Radiotherapy is a major treatment modality of cancer along with surgery and chemotherapy. More than half cancer patients receive ionizing radiation as part of their treatment and it is the main treatment modality at advanced stages of disease. A typical radiotherapy treatment scenario can generate a large pool of data “Big data” that comprise but not limited to patient demographics, volumetric dosimetric data about radiation exposure to the tumor and surrounding tissues, 3D and 4D anatomical and functional disease longitudinal imaging features (radiomics), in addition to genomics and proteomics data derived from peripheral blood and tissue specimens. Such panomics data could be used to guide radiation safety delivery and quality assurance procedures, improve treatment planning and monitoring efficacy, optimize therapeutic ratio, and personalize treatment regimens leading to better outcomes and quality of life for cancer patients who receive radiotherapy as part of their treatment. However, it is noted that radiotherapy data constitutes a unique interface between physical and biological data interactions that can benefit from the general advances of big data research while still requires the development of specific technologies within this framework to address its specificity.
In this Research Topic, we review recent advances and discuss current challenges to interrogate big data in radiotherapy using top-bottom and bottom top-approaches, describe the specific nature of big data in radiotherapy, and the different application areas including radiation physics quality assurance, contouring and treatment planning, image-guided radiotherapy, motion management, and treatment response modeling and outcomes prediction. We will discuss issues related to bioinformatics tools for data aggregation, sharing, and confidentiality. We will also highlight the potential opportunities in this field for big data research for different stake holders including radiation oncologists, radiation physicists, radiobiologists, and bioinformaticians.
Radiotherapy is a major treatment modality of cancer along with surgery and chemotherapy. More than half cancer patients receive ionizing radiation as part of their treatment and it is the main treatment modality at advanced stages of disease. A typical radiotherapy treatment scenario can generate a large pool of data “Big data” that comprise but not limited to patient demographics, volumetric dosimetric data about radiation exposure to the tumor and surrounding tissues, 3D and 4D anatomical and functional disease longitudinal imaging features (radiomics), in addition to genomics and proteomics data derived from peripheral blood and tissue specimens. Such panomics data could be used to guide radiation safety delivery and quality assurance procedures, improve treatment planning and monitoring efficacy, optimize therapeutic ratio, and personalize treatment regimens leading to better outcomes and quality of life for cancer patients who receive radiotherapy as part of their treatment. However, it is noted that radiotherapy data constitutes a unique interface between physical and biological data interactions that can benefit from the general advances of big data research while still requires the development of specific technologies within this framework to address its specificity.
In this Research Topic, we review recent advances and discuss current challenges to interrogate big data in radiotherapy using top-bottom and bottom top-approaches, describe the specific nature of big data in radiotherapy, and the different application areas including radiation physics quality assurance, contouring and treatment planning, image-guided radiotherapy, motion management, and treatment response modeling and outcomes prediction. We will discuss issues related to bioinformatics tools for data aggregation, sharing, and confidentiality. We will also highlight the potential opportunities in this field for big data research for different stake holders including radiation oncologists, radiation physicists, radiobiologists, and bioinformaticians.