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

This book, now in a revised and updated second edition, covers the full spectrum of clinical applications of SPECT/CT in the diagnosis and therapy planning of benign and malignant diseases. All chapters have been thoroughly updated and some chapters have been completely rewritten by a new group of experts. The opening chapters discuss the technology and physics of SPECT/CT and its use in dosimetry. The role of SPECT/CT in the imaging of a range of pathologic conditions is then addressed in detail. Applications covered include imaging of the thyroid, neuroendocrine tumors, bone, cardiac scintigraphy, sentinel node scintigraphy and imaging of the lungs. Individual chapters are also devoted to therapy planning in selective internal radiation therapy of liver tumors and to Bremsstrahlung SPECT/CT. For Nuclear Medicine Physicians, Radiologists and medical students in this field, the book offers an essential and up-to-date source of information on this invaluable hybrid imaging technique.

Radiomics and Radiogenomics in Neuro-Oncology: An Artificial Intelligence Paradigm—Volume 2: Genetics and Clinical Applications provides readers with a broad and detailed framework for radiomics and radiogenomics (R-n-R) approaches with AI in neuro-oncology. It delves into the study of cancer biology and genomics, presenting methods and techniques for analyzing these elements. The book also highlights current solutions that R-n-R can offer for personalized patient treatments, as well as discusses the limitations and future prospects of AI technologies. Volume 1: Radiogenomics Flow Using Artificial Intelligence covers the genomics and molecular study of brain cancer, medical imaging modalities and their analysis in neuro-oncology, and the development of prognostic and predictive models using radiomics. Volume 2: Genetics and Clinical Applications extends the discussion to imaging signatures that correlate with molecular characteristics of brain cancer, clinical applications of R-n-R in neuro-oncology, and the use of Machine Learning and Deep Learning approaches for R-n-R in neuro-oncology. - Includes coverage of foundational concepts of the emerging fields of Radiomics and Radiogenomics - Covers imaging signatures for brain cancer molecular characteristics, including Isocitrate Dehydrogenase Mutations (IDH), TP53 Mutations, ATRX loss, MGMT gene, Epidermal Growth Factor Receptor (EGFR), and other mutations - Presents clinical applications of R-n-R in neuro-oncology such as risk stratification, survival prediction, heterogeneity analysis, as well as early and accurate prognosis - Provides in-depth technical coverage of radiogenomics studies for difference brain cancer types, including glioblastoma, astrocytoma, CNS lymphoma, meningioma, acoustic neuroma, and hemangioblastoma

More and more medical centers are now combining high-resolution CT scans well with deep learning and artificial intelligence for lung cancer screening, resulting in significantly improved diagnostic sensitivity. Furthermore, the increased molecular alterations in lung cancer were demonstrated not only in tumor tissue, but also in other body organs. For example, circulating tumor DNA combined with next-generation sequencing is now becoming a popular method for lung cancer diagnosis and therapeutic monitoring. Therefore, the first focus of this topic is on such achievements in early diagnosis of lung cancer, especially non-invasive tests such as liquid biopsy.

Artificial Intelligence, Machine Learning, and Deep Learning in Precision Medicine and Liver Diseases: Concept, Technology, Application, and Perspectives combines four major applications of artificial intelligence (AI) within the field of clinical medicine specific to liver diseases: radiology imaging, electronic health records, pathology, and multiomics. The book provides a state-of-the-art summary of AI in precision medicine in hepatology, clarifying the concept and technology of AI and pointing to the current and future applications of AI within the field of hepatology. Coverage includes data preparation, methodology and application within disease-specific cases in fibrosis, viral and steatohepatitis, cirrhosis, hepatocellular carcinoma, acute liver failure, liver transplantation, and more. The ethical and legal issues of AI and future challenges and perspectives are also discussed. By highlighting many new AI applications which can further research, diagnosis, and treatment, this reference is the perfect resource for both practicing hepatologists and researchers focused on AI applications in medicine. - Introduces the concept of AI and machine learning of precision medicine in the field of hepatology - Discusses current challenges of AI in healthcare and proposes future tasks for AI in new workflows of healthcare - Provides real-world applications from domain experts in clinical medicine