November 2022 is the World Lung Cancer Awareness Month and at Frontiers in Oncology we want to highlight the recent discoveries in the field and raise awareness of the importance of early diagnosis and prediction of these cancers.
Treating patients suffering from lung cancer is a challenge as the main symptoms include cough, chest infection, weight loss, loss of appetite, lack of energy, etc. These are general symptoms that most of us experience in our day-to-day lives. This eventually leads to late-stage diagnosis and a high immorality rate. But, the last few years have witnessed revolutionary changes in the management of Lung cancer. Refined nosology, precisely understood phenotypes, much improved surgical management with mini-invasive techniques, use of radiotherapy, and many others are a few of them. Besides, several advances in diagnosis are underway. The most commonly used detection of Lung cancer heavily relies upon low-dose CT scan but is a constrained method as it is bounded by age and thus faces resistance to the advantages it offers. Thus, either non-invasive or negligibly low-invasive biomarkers and Artificial Intelligence (AI) can complement the existing CT process and provide early detection, improve responses to treatments, build an efficient predictive model, and help improve the decision-making process. Deep-Learning techniques are being harnessed for lung nodule and image recognition, image classification, pattern detection, etc., and can also be used to assist in the effective clinical management of lung cancer. But, AI alone is incapable of solving problems and building an effective predictive model. It needs to be integrated with radiomics, genomics, and clinical and semantic features for building the best models for diagnosis and management. Since AI is associated with legal, ethical, and psychological issues, thus, patient favorable principles, regulations, and decisions need to be proposed and adopted. For promising clinical management of cancer, awareness workshops, counseling sessions to handle patients’ fear, and training the doctors for enabling acceptance of AI, should be organized.
This article collection intends to invite articles that critically analyse and demonstrate the validity of existing techniques, discuss recent breakthroughs in the use of AI techniques and their clinical applications, and also propose AI-enabled techniques that can handle the above challenges and pave the path for clinical adoption.
The topics may include:
- Artificial intelligence in clinical applications for lung cancer
- Diagnosis, treatment and prognosis using Deep Learning techniques
- Future clinical utilization of AI for lung cancer
- Developing decision support tools
- Achieving precision in diagnosis and prediction
- Building effective predictive models
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.
November 2022 is the World Lung Cancer Awareness Month and at Frontiers in Oncology we want to highlight the recent discoveries in the field and raise awareness of the importance of early diagnosis and prediction of these cancers.
Treating patients suffering from lung cancer is a challenge as the main symptoms include cough, chest infection, weight loss, loss of appetite, lack of energy, etc. These are general symptoms that most of us experience in our day-to-day lives. This eventually leads to late-stage diagnosis and a high immorality rate. But, the last few years have witnessed revolutionary changes in the management of Lung cancer. Refined nosology, precisely understood phenotypes, much improved surgical management with mini-invasive techniques, use of radiotherapy, and many others are a few of them. Besides, several advances in diagnosis are underway. The most commonly used detection of Lung cancer heavily relies upon low-dose CT scan but is a constrained method as it is bounded by age and thus faces resistance to the advantages it offers. Thus, either non-invasive or negligibly low-invasive biomarkers and Artificial Intelligence (AI) can complement the existing CT process and provide early detection, improve responses to treatments, build an efficient predictive model, and help improve the decision-making process. Deep-Learning techniques are being harnessed for lung nodule and image recognition, image classification, pattern detection, etc., and can also be used to assist in the effective clinical management of lung cancer. But, AI alone is incapable of solving problems and building an effective predictive model. It needs to be integrated with radiomics, genomics, and clinical and semantic features for building the best models for diagnosis and management. Since AI is associated with legal, ethical, and psychological issues, thus, patient favorable principles, regulations, and decisions need to be proposed and adopted. For promising clinical management of cancer, awareness workshops, counseling sessions to handle patients’ fear, and training the doctors for enabling acceptance of AI, should be organized.
This article collection intends to invite articles that critically analyse and demonstrate the validity of existing techniques, discuss recent breakthroughs in the use of AI techniques and their clinical applications, and also propose AI-enabled techniques that can handle the above challenges and pave the path for clinical adoption.
The topics may include:
- Artificial intelligence in clinical applications for lung cancer
- Diagnosis, treatment and prognosis using Deep Learning techniques
- Future clinical utilization of AI for lung cancer
- Developing decision support tools
- Achieving precision in diagnosis and prediction
- Building effective predictive models
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.
