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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
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
Cancer is a leading cause of death that affects numerous people at
every age and their relatives. In recent years, there has been a
tremendous advancement in imaging and biotechnology technologies
and techniques for aiding in the detection, diagnosis, and
treatment of cancer. Emerging Developments and Practices in
Oncology provides research on recent advances in oncology aiming to
improve early detection and personalized treatment of cancer. While
highlighting applied methods of therapy, such as body radiotherapy,
chemoradiotherapy, and immunotherapy, readers learn about the
transforming approach to oncology in modern medicine and new
technologies used to diagnose and treat cancer. This book is an
important resource for medical trainees, graduate students, active
practitioners, researchers, and clinical scientists seeking current
research on oncology trends and applications.
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.
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