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For Authors
Submit a ProposalInnovative Paradigms in Oncology
One Line Description
This comprehensive resource bridges the gap between cutting-edge research and clinical practice, equipping healthcare professionals, researchers, and students with the essential insights needed to master precision medicine, AI-driven diagnostics, and modern oncology therapeutics.
Description
The field of oncology has undergone a paradigm shift with the advent of precision medicine and advanced technologies. Breakthroughs in molecular biology and bioinformatics have enabled a deeper understanding of tumor heterogeneity, leading to personalized treatment strategies. Simultaneously, innovations such as AI-driven diagnostics, immunotherapy, and nanotechnology are revolutionizing patient care, offering unprecedented levels of efficacy and specificity. This book provides a comprehensive exploration of cutting-edge developments in oncology, focusing on the integration of advanced technologies, precision medicine, and novel therapeutic approaches. It delves into the transformative role of artificial intelligence, genomics, immunotherapy, and other emerging technologies in reshaping cancer care. The content is structured to offer a multidisciplinary approach, covering foundational principles of precision medicine, technological advancements in cancer diagnostics and therapeutics, and the implementation of personalized care strategies. Each chapter highlights key innovations and their clinical implications, supported by case studies and real-world applications. The book also examines challenges such as ethical considerations, regulatory hurdles, and the need for equitable access to advanced care. This book is a consolidated resource that integrates diverse aspects of modern oncology, catering to an audience of healthcare professionals, researchers, and students who seek to stay at the forefront of this dynamic field.
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The field of oncology has undergone a paradigm shift with the advent of precision medicine and advanced technologies. Breakthroughs in molecular biology and bioinformatics have enabled a deeper understanding of tumor heterogeneity, leading to personalized treatment strategies. Simultaneously, innovations such as AI-driven diagnostics, immunotherapy, and nanotechnology are revolutionizing patient care, offering unprecedented levels of efficacy and specificity. This book provides a comprehensive exploration of cutting-edge developments in oncology, focusing on the integration of advanced technologies, precision medicine, and novel therapeutic approaches. It delves into the transformative role of artificial intelligence, genomics, immunotherapy, and other emerging technologies in reshaping cancer care. The content is structured to offer a multidisciplinary approach, covering foundational principles of precision medicine, technological advancements in cancer diagnostics and therapeutics, and the implementation of personalized care strategies. Each chapter highlights key innovations and their clinical implications, supported by case studies and real-world applications. The book also examines challenges such as ethical considerations, regulatory hurdles, and the need for equitable access to advanced care. This book is a consolidated resource that integrates diverse aspects of modern oncology, catering to an audience of healthcare professionals, researchers, and students who seek to stay at the forefront of this dynamic field.
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Author / Editor Details
Mudassir Khan, PhD is an Assistant Professor in the Department of Computer Science at King Khalid University, Saudi Arabia, with more than 16 years of teaching and research experience. He has published more than 150 research articles in reputed journals, edited numerous books, and authored four books. His research expertise includes big data, deep learning, machine learning, data science, IoT, and artificial intelligence.
Mudassir Khan, PhD is an Assistant Professor in the Department of Computer Science at King Khalid University, Saudi Arabia, with more than 16 years of teaching and research experience. He has published more than 150 research articles in reputed journals, edited numerous books, and authored four books. His research expertise includes big data, deep learning, machine learning, data science, IoT, and artificial intelligence.
Shaik Karimullah, PhD is an Assistant Professor at Annamacharya University. He has published numerous research papers in reputed journals and international conferences. His research focuses on VLSI architectures, design optimization, and image processing, contributing to advancements in modern electronics and computing.
Fahimuddin Shaik, PhD is an Associate Professor at the Annamacharya Institute of Technology and Sciences, with more than 18 years of experience. He is dedicated to teaching, mentoring engineering students, and advancing research in emerging areas of technology. His academic and research expertise focuses on image processing, embedded systems, and wireless communication.
Surajit Mondal, PhD is an Associate Professor at Amity University, Bengaluru, with more than 12 years of experience in teaching, research, and project management. He has published more than 100 research articles and contributed to several edited books. He holds 98 patents, including 46 granted patents, with research interests in renewable energy, sustainability, energy management, and smart technologies.
Paulami Ghosh, PhD is an Assistant Professor at Shoolini University, Himachal Pradesh, with publications in several peer-reviewed journals. Her research focuses on fermented probiotic plant-based foods, nutritional enhancement, and functional properties.
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Table of Contents
Preface
1. Artificial Intelligence and Machine Learning in Cancer Diagnostics
Debosree Ghosh and Sutapa Datta
1.1 Introduction
1.2 Methodology
1.3 Artificial Intelligence and Machine Learning in the Diagnosis of Lung Cancer
1.4 Artificial Intelligence and Machine Learning in the Diagnosis of Skin Cancer
1.5 Artificial Intelligence and Machine Learning in the Diagnosis of Brain Cancer
1.6 Artificial Intelligence and Machine Learning in the Diagnosis of Breast Cancer
1.7 Artificial Intelligence and Machine Learning in the Diagnosis of Prostate Cancer
1.8 Artificial Intelligence and Machine Learning in Cervical
Cancer
1.9 Artificial Intelligence and Machine Learning in Cancer Immunotherapy
1.10 Limitations and Future Prospects of the Application of Artificial Intelligence and Machine Learning in Cancer Diagnosis
1.11 Conclusion
Acknowledgment
References
2. Enhanced Automatic Lung Segmentation Using Novel Modified Adaptive Granular Multilevel Rough Entropy Threshold Computing
S. Nivetha, H. Hannah Inbarani and Mudassir Khan
2.1 Introduction
2.1.1 Research Motivation
2.1.2 Research Objective
2.1.3 Research Challenges
2.1.4 Advantages of the Novel Modified Adaptive Granular Multilevel Rough Entropy Threshold
2.1.5 Research Contributions
2.1.6 Research Justification
