Radiomics and Radiogenomics in Neuro-Oncology
An Artificial Intelligence Paradigm - Volume 1: Radiogenomics Flow Using Artificial Intelligence
- 1st Edition - March 29, 2024
- Imprint: Academic Press
- Editors: Sanjay Saxena, Jasjit S. Suri
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 8 5 0 8 - 3
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 8 5 0 7 - 6
Radiomics and Radiogenomics in Neuro-Oncology: An Artificial Intelligence Paradigm, Volume One: Radiogenomics Flow Using Artificial Intelligence broadly encompasses the study… Read more
Purchase options
Institutional subscription on ScienceDirect
Request a sales quoteRadiomics and Radiogenomics in Neuro-Oncology: An Artificial Intelligence Paradigm, Volume One: Radiogenomics Flow Using Artificial Intelligence broadly encompasses the study of life-threatening brain and spinal cord malignancies, including primary lesions and those metastasizing to the central nervous system. Radiomics is an approach to medical imaging used to extract many quantitative imaging features using different data characterization algorithms, while radiogenomics, which has recently emerged as a novel mechanism in neuro-oncology research, focuses on the relationship of imaging phenotype and genetics of cancer. Sections in this book discuss several AI approaches that have been applied to conventional and advanced medical imaging data. These AI-based R-n-R tools have the potential to stratify patients into more precise initial diagnostic and therapeutic pathways and permit better dynamic treatment monitoring in this period of personalized medicine. While extremely promising, the clinical acceptance of R-n-R methods and approaches will primarily hinge on their resilience to non-standardization across imaging protocols and their capability to show reproducibility.
- Includes coverage on the foundational concepts of the emerging fields of radiomics and radiogenomics
- Covers neural engineering modeling and AI algorithms for the imaging, diagnosis, and predictive modeling of neuro-oncology
- Presents crucial technologies and software platforms, along with advanced brain imaging techniques such as quantitative imaging using CT, PET, and MRI
- Provides in-depth technical coverage of computational modeling techniques and applied mathematics for brain tumor segmentation and radiomics features such as extraction and selection
Biomedical Engineers and researchers in Neural Engineering, medical imaging, and neural networks. Other interested audiences will be comprised of radiologists, neurologists, neurosurgeons, computer scientists, AI researchers, and designers of Machine Learning applications. Another audience includes those interested in signal processing of the brain and classifying brain signals, Clinicians and researchers interested in neurological diseases and disorders, including their diagnosis and treatment, Tumor imaging oncologists will also be a secondary audience
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- About the editors
- Preface
- Acknowledgments
- Section 1: Introduction
- Chapter 1 Fundamentals pipelines of radiomics and radiogenomics (R-n-R)
- Abstract
- 1.1 Introduction
- 1.2 Pipeline of radiomics
- 1.3 Pipeline of radiogenomics
- 1.4 Discussion and conclusion
- References
- Chapter 2 Artificial intelligence, its components, and crucial technologies for its implementation
- Abstract
- 2.1 Introduction
- 2.2 Advances in disease management with AI algorithms
- 2.3 State-of-the-art algorithms
- 2.4 Challenges in AI implementation for medical imaging
- 2.5 Strategies for overcoming hurdles to implementing AI in disease management
- 2.6 Recent scope of developments for AI in medicine
- 2.7 AI beyond classical learning
- 2.8 Challenges of AI for the future
- 2.9 Conclusion
- 2.10 Final say
- References
- Chapter 3 Radiomics and radiogenomics with artificial intelligence: Approaches, applications, advances, current challenges, and future perspectives
- Abstract
- 3.1 Introduction
- 3.2 Overview of radiomics and radiogenomics
- 3.3 Radiogenomics
- 3.4 Artificial intelligence
- 3.5 Discussion
- 3.6 Conclusion
- References
- Section 2: Genomics and molecular study of brain cancer
- Chapter 4 Brain cancer and World Health Organization
- Abstract
- 4.1 Introduction
- 4.2 Brain cancer and its types
- 4.3 WHO perspectives on brain cancer
- 4.4 Biology of brain cancer
- 4.5 Challenges to cure brain cancer
- 4.6 Role of AI in brain cancer survey
