[Ebooks] Radiomics and Its Clinical Application: Artificial Intelligence and Medical Big Data

[cheapebooks.org]

Radiomics and Its Clinical Application: Artificial Intelligence and Medical Big Data (The MICCAI Society book Series) 1st Edition 2021

With the expeditious growth of medical imaging data and the rapid advancement of artificial intelligence techniques, image-derived diagnosis and prognosis of multifold diseases has broken through the scope of conventional computer-aided diagnosis. Toward the era of intelligent analysis, a new product that combines big data of medical imaging and artificial intelligence, radiomics, has emerged.

In 2012, Professor Philippe Lambin and Professor Robert Gillies first proposed the concept of radiomics, which converts medical images such as computer tomography, magnetic resonance imaging, positron emission tomography, and ultrasound into excavable data, mines massive quantitative imaging characteristics related to diseases, and builds intelligent analysis models by artificial intelligence techniques to assist clinical diagnosis and prognosis. Radiomics originates from clinical issues and eventually returns to clinical guidance applications. It is currently one of the most important research hotspots with cutting-edge directions and has definitely shown great clinical application prospects. Up to now, many mainstream international imaging conferences, such as those of the Radiological Society of North America, the International Society for Magnetic Resonance in Medicine, and the World Molecular Imaging Congress, and clinical oncology conferences (such as those of the American Association for Cancer Research, American Society of Clinical Oncology), have set up special sessions for radiomics. There is also a trend of rapid growth in international research papers related to radiomics year by year.

We have been following the research hotspot of radiomics for many years and have participated in the international radiomics seminars hosted by Professor Robert Gillies for six consecutive years. While witnessing the rapid development of radiomics and the endless novel methods and clinical applications, we are deeply concerned about the lacunae of books dedicated to radiomics in China. In view of this, we have systematically sorted out the radiomics technique procedures and typical clinical applications and compiled this book, hoping to attract more domestic clinical and scientific researchers to jointly launch radiomics researches and provide a potential technical tool for promoting the precise diagnosis and treatment of cancers and other diseases.

The publication of this book has received much help and support. We appreciate the National Science and Technology Academic Publication Fund (2017-H-017), the National Key Research and Development Program of China (2017YFA0205200), the National Natural Science Foundation of China (81930053), and the Science Press for their long-term strong support. This book is based on radiomics research studies accumulated over years by the Key Laboratory of Molecular Imaging of the Chinese Academy of Sciences and the preliminary work of many doctoral students, master’s students, postdoctoral fellows, and young teachers. We are especially grateful to three authoritative experts in the field of radiomics, Robert Gillies, Philippe Lambin, and Sandy Napel, for writing prefaces to this book and supporting the research of radiomics in China. We thank Di Dong, Zhenyu Liu, Jingwei Wei, xiZhenchao Tang, Shuo Wang, Hailin Li, Siwen Wang, Lianzhen Zhong, Mengjie Fang, Lixin Gong, Runnan Cao, Caixia Sun, Kai Sun, Dongsheng Gu, and Shuaitong Zhang for participating in the writing and organization of this book. They contributed a lot to the final completion of this book.

Title: Medical Microbiology: Radiomics and Its Clinical Application: Artificial Intelligence and Medical Big Data (The MICCAI Society book Series)

Author: Jie Tian (Author), Di Dong, Zhenyu Liu, Jingwei Wei

Publisher: Elsevier Ltd

Publication: 2021

Edition: 1st

Language: English

Pages: 294

Ebook PDF

File size: 12MB

CBID: CBM079

Radiomics and Its Clinical Application: Artificial Intelligence and Medical Big Data