Buch, Englisch, 608 Seiten, Format (B × H): 155 mm x 231 mm, Gewicht: 975 g
Ai-Based Tools in Cancer Diagnosis and Therapy
Buch, Englisch, 608 Seiten, Format (B × H): 155 mm x 231 mm, Gewicht: 975 g
ISBN: 978-1-394-38486-0
Verlag: Wiley
Transform the future of oncology with this groundbreaking volume, which details how the synergy of AI-driven machine learning and intelligent nanocarriers can bypass systemic toxicity and drug resistance to deliver the future of personalized cancer care.
This groundbreaking volume explores the powerful intersection of nanotechnology and artificial intelligence in the fight against cancer. As traditional diagnostic and therapeutic strategies struggle with challenges like non-specific drug delivery, systemic toxicity, and drug resistance, this book presents a revolutionary alternative—intelligent nanocarriers enhanced by AI. These systems not only deliver drugs with remarkable precision and control, but also adapt and respond dynamically through machine learning algorithms, ushering in a new era of personalized, high-efficiency oncology. Structured across twelve expertly written chapters, the book covers foundational concepts, advanced applications, and future directions. Readers will gain deep insights into AI-assisted nanocarrier design, synthesis optimization, biomarker detection, and real-time diagnostic imaging. Real-world case studies, emerging innovations like AI-guided radiotherapy and quantum-enhanced analytics, and discussions on ethical and regulatory considerations make this book a comprehensive guide for both academic and industry professionals shaping the future of precision cancer therapy.
Readers will find the volume:
- Explores the powerful synergy between artificial intelligence and nanocarrier technology in cancer care;
- Covers key nanostructures and AI tools, drawing from biomedical, clinical, and data sciences;
- Includes case studies and clinical insights to connect theory with practice;
- Highlights emerging trends like AI-guided radiotherapy and quantum computing in oncology.
Audience
Academics, researchers, biomedical engineers, nanotechnologists, materials scientists, oncologists, clinical researchers, and medical professionals involved in the design and development of drug delivery systems.
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
- Naturwissenschaften Chemie Chemie Allgemein Pharmazeutische Chemie, Medizinische Chemie
- Technische Wissenschaften Technik Allgemein Nanotechnologie
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Klinische und Innere Medizin Onkologie, Krebsforschung
Weitere Infos & Material
Preface xv
1 Nanocarriers for Diagnosis and Treatment of Oncology 1
Mohammad Javad Javid-Naderi, Fatemeh Akbarzadeh-Ebrahimi, Dora Iliana Medina and Abbas Rahdar
1.1 Introduction 2
1.2 Types of Nanocarriers 3
1.2.1 Liposomes 4
1.2.2 Micelles 4
1.2.3 Polymer-Based Nanocarriers 5
1.2.4 Dendrimers 5
1.2.5 Carbon-Based Nanocarriers 6
1.3 Nanocarriers in Cancer Diagnosis 8
1.3.1 Imaging Techniques Enhanced by Nanocarriers 9
1.3.2 Theranostic Nanocarriers 13
1.4 Nanocarriers in Cancer Treatment 14
1.4.1 Targeted Drug Delivery Using Nanocarriers 14
1.4.2 Chemotherapy with Nanocarriers 18
1.4.3 Gene Therapy and RNA Delivery 20
1.4.4 Photothermal and Photodynamic Therapy 22
Outlook and Conclusion 23
References 24
2 Artificial Intelligence in Modern Healthcare 35
Hamidreza Hosseini, Abbas Rahdar, Abolfazl Tabatabaei, Sadanand Pandey and Maryam Shirzad
2.1 Introduction to AI 36
