Radiomics and Radiogenomics in Neuro-Oncology

An Artificial Intelligence Paradigm - Volume 1: Radiogenomics Flow Using Artificial Intelligence

1st Edition - March 29, 2024
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

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Radiomics 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.

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

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, India

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, USA

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