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Radiomics and radiogenomics in neuro-oncology an artificial intelligence paradigm. Volume 1, Radiogenomics Flow Using Artificial Intelligence /

Bibliographic Details
Corporate Author: ScienceDirect (Online service)
Other Authors: SAXENA, SANJAY, Suri, Jasjit
Format: eBook
Language:English
Published: [S.l.] : Academic press, 2024.
Subjects:
Brain > Cancer > Radiography.
Cerveau > Cancer > Radiographie.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • 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