2.1.1 Role of AI in Medical Imaging and Diagnostics 47
2.2 Features of Medical Images 50
2.3 Leading CAD Systems 53
2.4 Recent Developments in the Field of dl 56
2.5 Clinical Requirements and Obstacles 58
2.6 Applications in the Area of Medicine and Healthcare 59
2.7 CAD Pulmonary Artery Occlusion 60
2.8 CAD (Computer-Aided Diagnosis) Compatible with Lung Nodules 61
2.8.1 AI in Imaging Modalities 76
Conclusion 108
References 109
3 Foundation of AI-Enhanced Nanocarriers 121
Pejman Zarbanooei, Sanaz Aliakbarzadeh, Abbas Rahdar and Maryam Shirzad
3.1 Introduction 122
3.2 Introduction to Intelligent Nanocarriers: Bridging AI and Nanomedicine 124
3.2.1 Types of Nanocarriers 124
3.2.1.1 Liposomes 124
3.2.1.2 Micelles 124
3.2.1.3 Polymer-Based Nanocarriers 125
3.2.1.4 Dendrimers 125
3.2.1.5 Carbon-Based Nanocarriers 125
3.2.2 Nanocarriers in Cancer Diagnosis 126
3.2.2.1 Imaging Techniques Enhanced by Nanocarriers 126
3.2.2.2 Theranostic Nanocarriers 127
3.2.3 Nanocarriers in Cancer Treatment 127
3.2.3.1 Targeted Drug Delivery 127
3.2.3.2 Chemotherapy with Nanocarriers 128
3.2.3.3 Gene Therapy and RNA Delivery 128
3.2.3.4 Immunotherapy with Nanocarriers 129
3.3 Fundamentals of Artificial Intelligence in Oncology: A Primer 129
3.3.1 Introduction to Artificial Intelligence 129
3.3.2 Role of Artificial Intelligence in Precision Oncology 134
3.3.3 Implementation of Artificial Intelligence in Cancer Imaging 135
3.3.4 Artificial Intelligence and Translational Oncology 139
3.3.5 Challenges and Limitations 141
3.4 Nanocarriers in Cancer Therapy: An Overview of Traditional vs. AI-Enhanced Approaches 143
3.5 Conclusion and Future Prospects 145
References 146
4 Design and Development of AI-Based Nanocarriers 157
Ashkan Hajjafari, Narges Lotfalizadeh, Mansour Bayat, Abbas Rahdar, Maryam Shirzad and Guettari Moez
4.1 Introduction 158
4.2 Machine Learning Algorithms in Nanocarrier Design: Optimization and Innovation 161
4.3 AI-Driven Predictive Modeling for Nanocarrier Efficacy and Safety 167
4.4 Smart Materials (SMs) for Intelligent Nanocarriers: Integration of AI and Nanotechnology 174
4.5 Future and Challenges 181
Conclusion 183
References 184
5 Artificial Intelligence (AI) in Cancer Diagnosis and Targeting 195
Fatemeh Momtaz, Elham Momtaz, Mahdieh Momtaz, M. Ali Aboudzadeh and Abbas Rahdar
5.1 Introduction 196
5.2 AI-Powered Imaging Techniques for Early Cancer Detection 197
5.2.1 Ai 197
5.2.2 Imaging Techniques 199
5.2.3 Application of AI for Cancer Treatment 201
5.2.4 Imaging Techniques Based on AI for Cancer Diagnosis 202
5.3 Precision Targeting: AI’s Role in Nanocarrier-Based Drug Delivery Systems 205
5.3.1 Nanocarriers 205
5.3.2 Targeting Delivery of Nanocarriers 208
5.3.3 Precision Targeting: AIs Role in Nanocarrier-Based Drug Delivery Systems 209
5.4 TME and AI: Enhancing Targeted Delivery of Nanocarriers 212
5.4.1 Tme 212
5.4.2 Stimulus-Responsive Nanocarriers 214
5.4.3 Application of AI in TME 217
5.5 Challenges and Opportunities 218
5.6 Conclusion 219
References 220
6 Artificial Intelligence (AI)-Based Nanocarriers in Cancer Therapy 233
Mahdieh Momtaz, Elham Momtaz, Fatemeh Momtaz, Zeinab Moafian, Suresh Ghotekar and Abbas Rahdar
6.1 Introduction 234
6.2 AI for Predicting Nanocarrier Performance 235
6.2.1 Drug Encapsulation and Release Capacity 235
6.2.2 Pharmacokinetics Drugs 238
6.2.3 In Vitro/In Vivo 239
6.2.4 AI for Predicting Nanocarriers Performance 241
6.2.4.1 AI for Encapsulation 242
6.2.4.2 AI for Drug Release 243
6.2.4.3 AI for Pharmacokinetics and Pharmacodynamics 243
