Oncological treatment of children represents a compromise between chances and risks. Fortunately, the prognosis of children with cancer has substantially improved in the last decade, making the long-term sequelae of cancer therapy increasingly relevant. In order to find the best treatment for our most vulnerable patients, artificial intelligence presents a promising opportunity. The aim of this collection is to show an exciting variety of articles, illustrating both clinical reality and innovative visions.
Our goal of personalized therapy planning in children is still far away, but we certainly are on the way.
Radiation therapy in children represents an exciting dynamic evolving topic. The ever-improving prognosis of many diseases makes a deeper understanding essential for protection from long-term side effects. Increasing new systemic therapy options raise the intruding question of tolerability. At the same time, exciting translational results provide a better understanding of radiation therapy and enable promising advances. Exciting studies on the use of artificial intelligence are moving ever closer to the desire for personalized medicine for children. Increasingly, applications for artificial intelligence are gaining traction and treading nearly all areas of clinical activity from improving diagnostics and optimizing therapy to strengthened, individualized post-therapy follow-up.
The availability of large datasets, combined with advances in high-performance computing and innovative deep-learning architectures, opens a broad window for visions. In this dynamic time, some of these systemics are already reaching clinical application, raising understandable concerns about patient and physician trust. We do not yet know where this exciting journey will take us, but it is increasingly certain that the way we treat our patients today is under profound transformation. We would like to accompany the discerning readership of Frontiers in oncology during this process.
Our mission is helping to improve the treatment of our most vulnerable patients. To do justice to the exciting topic of this special issue, we would like to create a captivating combination of basic research studies and clinical trials concerning radiotherapy in childhood using artificial intelligence. We want to report on interesting innovations, but also consider clinical trials. At the same time, the use of artificial intelligence combined with the need for large amounts of data from many institutes also poses exciting ethical and legal questions that are of utmost importance prior to clinical implementation and the development of large cross-institutional data sets, which are an interesting addition. All areas from diagnosis to therapy and follow-up will be investigated. Exciting translational, basic research, and artificial intelligence questions concerning irradiation in childhood cancer that has a chance to change future countervailing paradigms.The work will be composed of mainly original papers with individual reviews.
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.
Oncological treatment of children represents a compromise between chances and risks. Fortunately, the prognosis of children with cancer has substantially improved in the last decade, making the long-term sequelae of cancer therapy increasingly relevant. In order to find the best treatment for our most vulnerable patients, artificial intelligence presents a promising opportunity. The aim of this collection is to show an exciting variety of articles, illustrating both clinical reality and innovative visions.
Our goal of personalized therapy planning in children is still far away, but we certainly are on the way.
Radiation therapy in children represents an exciting dynamic evolving topic. The ever-improving prognosis of many diseases makes a deeper understanding essential for protection from long-term side effects. Increasing new systemic therapy options raise the intruding question of tolerability. At the same time, exciting translational results provide a better understanding of radiation therapy and enable promising advances. Exciting studies on the use of artificial intelligence are moving ever closer to the desire for personalized medicine for children. Increasingly, applications for artificial intelligence are gaining traction and treading nearly all areas of clinical activity from improving diagnostics and optimizing therapy to strengthened, individualized post-therapy follow-up.
The availability of large datasets, combined with advances in high-performance computing and innovative deep-learning architectures, opens a broad window for visions. In this dynamic time, some of these systemics are already reaching clinical application, raising understandable concerns about patient and physician trust. We do not yet know where this exciting journey will take us, but it is increasingly certain that the way we treat our patients today is under profound transformation. We would like to accompany the discerning readership of Frontiers in oncology during this process.
Our mission is helping to improve the treatment of our most vulnerable patients. To do justice to the exciting topic of this special issue, we would like to create a captivating combination of basic research studies and clinical trials concerning radiotherapy in childhood using artificial intelligence. We want to report on interesting innovations, but also consider clinical trials. At the same time, the use of artificial intelligence combined with the need for large amounts of data from many institutes also poses exciting ethical and legal questions that are of utmost importance prior to clinical implementation and the development of large cross-institutional data sets, which are an interesting addition. All areas from diagnosis to therapy and follow-up will be investigated. Exciting translational, basic research, and artificial intelligence questions concerning irradiation in childhood cancer that has a chance to change future countervailing paradigms.The work will be composed of mainly original papers with individual reviews.
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.