2.1.7 Stronger Justification of Chosen Methodology
2.2 Summary of Existing Studies
2.3 Methods and Materials
2.3.1 Preprocessing
2.4 Ground Truth: Lung Mask Extraction
2.5 Proposed Novel Modified Adaptive Granular Multilevel Rough Entropy-Based Threshold Computing
2.6 Simulation Outcomes and Discussion
2.7 Evaluation Measures of Segmentation
2.8 Conclusion
References
3. Feature Optimization and Classification Performance for Prostate Cancer: Deep Learning vs. Machine Learning Approaches
Asmita Ray, Mostaque Md. Morshedur Hassan, Munsifa Firdaus Khan Barbhuyan, Md. Faruqul Islam and Mudassir Khan
3.1 Introduction
3.2 Related Work
3.3 Clinical Background and Epidemiology
3.3.1 Anatomy and Role of the Prostate Gland
3.3.2 Risk Factors: Age, Genetics, and Lifestyle
3.4 Prostate Cancer Screening Methods
3.4.1 Prostate-Specific Antigen Test
3.4.2 Digital Rectal Examination
3.4.3 Emerging Diagnostic Tools
3.5 Materials and Methods
3.5.1 Dataset
3.5.2 Feature Extraction
3.5.3 Texture Features
3.6 Machine Learning Method
3.7 Deep Learning Method
3.8 Proposed Methodology
3.9 Performance Measures
3.9.1 Receiver Operating Characteristic Curve
3.9.2 Area Under the Receiver Operating Characteristic Curve
3.10 Result and Discussion
3.11 Conclusion and Future Work
References
4. The Evolution of Oncology in the 21st Century
Lakshmi Shree P., Ananya B., Yuvaraj Sivamani and Sumitha Elayaperumal
4.1 Introduction
4.1.1 Cancer and Its Overview
4.1.2 Types of Cancer
4.1.2.1 Solid Cancers
4.1.2.2 Blood Cancers
4.1.2.3 Mixed Cancers
4.1.3 History of Cancer
4.1.3.1 Origin of the Word Cancer
4.1.4 Oncology
4.1.4.1 Medical Oncology
4.1.4.2 Surgical Oncology
4.1.4.3 Radiation Oncology
4.2 Discoveries from 2000–2005
4.2.1 First Molecularly Targeted Treatment Approved: Gleevec
4.2.2 Mechanism of Action Imatinib
4.2.3 Scientific Background of Imatinib
4.2.4 Clinical Trials of Imatinib
4.2.5 Case Study
4.2.6 Future Implication of Imatinib
4.2.7 Broader Implications of Imatinib
4.3 Discoveries from 2006–2010
4.3.1 Thalidomide
4.3.2 Mechanism of Action Thalidomide
4.3.3 Scientific Background of Thalidomide
4.3.4 Clinical Trials of Thalidomide
4.3.5 Case Studies
4.3.6 Future Implication of Thalidomide
4.3.7 Broader Implications of Thalidomide
4.4 Discoveries from 2011–2015
4.4.1 Cytotoxic T-Lymphocyte-Associated Protein 4 Inhibitor
4.4.2 Mechanism of Action Ipilimumab
4.4.3 Scientific Background of Ipilimumab
4.4.4 Clinical Trials of Ipilimumab
4.4.5 Future Implication of Ipilimumab
4.4.6 Broader Implication of Ipilimumab
4.5 Discoveries from 2016–2020
4.5.1 Methods of Precision Oncology
4.5.1.1 Tumor-Agnostic Therapy
4.5.1.2 Immunotherapy
4.5.2 Diagnostics
4.5.2.1 Liquid Biopsy
4.5.2.2 Artificial Intelligence in Cancer Diagnosis
4.6 Discoveries from 2021–2025
4.6.1 Advancement of Cancer Care with the Help of Artificial Intelligence
4.6.2 Drugging the “Undruggable” and Other Therapies
4.7 Ethics and Policies on Cancer Throughout the 21st Century
4.7.1 Autonomy and Informed Consent
4.7.2 Beneficence and Non-Maleficence
4.7.3 Justice and Equity
4.7.4 Truth-Telling and Cultural Competence
4.7.5 Professionalism and Marketing Ethics
4.7.6 Artificial Intelligence Chatbots, and Post-2020 Ethical Frontiers
4.7.7 Education and Continuing Competence
4.8 Conclusion
References
5. Emerging Therapeutics: Nanomedicine, Ribonucleic Acid
Therapies, and Beyond
Maryam, Aasma Bhatti, Muhammad Farrukh Tahir, Zunaira Munir and Qurat ul Ain Babar
5.1 Introduction
5.1.1 Overview of Emerging Cancer Therapeutics
5.1.2 The Need for Novel Approaches beyond Conventional Chemotherapy
5.1.3 Role of Precision Medicine in Oncology
5.2 Nanomedicine in Oncology
5.2.1 Principles of Nanomedicine for Cancer Therapy
5.2.2 Types of Nanoparticles
5.2.3 Tumor Targeting and Controlled Drug Release
5.2.4 Clinical Applications and Approved Nanodrugs
5.3 Ribonucleic Acid-Based Therapeutics
5.3.1 Mechanism of Ribonucleic Acid-Based Treatments
5.3.2 Messenger Ribonucleic Acid Vaccines and Immunotherapy
5.3.3 Antisense Oligonucleotides
5.3.4 Challenges in Ribonucleic Acid Therapy Delivery and Stability
5.4 Beyond Nanomedicines and Ribonucleic Acid: Next-Generation Therapies
5.4.1 Gene Editing Technologies (CRISPR/Cas9 in Cancer Therapy)
5.4.2 Exosome-Based Drug Delivery Systems
5.4.3 Biomimetic and Hybrid Nanoplatforms
5.4.4 Artificial Intelligence-Driven Drug Design and Therapeutic Optimization
5.5 Clinical Translation and Challenges
5.5.1 Regulatory Landscape and Approval Pathways
5.5.2 Safety Concerns and Immunogenicity Issues
5.5.3 Overcoming Therapeutic Resistance and Tumor Heterogeneity
5.5.4 Integration with Conventional Cancer Treatments
5.6 Future Perspectives and Conclusion
5.6.1 The Evolving Role of Precision Oncology
5.6.2 Synergistic Approaches Combining Multiple Modalities
5.6.3 Ethical Considerations and Patient-Centered Care in Advanced Therapeutics
5.6.4 Potential for Artificial Intelligence-Enhanced Personalization in Cancer Therapy
5.7 Conclusion
References
6. Blockchain in Oncology: Enhancing Data Security and Patient Privacy
R. Yuvaraj, N. Vishnuvarthan, N. Arisankar, P. Saranraj and M. Manigandan
6.1 Introduction
6.2 Blockchain: An Overview
6.3 Blockchain Technology and Architecture
6.3.1 Blockchain Components
6.3.2 Types of Blockchain
6.3.2.1 Data Contributors
6.3.2.2 Private Blockchains
6.3.2.3 Access Control
6.3.2.4 Permissioned Blockchains
6.3.2.5 Participant Equality
6.4 Blockchain Technology in Healthcare
6.4.1 Intelligent Healthcare System for the Patient
6.4.2 Enhancing Patient Data Privacy
6.4.3 Benefits of Blockchain in Maintaining Health Records
6.4.4 Blockchain Technology in Medical Healthcare
6.5 Technology for Building Smart Healthcare
6.6 Benefits and Drawbacks of Blockchains
6.6.1 Benefits of Blockchain
6.6.2 Drawbacks of Blockchain
6.7 Application of Blockchain in Healthcare
6.8 Challenges and Future Research Directions
6.9 Limitations of the Study
6.10 Conclusion
References
7. Global Oncology: Addressing Cancer Burden in Low-Resource Settings
Farhana Faruque Zerin, A. K. M. Shafiul Kadir, Jannatul Ferdause, M. Juel Khondakar, Rukaiya Akhter, Tasfi Jahan Tina, Mohammad Ullah Shemanto and Jim Rahman
7.1 Introduction
7.1.1 Overview of the Global Cancer Burden
7.1.2 Disparities in Cancer Care: High-Income vs. Low-Resource Settings
7.1.2.1 Low Public Understanding and Misconceptions
7.1.2.2 Insufficient Healthcare Funding and Infrastructure
7.1.2.3 Oncology Workforce
7.1.2.4 Lack of Access to Cancer Screening and Early Detection
7.1.2.5 Diagnostic Delays
7.1.2.6 Treatment Abandonment and Deficiencies in Palliative Care
7.1.3 Objectives and Scope of the Chapter
7.2 Cancer Epidemiology in Low-Resource Settings
7.2.1 Trends in Cancer Incidence and Mortality
7.2.2 Regional Variations and Unique Cancer Profiles
7.2.3 Risk Factors and Lifestyle Influences
7.3 Challenges in the Delivery of Oncology Services in Low-Resource Settings
7.3.1 Inadequate Infrastructure and Resource Limitations
7.3.2 Workforce Shortages and Training Gaps
7.3.3 Shortage of Technical Support Personnel
7.3.4 Economic and Policy Barriers to Access
7.3.5 Sociocultural Determinants and Their Impact
7.3.6 Lack of Structured Health Insurance and Organized Screening Programs
