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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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Dettagli Bibliografici
Altri autori: Saxena, Sanjay, 1986- (Redattore), Suri, Jasjit S. (Redattore)
Natura: eBook
Lingua:inglese
Pubblicazione: London, U.K. : Academic Press, [2025]
Soggetti:
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.
Artificial intelligence > Medical applications
Brain > Cancer > Treatment
Brain > Radiography > Data processing
Accesso online:ScienceDirect
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Sommario:
  • 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.

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