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.
1. Radiomics and Radiogenomics in Neuro-Oncology - p. i 2. Cover - p. i 3. Title Page - p. iii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of Contributors - p. xi 7. About the editors - p. xv 8. Preface - p. xvii 9. Acknowledgments - p. xix 10. Chapter 1 Radiogenomics and genetic diversity of glioblastoma characterization - p. 3 11. Chapter 2 Machine and deep learning-based methods for genotype O(6)-methylguanine-DNA-methyltransferase status prediction - p. 35 12. Chapter 3 AI-based image signature for brain cancer molecular analysis - p. 49 13. Chapter 4 Imaging signatures for different mutation estimation for brain cancer - p. 65 14. Chapter 5 Software solutions for managing radiomics and radiogenomics in neuro-oncology clinical settings - p. 103 15. Chapter 6 Survival estimation of brain tumor patients using radiogenomics-based studies - p. 137 16. Chapter 7 Brain tumor progression analysis: A comprehensive review - p. 167 17. Chapter 8 Integrative data analysis of MGMT methylation and IDH1 mutation in glioblastoma: A comprehensive review - p. 181 18. Chapter 9 AI enabled R-n-R for neurooncology: Clinical applications - p. 203 19. Chapter 10 AI for application solutions for healthcare services using AI detection and diagnosis of different diseases: A special emphasis on neuro-oncology - p. 241 20. Chapter 11 Traditional and advanced AI methods used in the area of neuro-oncology - p. 277 21. Chapter 12 AI in radiomics and radiogenomics for neuro-oncology: Achievements and challenges - p. 301 22. Index - p. 325
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.
1. Radiomics and Radiogenomics in Neuro-Oncology - p. i 2. Cover - p. i 3. Title Page - p. iii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of Contributors - p. xi 7. About the editors - p. xv 8. Preface - p. xvii 9. Acknowledgments - p. xix 10. Chapter 1 Radiogenomics and genetic diversity of glioblastoma characterization - p. 3 11. Chapter 2 Machine and deep learning-based methods for genotype O(6)-methylguanine-DNA-methyltransferase status prediction - p. 35 12. Chapter 3 AI-based image signature for brain cancer molecular analysis - p. 49 13. Chapter 4 Imaging signatures for different mutation estimation for brain cancer - p. 65 14. Chapter 5 Software solutions for managing radiomics and radiogenomics in neuro-oncology clinical settings - p. 103 15. Chapter 6 Survival estimation of brain tumor patients using radiogenomics-based studies - p. 137 16. Chapter 7 Brain tumor progression analysis: A comprehensive review - p. 167 17. Chapter 8 Integrative data analysis of MGMT methylation and IDH1 mutation in glioblastoma: A comprehensive review - p. 181 18. Chapter 9 AI enabled R-n-R for neurooncology: Clinical applications - p. 203 19. Chapter 10 AI for application solutions for healthcare services using AI detection and diagnosis of different diseases: A special emphasis on neuro-oncology - p. 241 20. Chapter 11 Traditional and advanced AI methods used in the area of neuro-oncology - p. 277 21. Chapter 12 AI in radiomics and radiogenomics for neuro-oncology: Achievements and challenges - p. 301 22. Index - p. 325