6.2.4.4 AI for In Vivo/In Vitro 244
6.3 AI-Powered High-Throughput Screening for Nanocarrier Formulation 245
6.3.1 Nanocarriers 245
6.3.2 Nanocarriers Formulation Optimization 246
6.3.3 High-Throughput Design and Formulation of Nanocarriers 247
6.3.3.1 Microfluidic Technology 247
6.3.3.2 Virtual Library Screening Methods 249
6.3.3.3 Molecular Dynamics (MD) Simulation 249
6.3.4 The Role of AI in Nanocarrier Design and High-Throughput Screening 250
6.4 Role of AI in Stimuli-Responsive Nanocarriers 251
6.4.1 Smart Nanocarriers 251
6.4.1.1 Dendrimer 251
6.4.1.2 Liposome 252
6.4.1.3 Micelles 252
6.4.1.4 Mesoporous Silica Nanoparticles (MSNs) 252
6.4.1.5 Gold Nanoparticles 253
6.4.1.6 Quantum Dots (QDs) 253
6.4.1.7 Carbon Nanotubes (CNTs) 254
6.4.2 Drug Release Control with Stimuli-Responsive Nanocarriers 255
6.4.2.1 Internal Stimulus for Drug Delivery Systems 255
6.4.2.2 External Stimulus for Drug Delivery Systems 259
6.4.2.3 Nanocarriers Responsive to Multiple Stimuli 262
6.4.3 Role of AI in Stimuli-Responsive Nanocarriers 263
6.5 Challenges and Opportunities 265
6.6 Conclusion 265
References 266
7 Artificial Intelligence (AI)-Enabled Nanocarriers in Cancer Diagnosis 283
Zakieh Sadat Hoseini, Amir Mohammad Najafi, Fatemeh Nouri Rouzbahani, Renato Nery Soriano and Abbas Rahdar
7.1 Introduction 284
7.2 Advancements in Nanotechnology and AI: Transforming Diagnostic Imaging 286
7.2.1 Traditional Medical Imaging and Its Limitations 286
7.2.2 High-Resolution Imaging of Extracellular Vesicles Using Nanotechnology 287
7.2.3 Nanoscale Imaging and AI Integration for Precise Disease Diagnosis 290
7.3 Nanocarrier-Assisted AI for Liquid Biopsy: Enhancing Circulating Tumor Cell Detection and Biomarker Extraction 294
7.3.1 Liquid Biopsy: Principles and Clinical Applications 294
7.3.2 Nanomaterial-Based Strategies for CTC Isolation 295
7.3.3 Artificial Intelligence Integration in Liquid Biopsy Data Analysis 296
7.4 Nanoparticle-Integrated, AI-Enhanced Biosensors for Real-Time Cancer Diagnostics 300
7.4.1 Biosensor Fundamentals and Nanoparticle Enhancement 300
7.4.2 Nanoparticle Applications in Biosensor Platforms 301
7.4.3 Integration of AI Algorithms in Biosensing 302
7.5 Future Prospects of AI-Driven Nanocarriers in Cancer Detection 306
7.6 Conclusion 307
References 308
8 AI-Based Nanocarriers in Gene Therapy and Immunotherapy 319
Heshmat Razlansari, Mahtab Razlansari, Masoud Kahrizi, Mohsen Kahrizi and Abbas Rahdar
8.1 Introduction 320
8.1.1 AI-Guided Nanocarriers in Chemotherapy Delivery 322
8.1.1.1 Revolutionizing Traditional Cancer Treatments with Targeted Systems 326
8.1.2 Immunotherapy and AI-Assisted Nanocarriers 329
8.1.2.1 Enhancing Immune Response Modulation with Intelligent Nanotechnologies 333
8.1.3 AI-Based Nanocarriers in Gene Therapy for Cancer 337
8.1.3.1 Innovations in Delivery of RNA/DNA-Based Therapeutics 339
Conclusion 344
References 345
9 Clinical Applications and Case Studies 357
Roghaieh Holghoomi, Abbas Rahdar and Renato Nery Soriano
9.1 Introduction 358
9.2 Clinical Applications of Artificial Intelligence-Enhanced Nanocarriers: Current Progress and Future Prospects 362
9.2.1 AI’s Diagnostics Application 363
9.2.1.1 AI in the Analysis of Molecular Profiling Identified through Nanotechnology 363
9.2.1.2 AI’s Utilization in Advanced Nanosensors for the Profiling of Biomarkers 366
9.2.2 AI’s Therapeutic Applications 367
9.2.2.1 AI’s Utilization in the Development of Drug-Loaded Nanoparticles for Nanomedicine 368
9.2.2.2 AI’s Utilization in Nanomedicine to Attain Therapeutic Synergism 370
