Radiomics and Its Clinical Application (eBook)

The rapid development of artificial intelligence technology in medical data analysis has led to the concept of radiomics. This book introduces the essential and latest technologies in radiomics, such as imaging segmentation, quantitative imaging feature extraction, and machine learning methods for model construction and performance evaluation, providing invaluable guidance for the researcher entering the field. It fully describes three key aspects of radiomic clinical practice: precision diagnosis, the therapeutic effect, and prognostic evaluation, which make radiomics a powerful tool in the clinical setting. This book is a very useful resource for scientists and computer engineers in machine learning and medical image analysis, scientists focusing on antineoplastic drugs, and radiologists, pathologists, oncologists, as well as surgeons wanting to understand radiomics and its potential in clinical practice. - An introduction to the concepts of radiomics - In-depth presentation of the core technologies and methods - Summary of current radiomics research, perspective on the future of radiomics and the challenges ahead - An introduction to several platforms that are planned to be built: cooperation, data sharing, software, and application platforms

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.

---

The rapid development of artificial intelligence technology in medical data analysis has led to the concept of radiomics. This book introduces the essential and latest technologies in radiomics, such as imaging segmentation, quantitative imaging feature extraction, and machine learning methods for model construction and performance evaluation, providing invaluable guidance for the researcher entering the field. It fully describes three key aspects of radiomic clinical practice: precision diagnosis, the therapeutic effect, and prognostic evaluation, which make radiomics a powerful tool in the clinical setting. This book is a very useful resource for scientists and computer engineers in machine learning and medical image analysis, scientists focusing on antineoplastic drugs, and radiologists, pathologists, oncologists, as well as surgeons wanting to understand radiomics and its potential in clinical practice. - An introduction to the concepts of radiomics- In-depth presentation of the core technologies and methods- Summary of current radiomics research, perspective on the future of radiomics and the challenges ahead- An introduction to several platforms that are planned to be built: cooperation, data sharing, software, and application platforms