Artificial Intelligence-Driven Precision Medicine for Triple-Negative Breast Cancer: Innovations and Insights in TNBC Management is a groundbreaking exploration into the transformative role of artificial intelligence in revolutionizing the management of triple-negative breast cancer (TNBC). In 19 chapters, this comprehensive guide navigates the intersection of cutting-edge technology and healthcare, offering insights that bridge the gap among medical professionals, researchers, and individuals affected by TNBC. Diving into the complexities of TNBC, this book unveils the potential of artificial intelligence (AI) in enhancing diagnostic accuracy, personalizing treatment strategies, and reshaping the landscape of breast cancer care. From radiomics and genomic data analysis to predictive modeling and patient engagement, each chapter unveils the latest innovations and provides the practical applications of AI to empower healthcare practitioners and improve patient outcomes. This book is a timely and valuable resource for health professionals, scientists and researchers, students, and all those who wish to broaden their knowledge in the allied field.
1. Chapter 1. Introduction of triple negative breast cancer and precision medicine - p. 3 2. Chapter 2. Understanding the molecular landscape of triple-negative breast cancer - p. 25 3. Chapter 3. Challenges in traditional TNBC therapies - p. 43 4. 1 Introduction - p. 43 5. 2 Biological and molecular heterogeneity - p. 44 6. 3 Current standard and therapeutic approaches - p. 53 7. 4 Side effects and toxicities - p. 60 8. 5 Toxicity related to breast cancer - p. 60 9. 6 Emerging targeted therapies - p. 62 10. 7 Novel approaches and combination therapies - p. 76 11. 8 Future directions and research needs - p. 81 12. 9 Conclusion - p. 84 13. Abbreviation - p. 85 14. Chapter 4. Fundamentals of artificial intelligence in healthcare - p. 99 15. Chapter 5. Artificial intelligence-driven precision medicine for triple negative breast cancer - p. 113 16. Chapter 6. Managing big data for TNBC research - p. 133 17. Section II. Applications and innovations in AI-driven precision medicine for TNBC - p. S2 18. Chapter 7. Enhancing early detection and diagnosis using AI technologies in artificial intelligence-driven precision medicine for triple-negative breast cancer - p. 171 19. Chapter 8. Predictive modeling for treatment response in TNBC - p. 187 20. Chapter 9. AI-driven biomarker discovery and validation - p. 209 21. Chapter 10. Precision treatment planning and decision support systems - p. 227 22. Chapter 11. AI in targeted drug discovery for TNBC - p. 243 23. Chapter 12. Integrating multiomics data for personalized TNBC management - p. 269 24. Chapter 13. Transforming clinical trials with AI integration in TNBC research - p. 285 25. Chapter 14. Ethical and regulatory dimensions of AI in TNBC management - p. 301 26. Chapter 15. AI-driven approaches for cancer recurrence monitoring - p. 319 27. Chapter 16. Advancements in immunotherapy through AI-driven approaches - p. 337 28. Chapter 17. AI-driven prognostic and predictive modeling in triple-negative breast cancer (TNBC) - p. 353 29. Chapter 18. Recent technologies for TNBC management - p. 365 30. Index - p. 377
Artificial Intelligence-Driven Precision Medicine for Triple-Negative Breast Cancer: Innovations and Insights in TNBC Management is a groundbreaking exploration into the transformative role of artificial intelligence in revolutionizing the management of triple-negative breast cancer (TNBC). In 19 chapters, this comprehensive guide navigates the intersection of cutting-edge technology and healthcare, offering insights that bridge the gap among medical professionals, researchers, and individuals affected by TNBC. Diving into the complexities of TNBC, this book unveils the potential of artificial intelligence (AI) in enhancing diagnostic accuracy, personalizing treatment strategies, and reshaping the landscape of breast cancer care. From radiomics and genomic data analysis to predictive modeling and patient engagement, each chapter unveils the latest innovations and provides the practical applications of AI to empower healthcare practitioners and improve patient outcomes. This book is a timely and valuable resource for health professionals, scientists and researchers, students, and all those who wish to broaden their knowledge in the allied field.
1. Chapter 1. Introduction of triple negative breast cancer and precision medicine - p. 3 2. Chapter 2. Understanding the molecular landscape of triple-negative breast cancer - p. 25 3. Chapter 3. Challenges in traditional TNBC therapies - p. 43 4. 1 Introduction - p. 43 5. 2 Biological and molecular heterogeneity - p. 44 6. 3 Current standard and therapeutic approaches - p. 53 7. 4 Side effects and toxicities - p. 60 8. 5 Toxicity related to breast cancer - p. 60 9. 6 Emerging targeted therapies - p. 62 10. 7 Novel approaches and combination therapies - p. 76 11. 8 Future directions and research needs - p. 81 12. 9 Conclusion - p. 84 13. Abbreviation - p. 85 14. Chapter 4. Fundamentals of artificial intelligence in healthcare - p. 99 15. Chapter 5. Artificial intelligence-driven precision medicine for triple negative breast cancer - p. 113 16. Chapter 6. Managing big data for TNBC research - p. 133 17. Section II. Applications and innovations in AI-driven precision medicine for TNBC - p. S2 18. Chapter 7. Enhancing early detection and diagnosis using AI technologies in artificial intelligence-driven precision medicine for triple-negative breast cancer - p. 171 19. Chapter 8. Predictive modeling for treatment response in TNBC - p. 187 20. Chapter 9. AI-driven biomarker discovery and validation - p. 209 21. Chapter 10. Precision treatment planning and decision support systems - p. 227 22. Chapter 11. AI in targeted drug discovery for TNBC - p. 243 23. Chapter 12. Integrating multiomics data for personalized TNBC management - p. 269 24. Chapter 13. Transforming clinical trials with AI integration in TNBC research - p. 285 25. Chapter 14. Ethical and regulatory dimensions of AI in TNBC management - p. 301 26. Chapter 15. AI-driven approaches for cancer recurrence monitoring - p. 319 27. Chapter 16. Advancements in immunotherapy through AI-driven approaches - p. 337 28. Chapter 17. AI-driven prognostic and predictive modeling in triple-negative breast cancer (TNBC) - p. 353 29. Chapter 18. Recent technologies for TNBC management - p. 365 30. Index - p. 377
