[PDF] A Guide To Outcome Modeling In Radiotherapy And Oncology eBook

Author : Issam El Naqa
Publisher : CRC Press
Page : 415 pages
File Size : 48,91 MB
Release : 2018-04-19
Category : Science
ISBN : 0429840349

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This book explores outcome modeling in cancer from a data-centric perspective to enable a better understanding of complex treatment response, to guide the design of advanced clinical trials, and to aid personalized patient care and improve their quality of life. It contains coverage of the relevant data sources available for model construction (panomics), ranging from clinical or preclinical resources to basic patient and treatment characteristics, medical imaging (radiomics), and molecular biological markers such as those involved in genomics, proteomics and metabolomics. It also includes discussions on the varying methodologies for predictive model building with analytical and data-driven approaches. This book is primarily intended to act as a tutorial for newcomers to the field of outcome modeling, as it includes in-depth how-to recipes on modeling artistry while providing sufficient instruction on how such models can approximate the physical and biological realities of clinical treatment. The book will also be of value to seasoned practitioners as a reference on the varying aspects of outcome modeling and their current applications. Features: Covers top-down approaches applying statistical, machine learning, and big data analytics and bottom-up approaches using first principles and multi-scale techniques, including numerical simulations based on Monte Carlo and automata techniques Provides an overview of the available software tools and resources for outcome model development and evaluation, and includes hands-on detailed examples throughout Presents a diverse selection of the common applications of outcome modeling in a wide variety of areas: treatment planning in radiotherapy, chemotherapy and immunotherapy, utility-based and biomarker applications, particle therapy modeling, oncological surgery, and the design of adaptive and SMART clinical trials

Author : Issam El Naqa
Publisher :
Page : pages
File Size : 46,29 MB
Release : 2018
Category : Electronic books
ISBN : 9781498768061

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"This book explores outcome modeling in cancer from a data-centric perspective to enable a better understanding of complex treatment response, to guide the design of advanced clinical trials, and to aid personalized patient care and improve their quality of life. It contains coverage of the relevant data sources available for model construction (panomics), ranging from clinical or preclinical resources to basic patient and treatment characteristics, medical imaging (radiomics), and molecular biological markers such as those involved in genomics, proteomics and metabolomics. It also includes discussions on the varying methodologies for predictive model building with analytical and data-driven approaches.This book is primarily intended to act as a tutorial for newcomers to the field of outcome modeling, as it includes in-depth how-to recipes on modeling artistry while providing sufficient instruction on how such models can approximate the physical and biological realities of clinical treatment. The book will also be of value to seasoned practitioners as a reference on the varying aspects of outcome modeling and their current applications."--Provided by publisher.

Author : Issam El Naqa
Publisher : CRC Press
Page : 368 pages
File Size : 30,59 MB
Release : 2018-04-19
Category : Science
ISBN : 0429840357

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This book explores outcome modeling in cancer from a data-centric perspective to enable a better understanding of complex treatment response, to guide the design of advanced clinical trials, and to aid personalized patient care and improve their quality of life. It contains coverage of the relevant data sources available for model construction (panomics), ranging from clinical or preclinical resources to basic patient and treatment characteristics, medical imaging (radiomics), and molecular biological markers such as those involved in genomics, proteomics and metabolomics. It also includes discussions on the varying methodologies for predictive model building with analytical and data-driven approaches. This book is primarily intended to act as a tutorial for newcomers to the field of outcome modeling, as it includes in-depth how-to recipes on modeling artistry while providing sufficient instruction on how such models can approximate the physical and biological realities of clinical treatment. The book will also be of value to seasoned practitioners as a reference on the varying aspects of outcome modeling and their current applications. Features: Covers top-down approaches applying statistical, machine learning, and big data analytics and bottom-up approaches using first principles and multi-scale techniques, including numerical simulations based on Monte Carlo and automata techniques Provides an overview of the available software tools and resources for outcome model development and evaluation, and includes hands-on detailed examples throughout Presents a diverse selection of the common applications of outcome modeling in a wide variety of areas: treatment planning in radiotherapy, chemotherapy and immunotherapy, utility-based and biomarker applications, particle therapy modeling, oncological surgery, and the design of adaptive and SMART clinical trials