7.3.7 The Imperative of Prevention and Early Detection
7.4 Innovative Solutions for Cancer Care
7.4.1 Affordable Diagnostic Tools and Technologies
7.4.2 Mobile Health Platforms and Telemedicine
7.4.3 Task-Shifting and Community Health Worker Models
7.4.4 Decentralization of Oncology Services
7.5 Capacity Building and Education
7.5.1 Training Programs for Oncology Professionals
7.5.2 Role of International Collaborations and Partnerships
7.5.3 Culturally Sensitive Approaches to Patient Care
7.6 Case Studies of Successful Interventions
7.6.1 Examples of Resource-Appropriate Oncology Programs
7.6.1.1 Rwanda’s National Cancer Control Program
7.6.1.2 India’s Tata Memorial Centre’s Hub-and-Spoke Model
7.6.1.3 Zambia’s Cervical Cancer Prevention Program
7.6.2 Impact of Public-Private Partnerships
7.6.2.1 Expanding Access through Collaborative Financing
7.6.2.2 Pharmaceutical Partnerships for Drug
7.6.3 Lessons Learned from Global Initiatives
7.7 Policy and Advocacy
7.7.1 Role of Governments and International Organizations
7.7.2 Policy Recommendations for Equitable Cancer Care
7.7.3 Addressing Financial Inequities and Promoting Structured Insurance
7.7.4 Funding Mechanisms and Sustainable Investments
7.8 Future Directions in Global Oncology
7.8.1 Emerging Technologies and Their Potential Impact
7.8.2 Integration of Precision Medicine in Low-Resource Settings
7.8.3 Vision for a Collaborative and Equitable Future
7.9 Limitations of the Study
7.10 Conclusion
Acknowledgments
References
8. An Optimization Strategy for Deep Learning Techniques to Enhance Clinical-Grade Bone Tumor Identification and Classification in Radiography
B. Omkar Lakshmi Jagan, Tsair-Fwu Lee, Sabuz Ahmed and N. Thirupathi Rao
8.1 Introduction
8.2 Literature Review
8.3 Methodology
8.3.1 Data Collection and Preparation
8.3.1.1 Image Loading and Labeling
8.3.1.2 Resizing and Normalization
8.3.2 Data Augmentation
8.3.2.1 Common Augmentation Techniques
8.3.2.2 Advanced Augmentation with a One-Class Generative Adversarial Network
8.3.3 Data Splitting
8.3.4 Model Selection and Architecture
8.3.4.1 Base Model: EfficientNetV2B3 with Transfer Learning
8.3.4.2 Custom Classification Head
8.3.4.3 Conceptual Integration of Xception and Vision Transformer Elements
8.3.5 Model Training
8.3.5.1 Compilation
8.3.5.2 Training Strategy
8.3.5.3 Callbacks
8.3.6 Model Evaluation
8.3.7 Hyperparameter Tuning
8.3.8 Tools and Libraries
8.3.9 Evaluation Metrics
8.4 Results and Discussion
8.4.1 Experimental Results: EfficientNetV2B3 Performance
8.4.2 Experimental Results: Xception Model Performance
8.4.3 Comparison and Discussion
8.4.4 Impact of Data Augmentation
8.4.5 Impact of Hybrid Architectural Concepts
8.4.6 Clinical Implications
8.4.7 Limitations
8.5 Conclusion and Future Work
References
9. Technological Innovations in Radiation and Surgical Oncology
Md. Akhtaruzzaman, Mohammad Ullah Shemanto, Martin A. Ebert, Tanvir Ahmed, Bhaskar Chakraborty, Md. Arifur Rahman, Md. Abdul Mannan, Md. Shariful Islam, Aditi Paul Chowdhury and A.F.M. Anwar Hossain
9.1 Introduction
9.1.1 Scope and Methodology
9.2 Advancements in Radiation Oncology
9.2.1 Modern Radiotherapy Techniques
9.2.2 Innovations in Imaging and Guidance
9.2.3 Synthesis and Future Directions
9.2.4 Emerging Modalities in Radiation Oncology
9.2.4.1 From Photons to Protons: Why Particle Therapy Matters
9.2.4.2 Clinical Gains and Practical Limits
9.2.4.3 FLASH Radiotherapy – an Ultrahigh Dose Rate Revolution
9.2.4.4 Molecular Targeted Radiotherapies
9.2.5 Integration of Artificial Intelligence in Radiation Oncology
9.2.5.1 Foundations of Artificial Intelligence and Deep Learning
9.2.5.2 Artificial Intelligence-Assisted Treatment Planning
9.2.5.3 Image Segmentation and Contouring
9.2.5.4 Predictive Analytics and Adaptive Decision Making
9.2.5.5 Challenges and Ethical Considerations
9.2.5.6 Future Directions
9.3 Technological Advancements in Surgical Oncology
9.3.1 Robotic-Assisted Surgery
9.3.2 Intraoperative Imaging Techniques
9.3.3 Three-Dimensional Printing in Surgical Oncology
9.3.4 Augmented Reality and Artificial Intelligence-Driven Decision Support
9.4 Multimodal Integration of Radiotherapy and Surgery in Complex Oncology
9.4.1 Combining Radiotherapy with Surgical Interventions for Complex Cases
9.4.2 Case Studies Demonstrating Improved Outcomes
9.4.3 Synergistic Mechanisms and Implementation Challenges
9.5 Challenges and Barriers to Implementation
9.5.1 High Costs and Accessibility in Low-Resource Settings
9.5.2 Need for Specialized Training and Multidisciplinary Collaboration
9.5.3 Ethical and Regulatory Considerations in Artificial Intelligence-Driven Oncology
9.6 Future Directions in Oncology
9.6.1 Emerging Technologies on the Horizon
9.6.2 Emphasis on Personalized and Precision-Based Approaches
9.6.3 Global Collaborations Accelerating Progress
9.7 Conclusion
References
10. Organoids for Cancer Modeling: Bridging the Gap Between
Bench Research and Clinical Applications
Sofi Imtiyaz Ali, Showkat Ul Nabi, Baby Summuna, Gulzar Ahmed Rather, Majid Shafi Kawsoo, Ibraq Khurshid, Mushatq Ahmad Lone, Pedro Brandão and Pedro Fonte
10.1 Introduction
10.2 Three-Dimensional Culture Methods for Organoid Production
10.2.1 Suspension Culture System
10.2.2 Organoid Fabrication Using Extracellular Matrix Scaffolds
10.2.3 Spinning Bioreactors Method
10.2.4 Air-Liquid Interface Method
10.3 Types of Organoids
10.3.1 Lung Organoids
10.3.1.1 Organoids for Pulmonary Disease Modeling
10.3.2 Intestinal Organoids
10.3.2.1 Organoids for Intestinal Disease Modeling
10.3.3 Liver Organoids
10.3.3.1 Organoids for Hepatic Disease Modeling
10.3.4 Gastric Organoids
10.3.4.1 Organoids for Gastric Disease Modeling
10.3.5 Brain Organoids
10.3.5.1 Organoids for Cerebral Disease Modeling
10.3.6 Kidney Organoids
10.3.6.1 Organoids for Kidney Disease Modeling
10.3.7 Pancreatic Organoids
10.3.7.1 Organoids for Pancreatic Disease Modeling
10.3.8 Prostate Organoids
10.3.9 Mammary Gland Organoids
10.3.10 Fallopian Tube and Ovarian Organoids
10.3.11 Salivary Gland Organoids
10.3.12 Retinal Organoids
10.4 Other Applications of Organoid Technology
10.4.1 Tissue Development
10.4.2 Genetic Diseases
10.4.3 Organoids in Drug Development
10.5 Limitations of Organoid Technology
10.6 Conclusions
Acknowledgements
References
11. Genomics and Proteomics: Unlocking the Molecular Basis
of Cancer
Pritee Chunarkar-Patil, Sahar Surve, Mohmmad Kaleem, Rupesh V. Chikhale, Kashif Ahmad and Pranjal Pradeep Salunkhe
11.1 Introduction
11.1.1 The Role of Genomics and Proteomics in Cancer Research
11.1.2 Historical Perspectives and Technological Advancements
11.1.3 The Importance of Molecular Profiling in Cancer
11.2 Genomics in Cancer Research
11.2.1 Overview of Genomic Technologies
11.2.1.1 Next-Generation Sequencing
11.2.1.2 Single-Cell Genomics
11.2.2 Identification of Cancer-Associated Genes and Mutations
11.2.2.1 Driver Mutations vs. Passenger Mutations
11.2.2.2 Common Mutations in Major Cancers
11.2.3 Tumor Classification Based on Molecular Profiles
11.2.3.1 Molecular Subtypes of Breast Cancer
11.2.3.2 Genomic Biomarkers of Therapeutic Relevance
11.2.3.3 Genomic Classification of Lung Cancer
11.2.3.4 Integration into Clinical Practice
