Artificial Intelligence in Gastrointestinal Cancers examines the transformative impact of AI technologies on diagnosing and treating gastrointestinal malignancies. This book explores how advanced algorithms, particularly deep learning models, enhance the analysis of medical imaging, including endoscopic, MRI, and CT scans, facilitating earlier and more accurate identification of cancers such as esophageal, stomach, colorectal, and pancreatic cancers. The content covers AI's multifaceted applications, from improving diagnostic accuracy to personalizing treatment strategies based on genomic and clinical data. It discusses how machine learning predicts patient outcomes and optimizes treatment plans while also addressing AI's role in accelerating drug discovery and clinical trials. For an academic audience, this book provides crucial insights into AI's current and future applications in GI oncology. Readers gain a comprehensive understanding of AI diagnostic tools and personalized treatment strategies, equipping researchers and clinicians with the knowledge to leverage these advancements for improved patient care and outcomes in gastrointestinal cancers.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. i 4. Table of Contents - p. iii 5. Volume in the bookseries Novel approaches to overcome cancer resistance to conventional therapies - p. ii 6. Copyright - p. iv 7. Dedication - p. v 8. Aims and Scope of Series “Novel Approaches to Overcome Cancer Resistance to Conventional Therapies” - p. vii 9. About the Series Editor - p. ix 10. Aims and scope of the volume - p. xi 11. About the editors - p. xiii 12. Preface - p. xv 13. Contributors - p. xxi 14. Chapter 1. Artificial intelligence in gastrointestinal cancers - p. 1 15. Chapter 2. The role of artificial intelligence (AI) and machine learning (ML) in gastrointestinal (GI) tract cancers: challenges and opportunities - p. 21 16. Chapter 3. Rise of machine learning applications in diagnosis and prognosis of gastric cancer: Imaging and omics data - p. 41 17. Chapter 4. Imaging to intervention: examining the potential of artificial intelligence for gastrointestinal cancers - p. 61 18. Chapter 5. Developments of AI based early detection and diagnosis method in pancreatic cancer - p. 91 19. Chapter 6. Exploring artificial intelligence approaches for studying intrinsically disorder proteins involvement in gastrointestinal cancers - p. 105 20. Chapter 7. Identification of diagnostic transcriptome regulatory network for prediction of early-onset Gastric Cancer using integrated bioinformatics analysis - p. 121 21. Chapter 8. Data to diagnosis: AI-powered insights for biomarker discovery in gastrointestinal tract cancers - p. 141 22. Chapter 9. The role of artificial intelligence in gastric cancer diagnosis, prognosis, surgery and drug discovery - p. 163 23. Chapter 10. Acceptance sampling approaches in discovering drugs for gastrointestinal cancer - p. 177 24. Chapter 11. Transforming gastrointestinal tract cancer with applications and advancements of artificial intelligence and machine learning: A next-generation patient care - p. 193 25. Chapter 12. Harnessing artificial intelligence for screening phytochemicals in gastrointestinal cancer therapeutics - p. 209 26. Chapter 13. Exploring AI-enabled drug discovery and repurposing approaches for gastrointestinal tract cancer - p. 223 27. Chapter 14. AI-driven drug repurposing strategies for hepatocellular carcinoma: Unlocking new therapeutic potential - p. 239 28. Chapter 15. Revolutionizing therapeutic possibilities for hepatocellular carcinoma by advanced bioinformatics and artificial intelligence-driven drug repurposing - p. 267 29. Chapter 16. Challenges and future perspectives of AI in gastrointestinal cancers - p. 283 30. Index - p. 305
Artificial Intelligence in Gastrointestinal Cancers examines the transformative impact of AI technologies on diagnosing and treating gastrointestinal malignancies. This book explores how advanced algorithms, particularly deep learning models, enhance the analysis of medical imaging, including endoscopic, MRI, and CT scans, facilitating earlier and more accurate identification of cancers such as esophageal, stomach, colorectal, and pancreatic cancers. The content covers AI's multifaceted applications, from improving diagnostic accuracy to personalizing treatment strategies based on genomic and clinical data. It discusses how machine learning predicts patient outcomes and optimizes treatment plans while also addressing AI's role in accelerating drug discovery and clinical trials. For an academic audience, this book provides crucial insights into AI's current and future applications in GI oncology. Readers gain a comprehensive understanding of AI diagnostic tools and personalized treatment strategies, equipping researchers and clinicians with the knowledge to leverage these advancements for improved patient care and outcomes in gastrointestinal cancers.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. i 4. Table of Contents - p. iii 5. Volume in the bookseries Novel approaches to overcome cancer resistance to conventional therapies - p. ii 6. Copyright - p. iv 7. Dedication - p. v 8. Aims and Scope of Series “Novel Approaches to Overcome Cancer Resistance to Conventional Therapies” - p. vii 9. About the Series Editor - p. ix 10. Aims and scope of the volume - p. xi 11. About the editors - p. xiii 12. Preface - p. xv 13. Contributors - p. xxi 14. Chapter 1. Artificial intelligence in gastrointestinal cancers - p. 1 15. Chapter 2. The role of artificial intelligence (AI) and machine learning (ML) in gastrointestinal (GI) tract cancers: challenges and opportunities - p. 21 16. Chapter 3. Rise of machine learning applications in diagnosis and prognosis of gastric cancer: Imaging and omics data - p. 41 17. Chapter 4. Imaging to intervention: examining the potential of artificial intelligence for gastrointestinal cancers - p. 61 18. Chapter 5. Developments of AI based early detection and diagnosis method in pancreatic cancer - p. 91 19. Chapter 6. Exploring artificial intelligence approaches for studying intrinsically disorder proteins involvement in gastrointestinal cancers - p. 105 20. Chapter 7. Identification of diagnostic transcriptome regulatory network for prediction of early-onset Gastric Cancer using integrated bioinformatics analysis - p. 121 21. Chapter 8. Data to diagnosis: AI-powered insights for biomarker discovery in gastrointestinal tract cancers - p. 141 22. Chapter 9. The role of artificial intelligence in gastric cancer diagnosis, prognosis, surgery and drug discovery - p. 163 23. Chapter 10. Acceptance sampling approaches in discovering drugs for gastrointestinal cancer - p. 177 24. Chapter 11. Transforming gastrointestinal tract cancer with applications and advancements of artificial intelligence and machine learning: A next-generation patient care - p. 193 25. Chapter 12. Harnessing artificial intelligence for screening phytochemicals in gastrointestinal cancer therapeutics - p. 209 26. Chapter 13. Exploring AI-enabled drug discovery and repurposing approaches for gastrointestinal tract cancer - p. 223 27. Chapter 14. AI-driven drug repurposing strategies for hepatocellular carcinoma: Unlocking new therapeutic potential - p. 239 28. Chapter 15. Revolutionizing therapeutic possibilities for hepatocellular carcinoma by advanced bioinformatics and artificial intelligence-driven drug repurposing - p. 267 29. Chapter 16. Challenges and future perspectives of AI in gastrointestinal cancers - p. 283 30. Index - p. 305
