Radiomics and Radiogenomics in Neuro-Oncology

An Artificial Intelligence Paradigm - Volume 2: Genetics and Clinical Applications
Erscheinungsjahr 2024
ISBN: 978-0-443-18509-0
Verlag: Elsevier Science

Buch, Englisch, 358 Seiten, Format (B × H): 150 mm x 229 mm, Gewicht: 540 g

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.

Autoren/Hrsg.

Saxena, Sanjay

Suri, Jasjit S

Fachgebiete

• Interdisziplinäres Wissenschaften Wissenschaften Interdisziplinär Neurowissenschaften, Kognitionswissenschaft

Inhaltsverzeichnis

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

Über Autor(innen)

Suri, Jasjit S
Dr. Jasjit Suri, PhD, MBA, is a renowned innovator and scientist. He received the Director General's Gold Medal in 1980 and is a Fellow of several prestigious organizations, including the American Institute of Medical and Biological Engineering and the Institute of Electrical and Electronics Engineers. Dr. Suri has been honored with lifetime achievement awards from Marcus, NJ, USA, and Graphics Era University, India. He has published nearly 300 peer-reviewed AI articles, 100 books, and holds 100 innovations/trademarks, achieving an H-index of nearly 100 with about 43,000 citations. Dr. Suri has served as chairman of AtheroPoint, IEEE Denver section, and as an advisory board member to various healthcare industries and universities globally.

Saxena, Sanjay
Dr. Sanjay Saxena is an Assistant Professor in the Department of Computer Science and Engineering at IIIT Bhubaneswar, India. He obtained his Ph.D. from the Indian Institute of Technology (BHU), Varanasi, and completed his postdoctoral research at the Artificial Intelligence in Biomedical Imaging Lab, University of Pennsylvania, USA. Dr. Saxena's primary research area involves developing AI-based methods for brain cancer analysis, with a broader focus on Data Science, Machine Learning, Deep Learning, and the emerging fields of Radiomics and Radiogenomics. His tenure under Prof. Davatzikos at the University of Pennsylvania was instrumental in his significant learning about Radiomics/Radiogenomics. Dr. Saxena is a member of prestigious organisations such as IEEE, the Society of Neuro-Oncology, ACM, and the New York Academy of Science. He has presented his work at globally recognised universities such as like Imperial College London, Stony Brook University, and Vienna University of Technology. and has made substantial scholarly contributions, with several peer-reviewed Journals, conference publications, book chapters, and two books to his name, advancing the intersection of AI and human cancer research.