Chapter 10 AI-driven advances in lung cancer care
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Abstract
Lung cancer continues to be a prominent source of cancer-related fatalities on a global scale, highlighting the necessity for enhanced methods of diagnosis and treatment. Artificial intelligence (AI) has the capacity to assist in the treatment of lung cancer by aiding in the detection, diagnostic, decision-making, and prognosis prediction processes. The rise of AI in recent years has generated significant interest in its possible application in the field of lung cancer. AI algorithms such as machine learning, deep learning, and radiomics have demonstrated impressive ability in identifying and describing lung nodules, thereby assisting in precise lung cancer screening and diagnosis. These systems have the capability to analyse different types of medical imaging, including low-dose computed tomography (CT) scans, positron emission tomography CT imaging, and chest radiographs. They can accurately detect and identify abnormal nodules, helping to enable prompt medical action. AI models have shown potential in using biomarkers and tumour markers as additional screening tools, effectively improving the precision and accuracy of early detection. This chapter presents a comprehensive summary of the present condition of AI applications in the fields of lung cancer screening, diagnosis, and treatment.
Abstract
Lung cancer continues to be a prominent source of cancer-related fatalities on a global scale, highlighting the necessity for enhanced methods of diagnosis and treatment. Artificial intelligence (AI) has the capacity to assist in the treatment of lung cancer by aiding in the detection, diagnostic, decision-making, and prognosis prediction processes. The rise of AI in recent years has generated significant interest in its possible application in the field of lung cancer. AI algorithms such as machine learning, deep learning, and radiomics have demonstrated impressive ability in identifying and describing lung nodules, thereby assisting in precise lung cancer screening and diagnosis. These systems have the capability to analyse different types of medical imaging, including low-dose computed tomography (CT) scans, positron emission tomography CT imaging, and chest radiographs. They can accurately detect and identify abnormal nodules, helping to enable prompt medical action. AI models have shown potential in using biomarkers and tumour markers as additional screening tools, effectively improving the precision and accuracy of early detection. This chapter presents a comprehensive summary of the present condition of AI applications in the fields of lung cancer screening, diagnosis, and treatment.
Chapters in this book
- Frontmatter I
- Preface V
- Foreword VII
- Contents IX
- About the authors XVII
- Chapter 1 Artificial intelligence in cancer treatment and management 1
- Chapter 2 AI-based approaches to cancer drug discovery 27
- Chapter 3 Integrating AI and digital twin technology in cancer therapy 47
- Chapter 4 AI for enhanced cancer detection and diagnosis 63
- Chapter 5 AI-guided surgical interventions for cancer and tumor removal 99
- Chapter 6 Artificial intelligence in breast cancer management 121
- Chapter 7 AI innovations in colorectal cancer detection and treatment 143
- Chapter 8 AI applications in brain cancer therapy 165
- Chapter 9 Leveraging AI for liver cancer diagnosis and treatment 191
- Chapter 10 AI-driven advances in lung cancer care 213
- Chapter 11 Artificial intelligence in prostate cancer detection and management 243
- Chapter 12 AI solutions for skin cancer diagnosis and treatment 265
- Index 345
- De Gruyter Series on the Applications of Mathematics in Engineering and Information Sciences
Chapters in this book
- Frontmatter I
- Preface V
- Foreword VII
- Contents IX
- About the authors XVII
- Chapter 1 Artificial intelligence in cancer treatment and management 1
- Chapter 2 AI-based approaches to cancer drug discovery 27
- Chapter 3 Integrating AI and digital twin technology in cancer therapy 47
- Chapter 4 AI for enhanced cancer detection and diagnosis 63
- Chapter 5 AI-guided surgical interventions for cancer and tumor removal 99
- Chapter 6 Artificial intelligence in breast cancer management 121
- Chapter 7 AI innovations in colorectal cancer detection and treatment 143
- Chapter 8 AI applications in brain cancer therapy 165
- Chapter 9 Leveraging AI for liver cancer diagnosis and treatment 191
- Chapter 10 AI-driven advances in lung cancer care 213
- Chapter 11 Artificial intelligence in prostate cancer detection and management 243
- Chapter 12 AI solutions for skin cancer diagnosis and treatment 265
- Index 345
- De Gruyter Series on the Applications of Mathematics in Engineering and Information Sciences