11.2.3.5 Lung Cancer Molecular Classification and Targeted Therapies
11.2.4 Applications of Genomics in Targeted Therapies
11.2.4.1 Case Example: FoundationOne CDx
11.2.4.2 Case Study: Epidermal Growth Factor Receptor Inhibitors in Non-Small Cell Lung Cancer
11.2.4.3 Key Advances and Future Directions in Therapy Targeted by Epidermal Growth Factor Receptors
11.3 Overview of Proteomic Technologies
11.3.1 Mass Spectrometry
11.3.2 Protein Microarrays
11.3.2.1 Applications in Oncology
11.3.2.2 Advancements in Protein Microarrays
11.3.2.3 Clinical Applications of Advanced Protein Microarrays
11.3.3 Protein Expression Patterns in Cancer
11.3.3.1 Altered Protein Signatures in Tumor Cells
11.3.3.2 Post-Translational Modifications
11.3.4 Proteomics and the Tumor Microenvironment
11.3.4.1 Role of Stromal Cells
11.3.4.2 Extracellular Matrix Remodeling
11.3.5 Proteomics in Biomarker Discovery
11.3.5.1 Early Detection Biomarkers
11.3.5.2 Prognostic and Predictive Biomarkers
11.3.5.3 Proteomics Platforms Used for Prognostic Studies
11.3.5.4 Clinical Applications and Emerging Approaches in Cancer Proteomics
11.4 Integration of Genomics and Proteomics
11.4.1 Multi-Omics Approaches in Cancer Research
11.4.1.1 Combining Genomics, Proteomics, and Metabolomics
11.4.1.2 Systems Biology and Network Analysis
11.4.2 Case Studies of Integrated Omics in Cancer
11.4.3 Challenges and Opportunities in Multi-Omics Integration
11.5 Advances in Technology and Methodology
11.5.1 Single-Cell Omics
11.5.1.1 Single-Cell Genomics and Proteomics
11.5.1.2 Applications in Tumor Heterogeneity
11.5.2 Artificial Intelligence and Machine Learning in Omics
11.5.2.1 Predictive Modeling for Biomarker Discovery
11.5.2.2 Artificial Intelligence-Driven Drug Target Identification
11.5.2.3 Case Studies of Artificial Intelligence-Discovered Targets
11.5.2.4 Challenges and Future Directions
11.5.2.5 Emerging Trends
11.5.3 Emerging Technologies
11.5.3.1 Spatial Transcriptomics and Proteomics
11.5.3.2 Liquid Biopsy and Circulating Tumor Deoxyribonucleic Acid
11.6 Clinical Applications and Translational Research
11.6.1 Genomics and Proteomics in Early Cancer Detection
11.6.1.1 Multi-Cancer Early Detection Tests
11.6.1.2 Proteomic Markers for Early Diagnosis
11.6.2 Precision Oncology and Targeted Therapies
11.6.2.1 Genomic-Driven Therapies
11.6.2.2 Proteomics in Predicting Drug Response
11.6.3 Immunotherapy and Omics
11.6.3.1 Immune Biomarkers
11.6.3.2 Proteomics in Immune Evasion
11.7 Challenges and Future Directions
11.7.1 Ethical and Privacy Issues in Handling Genomic Information
11.7.2 Technological Constraints in Current Approaches
11.7.3 Advances Shaping the Future of Precision Oncology
11.7.4 Global Collaboration and Data Exchange
11.8 Conclusion
11.8.1 Summary of Major Insights
11.8.2 The Role of Genomics and Proteomics in Cancer Advances
11.8.3 Looking Ahead: Bridging Research and Clinical Care
References
12. Advanced Nanocarriers and Smart Systems for Targeted
Cancer Therapy and Diagnosis
Paola Pirela, Girish Kumar, Tarun Virmani, Priti Choudhary, Geeta Mehlawat, Sabya Sachi Das, Bhavana Joshi, Ana Raquel Lima, Sofia O. D. Duarte and Pedro Fonte
12.1 Introduction
12.2 Physiopathology of Cancer
12.3 Nanocarriers for Cancer Treatment
12.4 Design and Synthesis of Nanotherapeutics
12.4.1 Lipid Nanocarriers
12.4.1.1 Liposomes
12.4.1.2 Solid Lipid Nanoparticles
12.4.1.3 Nanostructured Lipid Carriers
12.4.1.4 Nanoemulsions
12.4.2 Polymeric Nanocarriers
12.4.2.1 Polymeric Nanoparticles
12.4.2.2 Hybrid Nanoparticles
12.4.2.3 Dendrimers
12.4.2.4 Polymeric Micelles
12.4.3 Inorganic Nanocarriers
12.4.3.1 Metallic Nanoparticles
12.4.3.2 Mesoporous Silica Nanoparticles
12.4.3.3 Carbon Nanotubes
12.5 Targeting Mechanisms
12.5.1 Passive Targeting
12.5.2 Active Targeting
12.6 Diagnostic Applications
12.6.1 Nano-Imaging Agents
12.6.2 Quantum Dots and Contrast Agents
12.6.3 Nano-Based Biosensors
12.7 Conclusion and Future Perspectives
Acknowledgements
References
13. Nanotechnology-Based Strategies for Targeting Ion Channels: Toward Anti-Metastatic Cancer Therapy
Sanika Deshmukh, Suman Seervi, Sandip Ghosh, Sacchit Patel, Amit Sanghvi, Naisargi Modha, Biswarup Basu, Shuvomoy Banerjee and Juni Banerjee
13.1 Introduction
13.2 Types of Major Ion Channels in Human Malignancies
13.2.1 Voltage-Gated Ion Channels
13.2.1.1 L-Type Channels
13.2.1.2 N-Type Channels
13.2.1.3 P-Type Channels
13.2.1.4 Q-Type Channels
13.2.1.5 R-Type Channels
13.2.1.6 T-Type Channels
13.2.2 Voltage Potassium-Gated Ion Channel
13.2.2.1 Ether-a-Go-Go
13.2.2.2 Kv1.3 Channel
13.2.2.3 KCNQ
13.2.2.4 Calcium-Activated Potassium Ion Channel Subfamily
13.2.3 Transient Receptor Potential Channels
13.2.3.1 Transient Receptor Potential Vanilloid
13.2.3.2 Transient Receptor Potential Ankyrin
13.2.3.3 Transient Receptor Potential Melastatin
13.2.3.4 Transient Receptor Potential Canonical
13.2.3.5 Transient Receptor Potential Polycystin
13.2.4 Voltage-Gated Sodium Channels
13.2.4.1 α Subunits
13.2.4.2 β Subunit
13.2.5 Aquaporin
13.2.6 Piezo Channels
13.3 Mechanistic Overview of Ion Channels in Cancer Metastasis
13.3.1 Ionic Mechanisms for Cellular Migration Machinery
13.3.1.1 Cross-Talk of Ion Channels to Cancer Metastasis
13.4 Ion Channels as a Novel Candidate for Fighting Against Aggressive and Metastatic Cancers
13.5 Nanotechnology-Based Strategies and Models to Target Ion Channels in Cancer Research
13.6 Discussion
Acknowledgement
References
14. Three-Dimensional Bioprinting and Tumor Organoids
Yuvaraj Sivamani, Afrah Kounain P., Lamees Shekabba Haleyangdi and Sumitha Elayaperumal
14.1 Introduction
14.1.1 The Term Organoids
14.1.2 Objectives
14.1.3 Bioprinting Techniques
14.2 Materials and Strategies for Bioprinting
14.2.1 Bioinks
14.2.2 Alginate-Based Bioinks
14.2.3 Gelatin-Based Bioinks
14.2.4 Collagen-Based Bioinks
14.2.5 Hyaluronic Acid-Based Bioinks
14.2.6 Polyethylene Glycol-Based Bioinks
14.3 Extrusion-Based Bioprinting
14.3.1 Inkjet-Based Bioprinting
14.3.2 Stereolithography-Based Bioprinting
14.4 Emerging Three-Dimensional Bioprinting Technologies
14.4.1 Laser-Induced Forward Transfer
14.4.2 Volumetric Bioprinting via Tomographic Imaging
14.4.3 Electrospray Bioprinting
14.4.4 Plasma-Enhanced Bioprinting
14.4.5 Magnetic-Assisted Bioprinting
14.4.6 Acoustic Bioprinting
14.5 Integration of Three-Dimensional Bioprinting with Tumor Organoids
14.5.1 Strategies for Bioprinting Tumor Organoids
14.5.1.1 Scaffold-Based Bioprinting
14.5.1.2 Scaffold-Free Bioprinting
14.5.1.3 Support‑Bath Embedded (Hybrid) Methods
14.5.2 Bioprinted Tumor Microenvironment Recreation
14.5.3 Tumor Heterogeneity and Spatial Patterning Using Bioprinting
14.5.4 Spatial Patterning and Tumor Microenvironment Reconstruction
14.5.5 Examples of Cancer Types Bioprinted as Organoids – Breast, Glioblastoma, and Colorectal Cancers
14.5.5.1 Breast Cancer
14.5.5.2 Glioblastoma
14.5.5.3 Colorectal
14.6 Applications in Cancer Research and Precision Medicine
14.6.1 High-Throughput Drug Screening and Chemotherapy Testing
14.6.2 Metastasis and Invasion Studies
14.6.3 Personalized Medicine Using Patient-Derived Tumoroids
14.6.4 Tumor-on-Chip and Organoid-on-Chip Integrations
14.7 Technical Challenges and Limitations
14.7.1 Bioink Compatibility with Cancer Cell Phenotypes
