NASEM’s report on Digital Twins is now available!
“Foundational Research Gaps & Future Directions for Digital Twins,” published by the National Academies of Sciences, Engineering, and Medicine (NASEM) identifies cross-sector challenges and recommendations to support this potentially transformative approach for biomedical research. Priorities include development of multi-agency collaborations with industry to advance the mathematical, statistical, and computational…Close
Request for Information: Calling all Researchers and Healthcare Community Contributors
NEW NCI Childhood Cancer Data Initiative (CCDI) Request for Information (RFI) on Electronic Health Record (EHR) data! Responses due using the submission webform by February 29, 2024. NCI’s CCDI invites participation from all stakeholders across the cancer research and health care community including vendors and developers in understanding information on existing capabilities for automated EHR…Close
New Administrative Supplement Opportunity — Digital Twins in Radiation Oncology!
The NCI Division of Cancer Treatment and Diagnosis and the Center for Biomedical Informatics and Information Technology announce the Digital Twins Radiation Oncology (DTRO) administrative supplement opportunity. This administrative supplement funding opportunity seeks to leverage the existing infrastructure in radiation oncology, radiation biology and data science to facilitate new…Close
Predictive Radiation Oncology: News and Events
In March 2021, the NCI-DOE Collaboration held a series of interactive workshops with global experts in artificial intelligence, computing, and radiation. The Accelerating Precision Radiation Oncology through Advanced Computing and Artificial Intelligence series offered participants an opportunity to determine a roadmap for cutting-edge, multidisciplinary research that will drive development of new paradigms in radiation oncology. A report from the meeting is featured in Radiation Research.
Why is Predictive Radiation Oncology Important?
Radiation oncology is an area of cancer care that employs rich four-dimensional (4D) data to design and deliver highly personalized and technologically advanced treatments. Emerging approaches in physics, AI, advanced computing and mathematical modeling can be informed by the growing wealth of 4D data. New synergies can be created to predict response at various time scales and thereby support new treatment strategies with the potential for direct translation to the radiation oncology clinic.
The typical course of radiation treatment for cancer patients takes between one day and 8 weeks. This timespan creates opportunities to analyze dynamic changes and anticipate adaptive processes in cancer cells (e.g., radiation resistance) or to identify sensitivities of normal tissues to radiation damage.
Development of personalized, predictive models for these events enables adaptive, fine-tuned treatment and offers capabilities to leverage potentially vast amounts of diverse data to improve outcomes. The range of data includes areas such as circulating biomaterials, quantitative 3D imaging, and patient-reported outcomes.
Innovative multidisciplinary approaches that leverage advances in computing and measurement offer tremendous, untapped potential to shape the future of radiation treatments and oncology in general.
Moreover, radiation oncology clinical practice translates to many other areas of scientific discovery and societal impact. These include drug development, surgical practice, patient survivorship research, prevention of late effects, aeronautics and space travel, radiation safety, radiation biology, mitigation of radiation events, and disaster management.
Check back here for future predictive radiation oncology events.
Precision Medicine Applications in Radiation Oncology, a meeting sponsored by The Cancer informatics for Cancer Centers (Ci4CC), August 29–31, 2022. This in-person symposium in Santa Barbara, CA featured invited talks on innovative applications of computational, quantitative, and machine learning approaches to enhance the precision of biomarker development, theranostics, decision support, and workflow in radiation oncology. Fall Registration Page. Ci4CC is a nonprofit society providing a focused forum for NCI Designated & Community Cancer Centers that has a special focus on Precision Medicine, Data Science, Artificial Intelligence, Healthcare IT, Translational Research, & Digital Platforms targeting Executive Informatics & Research IT leaders nationally.
Radiation Oncology-Biology Integration Network (ROBIN), a previous RFA. Because this RFA has expired, interested parties are encouraged to apply to the more traditional mechanisms of the NCI R01 and P01. Guidance on the P01.
Questions? Contact ECICC_Community@nih.gov