving patient described by 4D computed tomography (4DCT) during the dose delivery process by linking timestamps of each on/off switch of proton spots, spills, energies, and fields to patient respiratory cycles. We introduce a clinically applicable metric for clinicians to use for determining the magnitude of the uncertainties caused by interplay effects. We then explore the techniques of fractionation and iso-layered re-scanning for mitigating these interplay effects. In the last part of the dissertation, we develop a robust adaptive optimization framework for intensity modulated radiation therapy (IMRT) for lung cancer, where temporal variation of tumor volume and its associated uncertainties throughout the course of the treatment are accounted for to re-optimize the treatment plan for the following sessions. This framework gives an insight into the trade-off between sparing the healthy tissues and ensuring that the tumor receives a sufficient dose. With this trade-off in mind, we demonstrate that our robust adaptive solution outperforms a non-adaptive solution and a nominal (no uncertainty) solution on a clinical case.

This publication provides guidance for designing and implementing radiotherapy programmes, taking into account clinical, medical physics, radiation protection and safety aspects. It reflects current requirements for radiotherapy infrastructure in settings with limited resources. It will be of use to professionals involved in the development, implementation and management of radiotherapy programmes

By recent estimates, just under 2 million cancer cases are diagnosed in the US each year, with a majority of those patients receiving radiation therapy. For each patient, a team of personnel manually designs a treatment plan with the intent to treat diseased tissue while minimizing damage to surrounding healthy organs. Treatment planning for these patients can be both time-consuming and labor intensive, especially for emerging treatment modalities where radiation is delivered from noncoplanar directions or with nonuniform gantry arcs. Radiation therapy treatment planning is a complicated process for determining various machine parameters that allow for the delivery of a desired dose distribution. As treatment planning can involve many conflicting objectives, no single plan can optimize performance on all objectives at once. Instead, planning typically involves the navigation of numerous clinical trade-offs, where planners iteratively adjust certain hyperparameters in order to achieve an acceptable treatment plan. To streamline the treatment planning process, we have developed a fully automated framework, which requires no active planning time. This dissertation will present the components of this framework--including treatment plan utility functions, meta-optimization, content-based image retrieval, etc.--as well as the applications of automated planning to emerging treatment modalities. Were this framework to be adopted in clinical settings, we would expect substantial reductions in planning times and improvements to overall plan quality.

Surface Guided Radiation Therapy provides a comprehensive overview of optical surface image guidance systems for radiation therapy. It serves as an introductory teaching resource for students and trainees, and a valuable reference for medical physicists, physicians, radiation therapists, and administrators who wish to incorporate surface guided radiation therapy (SGRT) into their clinical practice. This is the first book dedicated to the principles and practice of SGRT, featuring: Chapters authored by an internationally represented list of physicists, radiation oncologists and therapists, edited by pioneers and experts in SGRT Covering the evolution of localization systems and their role in quality and safety, current SGRT systems, practical guides to commissioning and quality assurance, clinical applications by anatomic site, and emerging topics including skin mark-less setups. Several dedicated chapters on SGRT for intracranial radiosurgery and breast, covering technical aspects, risk assessment and outcomes. Jeremy Hoisak, PhD, DABR is an Assistant Professor in the Department of Radiation Medicine and Applied Sciences at the University of California, San Diego. Dr. Hoisak’s clinical expertise includes radiosurgery and respiratory motion management. Adam Paxton, PhD, DABR is an Assistant Professor in the Department of Radiation Oncology at the University of Utah. Dr. Paxton’s clinical expertise includes patient safety, motion management, radiosurgery, and proton therapy. Benjamin Waghorn, PhD, DABR is the Director of Clinical Physics at Vision RT. Dr. Waghorn’s research interests include intensity modulated radiation therapy, motion management, and surface image guidance systems. Todd Pawlicki, PhD, DABR, FAAPM, FASTRO, is Professor and Vice-Chair for Medical Physics in the Department of Radiation Medicine and Applied Sciences at the University of California, San Diego. Dr. Pawlicki has published extensively on quality and safety in radiation therapy. He has served on the Board of Directors for the American Society for Radiology Oncology (ASTRO) and the American Association of Physicists in Medicine (AAPM).

This book, now in its second edition, provides a comprehensive overview of current re-irradiation strategies, with detailed discussion of re-irradiation methods, technical aspects, the role of combined therapy with anticancer drugs and hyperthermia, and normal tissue tolerance. In addition, disease specific chapters document recent clinical results and future research directions. All chapters from the first edition have been revised and updated to take account of the latest developments and research findings, including those from prospective studies. Due attention is paid to the exciting developments in the fields of proton irradiation and frameless image-guided ablative radiotherapy. The book documents fully how refined combined modality approaches and significant technical advances in radiation treatment planning and delivery have facilitated the re-irradiation of previously exposed volumes, allowing both palliative and curative approaches to be pursued at various disease sites. Professionals involved in radiation treatment planning and multimodal oncology treatment will find it to be an invaluable aid in understanding the benefits and limitations of re-irradiation and in designing prospective trials.

Stereotactic body radiation therapy (SBRT) has emerged as an important innovative treatment for various primary and metastatic cancers. This book provides a comprehensive and up-to-date account of the physical/technological, biological, and clinical aspects of SBRT. It will serve as a detailed resource for this rapidly developing treatment modality. The organ sites covered include lung, liver, spine, pancreas, prostate, adrenal, head and neck, and female reproductive tract. Retrospective studies and prospective clinical trials on SBRT for various organ sites from around the world are examined, and toxicities and normal tissue constraints are discussed. This book features unique insights from world-renowned experts in SBRT from North America, Asia, and Europe. It will be necessary reading for radiation oncologists, radiation oncology residents and fellows, medical physicists, medical physics residents, medical oncologists, surgical oncologists, and cancer scientists.

​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.

This publication provides guidelines, and highlights the milestones to be achieved by radiotherapy departments in the safe and effective introduction of image guided radiotherapy. Recent advances in external beam radiotherapy include the technology to image the patient in the treatment position, in the treatment room at the time of treatment. Since this technology and associated image techniques, termed image guided radiotherapy, are perceived as the cutting-edge of development in the field of radiotherapy, this publication addresses the concerns of personnel in radiotherapy departments as to the preparatory conditions and resources involved in implementation. Information is also presented on the current status of the evidence supporting the use of image guided radiotherapy in terms of patient outcomes.

Implementing safety practices in healthcare saves lives and improves the quality of care: it is therefore vital to apply good clinical practices, such as the WHO surgical checklist, to adopt the most appropriate measures for the prevention of assistance-related risks, and to identify the potential ones using tools such as reporting & learning systems. The culture of safety in the care environment and of human factors influencing it should be developed from the beginning of medical studies and in the first years of professional practice, in order to have the maximum impact on clinicians' and nurses' behavior. Medical errors tend to vary with the level of proficiency and experience, and this must be taken into account in adverse events prevention. Human factors assume a decisive importance in resilient organizations, and an understanding of risk control and containment is fundamental for all medical and surgical specialties. This open access book offers recommendations and examples of how to improve patient safety by changing practices, introducing organizational and technological innovations, and creating effective, patient-centered, timely, efficient, and equitable care systems, in order to spread the quality and patient safety culture among the new generation of healthcare professionals, and is intended for residents and young professionals in different clinical specialties.