This book highlights the use of artificial intelligence (AI), big data and precision oncology for medical decision making in cancer screening, diagnosis, prognosis and treatment. Precision oncology has long been thought of as ideal for the management and treatment of cancer. This strategy promises to revolutionize the treatment, control, and prevention of cancer by tailoring tests, treatments and predictions to specific individuals or population groups. In order to accomplish these goals, vast amounts of patient or population group specific data needs to be integrated and analysed to be able to identify key patterns or features which can be used to define or characterize the disease or the response to the disease in these individuals. These patterns or features can be as varied as molecular patterns or features in medical images. This level of data analysis and integration can only be achieved through the use of AI.
The book is divided into three parts starting with a section on the use of artificial intelligence for screening, diagnosis and monitoring in precision oncology. The second part: Artificial intelligence and Omics in precision oncology, highlights the use of AI and epigenetics, metabolomics, microbiomics in precision oncology. The third part covers artificial intelligence in cancer therapy and its clinical applications. It also highlights the use of AI tools for risk prediction, early detection, diagnosis and accurate prognosis.
This book, written by experts in the field from academia and industry, will appeal to cancer researchers, clinical oncologists, pathologists, medical students, academic teaching staff and medical residents interested in cancer research as well as those specialising as clinical oncologists.
Author(s): Zodwa Dlamini
Publisher: Springer
Year: 2023
Language: English
Pages: 316
City: Cham
Preface
Contents
Editor and Contributors
Chapter 1: The Application of AI in Precision Oncology: Tailoring Diagnosis, Treatment, and the Monitoring of Disease Progress...
1.1 Introduction
1.2 AI in Medicine
1.3 Biomarker Discovery and Application
1.4 Multi-omics Data
1.4.1 Genomics
1.4.2 Transcriptomics
1.4.3 Proteomics
1.4.4 Metabolomics
1.4.5 Microbiomics
1.5 Imaging
1.5.1 Radiogenomics
1.6 Drugs, AI and Precision Oncology
1.6.1 Drug Discovery and Re-purposing
1.6.2 Digital Twins
1.7 Conclusion
References
Part I: Artificial Intelligence for Screening, Diagnosis, Monitoring in Precision Oncology
Chapter 2: Application of AI in Novel Biomarkers Detection that Induces Drug Resistance, Enhance Treatment Regimens, and Advan...
2.1 Introduction
2.2 AI Advances in Healthcare and Precision Oncology
2.3 Classification of Biomarkers
2.4 Oncology Biomarkers: Solid Biomarkers vs Liquid Biomarkers
2.5 Advances in Biomarker Discovery: Liquid Biopsies
2.6 AI in Cancer Biomarker Discovery
2.7 AI in the Detection of Novel Biomarkers for Accurate Prognostication and Prediction of Drug Resistance to Enhance Treatment
2.8 Challenges, Limitations, and Opportunities
2.9 Conclusions and Perspectives
References
Chapter 3: Use of Artificial Intelligence in Implementing Mainstream Precision Medicine to Improve Traditional Symptom-driven ...
3.1 Introduction
3.2 Artificial Intelligence
3.3 Use of Artificial Intelligence for Early Interventions and Tailoring Better-personalised Treatment of Common Cancers
3.3.1 Breast Cancer
3.3.2 Colorectal Cancer
3.3.3 Lung Cancer
3.3.4 Cancer of the Cervix
3.3.5 Gastric Cancer
3.3.6 Prostate Cancer
3.3.7 Malignant Melanoma
3.3.8 Ovarian Cancer
3.3.9 Hepatocellular Carcinoma
3.3.9.1 Carcinoma of Oesophagus
3.3.10 Pancreatic Adenocarcinoma
3.3.11 Other Cancers
3.4 Limitations
3.5 Conclusion
References
Chapter 4: AI as a Novel Approach for Exploring ccfNAs in Personalized Clinical Diagnosis and Prognosis: Providing Insight int...