9.2.2.3 Artificial Intelligence in the Advancement of Nanomedicine for Personalized Drug Targeting and Dosage Optimization 371
9.3 Case Studies: Artificial Intelligence-Driven Nanocarrier Systems in Personalized Cancer Therapy 372
9.4 Overcoming Challenges: Artificial Intelligence and Nanocarrier Synergy in Complex Oncological Cases 374
9.5 Conclusion 375
References 376
10 AI-Guided Radiotherapy and Nanocarrier Synergy to Treat Cancers 381
Mehrab Pourmadadi, Zahra Omrani, Abbas Rahdar, Elvis Fosso-Kankeu and Sadanand Pandey
10.1 Introduction 382
10.2 Artificial Intelligence-Based Radiotherapy to Treat Cancers 384
10.2.1 Radiotherapy 384
10.2.2 Artificial Intelligence-Based Radiotherapy to Treat Cancers 388
10.3 AI-Guided Radiotherapy and Nanocarrier Synergy to Treat Cancers: “Combining Radiotherapy with AI-Enhanced Nanocarriers to Improve Therapeutic Outcomes” 395
10.4 Future Perspective 401
10.5 Conclusion 402
References 403
11 Integrating Artificial Intelligence and Quantum Computing with Omics Data for Cancer Therapy and Diagnosis 413
Ali Bakhshi, Mahya Bakhshi, Mojtaba Hosseine, Hossein Hasannezhad, Abbas Rahdar, Elvis Fosso-Kankeu and Sadanand Pandey
Abbreviations 414
11.1 Introduction 417
11.2 What is Omics? 422
11.2.1 Omics Technology 423
11.2.2 Technology-Based Omics 424
11.2.2.1 Sequencing-Based Omics 424
11.2.2.2 MS-Based Omics 426
11.2.3 Knowledge-Based Omics 427
11.2.4 Multi-Omics 428
11.3 Omics Data 430
11.3.1 Data Types in Omics 431
11.3.2 Datasets 435
11.4 AI-Driven Multi-Omics Integration for Cancer Diagnosis 444
11.4.1 Biomarker Discovery 446
11.4.2 Imaging and Histopathology 448
11.5 AI-Driven Multi-Omics Integration in Cancer Therapy 453
11.5.1 Drug Repurposing and Combination Therapy Design 454
11.5.2 Overcoming Resistance through AI-Driven Mechanistic Insights 467
11.5.3 AI in Immunotherapy Optimization 473
11.5.4 AI in Predicting Therapy-Related Adverse Effects 476
11.6 Integration of Artificial Intelligence and Omics Technologies for Precision Oncology 478
11.7 Quantum Artificial Intelligence in Oncology 482
11.7.1 Quantum Algorithms in Drug Discovery and Molecular Simulations 483
11.7.2 Quantum Machine Learning for Multi-Omics 487
11.7.3 Quantum Artificial Intelligence for Biomedical Instrumentation 487
11.7.4 Challenges Associated with Quantum Artificial Intelligence 490
11.8 Challenges and Opportunities 491
11.8.1 Challenges 492
11.8.1.1 Data Standardization and Interoperability 492
11.8.1.2 Computational Complexity and Scalability 492
11.8.1.3 Ethical and Regulatory Concerns 492
11.8.1.4 Validation and Clinical Translation 493
11.8.2 Opportunities 493
11.8.2.1 Advancements in Quantum Hardware and AI Models 493
11.8.2.2 AI-Driven Precision Oncology 493
11.8.2.3 Multi-Omics and Single-Cell Technologies 493
11.8.2.4 AI-QAI Hybrid Approaches for Drug Discovery 494
11.9 Future Perspective 494
Conclusion 497
References 498
12 Ethical, Regulatory, and Future Perspectives 525
Sanaz Aliakbarzadeh, Pejman Zarbanooei, Majid Abdouss, Abbas Rahdar and Luiz Fernando Romanholo Ferreira
12.1 Introduction 526
12.2 Ethical Issues with AI and Nanocarrier Applications in Oncology 529
12.2.1 Ethical Challenges 529
12.2.2 USA Food and Drug Administration 535
12.2.3 European Union 538
12.3 Regulatory Challenges and Pathways for AI-Enhanced Nanocarrier Therapies 543
12.4 The Future of Intelligent Nanocarriers: Innovations, Challenges, and Opportunities 548
12.5 Conclusion and Future Perspectives 561
References 563
Index 571
