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

Preface

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.

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Biographies
Dr. Jie Tian received his PhD (with honors) in Artificial Intelligence from the Chinese Academy of Sciences in 1993. Since 1997, he has been a Professor at the Chinese Academy of Sciences. Dr. Tian has been elected as a Fellow of ISMRM, AIMBE, IAMBE, IEEE, OSA, SPIE, and IAPR. He serves as an editorial board member of Molecular Imaging and Biology, European Radiology, IEEE Transactions on Medical Imaging, IEEE Transactions on Biomedical Engineering, IEEE Journal of Biomedical and Health Informatics, and Photoacoustics. He is the author of over 400 peer-reviewed journal articles, including publication in Nature Biomedical Engineering, Science Advances, Journal of Clinical Oncology, Nature Communications, Radiology, IEEE Transactions on Medical Imaging, and many other journals, and these articles have received over 25,000 Google Scholar citations (H-index 79). Dr. Tian is recognized as a pioneer and leader in the field of molecular imaging in China. In the last two decades, he has developed a series of new optical imaging models and reconstruction algorithms for in vivo optical tomographic imaging, including bioluminescence tomography, fluorescence molecular tomography, and Cerenkov luminescence tomography. He has developed new artificial intelligence strategies for medical imaging big data analysis in the field of radiomics and played a major role in establishing a standardized radiomics database with more than 100,000 cancer patients data collected from over 50 hospitals all over China. He has received numerous awards, including 5 national top awards for his outstanding work in medical imaging and biometrics recognition.

Dr. Di Dong is currently an Associate Professor at the Institute of Automation, Chinese Academy of Sciences. He received his PhD in Pattern Recognition and Intelligent Systems from the Institute of Automation, Chinese Academy of Sciences, China, in 2013. Dr. Dong is a member of the Youth Innovation Promotion Association of the Chinese Academy of Sciences, an active member of the American Association for Cancer Research (AACR), and a corresponding member of the European Society of Radiology (ESR). Dr. Dong has carried out long-term research work in the field of tumor radiomics and medical big data analysis. In recent years, Dr. Dong has published nearly 50 peer-reviewed papers in SCI journals, e.g., in Annals of Oncology, European Respiratory Journal, Clinical Cancer Research (three publications), BMC Medicine, etc. These articles have received over 1,600 Google Scholar citations (H-index 24). He has 6 ESI highly cited papers. He has applied for more than 20 patents and 10 software copyright licences in China.

Dr. Zhenyu Liu is currently a Professor at CAS Key Laboratory of Molecular Imaging, Institute of Automation. He received his PhD in Pattern Recognition and Intelligent Systems from the Institute of Automation, Chinese Academy of Sciences, China, in 2014. Dr. Liu got the outstanding youth fund of the Natural Science Foundation of China (NSFC) and is a member of the Youth Innovation Promotion Association of the Chinese Academy of Sciences. His research focuses on medical imaging analysis, especially radiomics and its applications in oncology research.
ixIn recent years, Dr. Liu has published nearly 30 papers in peer-reviewed journals, e.g., in Clinical Cancer Research, Theranostics, EBioMedicine, Radiotherapy and Oncology, etc. These articles received over 1,300 Google Scholar citations. He also holds more than 10 patents in China.

Dr. Jingwei Wei is currently an Assistant Professor at the Institute of Automation, Chinese Academy of Sciences. Her research focuses on radiomics and its clinical application in liver diseases, liver-specific feature engineering, traditional pattern recognition classifiers, and deep learning methods implemented towards liver disease-oriented research. Her primary work includes pre-operative prediction of microvascular invasion in hepatocellular carcinoma (HCC), prognosis prediction in HCC, and non-invasive imaging biomarker development for pathological factors prediction in liver diseases. Dr. Wei has published over 20 peer-reviewed papers in SCI journals, e.g., in Liver Cancer, Liver International, Clinical and Translational Gastroenterology, etc.