Automation and Artificial Intelligence in Radiation Oncology

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Editorial

12 October 2022

Editorial: Automation and artificial intelligence in radiation oncology

Savino Cilla

and

Jose Eduardo Villarreal Barajas

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Savino Cilla

Responsible Research Hospital

Jose Eduardo Villarreal Barajas

Royal Devon and Exeter Hospital

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Technology has always played a major role in the continuous development of radiation oncology. In recent years, technology-driven advances in planning and delivery of radiotherapy have been successfully accomplished and contributed to many clinical improvements. In particular, artificial intelligence techniques are becoming an increasingly widespread diffusion in radiation oncology, turning the corner from niche research endeavors to a standard clinical practice. A first example is the ability of AI techniques to generate automated solutions for treatment planning, with the potential to increase quality, and reduce variability and planning time. Neural networks have also been used in auto-contouring of normal structures and tumors; their application for organ segmentation for treatment planning are an active field of research.

Automation techniques can also play a major role in adaptive radiotherapy and online treatment planning. The potential of these techniques have been proven in the reconstruction of CT or MR images, able to generate “synthetic CT-images" needed for dose calculations from MRI scans. Several ongoing research is also highlighting the potential of automation to strengthen and speed up the quality assurance processes. Lastly, the extraction of image features from CT, MRI or PET imaging (radiomics) is proving to have great potential for personalized treatment, playing an important role in the development of prediction models of clinical outcomes.

The aim of this Research Topic is to provide information on the current and potential use of automation and advanced computing in radiation oncology. The present collection is aimed at summarizing the recent advances in this field of research, with a particular focus on their clinical utility and how their implementation will lead to the growing personalization of radiotherapy treatments.

This Research Topic welcomes contributions related to all aspects of automation in radiation oncology spanning the following topics:

- Latest developments of advanced computing systems for radiation oncology

- Automated segmentation (atlas-based, deep learning) of volumes in radiation oncology

- Knowledge-based, multicriteria optimization and template-based automation of treatment planning

- Online treatment planning and adaptive radiotherapy

- Automation of quality assurance and quality control procedures

- Safety considerations in processes automation in radiation oncology

Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (clinical 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.

Technology has always played a major role in the continuous development of radiation oncology. In recent years, technology-driven advances in planning and delivery of radiotherapy have been successfully accomplished and contributed to many clinical improvements. In particular, artificial intelligence techniques are becoming an increasingly widespread diffusion in radiation oncology, turning the corner from niche research endeavors to a standard clinical practice. A first example is the ability of AI techniques to generate automated solutions for treatment planning, with the potential to increase quality, and reduce variability and planning time. Neural networks have also been used in auto-contouring of normal structures and tumors; their application for organ segmentation for treatment planning are an active field of research.

Automation techniques can also play a major role in adaptive radiotherapy and online treatment planning. The potential of these techniques have been proven in the reconstruction of CT or MR images, able to generate “synthetic CT-images" needed for dose calculations from MRI scans. Several ongoing research is also highlighting the potential of automation to strengthen and speed up the quality assurance processes. Lastly, the extraction of image features from CT, MRI or PET imaging (radiomics) is proving to have great potential for personalized treatment, playing an important role in the development of prediction models of clinical outcomes.

The aim of this Research Topic is to provide information on the current and potential use of automation and advanced computing in radiation oncology. The present collection is aimed at summarizing the recent advances in this field of research, with a particular focus on their clinical utility and how their implementation will lead to the growing personalization of radiotherapy treatments.

This Research Topic welcomes contributions related to all aspects of automation in radiation oncology spanning the following topics:

- Latest developments of advanced computing systems for radiation oncology

- Automated segmentation (atlas-based, deep learning) of volumes in radiation oncology

- Knowledge-based, multicriteria optimization and template-based automation of treatment planning

- Online treatment planning and adaptive radiotherapy

- Automation of quality assurance and quality control procedures

- Safety considerations in processes automation in radiation oncology

Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (clinical 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.

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Editors

Savino Cilla

Responsible Research Hospital

Jose Eduardo Villarreal Barajas

Royal Devon and Exeter Hospital

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67,803 Total views

44,492 Article views

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Published In

Frontiers in Oncology

Radiation Oncology

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