We are integrating the mathematical modeling of tumor proliferation and invasion with advanced cancer imaging methods. We are applying this approach to gliomas, which are aggressive and highly invasive primary brain tumors associated with dismal prognoses. Because of the relative inaccessibility of tissue, the clinical management of gliomas are strongly directed by imaging, thus tools integrating changes on imaging with a dynamic understanding of the cancer system are sorely needed. The goals of our project are twofold: To impact current clinical challenges with treatment of gliomas, and to provide tools for the development of new therapies for these challenging cancers. Our first goal is to develop image-based response metrics based on the growth kinetics of each patient's tumor, as seen on both anatomical imaging (MR) and functional imaging (PET and advanced MR). We will use mathematical modeling to develop a patient-specific Untreated Virtual Imaging Control (UVIC) that quantifies the dynamics of each patient's tumor system. We will then test the UVIC model against a novel set of paired PET and MR images at multiple time-points (five on average) for each of 20 glioblastoma patients. The paired images will be acquired throughout the course of therapy and compared with the UVIC predicted images of hypoxia (FMISO-PET), necrosis (T1-Gd MR) and cellularity (DWI MR). The second, and overall, goal of this project is to extend the UVIC model to the early response assessment of individual patients in clinical trials. This will provide a tool for the development of much-needed therapies that are more effective for gliomas. The methodologies developed in the project could be extended by refining the biological modeling, and could also be applied to other cancers by the use of appropriate growth kinetic models.

Public Health Relevance

We are integrating the mathematical modeling of tumor proliferation and invasion with advanced cancer imaging methods. We are applying this approach to gliomas, which are aggressive and highly invasive primary brain tumors associated with dismal prognoses and since the clinical management of gliomas is strongly directed by imaging. The goals of our project are twofold: To impact current clinical challenges with treatment of gliomas, and to provide tools for the development of new therapies for these challenging cancers.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA164371-03
Application #
8531689
Study Section
Special Emphasis Panel (ZCA1-SRLB-9 (O1))
Program Officer
Menkens, Anne E
Project Start
2011-09-21
Project End
2016-07-31
Budget Start
2013-08-01
Budget End
2014-07-31
Support Year
3
Fiscal Year
2013
Total Cost
$806,109
Indirect Cost
$284,356
Name
University of Washington
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
605799469
City
Seattle
State
WA
Country
United States
Zip Code
98195
Gong, Kuang; Majewski, Stan; Kinahan, Paul E et al. (2016) Designing a compact high performance brain PET scanner-simulation study. Phys Med Biol 61:3681-97
Parsian, Sana; Giannakopoulos, Nadia V; Rahbar, Habib et al. (2016) Diffusion-weighted imaging reflects variable cellularity and stromal density present in breast fibroadenomas. Clin Imaging 40:1047-54
Rockne, Russell C; Trister, Andrew D; Jacobs, Joshua et al. (2015) A patient-specific computational model of hypoxia-modulated radiation resistance in glioblastoma using 18F-FMISO-PET. J R Soc Interface 12:
Vaquero, Juan José; Kinahan, Paul (2015) Positron Emission Tomography: Current Challenges and Opportunities for Technological Advances in Clinical and Preclinical Imaging Systems. Annu Rev Biomed Eng 17:385-414
Adair, Jennifer E; Johnston, Sandra K; Mrugala, Maciej M et al. (2014) Gene therapy enhances chemotherapy tolerance and efficacy in glioblastoma patients. J Clin Invest 124:4082-92
Harrison, Robert L; Elston, Brian F; Doot, Robert K et al. (2014) A Virtual Clinical Trial of FDG-PET Imaging of Breast Cancer: Effect of Variability on Response Assessment. Transl Oncol 7:138-46
Neal, Maxwell Lewis; Trister, Andrew D; Ahn, Sunyoung et al. (2013) Response classification based on a minimal model of glioblastoma growth is prognostic for clinical outcomes and distinguishes progression from pseudoprogression. Cancer Res 73:2976-86
Neal, Maxwell Lewis; Trister, Andrew D; Cloke, Tyler et al. (2013) Discriminating survival outcomes in patients with glioblastoma using a simulation-based, patient-specific response metric. PLoS One 8:e51951
Baldock, A L; Rockne, R C; Boone, A D et al. (2013) From patient-specific mathematical neuro-oncology to precision medicine. Front Oncol 3:62
Hawkins-Daarud, Andrea; Rockne, Russell C; Anderson, Alexander R A et al. (2013) Modeling Tumor-Associated Edema in Gliomas during Anti-Angiogenic Therapy and Its Impact on Imageable Tumor. Front Oncol 3:66

Showing the most recent 10 out of 11 publications