14.7.1.1 Reproducibility and Scalability Issues
14.7.1.2 Maintaining Long-Term Viability and Growth
14.7.1.3 Modeling Immune-Tumor Interactions In Vitro
14.7.1.4 Ethical and Regulatory Challenges in Clinical Translation
14.8 Conclusion
References
15. Smart Precision Healthcare: Artificial Intelligence of Things and Big Data Solutions for Oncology and Beyond
Rajesh Dey, Rupali Atul Mahajan, Mudassir Khan, Shaik Karimullah and Barga Mohammed Mujahid
15.1 Introduction to Smart Precision Healthcare
15.1.1 Definition and Scope of Smart Precision Healthcare
15.2 Artificial Intelligence in Healthcare
15.2.1 Applications of Artificial Intelligence in Oncology
15.3 Internet of Things in Healthcare
15.3.1 Advancements and Applications of the Internet of Things in Healthcare
15.4 Big Data Analytics in Healthcare
15.4.1 Importance and Impact of Big Data in Oncology
15.5 Challenges and Future Directions in Smart Precision Healthcare
References
16. Advancements in Lung Cancer Detection and Innovative
Techniques for Enhanced Diagnosis: A Review
Sheik Jamil Ahmed, Vishwanath Y. and Saira Banu Atham
16.1 Introduction
16.1.1 Lung Cancer Detection Using Machine Learning and Deep Learning Methodology
16.1.2 Related Studies
16.2 Methods and Materials
16.2.1 Datasets
16.2.2 Preprocessing
16.2.3 Feature Extraction and Selection
16.3 Advancements in Imaging Technologies for Lung Cancer Detection
16.4 Artificial Intelligence in Lung Cancer Detection
16.4.1 Generative Artificial Intelligence Approaches in Lung Cancer Detection
16.4.2 Enhancing Lung Cancer Diagnosis and Treatment
16.4.3 Integration with Deep Learning for Nodule Detection
16.4.4 Magnetic Resonance Imaging in Radiation Treatment Planning
16.5 Year-Wise Analysis of Detection Methods for Lung Tumor Detection Using CT/PET CT Scans and MRI
16.6 Challenges and Future Directions
16.7 Conclusion
References
17. Artificial Intelligence and Deep Learning in Medical Imaging: Enhancing Oncology Diagnosis and Treatment
Amar Saraswat, N. Ria, Neeta Sharma, Yojna Arora, Sarita and Saurav Mallik
17.1 Introduction
17.2 Background
17.2.1 Artificial Intelligence in Healthcare
17.2.2 Deep Learning in Healthcare
17.2.3 Current Climate of Medical Imaging
17.2.3.1 Proven Methods and Challenges
17.2.3.2 Relevance of Deep Learning Architectures in Medical Imaging
17.3 Applications in Medical Imaging
17.3.1 Automating Image Interpretation
17.3.1.1 Detection of Lesion
17.3.1.2 Segmentation
17.3.1.3 Quantification
17.3.2 Diagnostic Assistance
17.3.2.1 Flagging Suspicious Findings
17.3.2.2 Differential Diagnoses
17.3.2.3 Risk Stratification
17.3.2.4 Case Studies
17.3.3 Enabling Personalized Treatment
17.3.3.1 Predicting Disease Progression and Response to Treatment
17.3.3.2 Personalized Treatment Plans
17.4 Challenges
17.4.1 Data Privacy
17.4.2 Model Explainability
17.4.3 Regulatory Hurdles
17.4.4 Ethical Considerations
17.5 Conclusion
References
Index
Back to Top
Preface
1. Artificial Intelligence and Machine Learning in Cancer Diagnostics
Debosree Ghosh and Sutapa Datta
1.1 Introduction
1.2 Methodology
1.3 Artificial Intelligence and Machine Learning in the Diagnosis of Lung Cancer
1.4 Artificial Intelligence and Machine Learning in the Diagnosis of Skin Cancer
1.5 Artificial Intelligence and Machine Learning in the Diagnosis of Brain Cancer
1.6 Artificial Intelligence and Machine Learning in the Diagnosis of Breast Cancer
1.7 Artificial Intelligence and Machine Learning in the Diagnosis of Prostate Cancer
1.8 Artificial Intelligence and Machine Learning in Cervical
Cancer
1.9 Artificial Intelligence and Machine Learning in Cancer Immunotherapy
1.10 Limitations and Future Prospects of the Application of Artificial Intelligence and Machine Learning in Cancer Diagnosis
1.11 Conclusion
Acknowledgment
References
2. Enhanced Automatic Lung Segmentation Using Novel Modified Adaptive Granular Multilevel Rough Entropy Threshold Computing
S. Nivetha, H. Hannah Inbarani and Mudassir Khan
2.1 Introduction
2.1.1 Research Motivation
2.1.2 Research Objective
2.1.3 Research Challenges
2.1.4 Advantages of the Novel Modified Adaptive Granular Multilevel Rough Entropy Threshold
2.1.5 Research Contributions
2.1.6 Research Justification
2.1.7 Stronger Justification of Chosen Methodology
2.2 Summary of Existing Studies
2.3 Methods and Materials
2.3.1 Preprocessing
2.4 Ground Truth: Lung Mask Extraction
2.5 Proposed Novel Modified Adaptive Granular Multilevel Rough Entropy-Based Threshold Computing
2.6 Simulation Outcomes and Discussion
2.7 Evaluation Measures of Segmentation
2.8 Conclusion
References
3. Feature Optimization and Classification Performance for Prostate Cancer: Deep Learning vs. Machine Learning Approaches
Asmita Ray, Mostaque Md. Morshedur Hassan, Munsifa Firdaus Khan Barbhuyan, Md. Faruqul Islam and Mudassir Khan
3.1 Introduction
3.2 Related Work
3.3 Clinical Background and Epidemiology
3.3.1 Anatomy and Role of the Prostate Gland
3.3.2 Risk Factors: Age, Genetics, and Lifestyle
3.4 Prostate Cancer Screening Methods
3.4.1 Prostate-Specific Antigen Test
3.4.2 Digital Rectal Examination
3.4.3 Emerging Diagnostic Tools
3.5 Materials and Methods
3.5.1 Dataset
3.5.2 Feature Extraction
3.5.3 Texture Features
3.6 Machine Learning Method
3.7 Deep Learning Method
3.8 Proposed Methodology
3.9 Performance Measures
3.9.1 Receiver Operating Characteristic Curve
3.9.2 Area Under the Receiver Operating Characteristic Curve
3.10 Result and Discussion
3.11 Conclusion and Future Work
References
4. The Evolution of Oncology in the 21st Century
Lakshmi Shree P., Ananya B., Yuvaraj Sivamani and Sumitha Elayaperumal
4.1 Introduction
4.1.1 Cancer and Its Overview
4.1.2 Types of Cancer
4.1.2.1 Solid Cancers
4.1.2.2 Blood Cancers
4.1.2.3 Mixed Cancers
4.1.3 History of Cancer
4.1.3.1 Origin of the Word Cancer
4.1.4 Oncology
4.1.4.1 Medical Oncology
4.1.4.2 Surgical Oncology
4.1.4.3 Radiation Oncology
4.2 Discoveries from 2000–2005
4.2.1 First Molecularly Targeted Treatment Approved: Gleevec
4.2.2 Mechanism of Action Imatinib
4.2.3 Scientific Background of Imatinib
4.2.4 Clinical Trials of Imatinib
4.2.5 Case Study
4.2.6 Future Implication of Imatinib
4.2.7 Broader Implications of Imatinib
4.3 Discoveries from 2006–2010
4.3.1 Thalidomide
4.3.2 Mechanism of Action Thalidomide
4.3.3 Scientific Background of Thalidomide
4.3.4 Clinical Trials of Thalidomide
4.3.5 Case Studies
4.3.6 Future Implication of Thalidomide
4.3.7 Broader Implications of Thalidomide
4.4 Discoveries from 2011–2015
4.4.1 Cytotoxic T-Lymphocyte-Associated Protein 4 Inhibitor
4.4.2 Mechanism of Action Ipilimumab
4.4.3 Scientific Background of Ipilimumab
4.4.4 Clinical Trials of Ipilimumab
4.4.5 Future Implication of Ipilimumab
4.4.6 Broader Implication of Ipilimumab
4.5 Discoveries from 2016–2020
4.5.1 Methods of Precision Oncology
4.5.1.1 Tumor-Agnostic Therapy
4.5.1.2 Immunotherapy
4.5.2 Diagnostics
4.5.2.1 Liquid Biopsy
4.5.2.2 Artificial Intelligence in Cancer Diagnosis
4.6 Discoveries from 2021–2025
4.6.1 Advancement of Cancer Care with the Help of Artificial Intelligence
4.6.2 Drugging the “Undruggable” and Other Therapies
4.7 Ethics and Policies on Cancer Throughout the 21st Century
4.7.1 Autonomy and Informed Consent
