Abstract

Our overall goal is to improve the efficiency of clinical trials of new cancer therapies by enhancing the effectiveness of quantitative PET/CT imaging of tumor response. In this renewal application, we seek to deploy and validate in multi-center clinical trials the quantitative PET imaging tools and methods that we have developed within the Quantitative Imaging Network (QIN) during the current project period. While quantitative PET imaging is a uniquely powerful tool to assess response to potential cancer therapies, it is also subject to several sources of bias and variability that degrade study power. In addition, multi-center studies are needed to increase patient accrual rates, even in early-phase studies. These multi-center studies can confound quantitative accuracy, further reducing study power, leading to missed opportunities in evaluating new therapies To accelerate the evaluation of cancer therapies using quantitative PET imaging we have three distinct and linked aims: (1) Develop and implement a unified database and imaging platform that enables feasible deployment of our phantoms and software tools. (2) Extend our quantitative FDG PET imaging tools to FLT (proliferation) and FES (receptor status) in multicenter studies. (3) Prospectively test the integration of the above tools and methods in a clinical trial that uses 18F-FES PET imaging to evaluate new breast cancer therapies. These approaches can be readily extended to other cancers using quantitative PET imaging. The prospective testing of quantitative PET imaging tools in multisite clinical trials is an essential step towards adoption. In turn this will help accelerate the effective evaluation of improved cancer therapies by quantitative imaging, which will benefit investigators, clinicians, and cancer patients.

Public Health Relevance

Our goal is to improve the efficiency of multicenter clinical trials of cancer therapies by enhancing the effectiveness of quantitative PET/CT imaging of tumor response. For this will deploy and optimize our quantitative PET imaging tools in multicenter clinical trials.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01CA148131-07
Application #
9145153
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Tata, Darayash B
Project Start
2010-04-15
Project End
2020-08-31
Budget Start
2016-09-01
Budget End
2017-08-31
Support Year
7
Fiscal Year
2016
Total Cost
Indirect Cost

  Institution

Name
University of Washington
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
605799469
City
Seattle
State
WA
Country
United States
Zip Code
98195

  Publications

O'Sullivan, Finbarr; O'Sullivan, Janet N; Huang, Jian et al. (2018) Assessment of a statistical AIF extraction method for dynamic PET studies with 15O water and 18F fluorodeoxyglucose in locally advanced breast cancer patients. J Med Imaging (Bellingham) 5:011010
Zuki?, Dženan; Byrd, Darrin W; Kinahan, Paul E et al. (2018) Calibration Software for Quantitative PET/CT Imaging Using Pocket Phantoms. Tomography 4:148-158
Malyarenko, Dariya; Fedorov, Andriy; Bell, Laura et al. (2018) Toward uniform implementation of parametric map Digital Imaging and Communication in Medicine standard in multisite quantitative diffusion imaging studies. J Med Imaging (Bellingham) 5:011006
Byrd, Darrin; Christopfel, Rebecca; Arabasz, Grae et al. (2018) Measuring temporal stability of positron emission tomography standardized uptake value bias using long-lived sources in a multicenter network. J Med Imaging (Bellingham) 5:011016
Newitt, David C; Malyarenko, Dariya; Chenevert, Thomas L et al. (2018) Multisite concordance of apparent diffusion coefficient measurements across the NCI Quantitative Imaging Network. J Med Imaging (Bellingham) 5:011003
Linden, Hannah M; Peterson, Lanell M; Fowler, Amy M (2018) Clinical Potential of Estrogen and Progesterone Receptor Imaging. PET Clin 13:415-422
Kinahan, Paul E; Byrd, Darrin W; Helba, Brian et al. (2018) Simultaneous Estimation of Bias and Resolution in PET Images With a Long-Lived ""Pocket"" Phantom System. Tomography 4:33-41
Kurland, Brenda F; Peterson, Lanell M; Lee, Jean H et al. (2017) Estrogen Receptor Binding (18F-FES PET) and Glycolytic Activity (18F-FDG PET) Predict Progression-Free Survival on Endocrine Therapy in Patients with ER+ Breast Cancer. Clin Cancer Res 23:407-415
Wangerin, Kristen A; Muzi, Mark; Peterson, Lanell M et al. (2017) A virtual clinical trial comparing static versus dynamic PET imaging in measuring response to breast cancer therapy. Phys Med Biol 62:3639-3655
Beichel, Reinhard R; Smith, Brian J; Bauer, Christian et al. (2017) Multi-site quality and variability analysis of 3D FDG PET segmentations based on phantom and clinical image data. Med Phys 44:479-496

Showing the most recent 10 out of 57 publications