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Radiomics and radiogenomics in neuro-oncology
Radiogenomics Flow Using Artificial Intelligence
Intro -- Radiomics and Radiogenomics in Neuro-Oncology -- Copyright -- Contents -- Contributors -- About the editors -- Preface -- Acknowledgments -- Section 1: Introduction -- Chapter 1: Fundamentals pipelines of radiomics and radiogenomics (R-n-R) -- 1.1. Introduction -- 1.2. Pipeline of radiomics -- 1.2.1. Image acquisition -- 1.2.2. Image preprocessing -- 1.2.3. Tumor detection and segmentation using region of interest (ROI) -- 1.2.4. Conventional or deep learning-based feature extraction -- 1.2.5. Feature selection -- 1.2.6. Predictive models -- 1.2.6.1. Statistical analysis
1.2.6.2. Machine learning -- 1.2.6.3. Deep learning -- 1.2.7. Evaluation and diagnosis -- 1.3. Pipeline of radiogenomics -- 1.3.1. Tissue extraction and gene sequence -- 1.3.2. Data preprocessing -- 1.3.3. Association of radiomics and genomics -- 1.3.4. Correlation analysis and prediction -- 1.3.5. Outcomes -- 1.4. Discussion and conclusion -- References -- Chapter 2: Artificial intelligence, its components, and crucial technologies for its implementation -- 2.1. Introduction -- 2.1.1. Examples of AI usage in the medical domain -- 2.2. Advances in disease management with AI algorithms
2.3. State-of-the-art algorithms -- 2.4. Challenges in AI implementation for medical imaging -- 2.5. Strategies for overcoming hurdles to implementing AI in disease management -- 2.6. Recent scope of developments for AI in medicine -- 2.7. AI beyond classical learning -- 2.8. Challenges of AI for the future -- 2.9. Conclusion -- 2.10. Final say -- References -- Chapter 3: Radiomics and radiogenomics with artificial intelligence: Approaches, applications, advances, current challeng ... -- 3.1. Introduction -- 3.2. Overview of radiomics and radiogenomics -- 3.3. Radiogenomics
3.3.1. Neurooncology -- 3.3.2. Coronary heart disease -- 3.3.3. Cancer liver metastases (CRLM) -- 3.3.4. Diagnosis of lung cancer -- 3.3.5. Brain -- 3.3.6. Prostate -- 3.3.7. The application of radiomics in breast MRI -- 3.4. Artificial intelligence -- 3.4.1. Deep learning -- 3.4.2. Deep learning architectures in radiomics -- 3.5. Discussion -- 3.6. Conclusion -- References -- Section 2: Genomics and molecular study of brain cancer -- Chapter 4: Brain cancer and World Health Organization -- 4.1. Introduction -- 4.2. Brain cancer and its types -- 4.2.1. Gliomas -- 4.2.2. Medulloblastoma
4.2.3. Meningiomas -- 4.2.4. Ependymoma -- 4.3. WHO perspectives on brain cancer -- 4.3.1. CNS WHO grading and examples -- 4.3.2. Switching to Arabic numerals from Roman numerals for the grading system -- 4.3.3. Grading within types -- 4.3.4. Introduction of new entities -- 4.3.5. Diagnostic and therapeutic implications -- 4.4. Biology of brain cancer -- 4.4.1. Genetic mutations -- 4.4.2. Epigenetic changes -- 4.4.3. Aberrant cell signaling -- 4.4.4. Tumor microenvironment -- 4.4.5. Immunosuppression -- 4.5. Challenges to cure brain cancer -- 4.5.1. Multifaceted tumor characteristics