4.1 Introduction
4.2 Cancer Liquid Biopsies and Their Use in Precision Oncology
4.2.1 Cell-Free DNAs
4.2.2 Circulating Tumor DNA
4.2.3 Cell-free Mitochondrial DNA in Cancer
4.3 AI and Cell-free RNAs in Cancer
4.3.1 Non-coding RNA in Cancer
4.3.2 Long Non-coding RNA in Cancer
4.3.3 The Role of MicroRNAs in Human Cancer
4.3.4 Gene Silencing in Diagnosis and Prognosis of Cancer
4.4 Limitations and Future Perspectives
4.5 Conclusion
References
Chapter 5: AI-Enhanced Digital Pathology and Radiogenomics in Precision Oncology
5.1 Introduction
5.2 Medical Imaging in Precision Medicine
5.2.1 Magnetic Resonance Imaging (MRI) in Precision Medicine
5.2.2 Computed Tomography (CT) Scan in Precision Medicine
5.2.3 Positron Emission Tomography (PET)/Computed Tomography (CT) in Precision Medicine
5.2.4 CT vs. PET/CT Comparisons: The Preferred Choice
5.3 Digital Pathology and AI
5.3.1 Reporting the Results
5.4 Radiogenomics and Artificial Intelligence and Its Use in Precision Medicine
5.4.1 Acquisition of Raw Images
5.4.2 Pre-processing of Information
5.4.3 Extraction of Features
5.4.4 Data Analysis
5.4.5 Current Application of Radiogenomics in Oncology
5.5 Limitations
5.6 Conclusion
References
Part II: Artificial Intelligence and Omics in Precision Oncology
Chapter 6: Epigenetics Analysis Using Artificial Intelligence in the Era of Precision Oncology
6.1 Introduction
6.2 Types of Epigenetic Modifications
6.2.1 DNA Methylation
6.2.2 RNA Regulation
6.2.3 Histone Modifications
6.2.4 Chromosomal Structure
6.3 AI in the Analysis of Epigenomics
6.3.1 Supervised Learning
6.3.2 Unsupervised Learning
6.3.3 Deep Learning
6.4 The Practical Use of Epigenetic Data and AI in the Management of Cancer
6.5 Limitations of AI-Driven Epigenomics Applications
6.6 Conclusions
References
Chapter 7: Association of Metabolomics with AI in Precision Oncology: Emerging Perspectives for More Effective Cancer Care
7.1 Definitions and Broad Applications
7.1.1 Metabolomics
7.1.2 Analytical Techniques in Metabolomics
7.1.3 Limitations of Metabolomics
7.2 Precision Oncology
7.3 Artificial Intelligence
7.4 Cancer Management and AI
7.4.1 Diagnosis and Treatment of Cancer
7.4.2 Biomarkers
7.4.3 Challenges Facing the Application of AI to Cancer Diagnosis, Prognosis and Treatment
7.5 The Solution to the Challenges of AI Applications
7.6 Precision Medicine in Cancer Care
7.6.1 Introduction
7.6.2 A Summary of the Application of AI to Precision Medicine
7.7 Application of AI to Metabolomics
7.7.1 Application in Therapeutics
7.8 Application in Imaging Genomics (Radiomics/Radiogenomics)
7.9 The Future of Cancer Care
7.10 Conclusion
References
Chapter 8: Artificial Intelligence Application to Microbiomics Data for Improved Clinical Decision Making in Precision Oncology
8.1 Introduction
8.2 The Human Microbiome
8.3 The Microbiome and Cancer
8.4 Microbiomics
8.4.1 Techniques in Microbiomics
8.4.1.1 Quantitative Microbial Profiling Methods
8.4.1.2 Multi-omics Technologies
8.5 Artificial Intelligence: Big Data and Machine Learning
8.5.1 Big Data
8.5.2 Machine Learning in Microbiomics
8.6 Advancing Precision Oncology
8.7 Targeting the Microbiome in the Treatment of Cancer
8.8 Limitations
8.9 Conclusions
References
Part III: Artificial Intelligence in Cancer Therapy and Clinical Applications
Chapter 9: AI and Nanomedicine in Realizing the Goal of Precision Medicine: Tailoring the Best Treatment for Personalized Canc...
9.1 Introduction
9.2 Nanotechnology Solutions in Precision Medicine
9.2.1 Combining AI and Nanotechnology Solutions in Tailoring the Best Treatment for Cancer Treatment
9.3 Role of AI in Drug Development Optimization
9.3.1 Role of Artificial Intelligence in Clinical Therapy: Drug Dosing and Therapeutic Efficacy Correlation
9.3.2 Role of AI in Improved Targeting
9.3.3 Role of AI in Gene Therapy
9.4 Challenges with AI Integrated Nanotechnologies
9.4.1 AI-Enabled Nanomedicine
9.4.2 Current Nanotechnology Strategies
9.5 Conclusion and Perspectives
References
Chapter 10: Artificial Intelligence-Based Medical Devices Revolution in Cancer Screening: Impact into Clinical Practice
10.1 Introduction
10.2 The Definition and Characteristics of an AI Device
10.3 History of Artificial Intelligence (AI) Devices
10.4 The Basis of AIMDs
10.5 The Practical Use of AI Devices in Cancers
10.5.1 Radiology and the Analysis of Images for Pathology
10.5.2 Endoscopy
10.6 The Regulation of AI-based Devices
10.7 Drawbacks and Limitations of AI Devices
10.8 Conclusion and Future Perspectives
References
Chapter 11: Intelligent Drug Design and Use for Cancer Treatment: The Roles of AI and Precision Oncology in Targeting Patient-...