Author : Barry S. Rosenstein
Publisher : Academic Press
Page : 480 pages
File Size : 19,93 MB
Release : 2023-12-02
Category : Science
ISBN : 0128220015

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Machine Learning and Artificial Intelligence in Radiation Oncology: A Guide for Clinicians is designed for the application of practical concepts in machine learning to clinical radiation oncology. It addresses the existing void in a resource to educate practicing clinicians about how machine learning can be used to improve clinical and patient-centered outcomes. This book is divided into three sections: the first addresses fundamental concepts of machine learning and radiation oncology, detailing techniques applied in genomics; the second section discusses translational opportunities, such as in radiogenomics and autosegmentation; and the final section encompasses current clinical applications in clinical decision making, how to integrate AI into workflow, use cases, and cross-collaborations with industry. The book is a valuable resource for oncologists, radiologists and several members of biomedical field who need to learn more about machine learning as a support for radiation oncology. Presents content written by practicing clinicians and research scientists, allowing a healthy mix of both new clinical ideas as well as perspectives on how to translate research findings into the clinic Provides perspectives from artificial intelligence (AI) industry researchers to discuss novel theoretical approaches and possibilities on academic collaborations Brings diverse points-of-view from an international group of experts to provide more balanced viewpoints on a complex topic

Author : Issam El Naqa
Publisher : Springer
Page : 336 pages
File Size : 48,54 MB
Release : 2015-06-19
Category : Medical
ISBN : 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.

Author : Sarah R. Brown
Publisher : John Wiley & Sons
Page : 260 pages
File Size : 15,27 MB
Release : 2014-03-28
Category : Medical
ISBN : 1118763637

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How to identify optimal phase II trial designs Providing a practical guide containing the information needed to make crucial decisions regarding phase II trial designs, A Practical Guide to Designing Phase II Trials in Oncology sets forth specific points for consideration between the statistician and clinician when designing a phase II trial, including issues such as how the treatment works, choice of outcome measure and randomization, and considering both academic and industry perspectives. A comprehensive and systematic library of available phase II trial designs is included, saving time otherwise spent considering multiple manuscripts, and real-life practical examples of using this approach to design phase II trials in cancer are given. A Practical Guide to Designing Phase II Trials in Oncology: Offers a structured and practical approach to phase II trial design Considers trial design from both an academic and industry perspective Includes a structured library of available phase II trial designs Is relevant to both clinical and statistical researchers at all levels Includes real life examples of applying this approach For those new to trial design, A Practical Guide to Designing Phase II Trials in Oncology will be a unique and practical learning tool, providing an introduction to the concepts behind informed decision making in phase II trials. For more experienced practitioners, the book will offer an overview of new, less familiar approaches to phase II trial design, providing alternative options to those which they may have previously used.

Author : Issam El Naqa
Publisher : Springer Nature
Page : 514 pages
File Size : 20,5 MB
Release : 2022-02-02
Category : Science
ISBN : 3030830470

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This book, now in an extensively revised and updated second edition, provides a comprehensive overview of both machine learning and deep learning and their role in oncology, medical physics, and radiology. Readers will find thorough coverage of basic theory, methods, and demonstrative applications in these fields. An introductory section explains machine and deep learning, reviews learning methods, discusses performance evaluation, and examines software tools and data protection. Detailed individual sections are then devoted to the use of machine and deep learning for medical image analysis, treatment planning and delivery, and outcomes modeling and decision support. Resources for varying applications are provided in each chapter, and software code is embedded as appropriate for illustrative purposes. The book will be invaluable for students and residents in medical physics, radiology, and oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.

