This Project Grant from the National Science Foundation Division of Mathematical Sciences supports the development of a generalizable data framework to enable precision radiotherapy for individual cancer patients. Funded at $104,016 under the Mathematical and Physical Sciences program (CFDA 47.049), the award will support collaborative research between Jackson Laboratory and other organizations to build and validate a deep reinforcement learning model using multimodal imaging data from cancer patients. The model will analyze genetic and clinical data along with treatment plans and outcomes to provide clinical decision support for individualized radiotherapy regimens in lung and colorectal cancers. The goal is to develop a generalized tool that can analyze multiple data types and provide superior predictive power for assessing patient risk stratification, outcomes, and highly personalized management approaches.
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