11.1 Introduction
11.2 The Application of AI in Drug Design
11.3 The Role of AI in Drug Screening
11.3.1 Prediction of Physicochemical Properties and Bioactivity Using AI
11.3.2 AI Predictions of the Mode of Action of Potential Drugs
11.4 Techniques and Tools for Computational Drug Discovery
11.5 Protein Modelling and Docking
11.6 Drugs Targeting Alternative Splicing
11.7 Other Applications of AI in Drug Design
11.8 Limitations to AI-Based Drug Design
11.9 Conclusion
References
Chapter 12: Applying Artificial Intelligence Prediction Tools for Advancing Precision Oncology in Immunotherapy: Future Perspe...
12.1 Introduction
12.1.1 Cancer Immunotherapy
12.1.1.1 The Efficacy of Cancer Immunotherapy
12.1.2 AI and Biomarker Prediction Tools
12.1.2.1 Identification of Genomic Immune Signatures
12.1.2.2 Long Noncoding RNAs as Prognostic Markers
12.1.2.3 MicroRNAs as Prognostic Markers
12.1.2.4 Radiomics as Therapeutic Response Monitoring Tools
12.1.2.5 Other Approaches
12.2 Integration of AI Tools in the Enhancement of Cancer Immunotherapies
12.2.1 AI Tools for the Prediction of Novel Immune-Related Adverse Events
12.2.2 Implementation of AI Tools for Monitoring Patient Compliance to Cancer Immunotherapy
12.3 Challenges of AI in Cancer Immunotherapy
12.4 Future Perspectives
12.5 Conclusion
References
Chapter 13: Employing AI-Powered Decision Support Systems in Recommending the Most Effective Therapeutic Approaches for Indivi...
13.1 Introduction
13.2 AI-Tools in Optimising Drug Combinations and Enhancing Effective Cancer Therapeutics: From Drug Development to Personalis...
13.3 AI-Empowered Clinical Decision Support: Applications in Chemotherapy, Radiotherapy, Immunotherapy
13.4 AI-Enabled Adaptive Cancer Therapy
13.5 Challenges and Limitations
13.6 Conclusions
References
Chapter 14: AI-Pathway Companion in Clinical Decision Support: Enabling Personalized and Standardized Care Along Care Pathways...
14.1 Clinical Decision Support Systems
14.1.1 Defining Clinical Decision Support Systems (CDSS)
14.1.2 Clinical Decision Support Systems in Clinical Practice and Clinical Trials
14.1.3 Clinical Decision Support Systems Feature in Clinical Practice
14.1.4 Clinical Decision Support Systems in Pathology
14.2 AI-Pathway Companion in CDSS: Cancer Control and Prevention Includes Awareness, Screening and Early Diagnosis
14.2.1 AI-Pathway Companion in Clinical Decision Support
14.2.2 What Is an AI-Pathway Companion?
14.2.3 AI-Pathway Prostate Cancer
14.2.4 AI-Pathway Companion Breast Cancer
14.2.5 AI-Pathway Companion Coronary Artery Disease
14.2.6 AI-Pathway Companion Infectious Diseases
14.3 Clinical Uses of AI-Pathway Companion
14.4 Cancer Prevention and Control Using COMPAS
14.5 Overview of Clinical Decision Support Systems
14.6 Machine Learning Tools
14.7 Predictive Models Assist in Decision-making
14.8 Developing Treatment Responses
14.9 Limitations in the Application of AI in Precision Medicine
14.10 Conclusion
References
Chapter 15: AI Tools Offering Cancer Clinical Applications for Risk Predictor, Early Detection, Diagnosis, and Accurate Progno...
15.1 Introduction
15.2 AI-based Tools in Clinical Oncology Workflows
15.3 AI-Models for Predicting Clinically Relevant Parameters in Advancing Precision Oncology
15.4 AI-Enhanced Technologies in Early Cancer Detection, Diagnosis, Risk Stratification and Prognosis
15.5 AI from Bench to Bedside: Challenges and Limitations
15.6 Conclusions
References
Chapter 16: Conclusion and Insights into the Future of AI in Precision Oncology
16.1 Conclusion
