Full 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.
Contents
Section 1: Imaging signatures for brain cancer molecular characteristics
1. Isocitrate Dehydrogenase Mutations (IDH)
2. TP53 Mutations
3. ATRX Loss
4. MGMT (O6-Methylguanine-DNA-Methyltransferase Methylation) gene
5. EGFR (Epidermal Growth Factor Receptor)
6. Other mutations
Section 2: Clinical applications of R-n-R in Neuro-Oncology
7. Risk Stratification
8. Survival Prediction
9. Heterogeneity Analysis 10: Early and Accurate Prognosis
Section 3: Radiogenomics studies for different brain cancer types
11. Glioblastoma
12. Astrocytoma
13. CNS lymphoma
14. Others brain cancers: Meningioma, Acoustic neuroma, Haemangioblastoma
Section 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-oncology
16. Prospects and advances in R-n-R
17. Progress and future aspects
18. Limitations of AI in R-n-R study
