Jun Deng – författare

Big Data in Radiation Oncology gives readers an in-depth look into how big data is having an impact on the clinical care of cancer patients. While basic principles and key analytical and processing techniques are introduced in the early chapters, the rest of the book turns to clinical applications, in particular for cancer registries, informatics, radiomics, radiogenomics, patient safety and quality of care, patient-reported outcomes, comparative effectiveness, treatment planning, and clinical decision-making. More features of the book are: Offers the first focused treatment of the role of big data in the clinic and its impact on radiation therapy. Covers applications in cancer registry, radiomics, patient safety, quality of care, treatment planning, decision making, and other key areas. Discusses the fundamental principles and techniques for processing and analysis of big data. Address the use of big data in cancer prevention, detection, prognosis, and management. Provides practical guidance on implementation for clinicians and other stakeholders. Dr. Jun Deng is a professor at the Department of Therapeutic Radiology of Yale University School of Medicine and an ABR board certified medical physicist at Yale-New Haven Hospital. He has received numerous honors and awards such as Fellow of Institute of Physics in 2004, AAPM Medical Physics Travel Grant in 2008, ASTRO IGRT Symposium Travel Grant in 2009, AAPM-IPEM Medical Physics Travel Grant in 2011, and Fellow of AAPM in 2013. Lei Xing, Ph.D., is the Jacob Haimson Professor of Medical Physics and Director of Medical Physics Division of Radiation Oncology Department at Stanford University. His research has been focused on inverse treatment planning, tomographic image reconstruction, CT, optical and PET imaging instrumentations, image guided interventions, nanomedicine, and applications of molecular imaging in radiation oncology. Dr. Xing is on the editorial boards of a number of journals in radiation physics and medical imaging, and is recipient of numerous awards, including the American Cancer Society Research Scholar Award, The Whitaker Foundation Grant Award, and a Max Planck Institute Fellowship.

Big Data in Radiation Oncology gives readers an in-depth look into how big data is having an impact on the clinical care of cancer patients. While basic principles and key analytical and processing techniques are introduced in the early chapters, the rest of the book turns to clinical applications, in particular for cancer registries, informatics, radiomics, radiogenomics, patient safety and quality of care, patient-reported outcomes, comparative effectiveness, treatment planning, and clinical decision-making. More features of the book are: Offers the first focused treatment of the role of big data in the clinic and its impact on radiation therapy. Covers applications in cancer registry, radiomics, patient safety, quality of care, treatment planning, decision making, and other key areas. Discusses the fundamental principles and techniques for processing and analysis of big data. Address the use of big data in cancer prevention, detection, prognosis, and management. Provides practical guidance on implementation for clinicians and other stakeholders.Dr. Jun Deng is a professor at the Department of Therapeutic Radiology of Yale University School of Medicine and an ABR board certified medical physicist at Yale-New Haven Hospital. He has received numerous honors and awards such as Fellow of Institute of Physics in 2004, AAPM Medical Physics Travel Grant in 2008, ASTRO IGRT Symposium Travel Grant in 2009, AAPM-IPEM Medical Physics Travel Grant in 2011, and Fellow of AAPM in 2013. Lei Xing, Ph.D., is the Jacob Haimson Professor of Medical Physics and Director of Medical Physics Division of Radiation Oncology Department at Stanford University. His research has been focused on inverse treatment planning, tomographic image reconstruction, CT, optical and PET imaging instrumentations, image guided interventions, nanomedicine, and applications of molecular imaging in radiation oncology. Dr. Xing is on the editorial boards of a number of journals in radiation physics and medical imaging, and is recipient of numerous awards, including the American Cancer Society Research Scholar Award, The Whitaker Foundation Grant Award, and a Max Planck Institute Fellowship.

Big Data in Radiation Oncology gives readers an in-depth look into how big data is having an impact on the clinical care of cancer patients. While basic principles and key analytical and processing techniques are introduced in the early chapters, the rest of the book turns to clinical applications, in particular for cancer registries, informatics, radiomics, radiogenomics, patient safety and quality of care, patient-reported outcomes, comparative effectiveness, treatment planning, and clinical decision-making. More features of the book are:

Offers the first focused treatment of the role of big data in the clinic and its impact on radiation therapy.

Covers applications in cancer registry, radiomics, patient safety, quality of care, treatment planning, decision making, and other key areas.

Discusses the fundamental principles and techniques for processing and analysis of big data.

Address the use of big data in cancer prevention, detection, prognosis, and management.

Provides practical guidance on implementation for clinicians and other stakeholders.

Dr. Jun Deng is a professor at the Department of Therapeutic Radiology of Yale University School of Medicine and an ABR board certified medical physicist at Yale-New Haven Hospital. He has received numerous honors and awards such as Fellow of Institute of Physics in 2004, AAPM Medical Physics Travel Grant in 2008, ASTRO IGRT Symposium Travel Grant in 2009, AAPM-IPEM Medical Physics Travel Grant in 2011, and Fellow of AAPM in 2013.

