Radiomics and Radiogenomics in Neuro-Oncology (eBook)
An Artificial Intelligence Paradigm - Volume 2: Genetics and Clinical Applications
eBook Download:
EPUB
2024
|
1. Auflage
622 Seiten
Elsevier Science (Verlag)
978-0-443-18510-6 (ISBN)
622 Seiten
Elsevier Science (Verlag)
978-0-443-18510-6 (ISBN)
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. - Includes coverage of foundational concepts of the emerging fields of Radiomics and Radiogenomics - Covers imaging signatures for brain cancer molecular characteristics, including Isocitrate Dehydrogenase Mutations (IDH), TP53 Mutations, ATRX loss, MGMT gene, Epidermal Growth Factor Receptor (EGFR), and other mutations - Presents clinical applications of R-n-R in neuro-oncology such as risk stratification, survival prediction, heterogeneity analysis, as well as early and accurate prognosis - Provides in-depth technical coverage of radiogenomics studies for difference brain cancer types, including glioblastoma, astrocytoma, CNS lymphoma, meningioma, acoustic neuroma, and hemangioblastoma
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.
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. - Includes coverage of foundational concepts of the emerging fields of Radiomics and Radiogenomics- Covers imaging signatures for brain cancer molecular characteristics, including Isocitrate Dehydrogenase Mutations (IDH), TP53 Mutations, ATRX loss, MGMT gene, Epidermal Growth Factor Receptor (EGFR), and other mutations- Presents clinical applications of R-n-R in neuro-oncology such as risk stratification, survival prediction, heterogeneity analysis, as well as early and accurate prognosis- Provides in-depth technical coverage of radiogenomics studies for difference brain cancer types, including glioblastoma, astrocytoma, CNS lymphoma, meningioma, acoustic neuroma, and hemangioblastoma
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.
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. - Includes coverage of foundational concepts of the emerging fields of Radiomics and Radiogenomics- Covers imaging signatures for brain cancer molecular characteristics, including Isocitrate Dehydrogenase Mutations (IDH), TP53 Mutations, ATRX loss, MGMT gene, Epidermal Growth Factor Receptor (EGFR), and other mutations- Presents clinical applications of R-n-R in neuro-oncology such as risk stratification, survival prediction, heterogeneity analysis, as well as early and accurate prognosis- Provides in-depth technical coverage of radiogenomics studies for difference brain cancer types, including glioblastoma, astrocytoma, CNS lymphoma, meningioma, acoustic neuroma, and hemangioblastoma
| Erscheint lt. Verlag | 15.10.2024 |
|---|---|
| Sprache | englisch |
| Themenwelt | Naturwissenschaften ► Biologie ► Humanbiologie |
| Naturwissenschaften ► Biologie ► Zoologie | |
| ISBN-10 | 0-443-18510-7 / 0443185107 |
| ISBN-13 | 978-0-443-18510-6 / 9780443185106 |
| Haben Sie eine Frage zum Produkt? |
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