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Research Spending & Results

Award Detail

Awardee:UNIVERSITY OF WASHINGTON
Doing Business As Name:University of Washington
PD/PI:
  • Archis Ghate
  • (206) 616-5968
  • archis@u.washington.edu
Award Date:02/07/2011
Estimated Total Award Amount: $ 400,000
Funds Obligated to Date: $ 400,000
  • FY 2011=$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:
  • Russell Barton
  • (703) 292-2211
  • rbarton@nsf.gov

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.

If successful, the project will result in a rigorous mathematical foundation for a new individualized cancer treatment planning method that delivers the right radiation dose to the right location at the right time - potentially leading to improved health outcomes. The methodological ideas will also be applicable while planning treatment for other diseases. The PI will mentor graduate students, incorporate research findings into simulation workshops for pre-engineers, provide research opportunities for underrepresented undergraduates, and develop a short course for health professionals. Results will be disseminated through journal publications and scientific conferences.

For specific questions or comments about this information including the NSF Project Outcomes Report, contact us.