Description
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
Table of Contents
Section 1: Imaging signatures for brain cancer molecular characteristics1. Isocitrate Dehydrogenase Mutations (IDH)2. TP53 Mutations3. ATRX Loss4. MGMT (O6-Methylguanine-DNA-Methyltransferase Methylation) gene5. EGFR (Epidermal Growth Factor Receptor)6. Other mutationsSection 2: Clinical applications of R-n-R in Neuro-Oncology7. Risk Stratification8. Survival Prediction9. Heterogeneity Analysis 10: Early and Accurate PrognosisSection 3: Radiogenomics studies for different brain cancer types11. Glioblastoma12. Astrocytoma13. CNS lymphoma14. Others brain cancers: Meningioma, Acoustic neuroma, HaemangioblastomaSection 4: AI in R-n-R for Neuro-Oncology: What we have achieved so Far?15. A Survey on recent advancement of AI-enabled R-n-R in neuro-oncology16. Prospects and advances in R-n-R17. Progress and future aspects18. Limitations of AI in R-n-R study
