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Frederick National Laboratory for Cancer Research issued a request for proposal. To further advance AI in Medical Imaging (AIMI) large datasets, acquired through routine standard of care, are needed to train and evaluate the performance of the ML/AI algorithms. The datasets need to be correctly de-identified to maintain patient privacy while at the same time preserving as much scientifically relevant information as possible. Large datasets from the existing standard of care radiology…

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U.S. Department of Energy’s INCITE program seeks proposals for 2023: https://www.doeleadershipcomputing.org/. INCITE’s open call provides an opportunity for researchers to pursue transformational advances in science and technology through large allocations of computer time and supporting resources at the Argonne Leadership Computing Facility (ALCF) and the Oak Ridge Leadership Computing Facility (OLCF). Both are DOE Office of Science user facilities located at DOE’s Argonne…

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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.

Related Programs

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 will feature 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. There will also be a call for scientific abstracts that will be featured in a poster session. 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

Created by Petrina Hollingsworth Last Modified Tue April 19, 2022 8:57 am by Petrina Hollingsworth