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 different brain cancer types, including glioblastoma, astrocytoma, CNS lymphoma, meningioma, acoustic neuroma, and hemangioblastoma