- 4.7 Conclusion
- References
- Chapter 5 Genomic and genetic levels alteration in brain tumor
- Abstract
- 5.1 Introduction
- 5.2 Brain tumor and its types
- 5.3 Biology of genetic and genomic level changes in brain tumor
- 5.4 Brief description of different genomic level alterations
- 5.5 Types of brain tumors and corresponding genotypes
- 5.6 Conclusion
- References
- Chapter 6 Role of molecular markers in diagnosis and prognosis of gliomas
- Abstract
- 6.1 Introduction
- 6.2 Gliomas and their grades
- 6.3 Molecular markers of gliomas
- 6.4 Molecular markers in diagnosis of gliomas
- 6.5 Molecular markers in prognosis of gliomas
- 6.6 Summary
- References
- Chapter 7 Multiomics studies for neuro-oncology
- Abstract
- 7.1 Introduction
- 7.2 Multiomics studies
- 7.3 Discussion
- 7.4 Applications and challenges
- 7.5 Conclusion
- References
- Section 3: Medical imaging modalities and analysis in neuro-oncology
- Chapter 8 Medical image analysis steps: Medical image acquisition to classification (or regression) in neuro-oncology
- Abstract
- 8.1 Introduction
- 8.2 Steps involved in medical image analysis
- 8.3 Conclusion
- References
- Chapter 9 MRI: An important biomarker for radiomics study of brain cancer using machine learning
- Abstract
- 9.1 Introduction
- 9.2 Type of brain tumor
- 9.3 Choroid plexus carcinoma
- 9.4 Embryonal CNS tumors
- 9.5 Pediatric brain tumors
- 9.6 Various diagnostic methods for detecting brain cancer
- 9.7 Lumbar puncture or spinal tap
- 9.8 Neurological, vision, and hearing tests
- 9.9 Magnetic resonance imaging (MRI)
- 9.10 MRI and its uses in detecting cancer
- 9.11 Different MRI methods
- 9.12 Mechanism of MRI
- 9.13 Image processing technique
- 9.14 Image preprocessing
- 9.15 Contrast enhancement
- 9.16 Median filter
- 9.17 Wiener filter
- 9.18 Hybrid filter
- 9.19 Pixel brightness transform
- 9.20 Pixel intensity conversion
- 9.21 Geometric transformations
- 9.22 Image filtering
- 9.23 Classification
- 9.24 K-nearest neighbors (KNN)
- 9.25 Support vector machine (SVM)
- 9.26 Random forest (RF)
- 9.27 Decision tree (DT)
- 9.28 Hidden Markov model (HMM)
- 9.29 Probabilistic neural network (PNN)
- 9.30 Artificial neural networks (ANN)
- 9.31 Convolutional neural networks (CNN)
- 9.32 Conclusion
- References
- Chapter 10 Deep learning algorithms for imaging gliomas for diagnosis, prognosis and treatment strategies predictions
- Abstract
- 10.1 Introduction
- 10.2 Deep learning models
- 10.3 Deep learning in diagnosis, prognosis, survival and treatment strategy prediction of gliomas
- 10.4 Limitations and future prospects
- 10.5 Summary
- References
- Section 4: Radiomics and radiogenomics in neuro-oncology
- Chapter 11 Radiomics and radiogenomics of central nervous system metastatic lesions
- Abstract
- 11.1 Introduction
- 11.2 Brain metastases
- 11.3 Spinal metastasis
- 11.4 Limitations
- 11.5 Conclusions
- References
- Chapter 12 Clinical applications implementation in neuro-oncology using machine learning approaches
- Abstract
- 12.1 Introduction
- 12.2 Background
- 12.3 ML-based feature extraction strategies
- 12.4 ML-based feature selection strategies
- 12.5 Machine learning frameworks in neuro-oncology for clinical applications implementation
- 12.6 Radiogenomics: The current ML-based approaches in neuro-oncology
- 12.7 Challenges and solutions for implementing ML-based approaches in the clinical practice of neuro-oncology
- 12.8 Conclusion
- References
- Chapter 13 Application and constraints of AI in radiomics and radiogenomics (R-n-R) studies of neuro-oncology
- Abstract
- 13.1 Introduction
- 13.2 Radiomics and radiogenomics fundamentals
- 13.3 Applications of AI in R-n-R based on neuro-oncology
- 13.4 Present achievements from usage of AI in radiogenomics
- 13.5 Offers of AI
- 13.6 Limitations and clinical challenges
- 13.7 Conclusion
- References
- Index
- Edition: 1
- Published: March 29, 2024
- Imprint: Academic Press
- No. of pages: 622
- Language: English
- Paperback ISBN: 9780443185083
- eBook ISBN: 9780443185076
SS
Sanjay Saxena
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.
Affiliations and expertise
Assistant Professor, Department of Computer Science and Engineering, International Institute of Information Technology, Bhubaneswar, IndiaJS
Jasjit S. Suri
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.
Affiliations and expertise
Chairman, AtheroPoint LLC, USARead Radiomics and Radiogenomics in Neuro-Oncology on ScienceDirect