4.7.2 Beneficence and Non-Maleficence
4.7.3 Justice and Equity
4.7.4 Truth-Telling and Cultural Competence
4.7.5 Professionalism and Marketing Ethics
4.7.6 Artificial Intelligence Chatbots, and Post-2020 Ethical Frontiers
4.7.7 Education and Continuing Competence
4.8 Conclusion
References
5. Emerging Therapeutics: Nanomedicine, Ribonucleic Acid
Therapies, and Beyond
Maryam, Aasma Bhatti, Muhammad Farrukh Tahir, Zunaira Munir and Qurat ul Ain Babar
5.1 Introduction
5.1.1 Overview of Emerging Cancer Therapeutics
5.1.2 The Need for Novel Approaches beyond Conventional Chemotherapy
5.1.3 Role of Precision Medicine in Oncology
5.2 Nanomedicine in Oncology
5.2.1 Principles of Nanomedicine for Cancer Therapy
5.2.2 Types of Nanoparticles
5.2.3 Tumor Targeting and Controlled Drug Release
5.2.4 Clinical Applications and Approved Nanodrugs
5.3 Ribonucleic Acid-Based Therapeutics
5.3.1 Mechanism of Ribonucleic Acid-Based Treatments
5.3.2 Messenger Ribonucleic Acid Vaccines and Immunotherapy
5.3.3 Antisense Oligonucleotides
5.3.4 Challenges in Ribonucleic Acid Therapy Delivery and Stability
5.4 Beyond Nanomedicines and Ribonucleic Acid: Next-Generation Therapies
5.4.1 Gene Editing Technologies (CRISPR/Cas9 in Cancer Therapy)
5.4.2 Exosome-Based Drug Delivery Systems
5.4.3 Biomimetic and Hybrid Nanoplatforms
5.4.4 Artificial Intelligence-Driven Drug Design and Therapeutic Optimization
5.5 Clinical Translation and Challenges
5.5.1 Regulatory Landscape and Approval Pathways
5.5.2 Safety Concerns and Immunogenicity Issues
5.5.3 Overcoming Therapeutic Resistance and Tumor Heterogeneity
5.5.4 Integration with Conventional Cancer Treatments
5.6 Future Perspectives and Conclusion
5.6.1 The Evolving Role of Precision Oncology
5.6.2 Synergistic Approaches Combining Multiple Modalities
5.6.3 Ethical Considerations and Patient-Centered Care in Advanced Therapeutics
5.6.4 Potential for Artificial Intelligence-Enhanced Personalization in Cancer Therapy
5.7 Conclusion
References
6. Blockchain in Oncology: Enhancing Data Security and Patient Privacy
R. Yuvaraj, N. Vishnuvarthan, N. Arisankar, P. Saranraj and M. Manigandan
6.1 Introduction
6.2 Blockchain: An Overview
6.3 Blockchain Technology and Architecture
6.3.1 Blockchain Components
6.3.2 Types of Blockchain
6.3.2.1 Data Contributors
6.3.2.2 Private Blockchains
6.3.2.3 Access Control
6.3.2.4 Permissioned Blockchains
6.3.2.5 Participant Equality
6.4 Blockchain Technology in Healthcare
6.4.1 Intelligent Healthcare System for the Patient
6.4.2 Enhancing Patient Data Privacy
6.4.3 Benefits of Blockchain in Maintaining Health Records
6.4.4 Blockchain Technology in Medical Healthcare
6.5 Technology for Building Smart Healthcare
6.6 Benefits and Drawbacks of Blockchains
6.6.1 Benefits of Blockchain
6.6.2 Drawbacks of Blockchain
6.7 Application of Blockchain in Healthcare
6.8 Challenges and Future Research Directions
6.9 Limitations of the Study
6.10 Conclusion
References
7. Global Oncology: Addressing Cancer Burden in Low-Resource Settings
Farhana Faruque Zerin, A. K. M. Shafiul Kadir, Jannatul Ferdause, M. Juel Khondakar, Rukaiya Akhter, Tasfi Jahan Tina, Mohammad Ullah Shemanto and Jim Rahman
7.1 Introduction
7.1.1 Overview of the Global Cancer Burden
7.1.2 Disparities in Cancer Care: High-Income vs. Low-Resource Settings
7.1.2.1 Low Public Understanding and Misconceptions
7.1.2.2 Insufficient Healthcare Funding and Infrastructure
7.1.2.3 Oncology Workforce
7.1.2.4 Lack of Access to Cancer Screening and Early Detection
7.1.2.5 Diagnostic Delays
7.1.2.6 Treatment Abandonment and Deficiencies in Palliative Care
7.1.3 Objectives and Scope of the Chapter
7.2 Cancer Epidemiology in Low-Resource Settings
7.2.1 Trends in Cancer Incidence and Mortality
7.2.2 Regional Variations and Unique Cancer Profiles
7.2.3 Risk Factors and Lifestyle Influences
7.3 Challenges in the Delivery of Oncology Services in Low-Resource Settings
7.3.1 Inadequate Infrastructure and Resource Limitations
7.3.2 Workforce Shortages and Training Gaps
7.3.3 Shortage of Technical Support Personnel
7.3.4 Economic and Policy Barriers to Access
7.3.5 Sociocultural Determinants and Their Impact
7.3.6 Lack of Structured Health Insurance and Organized Screening Programs
7.3.7 The Imperative of Prevention and Early Detection
7.4 Innovative Solutions for Cancer Care
7.4.1 Affordable Diagnostic Tools and Technologies
7.4.2 Mobile Health Platforms and Telemedicine
7.4.3 Task-Shifting and Community Health Worker Models
7.4.4 Decentralization of Oncology Services
7.5 Capacity Building and Education
7.5.1 Training Programs for Oncology Professionals
7.5.2 Role of International Collaborations and Partnerships
7.5.3 Culturally Sensitive Approaches to Patient Care
7.6 Case Studies of Successful Interventions
7.6.1 Examples of Resource-Appropriate Oncology Programs
7.6.1.1 Rwanda’s National Cancer Control Program
7.6.1.2 India’s Tata Memorial Centre’s Hub-and-Spoke Model
7.6.1.3 Zambia’s Cervical Cancer Prevention Program
7.6.2 Impact of Public-Private Partnerships
7.6.2.1 Expanding Access through Collaborative Financing
7.6.2.2 Pharmaceutical Partnerships for Drug
7.6.3 Lessons Learned from Global Initiatives
7.7 Policy and Advocacy
7.7.1 Role of Governments and International Organizations
7.7.2 Policy Recommendations for Equitable Cancer Care
7.7.3 Addressing Financial Inequities and Promoting Structured Insurance
7.7.4 Funding Mechanisms and Sustainable Investments
7.8 Future Directions in Global Oncology
7.8.1 Emerging Technologies and Their Potential Impact
7.8.2 Integration of Precision Medicine in Low-Resource Settings
7.8.3 Vision for a Collaborative and Equitable Future
7.9 Limitations of the Study
7.10 Conclusion
Acknowledgments
References
8. An Optimization Strategy for Deep Learning Techniques to Enhance Clinical-Grade Bone Tumor Identification and Classification in Radiography
B. Omkar Lakshmi Jagan, Tsair-Fwu Lee, Sabuz Ahmed and N. Thirupathi Rao
8.1 Introduction
8.2 Literature Review
8.3 Methodology
8.3.1 Data Collection and Preparation
8.3.1.1 Image Loading and Labeling
8.3.1.2 Resizing and Normalization
8.3.2 Data Augmentation
8.3.2.1 Common Augmentation Techniques
8.3.2.2 Advanced Augmentation with a One-Class Generative Adversarial Network
8.3.3 Data Splitting
8.3.4 Model Selection and Architecture
8.3.4.1 Base Model: EfficientNetV2B3 with Transfer Learning
8.3.4.2 Custom Classification Head
8.3.4.3 Conceptual Integration of Xception and Vision Transformer Elements
8.3.5 Model Training
8.3.5.1 Compilation
8.3.5.2 Training Strategy
8.3.5.3 Callbacks
8.3.6 Model Evaluation
8.3.7 Hyperparameter Tuning
8.3.8 Tools and Libraries
8.3.9 Evaluation Metrics
8.4 Results and Discussion
8.4.1 Experimental Results: EfficientNetV2B3 Performance
8.4.2 Experimental Results: Xception Model Performance
8.4.3 Comparison and Discussion
8.4.4 Impact of Data Augmentation
8.4.5 Impact of Hybrid Architectural Concepts
8.4.6 Clinical Implications
8.4.7 Limitations
8.5 Conclusion and Future Work
References
9. Technological Innovations in Radiation and Surgical Oncology
Md. Akhtaruzzaman, Mohammad Ullah Shemanto, Martin A. Ebert, Tanvir Ahmed, Bhaskar Chakraborty, Md. Arifur Rahman, Md. Abdul Mannan, Md. Shariful Islam, Aditi Paul Chowdhury and A.F.M. Anwar Hossain
