RSR Award Detail
| Awardee: | UNIVERSITY OF WASHINGTON |
| Doing Business As Name: | University of Washington |
| PD/PI: |
|
| Award Date: | 02/07/2011 |
| Estimated Total Award Amount: | $ 400,000 |
| Funds Obligated to Date: |
$
400,000
|
| Award Start Date: | 02/15/2011 |
| Award Expiration Date: | 01/31/2016 |
| Transaction Type: | Grant |
| Agency: | NSF |
| Awarding Agency Code: | 4900 |
| Funding Agency Code: | 4900 |
| CFDA Number: | 47.041 |
| Primary Program Source: | 490100 NSF RESEARCH & RELATED ACTIVIT |
| Award Title or Description: | CAREER: Stochastic Control for Adaptive Biologically Conformal Radiotherapy |
| Federal Award ID Number: | 1054026 |
| DUNS ID: | 605799469 |
| Parent DUNS ID: | 042803536 |
| Program: | SERVICE ENTERPRISE SYSTEMS |
| Program Officer: |
|
Awardee Location | |
| Street: | 4333 Brooklyn Ave NE |
| City: | SEATTLE |
| State: | WA |
| ZIP: | 98195-9472 |
| Country: | US |
| Awardee Cong. District: | 07 |
Primary Place of Performance | |
| Organization Name: | University of Washington |
| Street: | 4333 Brooklyn Ave NE |
| City: | SEATTLE |
| State: | WA |
| ZIP: | 98195-9472 |
| Country: | US |
| Cong. District: | 07 |
Abstract at Time of Award | |
The research objective of this Faculty Early Career Development (CAREER) project is to mathematically develop a novel cancer radiotherapy paradigm that optimally adapts treatment to the spatiotemporal evolution of a tumor's biological condition. This will be accomplished by formulating dynamic optimization models that account for uncertainty in tumor-response to radiation and allow treatment planners to modulate radiation beam intensities depending on the tumor's condition, as observed in functional images acquired prior to each treatment session. The goal will be to minimize the number of tumor cells remaining at the end of the treatment course, while limiting toxic effects of radiation on nearby healthy tissue. Efficient algorithms will be designed to solve the resulting computationally challenging stochastic optimization problems. For truly patient-specific treatment, these will be integrated with statistical learning methods that estimate response-uncertainty using information gathered over the treatment course. Computer-generated test cases will be built to validate treatment strategies and to compare them from computational efficiency and treatment efficacy perspectives. | |
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