In treating cancer patients with radiation therapy, different patients may have different responses to the same type of radiotherapy. Hence, it is critical to individualize the radiation treatment based on the patient's health data, clinical conditions, as well as response over time. The goal of this project is to develop a generalizable data framework that can support precision radiotherapy for individual cancer patients. Specifically, a deep reinforcement learning model will be built and validated with multimodal imaging data acquired during diagnosis, treatment and follow-up of individual patients. Harmonization of the medical imaging data with genetic and clinical data will create an invaluable repository of knowledge to draw from, while calling for new analytics. The developed data framework will provide critical clinical decision support for individualized radiotherapy

By leveraging the wealth of data generated in the radiotherapy clinic, the project aims to develop a generalized deep reinforcement learning (DRL) tool for cancer risk stratification. Based on the DRL tool, an ensemble model will be built to analyze all the data types useful to patient outcome prediction. The model will be validated with independent datasets to ensure generalization. To account for information from multiple imaging modalities combined with treatment plans, a multimodal deep reinforcement learning (mDRL) model will be developed and trained with patient data stored in the electronic medical record system, as well as genomic information derived from blood and tissue specimens. The detection tool will be used in both lung cancer and colorectal cancer patients. Generalization to a variety of other cancers will be possible once the tools become available to the clinical research community. The ensemble model will allow integrated analysis of multiple data types recorded along the patient outcome trajectory, provide better discrimination between tumor phenotypes and superior predictive power. The framework will be designed to coordinate and synthesize various types of evidence and measurements into scores for the objective assessment and quantification of outcomes and endpoints. This strategy will ultimately provide novel patient re-stratification and support clinical decisions for highly individualized patient management.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

National Science Foundation (NSF)
Division of Mathematical Sciences (DMS)
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Christopher Stark
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Yale University
New Haven
United States
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