Fully Automated Radiation Therapy Treatment Planning Through Knowledge-Based Dose Predictions - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Fully Automated Radiation Therapy Treatment Planning Through Knowledge-Based Dose Predictions write by Angelia Landers. This book was released on 2018. Fully Automated Radiation Therapy Treatment Planning Through Knowledge-Based Dose Predictions available in PDF, EPUB and Kindle. Intensity-modulated radiotherapy treatment planning is an inverse problem that typically includes numerous parameters that have to be manually tuned by expert planners. This process can take hours or even days and can often lead to suboptimal plans. In this study, we developed a technique for fully automated radiotherapy treatment planning with the guidance of dose predictions using high quality or evolving knowledge bases. Knowledge-based planning (KBP) dose prediction provides patient-specific estimations for the capabilities and limitations of a plan. Statistical voxel dose learning (SVDL) was developed to predict the voxel dose of new patients. The method was compared to supervised machine learning methods, spectral regression (SR) and support vector regression (SVR), to evaluate the prediction accuracy and robustness of using small training sets. SVDL was found to have higher prediction accuracy than the more sophisticated machine learning methods and effective even with small training sets. To remove any dependence on hyperparameters that require manual tuning, voxel-based non-coplanar 4 radiotherapy and coplanar volumetric modulated arc therapy (VMAT) optimization problems were modified to include the KBP predicted doses. The new cost functions encourage the plans to meet or improve on the predicted doses. Because of this, the resulting plan quality is heavily reliant on the plan quality of the KBP training set. To ensure high quality plans, non-coplanar and coplanar IMRT plans were manually created using all available beams. The resulting automated plans were of superior quality compared to manually-created plans. In the case of no existing high quality training set, evolving-knowledge-base (EKB) planning was developed. An initial, low quality training set was used for the first epoch of automated planning. In subsequent epochs, the superior plans from the previous epoch were taken as the training set. Overall plan quality was observed to improve through epochs, plateauing after 3 and 6 epochs for lung and head & neck planning, respectively. The final EKB plans were significantly higher quality than manually-created VMAT plans and equivalent to manually-created 4 plans. Through the course of this work, we established a robust and accurate KBP dose prediction technique, which we then utilized in our automated planning protocol. Both the use of high quality training sets and EKB planning created high quality plans in a more efficient and consistent manner than hyperparameter tuning.
Machine Learning in Radiation Oncology
Machine Learning in Radiation Oncology - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning in Radiation Oncology write by Issam El Naqa. This book was released on 2015-06-19. Machine Learning in Radiation Oncology available in PDF, EPUB and Kindle. 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.
Knowledge-Based Automated Planning for Oropharyngeal Cancer
Knowledge-Based Automated Planning for Oropharyngeal Cancer - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Knowledge-Based Automated Planning for Oropharyngeal Cancer write by Aaron Babier. This book was released on 2017. Knowledge-Based Automated Planning for Oropharyngeal Cancer available in PDF, EPUB and Kindle. We automatically generate intensity-modulated radiation therapy plans for oropharyngeal cancer by combining knowledge-based planning (KBP) predictions with an inverse optimization (IO) pipeline into a single automated treatment planning pipeline. We extended two existing KBP methods, which use patients' anatomical geometry to predict achievable dose volume histograms (DVHs), and developed the first IO method that takes DVHs as direct inputs. The DVH predictions from KBP are put into the IO pipeline to automatically generate treatment plans via an intermediate step using objective function weights and an inverse planning problem. This step enables our automated planning pipeline to seamlessly fuse with the current treatment planning paradigm to increase its efficiency. Our automated pipeline can replicate, and often improve upon the clinical treatment plans by reducing the dose to healthy tissue and increasing primary target coverage. These results have been validated using a large cohort of 217 oropharyngeal cancer patients.
Artificial Intelligence In Radiation Oncology
Artificial Intelligence In Radiation Oncology - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Artificial Intelligence In Radiation Oncology write by Seong K Mun. This book was released on 2022-12-27. Artificial Intelligence In Radiation Oncology available in PDF, EPUB and Kindle. The clinical use of Artificial Intelligence (AI) in radiation oncology is in its infancy. However, it is certain that AI is capable of making radiation oncology more precise and personalized with improved outcomes. Radiation oncology deploys an array of state-of-the-art technologies for imaging, treatment, planning, simulation, targeting, and quality assurance while managing the massive amount of data involving therapists, dosimetrists, physicists, nurses, technologists, and managers. AI consists of many powerful tools which can process a huge amount of inter-related data to improve accuracy, productivity, and automation in complex operations such as radiation oncology.This book offers an array of AI scientific concepts, and AI technology tools with selected examples of current applications to serve as a one-stop AI resource for the radiation oncology community. The clinical adoption, beyond research, will require ethical considerations and a framework for an overall assessment of AI as a set of powerful tools.30 renowned experts contributed to sixteen chapters organized into six sections: Define the Future, Strategy, AI Tools, AI Applications, and Assessment and Outcomes. The future is defined from a clinical and a technical perspective and the strategy discusses lessons learned from radiology experience in AI and the role of open access data to enhance the performance of AI tools. The AI tools include radiomics, segmentation, knowledge representation, and natural language processing. The AI applications discuss knowledge-based treatment planning and automation, AI-based treatment planning, prediction of radiotherapy toxicity, radiomics in cancer prognostication and treatment response, and the use of AI for mitigation of error propagation. The sixth section elucidates two critical issues in the clinical adoption: ethical issues and the evaluation of AI as a transformative technology.
Strategies for Radiation Therapy Treatment Planning
Strategies for Radiation Therapy Treatment Planning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Strategies for Radiation Therapy Treatment Planning write by Ping Xia, PhD. This book was released on 2018-10-28. Strategies for Radiation Therapy Treatment Planning available in PDF, EPUB and Kindle. “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