Lei Xing, Ph.D., is the Jacob Haimson Professor of Medical Physics and Director of Medical Physics Division of Radiation Oncology Department at Stanford University. His research has been focused on inverse treatment planning, tomographic image reconstruction, CT, optical and PET imaging instrumentations, image guided interventions, nanomedicine, and applications of molecular imaging in radiation oncology. Dr. Xing is on the editorial boards of a number of journals in radiation physics and medical imaging, and is recipient of numerous awards, including the American Cancer Society Research Scholar Award, The Whitaker Foundation Grant Award, and a Max Planck Institute Fellowship.

Big Data in Radiation Oncology gives readers an in-depth look into how big data is having an impact on the clinical care of cancer patients. While basic principles and key analytical and processing techniques are introduced in the early chapters, the rest of the book turns to clinical applications, in particular for cancer registries, informatics, radiomics, radiogenomics, patient safety and quality of care, patient-reported outcomes, comparative effectiveness, treatment planning, and clinical decision-making. More features of the book are:

Offers the first focused treatment of the role of big data in the clinic and its impact on radiation therapy.

Covers applications in cancer registry, radiomics, patient safety, quality of care, treatment planning, decision making, and other key areas.

Discusses the fundamental principles and techniques for processing and analysis of big data.

Address the use of big data in cancer prevention, detection, prognosis, and management.

Provides practical guidance on implementation for clinicians and other stakeholders.

Dr. Jun Deng is a professor at the Department of Therapeutic Radiology of Yale University School of Medicine and an ABR board certified medical physicist at Yale-New Haven Hospital. He has received numerous honors and awards such as Fellow of Institute of Physics in 2004, AAPM Medical Physics Travel Grant in 2008, ASTRO IGRT Symposium Travel Grant in 2009, AAPM-IPEM Medical Physics Travel Grant in 2011, and Fellow of AAPM in 2013.

Lei Xing, Ph.D., is the Jacob Haimson Professor of Medical Physics and Director of Medical Physics Division of Radiation Oncology Department at Stanford University. His research has been focused on inverse treatment planning, tomographic image reconstruction, CT, optical and PET imaging instrumentations, image guided interventions, nanomedicine, and applications of molecular imaging in radiation oncology. Dr. Xing is on the editorial boards of a number of journals in radiation physics and medical imaging, and is recipient of numerous awards, including the American Cancer Society Research Scholar Award, The Whitaker Foundation Grant Award, and a Max Planck Institute Fellowship.

Based on the past 30-years' research on the technical and cultural values of China's Grand Canal, this book, based on interdisciplinary research, studies the natural and social background of the evolution and development of different sections of the Grand Canal in different historical periods, as well as the interrelations between the Grand Canal and the Chinese politics, economics, and culture. It also assesses the effects of the Grand Canal on the progress of the Chinese civilization, engineering technology achievement, the natural environment, and the society, providing the readers with an understanding of China's Grand Canal from the perspectives of hydraulic engineering and history.

This book, LNCS 16193, constitutes the proceedings of the First International Workshop on Digital Twin for Healthcare, DT4H 2025, held in conjunction with MICCAI 2025, in Daejeon, South Korea, during September 23, 2025.The 15 full papers included in the book were carefully reviewed and selected from 24 submissions. They deal with integrating Digital Twin frameworks with conventional medical imaging and computational tools, focusing on topics such as organ- and disease-specific DT modeling; multi-modal data integration; AI-driven simulation and prediction; DT-guided diagnostic and treatment planning; etc.

This book, LNCS 16193, constitutes the proceedings of the First International Workshop on Digital Twin for Healthcare, DT4H 2025, held in conjunction with MICCAI 2025, in Daejeon, South Korea, during September 23, 2025.The 15 full papers included in the book were carefully reviewed and selected from 24 submissions. They deal with integrating Digital Twin frameworks with conventional medical imaging and computational tools, focusing on topics such as organ- and disease-specific DT modeling; multi-modal data integration; AI-driven simulation and prediction; DT-guided diagnostic and treatment planning; etc.

This book provides a scientific basis for development of targeted inhibitors and directional inhibitors of preventing spontaneous combustion of coal. This book applied solvent extraction assisted by ultrasonic into the study of coal spontaneous combustion and hence broken through the technical bottlenecks of existing studies for mechanisms of coal spontaneous combustion. Further, the theories of particles physics were firstly combined with theories of coal chemistry and finally explained some previous conjectures scientifically in this book. Thus, the theory of spontaneous combustion of coal has been greatly broadened and deepened. Moreover, a new theory named “Chain self-promoted oxidizing coal spontaneous combustion theory induced by active group” was proposed in this book. This theory elucidates the correlation mechanism between coal active groups and indicator gases, explaining the mechanism of indicator gas generation in coal spontaneous combustion and providing a theoretical basis for establishing an early warning indicator system for coal spontaneous combustion. This is very easy to be understood by audience with working in the field of mining or coal chemistry. Besides, principles of theories used in this book were explained in detail in this book. That is to say, there are almost no challenges or pain points for the audiences to overcome.