Lung cancer is a prevalent and severe form of cancer worldwide. It is responsible for a significant number of cancer-related deaths annually, with around 1.76 million deaths recorded globally in 2020. In many countries, it remains the leading cause of cancer deaths. The development of advanced treatment options is crucial to combat this disease, and artificial intelligence technology has shown promise in aiding such advancements.
Machine learning is a field of artificial intelligence in which computer systems are able to learn from available databases. When used in the field of lung cancer radiotherapy, it has the ability to revolutionize areas of treatment, such as the analysis of medical images or predicting the adverse effects of treatment in non-small cell lung cancer. Similarly, deep learning is a subfield of machine learning that uses artificial neural networks to imitate the human brain’s learning processes. It has recently made significant advances in sequential decision-making problems, such as being able to defeat human experts in video games. These advances have been translated into initial research developments in lung cancer radiotherapy, such as the invention of a deep reinforcement learning based brachytherapy treatment planning framework. However, many more developments are needed to improve efficiency of such innovations.
The aim of this Research Topic is to explore the way that machine learning can be used to aid and advance radiation therapy in patients with lung cancer. To do so, it aims to collect Original Research, Reviews, Mini Reviews and Opinion Articles. We welcome manuscripts focusing on the most current achievements in the field, which include but are not limited to:
- Deep reinforcement learning
- Diagnosis and treatment using machine learning techniques
- Automatic treatment planning
- Radiomics
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.
Lung cancer is a prevalent and severe form of cancer worldwide. It is responsible for a significant number of cancer-related deaths annually, with around 1.76 million deaths recorded globally in 2020. In many countries, it remains the leading cause of cancer deaths. The development of advanced treatment options is crucial to combat this disease, and artificial intelligence technology has shown promise in aiding such advancements.
Machine learning is a field of artificial intelligence in which computer systems are able to learn from available databases. When used in the field of lung cancer radiotherapy, it has the ability to revolutionize areas of treatment, such as the analysis of medical images or predicting the adverse effects of treatment in non-small cell lung cancer. Similarly, deep learning is a subfield of machine learning that uses artificial neural networks to imitate the human brain’s learning processes. It has recently made significant advances in sequential decision-making problems, such as being able to defeat human experts in video games. These advances have been translated into initial research developments in lung cancer radiotherapy, such as the invention of a deep reinforcement learning based brachytherapy treatment planning framework. However, many more developments are needed to improve efficiency of such innovations.
The aim of this Research Topic is to explore the way that machine learning can be used to aid and advance radiation therapy in patients with lung cancer. To do so, it aims to collect Original Research, Reviews, Mini Reviews and Opinion Articles. We welcome manuscripts focusing on the most current achievements in the field, which include but are not limited to:
- Deep reinforcement learning
- Diagnosis and treatment using machine learning techniques
- Automatic treatment planning
- Radiomics
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.