9.1 Introduction
9.1.1 Scope and Methodology
9.2 Advancements in Radiation Oncology
9.2.1 Modern Radiotherapy Techniques
9.2.2 Innovations in Imaging and Guidance
9.2.3 Synthesis and Future Directions
9.2.4 Emerging Modalities in Radiation Oncology
9.2.4.1 From Photons to Protons: Why Particle Therapy Matters
9.2.4.2 Clinical Gains and Practical Limits
9.2.4.3 FLASH Radiotherapy – an Ultrahigh Dose Rate Revolution
9.2.4.4 Molecular Targeted Radiotherapies
9.2.5 Integration of Artificial Intelligence in Radiation Oncology
9.2.5.1 Foundations of Artificial Intelligence and Deep Learning
9.2.5.2 Artificial Intelligence-Assisted Treatment Planning
9.2.5.3 Image Segmentation and Contouring
9.2.5.4 Predictive Analytics and Adaptive Decision Making
9.2.5.5 Challenges and Ethical Considerations
9.2.5.6 Future Directions
9.3 Technological Advancements in Surgical Oncology
9.3.1 Robotic-Assisted Surgery
9.3.2 Intraoperative Imaging Techniques
9.3.3 Three-Dimensional Printing in Surgical Oncology
9.3.4 Augmented Reality and Artificial Intelligence-Driven Decision Support
9.4 Multimodal Integration of Radiotherapy and Surgery in Complex Oncology
9.4.1 Combining Radiotherapy with Surgical Interventions for Complex Cases
9.4.2 Case Studies Demonstrating Improved Outcomes
9.4.3 Synergistic Mechanisms and Implementation Challenges
9.5 Challenges and Barriers to Implementation
9.5.1 High Costs and Accessibility in Low-Resource Settings
9.5.2 Need for Specialized Training and Multidisciplinary Collaboration
9.5.3 Ethical and Regulatory Considerations in Artificial Intelligence-Driven Oncology
9.6 Future Directions in Oncology
9.6.1 Emerging Technologies on the Horizon
9.6.2 Emphasis on Personalized and Precision-Based Approaches
9.6.3 Global Collaborations Accelerating Progress
9.7 Conclusion
References
10. Organoids for Cancer Modeling: Bridging the Gap Between
Bench Research and Clinical Applications
Sofi Imtiyaz Ali, Showkat Ul Nabi, Baby Summuna, Gulzar Ahmed Rather, Majid Shafi Kawsoo, Ibraq Khurshid, Mushatq Ahmad Lone, Pedro Brandão and Pedro Fonte
10.1 Introduction
10.2 Three-Dimensional Culture Methods for Organoid Production
10.2.1 Suspension Culture System
10.2.2 Organoid Fabrication Using Extracellular Matrix Scaffolds
10.2.3 Spinning Bioreactors Method
10.2.4 Air-Liquid Interface Method
10.3 Types of Organoids
10.3.1 Lung Organoids
10.3.1.1 Organoids for Pulmonary Disease Modeling
10.3.2 Intestinal Organoids
10.3.2.1 Organoids for Intestinal Disease Modeling
10.3.3 Liver Organoids
10.3.3.1 Organoids for Hepatic Disease Modeling
10.3.4 Gastric Organoids
10.3.4.1 Organoids for Gastric Disease Modeling
10.3.5 Brain Organoids
10.3.5.1 Organoids for Cerebral Disease Modeling
10.3.6 Kidney Organoids
10.3.6.1 Organoids for Kidney Disease Modeling
10.3.7 Pancreatic Organoids
10.3.7.1 Organoids for Pancreatic Disease Modeling
10.3.8 Prostate Organoids
10.3.9 Mammary Gland Organoids
10.3.10 Fallopian Tube and Ovarian Organoids
10.3.11 Salivary Gland Organoids
10.3.12 Retinal Organoids
10.4 Other Applications of Organoid Technology
10.4.1 Tissue Development
10.4.2 Genetic Diseases
10.4.3 Organoids in Drug Development
10.5 Limitations of Organoid Technology
10.6 Conclusions
Acknowledgements
References
11. Genomics and Proteomics: Unlocking the Molecular Basis
of Cancer
Pritee Chunarkar-Patil, Sahar Surve, Mohmmad Kaleem, Rupesh V. Chikhale, Kashif Ahmad and Pranjal Pradeep Salunkhe
11.1 Introduction
11.1.1 The Role of Genomics and Proteomics in Cancer Research
11.1.2 Historical Perspectives and Technological Advancements
11.1.3 The Importance of Molecular Profiling in Cancer
11.2 Genomics in Cancer Research
11.2.1 Overview of Genomic Technologies
11.2.1.1 Next-Generation Sequencing
11.2.1.2 Single-Cell Genomics
11.2.2 Identification of Cancer-Associated Genes and Mutations
11.2.2.1 Driver Mutations vs. Passenger Mutations
11.2.2.2 Common Mutations in Major Cancers
11.2.3 Tumor Classification Based on Molecular Profiles
11.2.3.1 Molecular Subtypes of Breast Cancer
11.2.3.2 Genomic Biomarkers of Therapeutic Relevance
11.2.3.3 Genomic Classification of Lung Cancer
11.2.3.4 Integration into Clinical Practice
11.2.3.5 Lung Cancer Molecular Classification and Targeted Therapies
11.2.4 Applications of Genomics in Targeted Therapies
11.2.4.1 Case Example: FoundationOne CDx
11.2.4.2 Case Study: Epidermal Growth Factor Receptor Inhibitors in Non-Small Cell Lung Cancer
11.2.4.3 Key Advances and Future Directions in Therapy Targeted by Epidermal Growth Factor Receptors
11.3 Overview of Proteomic Technologies
11.3.1 Mass Spectrometry
11.3.2 Protein Microarrays
11.3.2.1 Applications in Oncology
11.3.2.2 Advancements in Protein Microarrays
11.3.2.3 Clinical Applications of Advanced Protein Microarrays
11.3.3 Protein Expression Patterns in Cancer
11.3.3.1 Altered Protein Signatures in Tumor Cells
11.3.3.2 Post-Translational Modifications
11.3.4 Proteomics and the Tumor Microenvironment
11.3.4.1 Role of Stromal Cells
11.3.4.2 Extracellular Matrix Remodeling
11.3.5 Proteomics in Biomarker Discovery
11.3.5.1 Early Detection Biomarkers
11.3.5.2 Prognostic and Predictive Biomarkers
11.3.5.3 Proteomics Platforms Used for Prognostic Studies
11.3.5.4 Clinical Applications and Emerging Approaches in Cancer Proteomics
11.4 Integration of Genomics and Proteomics
11.4.1 Multi-Omics Approaches in Cancer Research
11.4.1.1 Combining Genomics, Proteomics, and Metabolomics
11.4.1.2 Systems Biology and Network Analysis
11.4.2 Case Studies of Integrated Omics in Cancer
11.4.3 Challenges and Opportunities in Multi-Omics Integration
11.5 Advances in Technology and Methodology
11.5.1 Single-Cell Omics
11.5.1.1 Single-Cell Genomics and Proteomics
11.5.1.2 Applications in Tumor Heterogeneity
11.5.2 Artificial Intelligence and Machine Learning in Omics
11.5.2.1 Predictive Modeling for Biomarker Discovery
11.5.2.2 Artificial Intelligence-Driven Drug Target Identification
11.5.2.3 Case Studies of Artificial Intelligence-Discovered Targets
11.5.2.4 Challenges and Future Directions
11.5.2.5 Emerging Trends
11.5.3 Emerging Technologies
11.5.3.1 Spatial Transcriptomics and Proteomics
11.5.3.2 Liquid Biopsy and Circulating Tumor Deoxyribonucleic Acid
11.6 Clinical Applications and Translational Research
11.6.1 Genomics and Proteomics in Early Cancer Detection
11.6.1.1 Multi-Cancer Early Detection Tests
11.6.1.2 Proteomic Markers for Early Diagnosis
11.6.2 Precision Oncology and Targeted Therapies
11.6.2.1 Genomic-Driven Therapies
11.6.2.2 Proteomics in Predicting Drug Response
11.6.3 Immunotherapy and Omics
11.6.3.1 Immune Biomarkers
11.6.3.2 Proteomics in Immune Evasion
11.7 Challenges and Future Directions
11.7.1 Ethical and Privacy Issues in Handling Genomic Information
11.7.2 Technological Constraints in Current Approaches
11.7.3 Advances Shaping the Future of Precision Oncology
11.7.4 Global Collaboration and Data Exchange
11.8 Conclusion
11.8.1 Summary of Major Insights
11.8.2 The Role of Genomics and Proteomics in Cancer Advances
11.8.3 Looking Ahead: Bridging Research and Clinical Care
References
12. Advanced Nanocarriers and Smart Systems for Targeted
Cancer Therapy and Diagnosis
