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...

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Bibliographic Details
Corporate Author:	ScienceDirect (Online service)
Other Authors:	Saxena, Sanjay, 1986- (Editor), Suri, Jasjit S. (Editor)
Format:	eBook
Language:	English
Published:	London, U.K. : Academic Press, [2025]
Subjects:	Brain > Cancer > Treatment. Radiogenetics > Data processing. Brain > Radiography > Data processing. Artificial intelligence > Medical applications. Brain > Cancer > Radiography. Cerveau > Cancer > Radiographie. Cerveau > Cancer > Traitement. Radiogénétique > Informatique. Cerveau > Radiographie > Informatique. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Section 1. Imaging signatures for brain cancer molecular characteristics. Chapter 1. Radiogenomics and genetic diversity of glioblastoma characterization
Chapter 2. Machine and deep learning-based methods for genotype O(6)-methylguanine-DNA-methyltransferase status prediction
Chapter 3. AI-based image signature for brain cancer molecular analysis
Chapter 4. Imaging signatures for different mutation estimation for brain cancer
Chapter 5. Software solutions for managing radiomics and radiogenomics in neuro-oncology clinical settings
Section 2. Clinical applications of R-n-R in neuro-oncology. Chapter 6. Survival estimation of brain tumor patients using radiogenomics-based studies
Chapter 7. Brain tumor progression analysis: a comprehensive review
Chapter 8. Integrative data analysis of MGMT methylation and IDH1 mutation in glioblastoma: a comprehensive review
Chapter 9. AI enabled R-n-R for neurooncology: clinical applications
Section 3. AI in R-n-R for neuro-oncology: what have we achieved so far? Chapter 10. AI for application solutions for healthcare services using AI detection and diagnosis of different diseases: a special emphasis on neuro-oncology
Chapter 11. Traditional and advanced AI methods used in the area of neuro-oncology
Chapter 12. AI in radiomics and radiogenomics for neuro-oncology: achievements and challenges.