Author : Gilmer Valdes
Publisher : CRC Press
Page : 185 pages
File Size : 26,67 MB
Release : 2023-08-14
Category : Computers
ISBN : 1000903753

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This pioneering book explores how machine learning and other AI techniques impact millions of cancer patients who benefit from ionizing radiation. It features contributions from global researchers and clinicians, focusing on the clinical applications of machine learning for medical physics. AI and machine learning have attracted much recent attention and are being increasingly adopted in medicine, with many clinical components and commercial software including aspects of machine learning integration. General principles and important techniques in machine learning are introduced, followed by discussion of clinical applications, particularly in radiomics, outcome prediction, registration and segmentation, treatment planning, quality assurance, image processing, and clinical decision-making. Finally, a futuristic look at the role of AI in radiation oncology is provided. This book brings medical physicists and radiation oncologists up to date with the most novel applications of machine learning to medical physics. Practitioners will appreciate the insightful discussions and detailed descriptions in each chapter. Its emphasis on clinical applications reaches a wide audience within the medical physics profession.

Author : André Diamant Boustead
Publisher :
Page : pages
File Size : 35,86 MB
Release : 2020
Category :
ISBN :

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"Prognosis after cancer treatment is a constant concern for physicians, patients and their surrounding friends and family. This is one of the reasons that treatment outcomes prediction is such a critical field of research. The sheer magnitude of data generated within a typical radiation oncology clinic each year facilitates the development and eventual validation of predictive and prognostic models. Furthermore, the technological advances driven by data science have enabled the usage of advanced machine learning techniques which can far exceed the performance of previously used conventional techniques.Most cancer patients follow a standard radiation oncology workflow, which among other things includes medical imaging (CT/PET) and the creation of a radiation therapy treatment plan. As these sorts of data are (in theory) present for every patient, they are ideal variables to input into a predictive model. The goal of this thesis was to investigate these two types of pre-treatment input data (diagnostic imaging and dosimetric data) along with patient characteristics to identify associations and create models capable of predicting a cancer patient's treatment response following radiation therapy. The first objective was to investigate dose-volume metrics as predictors of clinical outcomes in a cohort of 422 non-small cell lung cancer (NSCLC) patients who received stereotactic body radiation therapy (SBRT). A correlation between the dose delivered to the region outside the tumor and the occurrence of distant metastasis was revealed. In particular, patients who received above a certain threshold dose were shown to have significantly reduced distant metastasis recurrence rates compared to the rest of the population. This was first shown on 217 patients all of whom were treated with conventional SBRT treatment modalities. Next, a similar analysis was done on 205 patients who were treated with a robotic arm linear accelerator (CyberKnife). It was found that the CyberKnife cohort had both superior distant control and local control, suggesting that under current prescription practices, CyberKnife, as a delivery device, could be superior for treating NSCLC patients with SBRT. The second objective of this thesis was to investigate the usage of a deep learning framework applied to raw medical imaging data in order to predict the overall prognosis of head & neck cancer patients post-radiation therapy. A de novo architecture was built incorporating CT images, resulting in comparable performance to a state-of-the-art study. Furthermore, our model was shown to recognize imaging features (`radiomics') previously shown to be predictive without being explicitly presented with their definition. The final portion of this work was the development of a multi-modal deep learning framework which incorporated CT & PET images along with clinical information. This was compared to the previous architecture built, showing substantial increase in prediction performance for both overall survival and local recurrence. It was also shown to function in the presence of missing data, a common occurrence within the medical landscape.This work demonstrates that pre-treatment prediction of a cancer patient's post-radiation therapy outcomes is possible by learning correlations and building models from readily available data. Future efforts should be put towards data sharing & data curation to enable the creation and validation of models that eventually can be used in the clinic. Ultimately, predictive models should evolve into generative models whereupon one's treatment could be automatically created with the explicit intention of statistically optimizing that patient's outcomes"--