Paola Pirela, Girish Kumar, Tarun Virmani, Priti Choudhary, Geeta Mehlawat, Sabya Sachi Das, Bhavana Joshi, Ana Raquel Lima, Sofia O. D. Duarte and Pedro Fonte
12.1 Introduction
12.2 Physiopathology of Cancer
12.3 Nanocarriers for Cancer Treatment
12.4 Design and Synthesis of Nanotherapeutics
12.4.1 Lipid Nanocarriers
12.4.1.1 Liposomes
12.4.1.2 Solid Lipid Nanoparticles
12.4.1.3 Nanostructured Lipid Carriers
12.4.1.4 Nanoemulsions
12.4.2 Polymeric Nanocarriers
12.4.2.1 Polymeric Nanoparticles
12.4.2.2 Hybrid Nanoparticles
12.4.2.3 Dendrimers
12.4.2.4 Polymeric Micelles
12.4.3 Inorganic Nanocarriers
12.4.3.1 Metallic Nanoparticles
12.4.3.2 Mesoporous Silica Nanoparticles
12.4.3.3 Carbon Nanotubes
12.5 Targeting Mechanisms
12.5.1 Passive Targeting
12.5.2 Active Targeting
12.6 Diagnostic Applications
12.6.1 Nano-Imaging Agents
12.6.2 Quantum Dots and Contrast Agents
12.6.3 Nano-Based Biosensors
12.7 Conclusion and Future Perspectives
Acknowledgements
References
13. Nanotechnology-Based Strategies for Targeting Ion Channels: Toward Anti-Metastatic Cancer Therapy
Sanika Deshmukh, Suman Seervi, Sandip Ghosh, Sacchit Patel, Amit Sanghvi, Naisargi Modha, Biswarup Basu, Shuvomoy Banerjee and Juni Banerjee
13.1 Introduction
13.2 Types of Major Ion Channels in Human Malignancies
13.2.1 Voltage-Gated Ion Channels
13.2.1.1 L-Type Channels
13.2.1.2 N-Type Channels
13.2.1.3 P-Type Channels
13.2.1.4 Q-Type Channels
13.2.1.5 R-Type Channels
13.2.1.6 T-Type Channels
13.2.2 Voltage Potassium-Gated Ion Channel
13.2.2.1 Ether-a-Go-Go
13.2.2.2 Kv1.3 Channel
13.2.2.3 KCNQ
13.2.2.4 Calcium-Activated Potassium Ion Channel Subfamily
13.2.3 Transient Receptor Potential Channels
13.2.3.1 Transient Receptor Potential Vanilloid
13.2.3.2 Transient Receptor Potential Ankyrin
13.2.3.3 Transient Receptor Potential Melastatin
13.2.3.4 Transient Receptor Potential Canonical
13.2.3.5 Transient Receptor Potential Polycystin
13.2.4 Voltage-Gated Sodium Channels
13.2.4.1 α Subunits
13.2.4.2 β Subunit
13.2.5 Aquaporin
13.2.6 Piezo Channels
13.3 Mechanistic Overview of Ion Channels in Cancer Metastasis
13.3.1 Ionic Mechanisms for Cellular Migration Machinery
13.3.1.1 Cross-Talk of Ion Channels to Cancer Metastasis
13.4 Ion Channels as a Novel Candidate for Fighting Against Aggressive and Metastatic Cancers
13.5 Nanotechnology-Based Strategies and Models to Target Ion Channels in Cancer Research
13.6 Discussion
Acknowledgement
References
14. Three-Dimensional Bioprinting and Tumor Organoids
Yuvaraj Sivamani, Afrah Kounain P., Lamees Shekabba Haleyangdi and Sumitha Elayaperumal
14.1 Introduction
14.1.1 The Term Organoids
14.1.2 Objectives
14.1.3 Bioprinting Techniques
14.2 Materials and Strategies for Bioprinting
14.2.1 Bioinks
14.2.2 Alginate-Based Bioinks
14.2.3 Gelatin-Based Bioinks
14.2.4 Collagen-Based Bioinks
14.2.5 Hyaluronic Acid-Based Bioinks
14.2.6 Polyethylene Glycol-Based Bioinks
14.3 Extrusion-Based Bioprinting
14.3.1 Inkjet-Based Bioprinting
14.3.2 Stereolithography-Based Bioprinting
14.4 Emerging Three-Dimensional Bioprinting Technologies
14.4.1 Laser-Induced Forward Transfer
14.4.2 Volumetric Bioprinting via Tomographic Imaging
14.4.3 Electrospray Bioprinting
14.4.4 Plasma-Enhanced Bioprinting
14.4.5 Magnetic-Assisted Bioprinting
14.4.6 Acoustic Bioprinting
14.5 Integration of Three-Dimensional Bioprinting with Tumor Organoids
14.5.1 Strategies for Bioprinting Tumor Organoids
14.5.1.1 Scaffold-Based Bioprinting
14.5.1.2 Scaffold-Free Bioprinting
14.5.1.3 Support‑Bath Embedded (Hybrid) Methods
14.5.2 Bioprinted Tumor Microenvironment Recreation
14.5.3 Tumor Heterogeneity and Spatial Patterning Using Bioprinting
14.5.4 Spatial Patterning and Tumor Microenvironment Reconstruction
14.5.5 Examples of Cancer Types Bioprinted as Organoids – Breast, Glioblastoma, and Colorectal Cancers
14.5.5.1 Breast Cancer
14.5.5.2 Glioblastoma
14.5.5.3 Colorectal
14.6 Applications in Cancer Research and Precision Medicine
14.6.1 High-Throughput Drug Screening and Chemotherapy Testing
14.6.2 Metastasis and Invasion Studies
14.6.3 Personalized Medicine Using Patient-Derived Tumoroids
14.6.4 Tumor-on-Chip and Organoid-on-Chip Integrations
14.7 Technical Challenges and Limitations
14.7.1 Bioink Compatibility with Cancer Cell Phenotypes
14.7.1.1 Reproducibility and Scalability Issues
14.7.1.2 Maintaining Long-Term Viability and Growth
14.7.1.3 Modeling Immune-Tumor Interactions In Vitro
14.7.1.4 Ethical and Regulatory Challenges in Clinical Translation
14.8 Conclusion
References
15. Smart Precision Healthcare: Artificial Intelligence of Things and Big Data Solutions for Oncology and Beyond
Rajesh Dey, Rupali Atul Mahajan, Mudassir Khan, Shaik Karimullah and Barga Mohammed Mujahid
15.1 Introduction to Smart Precision Healthcare
15.1.1 Definition and Scope of Smart Precision Healthcare
15.2 Artificial Intelligence in Healthcare
15.2.1 Applications of Artificial Intelligence in Oncology
15.3 Internet of Things in Healthcare
15.3.1 Advancements and Applications of the Internet of Things in Healthcare
15.4 Big Data Analytics in Healthcare
15.4.1 Importance and Impact of Big Data in Oncology
15.5 Challenges and Future Directions in Smart Precision Healthcare
References
16. Advancements in Lung Cancer Detection and Innovative
Techniques for Enhanced Diagnosis: A Review
Sheik Jamil Ahmed, Vishwanath Y. and Saira Banu Atham
16.1 Introduction
16.1.1 Lung Cancer Detection Using Machine Learning and Deep Learning Methodology
16.1.2 Related Studies
16.2 Methods and Materials
16.2.1 Datasets
16.2.2 Preprocessing
16.2.3 Feature Extraction and Selection
16.3 Advancements in Imaging Technologies for Lung Cancer Detection
16.4 Artificial Intelligence in Lung Cancer Detection
16.4.1 Generative Artificial Intelligence Approaches in Lung Cancer Detection
16.4.2 Enhancing Lung Cancer Diagnosis and Treatment
16.4.3 Integration with Deep Learning for Nodule Detection
16.4.4 Magnetic Resonance Imaging in Radiation Treatment Planning
16.5 Year-Wise Analysis of Detection Methods for Lung Tumor Detection Using CT/PET CT Scans and MRI
16.6 Challenges and Future Directions
16.7 Conclusion
References
17. Artificial Intelligence and Deep Learning in Medical Imaging: Enhancing Oncology Diagnosis and Treatment
Amar Saraswat, N. Ria, Neeta Sharma, Yojna Arora, Sarita and Saurav Mallik
17.1 Introduction
17.2 Background
17.2.1 Artificial Intelligence in Healthcare
17.2.2 Deep Learning in Healthcare
17.2.3 Current Climate of Medical Imaging
17.2.3.1 Proven Methods and Challenges
17.2.3.2 Relevance of Deep Learning Architectures in Medical Imaging
17.3 Applications in Medical Imaging
17.3.1 Automating Image Interpretation
17.3.1.1 Detection of Lesion
17.3.1.2 Segmentation
17.3.1.3 Quantification
17.3.2 Diagnostic Assistance
17.3.2.1 Flagging Suspicious Findings
17.3.2.2 Differential Diagnoses
17.3.2.3 Risk Stratification
17.3.2.4 Case Studies
17.3.3 Enabling Personalized Treatment
17.3.3.1 Predicting Disease Progression and Response to Treatment
17.3.3.2 Personalized Treatment Plans
17.4 Challenges
17.4.1 Data Privacy
17.4.2 Model Explainability
17.4.3 Regulatory Hurdles
17.4.4 Ethical Considerations
17.5 Conclusion
References
Index
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